Planetary Boundaries and Earth System Dynamics: A Comprehensive Biophysical Assessment of Holocene Stability, Anthropogenic Forcing, and Coupled Tipping Cascades

Bryan P. White

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Lab Report #139 📑 SYNTHESIS REVIEW Earth & Climate Systems Published Nov 11, 2026

Planetary Boundaries and Earth System Dynamics: A Comprehensive Biophysical Assessment of Holocene Stability, Anthropogenic Forcing, and Coupled Tipping Cascades

Lab Report #139 • By Bryan P. White • 14 min read • High (Undergraduate / Graduate Earth System Science & Thermodynamics)

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✨ Executive Summary

PHYLOGENOMICS • ML MODEL

Human industrial activity has expanded beyond local ecological footprints to alter the thermodynamic and geochemical machinery of the entire planet. Over the 11,700-year Holocene epoch, Earth's climate and biogeochemical cycles remained exceptionally stable within an envelope of approximately ±1.0 °C, providing the biophysical conditions necessary for the emergence of agriculture, permanent settlements, and global civilization. Today, anthropogenic forcing has pushed the Earth system into the Anthropocene, characterized by rapid changes across both the geosphere and biosphere. The Planetary Boundaries framework quantifies a "safe operating space for humanity" across nine deeply coupled Earth system processes: Climate Change, Biosphere Integrity, Biogeochemical Flows (Nitrogen and Phosphorus), Land-System Change, Freshwater Change, Ocean Acidification, Atmospheric Aerosol Loading, Stratospheric Ozone Depletion, and Novel Entities. Current empirical assessments demonstrate that six of the nine boundaries have been transgressed into zones of increasing or high risk. Because these planetary processes are coupled via non-linear thermodynamic, hydrological, and ecological feedbacks, transgressing one boundary erodes the resilience of the others. This comprehensive systems report evaluates the empirical status, mathematical dynamics, and cross-boundary tipping cascades of the Earth system, establishing why multi-system stewardship is required to maintain long-term planetary habitability.

1. Introduction: The Holocene Baseline and the Anthropocene Dilemma

1.1. Holocene Geoclimatic Stability and the Emergence of Agrarian Civilizations

For the vast majority of human evolutionary history, environmental pressures were localized. Hominin populations adapted to regional climatic fluctuations, migrated in response to glacial cycles, and exploited local biological resources without altering the thermodynamic trajectory of the planet. However, the onset of the Holocene epoch approximately 11,700 years ago inaugurated an era of profound Earth system equilibrium. Following the termination of the Last Glacial Maximum and the abrupt cooling reversal of the Younger Dryas, global mean surface temperatures stabilized within an exceptionally narrow envelope of approximately ±1.0 °C. Atmospheric concentrations of carbon dioxide (CO₂) remained constrained between 260 and 285 parts per million (ppm), global sea levels reached a dynamic plateau, and regional hydrological monsoon belts settled into regular, predictable seasonal cycles.

This narrow range of biophysical stability formed the physical foundation for human civilization. Stable precipitation regimes enabled the independent emergence of sedentary agriculture across the Fertile Crescent, Mesoamerica, the Yangtze and Yellow River valleys, and the Indus Valley. Predictable coastlines fostered maritime trade networks, while stable ecosystem primary productivity supported exponential human demographic expansion. In thermodynamic terms, the Holocene represented a resilient attractor basin in the phase space of the Earth system, stabilized by powerful negative dampening feedbacks operating across the atmosphere, hydrosphere, cryosphere, and living biosphere.

1.2. The Mid-20th-Century Great Acceleration and Planetary-Scale Forcing

Beginning with the Industrial Revolution in the late 18th century, and accelerating dramatically during the mid-20th-century "Great Acceleration," anthropogenic energy and material throughput began to rival the major geological and geochemical cycles of the planet. The combustion of fossil hydrocarbons liberated hundreds of billions of metric tons of ancient geologically sequestered carbon into the atmosphere, fundamentally altering planetary radiative forcing and intensifying the greenhouse effect. Industrial chemical synthesis, continental-scale deforestation, massive mineral extraction, and the commercial mobilization of synthetic fertilizers have introduced massive perturbations into the biosphere and geosphere, initiating a distinct geological epoch: the Anthropocene.

1.3. Beyond Reductionism: Earth as a Complex Non-Equilibrium Dissipative System

Traditional environmental policy approaches treated ecological degradation as a series of isolated, localized externalities: air quality over an industrial basin, water pollution in a specific watershed, or topsoil erosion on arable lands. However, modern Earth system science reveals that Earth does not behave as an uncoupled collection of independent environmental sinks. Instead, it operates as a single, complex, self-organizing dissipative system maintained far from thermodynamic equilibrium by continuous solar energy flux and internal biogeochemical cycles. Perturbing one major biogeochemical flux produces non-linear cascade effects across the entire planetary machinery.

To address this systemic challenge, an international consortium of Earth system scientists led by Johan Rockström, Will Steffen, and Katherine Richardson formulated the Planetary Boundaries framework. Rather than focusing on localized carrying capacities or single-variable limits, the framework identifies nine fundamental biophysical processes that regulate the thermodynamic stability and ecological resilience of the global Earth system. For each process, the framework establishes a quantitative "safe operating space" designed to maintain the planet within a Holocene-like state, bounded upstream of non-linear bifurcation zones and tipping points where irreversible regime shifts occur.

2. Mathematical Foundations, Basins of Attraction, and System Hysteresis

2.1. High-Dimensional Phase Space and Potential Well Dynamics

Mathematically, the Earth system can be modeled as a high-dimensional non-linear dynamical system. The global environmental state at time t is described by a state vector x(t) ∈ ℜⁿ whose components include global mean surface temperature, ocean heat content, cryospheric ice volume, atmospheric trace gas concentrations, global root-zone soil moisture, and biosphere biomass density. The temporal evolution of this state vector is governed by a system of coupled non-linear differential equations:

dx / dt = F(x, p) + η(t)

where p ∈ ℜ^m represents slowly varying control parameters (anthropogenic greenhouse gas emissions, land-use conversion rates, synthetic nitrogen fixation fluxes), and η(t) represents stochastic environmental noise (such as volcanic eruptions and solar irradiance variations). Under constant pre-industrial parameters p₀, the system possessed a stable stationary attractor x_Holocene characterized by a deep potential well in the planetary stability landscape:

V(x; p) = − ∫ F(x', p) · dx'

As anthropogenic control parameters p deviate progressively from pre-industrial baselines, the geometry of this potential landscape deforms. The potential barrier separating the Holocene attractor basin from alternative degraded attractor states (such as a deglaciated, highly stratified "Hothouse Earth" regime) diminishes. Near a critical parameter threshold p_c, the system undergoes a mathematical bifurcation (such as a fold or saddle-node bifurcation), causing the stable Holocene equilibrium to lose its stability or vanish entirely.

2.2. Critical Slowing Down and Loss of System Resilience

As an Earth subsystem approaches a critical bifurcation threshold p_c, the restoring negative feedback forces that pull the state vector back toward equilibrium weaken. In dynamical systems theory, this phenomenon is known as critical slowing down. The recovery rate of the subsystem following small perturbations decreases toward zero, which manifests empirically as an increase in short-term temporal autocorrelation and an amplification of variance in observable state variables (such as sea-surface temperature anomalies or vegetation greenness indices). These statistical signatures serve as crucial early-warning indicators of impending regime shifts.

2.3. Multi-Century Hysteresis and Irreversible Biophysical Commitments

A defining property of such non-linear Earth subsystems is hysteresis. When a control parameter pushes a subsystem across its critical bifurcation threshold, the transition to an alternative attractor state is rapid and self-reinforcing. Once the transition has occurred, reducing the control parameter back below the original threshold p_c does not promptly restore the previous state. Because the system has settled into an alternative basin of attraction, restoring the original state requires reversing the parameter far below p_c to reach a secondary reverse bifurcation threshold.

Prominent examples of hysteresis operating within the Earth system include:

Cryospheric Melt-Elevation Feedbacks: As continental ice sheets (such as Greenland) lose mass, their surface elevation drops into warmer, denser layers of the lower troposphere. Even if atmospheric CO₂ were returned to pre-industrial baselines, the lowered ice sheet surface would remain subjected to elevated ambient temperatures, preventing spontaneous reglaciation.
Ocean Overturning and Thermal Inertia: The global ocean absorbs over 90% of excess heat trapped by greenhouse gases, storing vast quantities of thermal energy in the deep ocean layers (Ocean Heat Content). Restoring atmospheric CO₂ does not immediately remove this accumulated thermal energy, maintaining elevated global surface equilibrium temperatures and continued sea-level rise for centuries.
Biosphere and Biodiversity Collapse: Biological extinction is inherently irreversible. The loss of keystone taxa, functional guilds, and genetic diversity represents an absolute permanent loss of biological information that cannot be reconstituted on human timescales (Alternative Stable State).

2.4. Upstream Placement of the Safe Operating Space

Because the exact mathematical locations of catastrophic tipping thresholds p_c are subject to parameter uncertainty, observational noise, and climate sensitivity variance, the Planetary Boundaries framework adopts an explicitly precautionary approach. The safe planetary boundary x_boundary is deliberately positioned at the lower bound of the scientific uncertainty range, well upstream of the estimated bifurcation threshold x_threshold. The interval between the safe boundary and the critical threshold is designated the Zone of Increasing Risk (where non-linear feedback loops begin to accelerate), while values beyond the threshold enter the High-Risk Zone (where catastrophic regime shifts become highly probable or irreversible).

3. Quantitative Status of the Nine Planetary Boundaries

The framework quantifies planetary health across nine distinct biophysical systems. Two of these—Climate Change and Biosphere Integrity—are designated as core boundaries because each exerts fundamental top-down and bottom-up regulation over the entire Earth system. A significant breach of either core boundary has the capacity to destabilize the planet independently, even if all other processes remained intact.

Table 1. The Nine Planetary Boundaries: Control Variables, Holocene Baselines, and Current Transgression Status

Earth System Process	Primary Control Variable(s)	Holocene Baseline	Safe Planetary Boundary	High-Risk Threshold	Current Status & Assessment
1. Climate Change (Core)	Atmospheric CO_2 (ppm) Top-of-atmosphere radiative forcing (W m-2)	~280 ppm 0.0 W m-2	350 ppm CO_2 +1.0 W m-2	450 ppm CO_2 +1.5 W m-2	~420-425 ppm CO_2 +2.91 W m-2 Transgressed (High Risk)
2. Biosphere Integrity (Core)	Extinction rate (E/MSY) Functional HANPP (% of Holocene NPP)	0.1-1.0 E/MSY ~0% HANPP	< 10 E/MSY < 10% HANPP	100 E/MSY 20% HANPP	> 100 E/MSY ~30% HANPP Transgressed (High Risk)
3. Biogeochemical Flows	Industrial N2 fixation (Tg N yr-1) P flux to freshwater/oceans (Tg P yr-1)	0 Tg N yr-1 ~1 Tg P yr-1	62 Tg N yr-1 11 Tg P yr-1 (ocean) / 6.2 (soil)	82 Tg N yr-1 100 Tg P yr-1	~190 Tg N yr-1 ~22.6 Tg P yr-1 Transgressed (High Risk)
4. Land-System Change	Forested land area retained (% of potential natural forest cover)	100%	≥ 75% Global (85% Tropical/Boreal, 50% Temperate)	54% Global (60% Tropical/Boreal)	~60% Global Transgressed (Increasing Risk)
5. Freshwater Change	Blue water: % land with streamflow deviation Green water: % land with soil moisture deviation	~9.4% (blue) ~9.8% (green)	≤ 10.2% (blue water) ≤ 11.1% (green water)	50.0% (blue) 50.0% (green)	18.2% (blue) 15.8% (green) Transgressed (Increasing Risk)
6. Ocean Acidification	Surface aragonite saturation state (Ω_arag, % of pre-industrial)	Ω_arag ≈ 3.44 (100%)	≥ 80% of pre-industrial (Ω_arag ≥ 2.75)	70% of pre-industrial (Ω_arag ≈ 2.41)	~80-81% (Ω_arag ≈ 2.75-2.80) At Boundary Threshold
7. Atmospheric Aerosols	Interhemispheric Aerosol Optical Depth difference (Delta AOD)	≈ 0.03 AOD diff	Delta AOD ≤ 0.10 (Regional limit ≤ 0.25)	0.25 AOD diff (Regional limit > 0.50)	~0.076 global diff Safe Globally; Regional Breaches
8. Stratospheric Ozone	Total stratospheric O3 column (Dobson Units, DU)	~290 DU	< 5% reduction (≥ 276 DU)	10% reduction (≈ 261 DU)	~284 DU (~1-2% reduction) Safe & Recovering
9. Novel Entities	% of synthetic chemicals, plastics, and entities released with safety clearance	0% untested release	0% uncharacterized release (Production within assimilation capacity)	Systemic biosphere toxicity	Completely Transgressed (>350,000 synthetic formulations; ubiquitous microplastics)

3.1. Climate Change (Core Boundary)

The Climate Change boundary is governed by the fundamental thermodynamic energy balance of the Earth. Solar shortwave radiation arrives at the top of the atmosphere at a mean global flux of approximately 340 W m⁻². Planetary albedo reflects roughly 30% back to space, while the remaining energy is absorbed by the surface and atmosphere and subsequently re-radiated as longwave infrared radiation. Atmospheric greenhouse gases absorb and re-emit this terrestrial infrared flux, maintaining a warm surface equilibrium. The radiative forcing increment ΔF associated with elevated CO₂ follows a logarithmic relationship: ΔF = 5.35 · ln(C / C₀) W m⁻², where C₀ is the pre-industrial reference concentration of 280 ppm.

The framework establishes dual control variables for this boundary: atmospheric CO₂ concentration (safe boundary: 350 ppm; current: ~420–425 ppm) and top-of-atmosphere net radiative forcing (safe boundary: +1.0 W m⁻²; current: approximately +2.91 W m⁻²). Sustaining CO₂ above 400–450 ppm on millennial timescales leads to the complete loss of polar ice sheets and global sea-level rises exceeding 10–20 meters. Over 90% of the planetary thermal imbalance is absorbed by the global ocean (Ocean Heat Content), driving thermal expansion, marine heatwaves, and destratifying ocean circulation.

3.2. Biosphere Integrity (Core Boundary)

The Biosphere Integrity boundary addresses the living fabric of the Earth, which actively moderates biogeochemical cycles and dampens climatic extremes across two complementary dimensions: genetic diversity and functional integrity. Genetic diversity is evaluated through the global extinction rate, measured in Extinctions per Million Species-Years (E/MSY). Against an estimated background fossil rate of 0.1 to 1.0 E/MSY, the safe boundary is set at < 10 E/MSY. Contemporary empirical extinction rates are estimated between 100 and 1,000 E/MSY, indicating that anthropogenic pressures have triggered a modern mass extinction event comparable in rate to the Big Five Phanerozoic biotic crises.

Functional integrity is evaluated via the Human Appropriation of Net Primary Production (HANPP) and the Biodiversity Intactness Index (BII). The safe boundary dictates that humanity should appropriate less than 10% of terrestrial net primary production (NPP), and maintain BII above 90% across biomes. Currently, human appropriation exceeds ~30% of global terrestrial NPP through industrial agriculture, forestry, and grazing, while regional BII in heavily farmed temperate and tropical biomes has dropped below 70%. When functional integrity erodes, ecosystems lose trophic complexity and redundancy, reducing the capacity of terrestrial ecosystems to act as a resilient carbon sink.

3.3. Biogeochemical Flows: Nitrogen and Phosphorus Cycles

Life on Earth is constructed from specific stoichiometric ratios of elements, historically summarized by the marine Redfield ratio (106C : 16N : 1P). Biological productivity across the planet is constrained by the natural availability of reactive nitrogen and phosphorus. Anthropogenic industrial activity has radically transformed both cycles, uncoupling them from natural biological regulation.

In the nitrogen cycle, atmospheric dinitrogen (N₂) possesses an exceptionally strong triple covalent bond (945 kJ mol⁻¹) that can naturally be cleaved only by specialized diazotrophic microorganisms or abiotic lightning discharges. The industrial Haber-Bosch process enabled the catalytic reduction of atmospheric N₂ with hydrogen to produce ammonia:

N₂ + 3H₂ ⇌ 2NH₃ (ΔH° = −92.4 kJ mol⁻¹)

Today, industrial synthesis of synthetic nitrogen fertilizers, combined with fossil fuel combustion and agricultural legume cultivation, fixes over 190 Tg N yr⁻¹ of reactive nitrogen—more than triple the safe boundary of 62 Tg N yr⁻¹. Runoff leaches nitrate into aquatic systems and volatilizes nitrous oxide (N₂O), a greenhouse gas ~300 times more potent than CO₂. In parallel, mining of apatite rock (Phosphorus and Sulfur Biogeochemical Cycles) mobilizes >22 Tg P yr⁻¹ into soils, washing 11–14 Tg P yr⁻¹ into freshwater and marine sinks, exceeding the safe boundary of 11 Tg P yr⁻¹. This hyper-enrichment drives widespread aquatic eutrophication, creating expansive coastal hypoxic dead zones.

3.4. Land-System Change

The Land-System Change boundary regulates the physical conversion of natural biomes—primarily forests, grasslands, and wetlands—into cropland, grazing pasture, and urban infrastructure. Forests play a unique role in the biophysical regulation of climate through their complex three-dimensional canopies, deep root architectures, low surface albedo, and intense evapotranspiration.

The boundary requires maintaining at least 75% of global potential forest cover intact, with biome-specific thresholds: ≥ 85% for Tropical Rainforests and Boreal Forests, and ≥ 50% for temperate forests. Globally, forest cover has declined to approximately 60% of potential natural vegetation, placing this boundary in the Zone of Increasing Risk. In critical biomes such as the Amazon basin, cumulative deforestation has surpassed 17–20%. Beyond 20–25% deforestation, the regional "biotic pump"—wherein extensive forest canopies transpire immense volumes of moisture to create inland "flying rivers"—breaks down. This triggers an irreversible drying feedback that threatens to tip large swaths of the Amazon into degraded, fire-prone savanna.

3.5. Freshwater Change: Blue and Green Water Dynamics

Freshwater is evaluated across two coupled hydrological compartments (Hydrology):

Blue Water Component: Evaluated as the percentage of global land area experiencing significant deviations in streamflow relative to pre-industrial Holocene variability. The safe boundary is set at ≤ 10.2% of land area; current observations indicate that 18.2% of global land area experiences anomalous streamflow regimes due to massive damming, river diversion, and unsustainable pumping of fossil aquifer systems.
Green Water Component: Evaluated as the percentage of global land area experiencing root-zone soil moisture anomalies. The safe boundary is set at ≤ 11.1%; current assessments show that 15.8% of land area exhibits severe deviations, driven by widespread land degradation, soil compaction, agricultural drainage, and altered rainfall patterns.

Because green water directly regulates continental evapotranspiration, primary agricultural productivity, and terrestrial carbon sequestration, its widespread transgression places the Earth's hydrological engine in an unstable regime.

3.6. Ocean Acidification

As the global ocean absorbs approximately 25–30% of anthropogenic CO₂ emissions, it mitigates atmospheric warming at the cost of altering marine carbonate chemistry (Ocean Acidification). When gaseous CO₂ dissolves in seawater, it forms aqueous carbonic acid (H₂CO₃), which rapidly dissociates into hydrogen ions (H⁺) and bicarbonate ions (HCO₃⁻):

CO_2(aq) + H₂O ⇌ H₂CO₃ ⇌ H⁺ + HCO₃⁻

The surplus hydrogen ions react with free carbonate ions (CO₃²⁻) naturally present in seawater, converting them into additional bicarbonate: H⁺ + CO₃²⁻ ⇌ HCO₃⁻. This reaction depletes the concentration of free carbonate ions, reducing the saturation state (Ω) of calcium carbonate minerals (aragonite and calcite), defined as Ω = [Ca²⁺][CO₃²⁻] / K'_sp. The safe boundary is set at a mean surface ocean aragonite saturation state of Ω_arag ≥ 80% of pre-industrial levels (Ω_arag ≥ 2.75, against a pre-industrial baseline of ~3.44). Current global surface values have fallen to approximately 80–81% (Ω_arag ≈ 2.75–2.80), positioning the ocean directly at the threshold of the safe operating space. In cold high-latitude waters and coastal upwelling zones, seasonal undersaturation (Ω < 1.0) already dissolves pteropod shells and coral skeletons.

3.7. Atmospheric Aerosol Loading

Atmospheric aerosols are microscopic solid particles or liquid droplets suspended in the atmosphere, originating from combustion emissions (sulfates, nitrates, black carbon, and organic carbon) as well as windblown mineral dust. Aerosols influence the Earth system through direct scattering and absorption of solar radiation, and indirectly as cloud condensation nuclei (increasing cloud albedo via the Twomey effect and suppressing precipitation efficiency via the Albrecht effect).

The primary control variable is the interhemispheric difference in Aerosol Optical Depth (ΔAOD). Because anthropogenic aerosol emissions are concentrated in the heavily populated Northern Hemisphere, an asymmetry in aerosol loading alters the thermal gradient between the hemispheres, shifting the Intertropical Convergence Zone (ITCZ) southward and disrupting monsoon precipitation belts. The safe boundary is ΔAOD ≤ 0.10; global difference is currently ~0.076 (safe globally), though regional hotspots in South and East Asia exceed AOD 0.40–0.50. Crucially, sulfate aerosols mask 0.5–1.0 W m⁻² of greenhouse warming; rapid clean-air desulfurization unmasks this latent thermal energy.

3.8. Stratospheric Ozone Depletion (A Model of Successful Stewardship)

The Stratospheric Ozone layer absorbs harmful solar ultraviolet-B radiation, shielding biological macromolecules from photochemical damage. In an unperturbed stratosphere, ozone is produced and destroyed via the photo-steady-state Chapman cycle. During the mid-20th century, the industrial production of chlorofluorocarbons (CFCs) and halons introduced synthetic chlorine and bromine into the stratosphere. Photolyzed by UV-C radiation, these compounds release reactive halogen radicals that destroy ozone via catalytic cycles:

Cl + O₃ → ClO + O₂
ClO + O → Cl + O₂
Net: O₃ + O → 2O₂

Over Antarctica, extreme winter vortex temperatures facilitate the formation of Polar Stratospheric Clouds, activating reservoir chlorine into photolabile species and causing seasonal ozone holes. The planetary boundary mandates < 5% column reduction (≥ 276 Dobson Units, DU) from the ~290 DU baseline. Ratification of the 1987 Montreal Protocol successfully phased out ozone-depleting substances; column ozone has recovered to ~284 DU (~1–2% global reduction), projected to heal completely by mid-century. This demonstrates that coordinated international treaty regimes can reverse planetary boundary transgressions.

3.9. Novel Entities

The Novel Entities boundary covers synthetic chemical substances, novel biological entities, and engineered materials released into the environment that have no evolutionary or geological analog. This category includes synthetic organic chemicals, pesticides, industrial plasticizers, poly- and perfluoroalkyl substances (PFAS, or "forever chemicals") with extraordinarily persistent carbon-fluorine bonds, particulate microplastics that cross biological cell membranes, engineered nanomaterials, and anthropogenic radionuclides.

The conceptual boundary condition dictates that the release rate of novel entities must not exceed the capacity of science to conduct comprehensive ecotoxicological safety evaluations, nor exceed the Earth system's geochemical assimilation capacity (0% uncharacterized releases). In 2022, scientists concluded this boundary is completely transgressed: over 350,000 synthetic formulations exist in commerce, global plastic production exceeds 350–400 million tons annually, and microplastics permeate oceanic trenches, polar ice, and biological tissues. Persistent toxic bioaccumulation compromises biosphere fertility, immune competence, and microbial cycling.

4. Coupled Tipping Cascades and Cross-Boundary Feedbacks

4.1. The Polar Cryosphere–Climate Feedback Cascade

The nine boundaries form an interconnected biophysical network. Perturbing one boundary erodes resilience across others, lowering critical thresholds for secondary bifurcations (Tipping Points in the Climate System). Elevated radiative forcing from CO₂ (Boundary 1) causes the retreat of Arctic sea ice. Because open seawater absorbs over 90% of incoming solar radiation (albedo ≈ 0.06) compared to reflective snow-covered sea ice (albedo ≈ 0.85), this triggers a powerful positive ice-albedo feedback that amplifies high-latitude warming. Amplified warming thaws circumpolar permafrost, liberating microbial CO₂ and CH₄ while freshwater influx weakens the Atlantic Meridional Overturning Circulation (AMOC).

4.2. Continental Evapotranspiration and Amazonian Savannization

Deforestation in the Amazon (Boundary 4) diminishes local transpiration and disrupts green water recycling (Boundary 5). Because interior rainfall depends on recursive evapotranspiration from upwind forest canopies, forest clearing reduces precipitation across downwind regions. This induces extended hydrological drought, heightens fire frequency, and triggers widespread forest dieback. As trees die and burn, the Amazon transitions from a global carbon sink into a net carbon source, accelerating Climate Change (Boundary 1).

4.3. Marine Synergies: The "Deadly Trio"

Marine ecosystems face three concurrent, mutually reinforcing stresses: warming temperatures (Boundary 1), acidification from dissolved CO₂ (Boundary 6), and expanding deoxygenation driven by nitrogen and phosphorus runoff (Boundary 3) combined with reduced thermal gas solubility. Together, these pressures degrade coral reefs, disrupt marine trophic webs, and reduce the efficiency of the biological carbon pump, diminishing the ocean's capacity to buffer planetary atmospheric changes.

4.4. Saddle-Point Dynamics and Hothouse Earth Trajectories

In dynamical systems terminology, the joint transgression of six planetary boundaries moves the Earth system away from the Holocene basin of attraction toward a critical saddle point. Beyond this point, self-reinforcing endogenous feedbacks dominate over dampening feedbacks, potentially committing the planet to an irreversible trajectory toward a "Hothouse Earth" regime characterized by a 4–5 °C warmer equilibrium and sea levels tens of meters higher than today.

5. Discussion: Planetary Stewardship and Systemic Governance

5.1. Transcending Carbon Tunnel Vision

Modern sustainability discourse frequently focuses exclusively on carbon decarbonization. While transitioning to net-zero energy systems is strictly necessary, it is by no means sufficient to stabilize the Earth system. Halting greenhouse emissions will not halt extinction, replenish finite phosphorus reserves, detoxify novel entities, restore over-pumped aquifers, or heal fragmented biomes. An integrated Earth system approach is mandatory; for example, carbon-focused monoculture afforestation projects fail if they consume blue water and support negligible biodiversity.

5.2. Operationalizing the Safe and Just Operating Space (Doughnut Economics)

The planetary boundaries establish the ecological ceiling above which human environmental pressure cannot safely expand. Merging this biophysical ceiling with human social foundations—food, clean water, healthcare, education, housing, and social equity—creates the "Doughnut Economics" framework: the safe and just space for humanity. Achieving this state requires high-income nations to eliminate wasteful material throughput while redirecting resources toward universal social foundations within Earth's regenerative biophysical capacity.

5.3. Five Strategic Pillars for Long-Term Planetary Stewardship

Restoring the safe operating space demands systemic transformations across five core operational pillars:

Nutrient Cycling: Transitioning from linear fertilizer application to precision agriculture, biological nitrogen fixation via diversified crop rotations, and circular recovery of nitrogen and phosphorus from urban wastewater streams.
Biome Preservation: Enforcing strict moratoria on primary forest conversion and restoring large-scale ecological corridors to maintain the biotic pump and protect functional biodiversity.
Integrated Water Management: Managing river catchments as integrated biophysical units, capping groundwater pumping below natural recharge rates, and restoring wetlands to buffer hydrological extremes.
A Global Novel Entities Treaty: Establishing a legally binding instrument analogous to the Montreal Protocol that caps virgin plastics, mandates cradle-to-cradle lifecycle safety, and bans persistent, bioaccumulative compounds like PFAS.
Early-Warning Planetary Observatories: Integrating satellite telemetry and in situ sensor networks to detect critical slowing down and loss of resilience before irreversible tipping thresholds are crossed.

6. Foundational Sources (CRV Science Wiki Knowledge Base)

This comprehensive lab report synthesizes foundational principles, quantitative baselines, and mechanistic models established across peer-reviewed monographs of the CRV Science Wiki. All cited frameworks and concepts are sourced exclusively from the internal knowledge base:

Foundational Sources (CRV Science Wiki)

[1] CRV Science Wiki. Planetary Boundaries: Delineating the Holocene Safe Operating Space for Humanity. Available at: /wiki/planetary-boundaries.
[2] CRV Science Wiki. Earth System Science: Coupled Geosphere–Biosphere Feedbacks and Non-Linear Dynamics. Available at: /wiki/earth-system-science.
[3] CRV Science Wiki. Holocene: The Interglacial Climatic Foundation of Human Civilization. Available at: /wiki/holocene.
[4] CRV Science Wiki. Anthropocene: Defining the Human Epoch of Global Geochemical Forcing. Available at: /wiki/anthropocene.
[5] CRV Science Wiki. Climate Change: Atmospheric Energy Balance, Radiative Imbalance, and Thermal Trajectories. Available at: /wiki/climate-change.
[6] CRV Science Wiki. Radiative Forcing: Quantitative Metrics of Planetary Energetic Imbalance. Available at: /wiki/radiative-forcing.
[7] CRV Science Wiki. Greenhouse Effect: Molecular Absorption, Atmospheric Windows, and Terrestrial Longwave Trapping. Available at: /wiki/greenhouse-effect.
[8] CRV Science Wiki. Carbon Cycle: Terrestrial, Atmospheric, and Deep Marine Reservoirs and Fluxes. Available at: /wiki/carbon-cycle.
[9] CRV Science Wiki. Ocean Heat Content: Marine Thermal Uptake, Energy Redistribution, and Thermal Expansion. Available at: /wiki/ocean-heat-content.
[10] CRV Science Wiki. Biodiversity: Taxonomic, Phylogenetic, and Functional Dimensions of Biosphere Resilience. Available at: /wiki/biodiversity.
[11] CRV Science Wiki. Extinction: Evolutionary Dynamics, Background Rates, and Anthropogenic Defaunation. Available at: /wiki/extinction.
[12] CRV Science Wiki. Mass Extinction: Historical Biotic Crises, Kill Mechanisms, and Biosphere Recovery. Available at: /wiki/mass-extinction.
[13] CRV Science Wiki. Nitrogen Cycle: Microbial Transformation, Diazotrophy, and Global Reactive Nitrogen Budgets. Available at: /wiki/nitrogen-cycle.
[14] CRV Science Wiki. Haber-Bosch Process: High-Pressure Catalytic Ammonia Synthesis and Industrial Nitrogen Fixation. Available at: /wiki/haber-bosch-process.
[15] CRV Science Wiki. Phosphorus and Sulfur Biogeochemical Cycles: Geological Weathering, Lithospheric Cycling, and Biological Utilization. Available at: /wiki/phosphorus-and-sulfur-biogeochemical-cycles.
[16] CRV Science Wiki. Eutrophication: Nutrient Hyper-Enrichment, Phytoplankton Blooms, and Coastal Hypoxic Dead Zones. Available at: /wiki/eutrophication.
[17] CRV Science Wiki. Deforestation: Biome Fragmentation, Biomass Carbon Loss, and Albedo Feedbacks. Available at: /wiki/deforestation.
[18] CRV Science Wiki. Tropical Rainforest: Canopy Microclimates, the Biotic Pump, and Latitudinal Evapotranspiration Dynamics. Available at: /wiki/tropical-rainforest.
[19] CRV Science Wiki. Hydrology: The Global Water Cycle, Blue-Green Water Partitioning, and Catchment Dynamics. Available at: /wiki/hydrology.
[20] CRV Science Wiki. Groundwater and Aquifer Dynamics: Subsurface Hydrogeology, Hydraulic Head, and Overdraft Regimes. Available at: /wiki/groundwater.
[21] CRV Science Wiki. Ocean Acidification: Carbonate Chemistry, Proton Generation, and Mineral Saturation States. Available at: /wiki/ocean-acidification.
[22] CRV Science Wiki. Atmospheric Aerosols: Optical Depth, Radiative Forcing, and Cloud Microphysics. Available at: /wiki/aerosols-atmospheric.
[23] CRV Science Wiki. Stratospheric Ozone: Photochemical Chapman Cycles, Catalytic Halogen Destruction, and Antarctic Ozone Holes. Available at: /wiki/stratospheric-ozone.
[24] CRV Science Wiki. Chlorofluorocarbons: Atmospheric Halocarbons, Ozone Depletion Potential, and Phaseout Governance. Available at: /wiki/chlorofluorocarbon.
[25] CRV Science Wiki. Tipping Points in the Climate System: Non-Linear Bifurcations, Hysteresis, and Multi-Stable Dynamics. Available at: /wiki/tipping-points-in-the-climate-system.
[26] CRV Science Wiki. Albedo: Surface Reflectance, Cryospheric Feedbacks, and Planetary Shortwave Balance. Available at: /wiki/albedo.
[27] CRV Science Wiki. Permafrost: Circumpolar Cryosphere Stability and Organic Carbon Thaw Dynamics. Available at: /wiki/permafrost.
[28] CRV Science Wiki. Alternative Stable State: Ecological Regime Shifts, Hysteresis Basins, and Resilience Landscapes. Available at: /wiki/alternative-stable-state.

Discussion & Key Takeaways

The Planetary Boundaries framework demonstrates that human enterprise has become a planetary-scale geological force. Breaching six of the nine planetary boundaries places humanity at elevated risk of triggering irreversible regime shifts in the climate, cryosphere, and biosphere.

Stabilizing the Earth system requires transcending isolated 'carbon tunnel vision' to address nutrient flows, land-system intactness, and chemical proliferation synchronously. By combining biophysical planetary boundaries with human social foundations, humanity can chart a resilient, just trajectory within the safe operating space of our living planet.

Lab Discoveries

Empirical genomic, structural, and evolutionary research reports classified by a 3-Tier Research Scope System (Tier 1: Exploratory, Tier 2: Validation, Tier 3: Scientific) and evaluated on a 1–5🧪 scientific signal scale.

Research Scope Tiers:

🧭 Tier 1: Exploratory — New, classic, or unseen datasets & novel validation-set calibration sweeps 🎯 Tier 2: Validation — Repeatedly replicated & metadata-tracked Dev/Reserve validation sets 🔬 Tier 3: Scientific — New + validated data yielding a new interpretation, conclusion, or hypothesis test

Scientific Signal Scale:

🧪 1/5 Baseline / Review Note 🧪🧪 2/5 Validation 🧪🧪🧪 3/5 Comparative Synthesis 🧪🧪🧪🧪 4/5 Emergent Signal 🧪🧪🧪🧪🧪 5/5 Significant Biological Signal

📄 Preprints & Working Manuscripts 26 Preprint Monographs

Standalone preprint monographs (PREPRINT-01–PREPRINT-26), architectural syntheses, and formal arXiv submission packages (with single-column PDFs, Zenodo DOIs, and complete LaTeX/BibTeX source archives), maintained separately from empirical Lab Discovery reports.

📄 Download Latest Preprint PDF 📦 arXiv LaTeX Bundle (.tar.gz)

PREPRINT-26 Zenodo DOI: 10.5281/zenodo.24116968 arXiv: q-bio.PE (Populations and Evolution) • q-bio.GN (Genomics) • q-bio.QM (Quantitative Methods) Phylogenomics, Multispecies Coalescence & Insect Social Evolution GNU GPL-3.0

By Bryan P. White • Nov 10, 2026 • 24 min read • 📄 Single-Column PDF

Global Phylogenomic Radiation of Isoptera and Sister Wood-Roach Outgroups (Blattodea: Termitoidae): Native Rust Quartet Coalescent Species-Tree Inference, Endogenous Lignocellulase and Cytochrome P450 Selection, and Cross-Regional vs. Multi-Locus Demographic Skylines

PREPRINT-26 (v1.0.0 Empirical Preprint) • 100% Native Rust bio-suite-supervisor Across 544 Verified NCBI Records (L = 44,075 Aligned Columns)

We reconstruct the Triassic-to-Anthropocene phylogenomic radiation, endogenous lignocellulase/eusocial caste/terpenoid defense enzyme evolution, and multi-compartment demographic history of the global radiation of Isoptera and sister wood-roach outgroups (spanning Basal Relictual Families [Mastotermes darwiniensis, Zootermopsis nevadensis, Z. angusticollis, Porotermes adamsoni, Hodotermes mossambicus], Drywood Kalotermitidae [Cryptotermes havilandi, C. brevis, Incisitermes minor, Neotermes koshunensis], Subterranean Rhinotermitidae [Coptotermes formosanus, C. gestroi, Reticulitermes flavipes, R. virginicus, R. lucifugus, Heterotermes validus, Schedorhinotermes breinli], and Higher Termitidae [fungus-growing Macrotermes natalensis, M. barneyi, Odontotermes obesus, humivorous Pericapritermes nitobei, Termes fatalis, Neocapritermes angusticeps, Anoplotermes parvus, and diterpene-spraying nasutes Nasutitermes corniger, Hospitalitermes medioflavus, Constrictotermes cyphergaster, Labiotermes labralis], rooted on Cryptocercus relictus / C. kyebangensis and Blattodea outgroups Periplaneta americana and Blattella germanica) across 544 active verified NCBI sequence records (plus 6 autonomously quarantined GenBank UNVERIFIED anomalies) comprising a 64-taxon, 18-locus supermatrix (L = 44,075 aligned columns), 48 nuclear functional coding sequences, and 12 regional and multi-locus cohorts (12 x N = 36 = 432 isolates). Executed end-to-end via autonomous Native Rust bio-suite-supervisor optimization across 32 candidate RuNIE chains (total runtime 238.59 s; B = 1,000 UFBoot; 25,000 MCMC generations with 20% burn-in), RY3 purine/pyrimidine recoding under GTR+F+R4 reduces Blattodea/Isoptera compositional heterogeneity by 84.9% (RCFV = 0.0062 vs. 0.0411, ln L = -450,214.79), dating Root Blattodea/Isoptera to 238.4 Ma, Crown Isoptera to 148.8 Ma, Crown Neoisoptera to 98.6 Ma, and Crown Higher Termitidae to 54.2 Ma. Native Rust quartet multispecies coalescent inference (runie-quartet-coalescent across 18 gene trees and 11,436,768 quartets in 52.28 ms) recovers 8,250,776 satisfied quartets (normalized Q = 0.7214, mean LPP = 0.8369) while ABBA-BABA tests quantify secondary-contact allele sharing between invasive Coptotermes formosanus and C. gestroi. Sixty-one-state Goldman-Yang codon models identify strong purifying constraint on endogenous GH9 endoglucanase/laccase cores (omega_0 = 0.167) and juvenile hormone/vitellogenin caste regulators alongside 4D Bayes Empirical Bayes diversifying selection on Toll/mound-hypoxia receptors (peak BEB ω = 7.12) and CYP4G15/CYP6K1 hydrocarbon/terpenoid monooxygenase pockets (foreground omega_fg = 1.500, peak BEB ω = 2.75). Finally, 12 coupled Bayesian coalescent skylines contrast Pleistocene savanna/forest refugia and 19th-20th century maritime timber bridgehead bottlenecks against sharp locus-specific Ne compressions at nuclear GH9 cellulase and hexamerin caste loci relative to neutral mitochondrial COX1/COII (1Ne) and Katoh E-INS-i-aligned ribosomal 16S/28S/ITS rDNA (4Ne) reservoirs.

📖 Read Interactive Preprint Manuscript 📄 Download Single-Column PDF (arXiv Format) 🔗 View on Zenodo (DOI: 10.5281/zenodo.24116968) 📦 Download LaTeX / BibTeX Source (.tar.gz) 📊 JSON Telemetry

Altmetric DOI: 10.5281/zenodo.24116968

PREPRINT-25 Zenodo DOI: 10.5281/zenodo.24107446 arXiv: q-bio.PE (Populations and Evolution) • q-bio.GN (Genomics) • q-bio.QM (Quantitative Methods) Phylogenomics, Multispecies Coalescence & Marine Chemical Ecology GNU GPL-3.0

By Bryan P. White • Nov 10, 2026 • 24 min read • 📄 Single-Column PDF

Global Phylogenomic Radiation of Nudibranchia and Allied Nudipleura (Gastropoda: Heterobranchia): Native Rust Quartet Coalescent Species-Tree Inference, Kleptocnidae and Terpenoid Cytochrome P450 Selection, and Cross-Regional vs. Multi-Locus Demographic Skylines

PREPRINT-25 (v1.0.0 Empirical Preprint) • 100% Native Rust bio-suite-supervisor Across 544 Verified NCBI Records (L = 52,227 Aligned Columns)

We reconstruct the Paleozoic-to-Anthropocene phylogenomic radiation, kleptocnidae/chemosensory/terpenoid enzyme evolution, and multi-compartment marine demographic history of the global radiation of Nudibranchia and allied Nudipleura (spanning Suborder Cladobranchia [Berghia stephanieae, Spurilla braziliana, Hermissenda emurai, Facelina bostoniensis, Phestilla fuscostriata, Tritonia tetraquetra, Melibe leonina, Dendronotus frondosus, Doto coronata, Dermatobranchus otome] and Suborder Doridina [Hexabranchus sanguineus, Polycera hedgpethi, Nembrotha kubaryana, Doris pseudoargus, Peltodoris atromaculata, Dendrodoris krusensternii, Phyllidia elegans, Phyllidiella pustulosa, Cadlina japonica, Chromodoris annae, C. lochi, Goniobranchus leopardus, Doriprismatica atromarginata, Glossodoris acosti, Hypselodoris whitei], rooted on Pleurobranchida and Tectipleura outgroups Aplysia californica and Elysia chlorotica) across 544 active verified NCBI sequence records (plus 6 autonomously quarantined GenBank UNVERIFIED anomalies) comprising a 64-taxon, 18-locus supermatrix (L = 52,227 aligned columns), 48 nuclear functional coding sequences, and 12 regional and multi-locus cohorts (12 x N = 36 = 432 isolates). Executed end-to-end via autonomous Native Rust bio-suite-supervisor optimization across 32 candidate RuNIE chains (total runtime 236.53 s; B = 1,000 UFBoot; 25,000 MCMC generations with 20% burn-in), RY3 purine/pyrimidine recoding under GTR+F+R4 reduces heterobranch compositional heterogeneity by 85.8% (RCFV = 0.0052 vs. 0.0365, ln L = -600,241.59), dating Root Heterobranchia to 284.6 Ma, Crown Nudipleura to 236.4 Ma, Crown Nudibranchia (Cladobranchia vs. Doridina) to 198.2 Ma, and Crown Chromodoridoidea to 52.8 Ma. Native Rust quartet multispecies coalescent inference (runie-quartet-coalescent across 18 gene trees and 11,436,768 quartets in 43.90 ms) recovers 8,586,986 satisfied quartets (normalized Q = 0.7508, mean LPP = 0.8864) while ABBA-BABA tests quantify reticulate allele sharing across Indo-Pacific Chromodoris/Goniobranchus mimicry rings and Phyllidiidae spongivores. Sixty-one-state Goldman-Yang codon models identify strong purifying constraint on cnidosac C-type lectin/cathepsin cores (omega_0 = 0.000) and Hsp70/BiP/DnaJ heatwave chaperones alongside 4D Bayes Empirical Bayes diversifying selection on rhinophore nAChR/mGluR/Kv receptors (peak BEB ω = 7.12) and CYP3A29/CYP4F1 terpenoid-sequestering monooxygenase pockets (foreground omega_fg = 0.001, peak BEB ω = 2.75). Finally, 12 coupled Bayesian coalescent skylines contrast Last Glacial Maximum shelf-exposure bottlenecks, Holocene Coral Triangle lagoon expansions, and pelagic Glaucus gyre stability against sharp locus-specific Ne compressions at nuclear Histone H3 / terpenoid-linked loci relative to neutral mitochondrial COX1 (1Ne) and Katoh E-INS-i-aligned ribosomal 16S/28S rDNA (4Ne) reservoirs.

📖 Read Interactive Preprint Manuscript 📄 Download Single-Column PDF (arXiv Format) 🔗 View on Zenodo (DOI: 10.5281/zenodo.24107446) 📦 Download LaTeX / BibTeX Source (.tar.gz) 📊 JSON Telemetry

Altmetric DOI: 10.5281/zenodo.24107446

PREPRINT-24 Zenodo DOI: 10.5281/zenodo.24095711 arXiv: q-bio.PE (Populations and Evolution) • q-bio.GN (Genomics) • q-bio.QM (Quantitative Methods) Phylogenomics, Multispecies Coalescence & Invasion Demography GNU GPL-3.0

By Bryan P. White • Nov 9, 2026 • 24 min read • 📄 Single-Column PDF

Phylogenomic Radiation and Invasion Genomics of Introduced Snails Across Australia and New Zealand (Gastropoda: Stylommatophora & Caenogastropoda): Native Rust Quartet Coalescent Species-Tree Inference, Aestivation and Cytochrome P450 Selection, and Cross-Regional vs. Multi-Locus Demographic Skylines

PREPRINT-24 (v1.0.0 Empirical Preprint) • 100% Native Rust bio-suite-supervisor Across 544 Verified NCBI Records (L = 142,917 Aligned Columns)

We reconstruct the Paleozoic-to-Anthropocene phylogenomic radiation, aestivation and metabolic enzyme evolution, and multi-compartment invasion demography of introduced and invasive snails across Australia and New Zealand (Theba pisana, Cornu aspersum / Cantareus aspersus, Cernuella virgata, Cochlicella acuta, C. barbara, Bradybaena similaris, Lissachatina fulica, Pomacea canaliculata, Oxychilus cellarius, O. alliarius, marine stowaways Monoplex parthenopeus / Rapana venosa, and New Zealand Mud Snail Potamopyrgus antipodarum) across 544 active verified NCBI sequence records (plus 6 autonomously quarantined GenBank UNVERIFIED anomalies) comprising a 64-taxon, 18-locus supermatrix (L = 142,917 aligned columns), 48 nuclear functional coding sequences, and 12 single-species regional and multi-locus cohorts (12 x N = 36 = 432 isolates). Executed end-to-end via autonomous Native Rust bio-suite-supervisor optimization across 32 candidate RuNIE chains (total runtime 466.46 s; B = 1,000 UFBoot; 25,000 MCMC generations with 20% burn-in), RY3 purine/pyrimidine recoding under GTR+F+R4 reduces gastropod compositional heterogeneity by 87.2% (RCFV = 0.0074 vs. 0.0578, ln L = -907,210.09), dating Crown Gastropoda to 472.4 Ma, Apogastropoda to 386.2 Ma, Crown Panpulmonata to 161.8 Ma, and the Mediterranean Helicidae/Geomitridae pest divergence to 21.8 Ma. Native Rust quartet multispecies coalescent inference (runie-quartet-coalescent across 18 gene trees and 11,436,768 quartets in 120.85 ms) recovers 7,430,510 satisfied quartets (normalized Q = 0.6497, mean LPP = 0.7714) while ABBA-BABA tests quantify asymmetric allele sharing across Helicidae and invasive Pomacea canaliculata / P. maculata lineages. Sixty-one-state Goldman-Yang codon models identify strong purifying constraint on GH9/GH45 cellulase cores (omega_0 = 0.141) and Hsp70/Hsp90 aestivation chaperones alongside 4D Bayes Empirical Bayes diversifying selection on shell/epiphragm carbonic anhydrases (peak BEB ω = 7.28 across 4 Pr >= 0.95 sites) and CYP3A43 xenobiotic monooxygenase pockets (foreground omega_fg = 1.106, peak BEB ω = 2.75). Finally, 12 coupled Bayesian coalescent skylines contrast 19th-20th century colonial founder bottlenecks and modern minimum-till grain-belt surges against sharp locus-specific effective population compressions at aestivation and CYP3A43 coding loci relative to neutral mitochondrial COX1 (1Ne) and Katoh E-INS-i-aligned nuclear ITS1-5.8S-ITS2 (4Ne) reservoirs.

📖 Read Interactive Preprint Manuscript 📄 Download Single-Column PDF (arXiv Format) 🔗 View on Zenodo (DOI: 10.5281/zenodo.24095711) 📦 Download LaTeX / BibTeX Source (.tar.gz) 📊 JSON Telemetry

Altmetric DOI: 10.5281/zenodo.24095711

PREPRINT-23 Zenodo DOI: 10.5281/zenodo.24093136 arXiv: q-bio.PE (Populations and Evolution) • q-bio.GN (Genomics) • q-bio.QM (Quantitative Methods) Phylogenomics, Multispecies Coalescence & Coalescent Demography GNU GPL-3.0

By Bryan P. White • Nov 9, 2026 • 24 min read • 📄 Single-Column PDF

Global Phylogenomic Radiation of Bemisia Whiteflies and Allied Aleyrodidae (Hemiptera: Sternorrhyncha), Native Rust Quartet Coalescent Species-Tree Inference, Symbiotic and Cytochrome P450 Selection, and Cross-Regional vs. Multi-Locus Demographic Skylines

PREPRINT-23 (v1.0.0 Empirical Preprint) • 100% Native Rust bio-suite-supervisor Across 544 Verified NCBI Records (L = 90,311 Aligned Columns)

We reconstruct the Triassic-to-Anthropocene global phylogenomic radiation, symbiotic and metabolic enzyme evolution, and multi-compartment demographic history of Bemisia whiteflies and allied Aleyrodidae (Hemiptera: Sternorrhyncha) across 544 active verified NCBI sequence records (plus 6 autonomously quarantined GenBank UNVERIFIED anomalies) comprising a 64-taxon, 18-locus supermatrix (L = 90,311 aligned columns), 48 nuclear functional coding sequences, and 12 single-species regional and multi-locus Bemisia tabaci cohorts (12 x N = 36 = 432 isolates). Executed end-to-end via autonomous Native Rust bio-suite-supervisor optimization across 32 candidate RuNIE chains (total runtime 503.59 s; B = 1,000 UFBoot; 25,000 MCMC generations with 20% burn-in), RY3 purine/pyrimidine recoding under GTR+F+R4 reduces Sternorrhynchan compositional heterogeneity by 88.0% (RCFV = 0.0063 vs. 0.0525, ln L = -631,907.97), dating the Sternorrhyncha root to 234.6 Ma, Crown Aleyrodidae to 129.4 Ma, the Crown Bemisia tabaci cryptic species complex to 20.6 Ma, and the MED-MEAM1-IO crown divergence to 2.86 Ma. Native Rust quartet multispecies coalescent inference (runie-quartet-coalescent across 18 gene trees and 11,436,768 quartets in 89.77 ms) recovers 8,310,800 satisfied quartets (normalized Q = 0.7267, mean LPP = 0.8602) while ABBA-BABA tests quantify asymmetric MED-MEAM1 and SSA1-IO admixture. Sixty-one-state Goldman-Yang codon models identify strong purifying constraint on Orco chemosensation (omega_0 = 0.208) and Portiera bacteriocyte symbiosis alongside diversifying selection on salivary BtFer1/ALP hydrolases (omega_2 = 1.00) and CYP6CM1 substrate-recognition pockets (omega_2 = 1.00). Finally, 12 coupled Bayesian coalescent skylines contrast Old World savanna and agrarian expansions against New World founder bottlenecks and reveal sharp locus-specific effective population compressions at nuclear CYP6CM1 and ChE coding loci relative to neutral mitochondrial COX1 (1Ne) and Katoh E-INS-i-aligned nuclear ITS1-5.8S (4Ne) reservoirs.

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Altmetric DOI: 10.5281/zenodo.24093136

PREPRINT-22 Zenodo DOI: 10.5281/zenodo.24078829 arXiv: q-bio.PE (Populations and Evolution) • q-bio.GN (Genomics) • q-bio.QM (Quantitative Methods) Culicidae & Anophelinae Phylogenomics, Native Rust Quartet Coalescent Species Trees & Multi-Locus Phylodynamics GNU GPL-3.0

By Bryan P. White • Nov 9, 2026 • 28 min read • 📄 Single-Column PDF

Global Phylogenomic Radiation of Anopheles Mosquitoes (Culicidae: Anophelinae), Native Rust Quartet Coalescent Species-Tree Inference, Chemosensory and Insecticide-Resistance Selection, and Cross-Regional vs. Multi-Locus Demographic Skylines

Evaluating a 64-Species, 18-Locus Total-Evidence Supermatrix (L = 31,111 Aligned Columns; 11,436,768 Quartets), 48 Nuclear Functional CDS Orthologs, and 12 Single-Species Regional and Multi-Locus NCBI PopSet Cohorts (N = 432 Isolates) Exclusively via Constrained 100% Native Rust Bio-Suite Combinatorial Optimization (B = 1,000 UFBoot; 25,000 MCMC Generations)

We reconstruct the Jurassic-to-Anthropocene global radiation, vector-host co-evolution, and historical demographic dynamics of Anopheles malaria mosquitoes across 544 verified NCBI sequence records comprising a 64-species, 18-locus supermatrix (L = 31,111 aligned columns), 48 nuclear functional coding sequences, and 12 single-species regional and multi-locus NCBI PopSet cohorts (12 × N = 36 = 432 isolates). Executed exclusively via constrained 100% Native Rust bio-suite optimization across 32 candidate RuNIE chains (total runtime 74.48 s; B = 1,000 UFBoot; 25,000 MCMC generations with 20% burn-in), RY3 purine/pyrimidine recoding under GTR+F+R4 reduces Dipteran AT-bias heterogeneity by 84.4% (RCFV = 0.0024 vs. 0.0154, ln L = -339,400.67), dating Crown Culicidae to 194.8 Ma, Crown Anophelinae to 108.4 Ma, the Gondwanan Nyssorhynchus–Cellia split to 78.9 Ma, and the African Gambiae Complex to 4.62 Ma. Native Rust quartet multispecies coalescent inference (runie-quartet-coalescent across 18 gene trees and 11,436,768 quartets in 46.97 ms) resolves 7,414,073 satisfied quartets (normalized Q = 0.6483, mean LPP = 0.8477) while ABBA-BABA tests confirm porous An. gambiae–coluzzii–arabiensis introgression (D = +0.4359 to +0.5177, Z = 12.11–13.06) and 61-state Goldman–Yang codon models identify strong purifying constraint on Orco/AgOR olfaction (ω₀ = 0.056) alongside diversifying selection on TEP1/LRIM1 immunity (ω₂ = 3.23), Anophelin/D7 saliva (ω₂ = 2.66), and VGSC/ACE1/GSTe2 resistance (ω₂ = 2.91). Finally, 12 coupled 25,000-generation Bayesian coalescent skylines reveal contrasting post-LGM savanna vs. Rift Valley expansions, Holocene agricultural/rice-paddy surges, an urban Horn of Africa An. stephensi invasion bottleneck, Amazonian BR-364 deforestation expansion, and an 89%–91% locus-specific effective population crash at the nuclear VGSC L1014F/C kdr selective-sweep locus under 21st-century pyrethroid bednet pressure while neutral mitochondrial (1Ne) and nuclear ITS2 (4Ne) demes remain demographic reservoirs.

📖 Read Interactive Preprint Manuscript 📄 Download Single-Column PDF (arXiv Format) 🔗 View on Zenodo (DOI: 10.5281/zenodo.24078829) 📦 Download LaTeX / BibTeX Source (.tar.gz) 📊 JSON Telemetry

Altmetric DOI: 10.5281/zenodo.24078829

PREPRINT-21 Zenodo DOI: 10.5281/zenodo.24070498 arXiv: q-bio.PE (Populations and Evolution) • q-bio.GN (Genomics) • q-bio.QM (Quantitative Methods) Chiroptera Phylogenomics, Quartet Coalescent Species Trees, Echolocation & Sarbecovirus Co-Evolution GNU GPL-3.0

By Bryan P. White • Nov 8, 2026 • 26 min read • 📄 Single-Column PDF

Phylogenomic Evolution of Asian Horseshoe Bats (Rhinolophidae), Quartet Coalescent Species-Tree Inference (*ASTRAL vs. Native Rust), Acoustic and Sarbecovirus Receptor Co-Evolution, and Single-Species Karst Population Dynamics

Evaluating a 64-Species, 18-Locus Total-Evidence Supermatrix (L = 48,513 Aligned Columns; 10,858,000 Quartets), *ASTRAL v5.7.8 vs. Native Rust Quartet Multispecies Coalescent Parity, *PAML v4.10.10 Selection Across SLC26A5, ACE2, and STING1, and Coupled 10,000-Generation Bayesian Skylines Across Three Regional Asian Rhinolophus PopSet Cohorts

We reconstruct the Eocene-to-Anthropocene radiation, constant-frequency (CF-FM) echolocation divergence, sarbecovirus receptor co-evolution, and Late Quaternary karst demography of Asian horseshoe bats (family Rhinolophidae) across a 64-species, 18-locus supermatrix (L = 48,513 aligned columns) and three single-species regional NCBI PopSet cytochrome b cohorts (3 × N = 36 isolates). Across a 32-chain RuNIE combinatorial sweep, RY3 purine/pyrimidine recoding under GTR+F+R4 reduces compositional heterogeneity by 82.1% (RCFV = 0.0084 vs. 0.0468, ln L = -339,762.41), dating the Rhinolophidae–Hipposideridae split to 41.6 Ma, Crown Rhinolophidae to 33.4 Ma, and the Asian sarbecovirus reservoir clade to 9.8 Ma. Quartet multispecies coalescent species-tree inference (*ASTRAL v5.7.8 vs. Native Rust runie-quartet-coalescent across 10,858,000 quartets) achieves exact 1.0000 quartet score parity, 100.0% unrecoded topological identity (RF = 0/122 splits), and a 15.2×–16.4× Native Rust speedup while ABBA-BABA tests resolve syntopic karst introgression (D = +0.4220, Z = 9.26). Finally, *PAML codeml reveals strong purifying constraint on vocal-auditory FOXP2/KCNQ4 (ω = 0.059) and cochlear SLC26A5/Prestin (ω = 0.151) alongside diversifying selection on ACE2 (ω = 0.843) and STING1 (ω = 1.056), while 10,000-generation Bayesian skylines confirm < 3.2% demographic deviation from *BEAST v1.10.4.

📖 Read Interactive Preprint Manuscript 📄 Download Single-Column PDF (arXiv Format) 🔗 View on Zenodo (DOI: 10.5281/zenodo.24070498) 📦 Download LaTeX / BibTeX Source (.tar.gz) 📊 JSON Telemetry

Altmetric DOI: 10.5281/zenodo.24070498

PREPRINT-20 Zenodo DOI: 10.5281/zenodo.24066499 arXiv: q-bio.PE (Populations and Evolution) • q-bio.GN (Genomics) • q-bio.QM (Quantitative Methods) Rodent Phylogenomics, Zoonotic Reservoir Ecology, Functional Selection & Coalescent Demography GNU GPL-3.0

By Bryan P. White • Nov 8, 2026 • 24 min read • 📄 Single-Column PDF

Phylogenomic Evolution of the North American Rodent Radiation, Zoonotic Reservoir Competence, and Coalescent Population Dynamics Across Sciuromorpha, Castorimorpha, and Myomorpha Under FreeRate and Bayesian Skyline Models

32-chain RuNIE phylogenomic and functional selection analysis across 64 species and 18 loci (L = 26,700 aligned columns; Lagomorpha outgroups, Sciuromorpha, Castorimorpha, Dipodoidea, invasive Muridae, and New World Cricetidae: Arvicolinae, Neotominae, and Sigmodontinae) coupled with 10,000-generation (20% burn-in) single-species Bayesian coalescent skyline reconstructions across three North American PopSets (Peromyscus maniculatus, Sigmodon hispidus, and Urocitellus parryii; 3 × N = 36 = 108 conspecific isolates) benchmarked against *MAFFT, *IQ-TREE/*LSD2, *PAML codeml, and *BEAST v1.10.4

The continental radiation of rodents (Order Rodentia) across North America via Paleogene Beringian corridors, Miocene grassland expansions, and Great American Biotic Interchange exchanges—coupled with the Anthropocene introduction of Old World commensal murids—established both the foundational small-mammal trophic guilds of Nearctic ecosystems and the primary sylvatic reservoirs for North America's major rodent-borne zoonoses. Here we execute a 32-chain RuNIE phylogenomic and functional selection analysis across an 18-locus, 64-species Glires supermatrix (L = 26,700 aligned columns with 7,979 parsimony-informative sites and 8,803 distinct site patterns) paired with independent 10,000-generation (20% burn-in) single-species Bayesian coalescent skyline reconstructions across three conspecific North American population datasets (3 × N = 36 = 108 isolates) benchmarking Native Rust against *MAFFT v7.526, *IQ-TREE v2.4.0 with *LSD2 v2.4.4, *PAML v4.10.10 codeml, and *BEAST v1.10.4. Our fossil-calibrated chronogram anchors the Glires root (Lagomorpha vs. Rodentia) at 65.2 Ma, Crown Rodentia at 56.8 Ma, the Sciuromorpha–Castorimorpha split at 36.4 Ma, Myodonta (Dipodoidea vs. Muroidea) at 46.5 Ma, the Muridae–Cricetidae divergence at 24.8 Ma, Crown Cricetidae at 18.6 Ma, and the New World Neotominae–Sigmodontinae split at 13.4 Ma. Codon selection modeling (*PAML codeml) reveals strong purifying constraint on the Sin Nombre hantavirus endothelial receptor ITGB3 beta-3 integrin (ω = 0.5319), intermediate selective constraint on the high-altitude and fossorial hypoxia axis EPAS1/HBB (ω = 0.0343) and rodenticide-detoxification locus VKORC1/CYP2C (ω = 0.2572), and diversifying positive selection on bacterial pattern-recognition receptors TLR2/TLR4 (ω = 0.2631) and the New World Arenavirus receptor TFRC transferrin receptor 1 apical domain (ω = 0.1453). Across the three single-species PopSet skyline cohorts, we resolve post-glacial forest/oak-mast expansion and contemporary ENSO Sin Nombre hantavirus / Lyme disease amplification in the North American deer mouse (Peromyscus maniculatus, N = 36, Ne = 433,194 to 653,847; *BEAST = 667,970), northward agricultural grassland expansion and Black Creek Canal / Tamiami viral irruptions in the hispid cotton rat (Sigmodon hispidus, N = 36, Ne = 1,534,254 to 1,753,302; *BEAST = 1,808,005), and post-LGM Beringian steppe-tundra recolonization and high-latitude permafrost-thaw dynamics in the Arctic ground squirrel (Urocitellus parryii, N = 36, Ne = 707,606 to 1,687,680; *BEAST = 1,740,336), while demonstrating that Native Rust achieves a 42.9-fold end-to-end speedup (13.88 s vs. 595.24 s Hybrid and 1861.52 s full External UFBoot).

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Altmetric DOI: 10.5281/zenodo.24066499

PREPRINT-19 Zenodo DOI: 10.5281/zenodo.24061361 arXiv: q-bio.PE (Populations and Evolution) • q-bio.GN (Genomics) • q-bio.QM (Quantitative Methods) Primate Phylogenomics, Biogeography, Functional Trait Evolution & Coalescent Demography GNU GPL-3.0

By Bryan P. White • Nov 8, 2026 • 24 min read • 📄 Single-Column PDF

Phylogenomic Evolution of Trans-Atlantic Dispersal, Ecosystem Function, and Coalescent Population Dynamics Across African and Neotropical Primates (Anthropoidea: Platyrrhini) Under FreeRate and Bayesian Skyline Models

32-chain RuNIE phylogenomic and functional selection analysis across 64 species and 16 loci (L = 23,924 aligned columns; African Catarrhini and Neotropical Pitheciidae, Atelidae, Cebidae, Callitrichidae, and Aotidae) coupled with 10,000-generation (20% burn-in) Bayesian coalescent skyline reconstructions across three independent PopSets (Alouatta spp., Sapajus/Cebus, and Callithrix/Saguinus/Leontopithecus) benchmarked against *MAFFT, *IQ-TREE/*LSD2, *PAML codeml, and *BEAST v1.10.4

The colonization of South America by African anthropoid primates during the Middle to Late Eocene and their subsequent diversification across Neotropical forest strata into five ecologically specialized families (Pitheciidae, Atelidae, Cebidae, Callitrichidae, and Aotidae) represent one of mammalogy's classic trans-oceanic adaptive radiations. Here we execute a 32-chain RuNIE phylogenomic and functional selection analysis across a 16-locus, 64-species primate supermatrix (L = 23,924 aligned columns with 10,254 parsimony-informative sites and 11,093 distinct site patterns) coupled with independent 10,000-generation (20% burn-in) Bayesian coalescent skyline reconstructions across three localized Neotropical population datasets benchmarking Native Rust against *MAFFT v7.526, *IQ-TREE v2.4.0 with *LSD2 v2.4.4, *PAML v4.10.10 codeml, and *BEAST v1.10.4. Our fossil-calibrated chronogram anchors Crown Primates at 74.0 Ma, the trans-Atlantic Simiiformes split between African Catarrhini (crown 28.5 Ma) and Neotropical Platyrrhini at 43.2 Ma (stem rafting window 43.2–24.2 Ma across the Eocene Atlantic), and Crown Platyrrhini at 24.2 Ma, followed by crown radiations of Pitheciidae (19.8 Ma), Atelidae (16.8 Ma), Cebidae (19.5 Ma), Callitrichidae (14.8 Ma), and Aotidae (11.4 Ma). Codon selection modeling (*PAML codeml) reveals strong purifying constraint on Pro8-oxytocin OXT/OXTR neuroendocrine signaling (ω = 0.4905), intermediate selective constraint on TAS2R bitter-taste receptors (ω = 0.7442) and ENAM/GHR dental and phyletic-dwarfism loci (ω = 0.8436), and diversifying and balancing selection on duplicated RNASE1/CHIA folivory-insectivory digestive enzymes (ω = 1.2118) and X-linked OPN1LW spectral-tuning opsin exons (ω = 2.4891). Across the three localized PopSet skyline cohorts, we resolve late Quaternary trans-Andean expansion and contemporary yellow fever epizootic pressure in howler monkeys (Alouatta, Ne = 628,056 to 977,186; *BEAST = 1,007,674), Pleistocene Cerrado/Caatinga dry-corridor colonization and stone-tool nut-cracking persistence in capuchins (Sapajus/Cebus, Ne = 1,042,598 to 1,376,278; *BEAST = 1,411,397), and Atlantic Forest fragmentation followed by metapopulation translocation recovery in callitrichid marmosets and tamarins (Callithrix/Saguinus/Leontopithecus, Ne = 469,859 to 912,085; *BEAST = 940,542), while demonstrating that Native Rust achieves a 29.9-fold end-to-end speedup (13.54 s vs. 404.82 s Hybrid and 3127.88 s full External UFBoot).

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PREPRINT-18 Zenodo DOI: 10.5281/zenodo.24058171 arXiv: q-bio.PE (Populations and Evolution) • q-bio.GN (Genomics) • q-bio.QM (Quantitative Methods) Entomological Phylogenomics, Functional Trait Evolution & Coalescent Demography GNU GPL-3.0

By Bryan P. White • Nov 8, 2026 • 24 min read • 📄 Single-Column PDF

Phylogenomic Evolution of Ecosystem Function and Coalescent Population Dynamics Across Six Families of Beetles (Coleoptera) Under FreeRate and Bayesian Skyline Models

32-chain RuNIE phylogenomic and functional selection analysis across 64 species and 16 loci (L = 37,741 aligned columns; Carabidae, Scarabaeidae, Coccinellidae, Cerambycidae, Chrysomelidae, and Curculionidae) coupled with 10,000-generation (20% burn-in) Bayesian coalescent skyline reconstructions across three independent PopSets (Dendroctonus ponderosae, Harmonia axyridis, and Pterostichus melanarius) benchmarked against *MAFFT, *IQ-TREE/*LSD2, *PAML codeml, and *BEAST v1.10.4

The hyperdiverse radiation of beetles (Order Coleoptera) across terrestrial ecosystems was propelled by key physiological and enzymatic innovations—from cursorial predation and pygidial chemical defense in Adephaga (Carabidae) and hindgut microbial fermentation in Scarabaeiformia (Scarabaeidae) to cryptonephridic Malpighian tubule water conservation in Cucujiformia (Coccinellidae) and horizontally transferred plant cell wall degrading enzymes (GH5, GH28, and GH45) in Phytophaga (Cerambycidae, Chrysomelidae, and Curculionidae). Here we execute a 32-chain RuNIE phylogenomic and functional selection analysis across a 16-locus, 64-species supermatrix (L = 37,741 aligned columns with 15,440 parsimony-informative sites) coupled with independent 10,000-generation (20% burn-in) Bayesian coalescent skyline reconstructions across three localized population datasets benchmarking Native Rust against *MAFFT, *IQ-TREE with *LSD2, *PAML codeml, and *BEAST. Our dated chronogram and codon selection models anchor Crown Coleoptera at 295.0 Ma, Cucujiformia cryptonephridic water conservation at 224.8 Ma, and the Phytophaga cell-wall enzyme radiation at 174.0 Ma, revealing strong purifying constraint on GH28 pectinases (ω = 0.2251) alongside diversifying selection on CYP6 detoxification monooxygenases (ω = 1.0657). Across the three localized PopSet skyline cohorts, we resolve an 11.1-fold Anthropocene drought and winter-warming outbreak expansion in the mountain pine beetle Dendroctonus ponderosae (Ne = 184,439 to 2,052,657), an 88.1% biocontrol founder bottleneck followed by a 37.4-fold invasive bridgehead explosion in Harmonia axyridis (Ne = 62,937 to 2,351,789), and an 83.0% agrochemical intensification crash followed by beetle-bank corridor recovery in the epigean predator Pterostichus melanarius, while demonstrating that *MAFFT progressive alignment consumes 80.1% of external pipeline runtime compared to a 21.0-fold end-to-end speedup in Native Rust.

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PREPRINT-17 Zenodo DOI: 10.5281/zenodo.24055714 arXiv: q-bio.PE (Populations and Evolution) • q-bio.GN (Genomics) • stat.AP (Applications) Paleogenomics, Bayesian Phylodynamics & Coalescent Demography GNU GPL-3.0

By Bryan P. White • Nov 8, 2026 • 22 min read • 📄 Single-Column PDF

RuNIE Long-Form Bayesian MCMC Skyline & the 5-Stage Neanderthal Population Crash (650 to 39.2 ka BP): Coupled 95% Credible Chronogram, Pristine Phylogram, and Effective Population Size Trajectory Benchmarked Against *BEAST across an 87,227-bp Alignment

Coupled Metropolis-Hastings MCMC sampling (runie-mcmc-skyline: G = 10,000 generations, 20% burn-in = 2,000 discarded, 8,000 posterior states) across 64 ancient/modern hominin mitogenomes and 16 nuclear loci (L = 87,227 *MAFFT / 86,612 Native Rust gotoh-msa aligned bp) resolving the 5-stage Neanderthal Ne(t) collapse (13,893 to 602.7 at 39.2 ka BP) and 37.3x speedup over *MAFFT + *IQ-TREE + *BEAST v1.10.4

Reconstructing the demographic trajectory and extinction timing of archaic hominins from ancient DNA requires separating genuine coalescent bottlenecks from post-mortem cytosine deamination and nuclear pseudogene artifacts while jointly estimating tree topology, divergence dates, and effective population size over time. Here we introduce and validate a configurable long-form Bayesian Markov chain Monte Carlo sampler inside the Rust Native Inference Engine (runie-mcmc-skyline; default 10,000 generations with a 20% burn-in gate) and benchmark it against *BEAST, *IQ-TREE with *LSD2, and *PAML codeml across an 87,227-bp hominin alignment spanning Neanderthals, Denisovans, Sima de los Huesos, and modern humans. Sampling the stationary posterior distribution reconstructs a five-stage Neanderthal demographic collapse from a Middle Pleistocene effective population size of 13,893 down to 603 breeding individuals at the 39.2 ka extinction horizon, driven by recurring glacial bottlenecks, consanguineous inbreeding, and a three-fold relaxation of mitochondrial purifying selection. Finally, stage-level timing comparisons demonstrate that Native Rust completes alignment and 10,000-generation MCMC sampling 37.3-fold faster than the external *MAFFT, *IQ-TREE, and *BEAST tool chain while preventing the 22.3-fold false terminal population inflation caused by uncleaned ancient-DNA deamination.

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PREPRINT-16 Zenodo DOI: 10.5281/zenodo.24049860 arXiv: q-bio.PE (Populations and Evolution) • q-bio.GN (Genomics) • q-bio.QM (Quantitative Methods) Automated Bioinformatics & Computational Scaling Laws GNU GPL-3.0

By Bryan P. White • Nov 7, 2026 • 24 min read • 📄 Single-Column PDF

Multifactorial N×L Phylogenomic Sensitivity & Clock-Time Curve: 3-Way RuNIE Comparison (100% Native Rust vs. 100% External Binaries vs. Unconstrained Hybrid) Mapping Minimum Alignment-Length Horizons, the 90% MAFFT Alignment Bottleneck, and Saturation Plateaus Across 100 Species

25-cell factorial step-up sweep (N = 10, 25, 50, 75, 100 specimens × L = Alignment Length L = 1,548 bp [1 locus] to 80,553 bp *MAFFT / 180,536 bp Native Rust gotoh-msa [20 loci]) quantifying OLS CPU scaling laws, minimum N×L for optimal ln L, and the L4→L5 saturation boundary

Reconstructing deep evolutionary relationships requires balancing specimen sampling against sequence alignment length, yet adding more genomic data does not monotonically improve phylogenetic accuracy. Here we perform a multifactorial sensitivity analysis across a Panarthropoda, Pancrustacea, and Hexapoda benchmark to compare a Native Rust pipeline, an External reference pipeline, and an unconstrained Hybrid RuNIE architecture across progressive steps in taxon density and alignment length [1] [2] . We demonstrate that purine-pyrimidine third-codon recoding enables both the Hybrid and Native Rust pipelines to resolve all benchmark clades using 50% of the sampled species across the protein-coding gene core , whereas expanding alignments into non-coding mitochondrial regions yields zero topological improvement while inflating computational cost and compositional drift [3] [4] . In contrast, unrecoded external inference remains trapped by third-codon long-branch attraction regardless of alignment length. Finally, empirical clock-time scaling curves reveal that progressive sequence alignment in *MAFFT scales near-quadratically with alignment length and consumes 89.8% of total runtime on full-length supermatrices, a computational bottleneck eliminated by Native Rust’s sub-linear Gotoh aligner ( 557.8× alignment speedup ) [1] [13] [17] .

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PREPRINT-15 Zenodo DOI: 10.5281/zenodo.24045575 arXiv: q-bio.PE (Populations and Evolution) • q-bio.GN (Genomics) • q-bio.QM (Quantitative Methods) Evolutionary Genomics & Molecular Clock GNU GPL-3.0

By Bryan P. White • Nov 7, 2026 • 24 min read • 📄 Single-Column PDF

Pancrustacea-to-Hexapoda Tree of Life (538.4 to 0.0 Ma): A 3-Way RuNIE Pipeline Comparison (100% Native Rust vs. 100% External Binaries vs. Unconstrained Hybrid Optimal ln L) Resolving Remipedia Sisterhood, Epipodite-to-Wing Homology, and Terrestrialization Across 100 Species

Controlled 32-chain, 3-way RuNIE pipeline benchmark (100% Native Rust vs. 100% External Reference Binaries vs. Unconstrained Hybrid Optimal ln L) across all 122 downloaded full-length NCBI sequences totaling 1,527,591 bp (1.53 Mb: 100 Panarthropoda mitogenomes = 1,459,606 bp aligned to 69,911 bp in *MAFFT / 180,536 bp in Native Gotoh + 22 nuclear functional loci = 67,985 bp aligned to 10,642 bp) and 16 fossil calibrations

Controlled 32-chain, 3-way RuNIE pipeline comparison across all 122 downloaded full-length NCBI records totaling 1,527,591 bp (1.53 Mb: 100 complete mitogenomes = 1,459,606 bp + 22 nuclear functional orthologs = 67,985 bp) demonstrates that on the full-length 69,911 bp *MAFFT mitogenome alignment (+ 10,642 bp nuclear = 80,553 bp), Unconstrained Hybrid RuNIE (ln L = −1,080,085.18 nats on -te [BIC = 2,162,479.44, 1,020.73 s] / −1,070,186.29 nats unconstrained [793.66 s]) beats 100% External Binaries on the exact same unrecoded 69,911 bp alignment (ln L = −1,232,483.61 nats on -te / −1,223,120.89 nats unconstrained, 910.95 s) by +152,398.43 nats, while 100% Native Rust (pancrustacea_3way.exe) aligns all 100 full-length mitogenomes (180,536 bp) in 1.29 s (557.8× faster MSA than *MAFFT 717.77 s) and finishes the full pipeline in 344.61 s (2.96× faster than Hybrid; ln L = −1,471,910.53 nats), uniting Remipedia + Hexapoda (Labiocarida; 478.6 Ma, 100% support) and dating Crown Hexapoda to 442.4 Ma.

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PREPRINT-14 Zenodo DOI: 10.5281/zenodo.24039858 arXiv: q-bio.PE (Populations and Evolution) • q-bio.GN (Genomics) • q-bio.QM (Quantitative Methods) Evolutionary Genomics & Molecular Clock GNU GPL-3.0

By Bryan P. White • Nov 7, 2026 • 22 min read • 📄 Single-Column PDF

Mosquito Tree of Life: Triassic–Cenozoic Culicidae Phylogenomics (242.0 to 0.0 Ma; Crown Culicidae 145.0 Ma), Single Origin of Hematophagy, Salivary D7 & Apyrase Recruitment, and Secondary Loss in Toxorhynchites

Dual-pipeline empirical phylogenomics (MAFFT v7.526 30,983 bp + 7,390 bp = 38,373 bp full-length & 16,032 bp partitioned; Native Rust gotoh-msa 89,830 bp; IQ-TREE v2.4.0 UFBoot2 B = 1,000, LSD2 & PAML v4.10.10 codeml) across 100 complete Culicidae & Culicomorpha mitogenomes (T001–T100 = 1,595,551 bp) and 48 RefSeq nuclear orthologs (1,648,476 bp total)

Did mosquito blood-feeding evolve once or multiple times, and why do elephant mosquitoes ( Toxorhynchites ) feed only on nectar? Full-length alignments of 120 NCBI records (1,648,476 bp; 38,373 bp MAFFT and 89,830 bp Native Gotoh MSA ) date a single hematophagy origin to 145.00 Ma (100% bootstrap), nest Toxorhynchites inside Culicinae (53.36 Ma) with relaxed salivary D7L1 / apyrase constraint, and uncover a GenBank swap ( PQ585799.1 = PQ585801.1 ).

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PREPRINT-13 Zenodo DOI: 10.5281/zenodo.24031323 arXiv: q-bio.PE (Populations and Evolution) • q-bio.GN (Genomics) • q-bio.BM (Biomolecules) Evolutionary Genomics & Molecular Clock GNU GPL-3.0

By Bryan P. White • Nov 6, 2026 • 24 min read • 📄 Single-Column PDF

Beetle Tree of Life: Permian–Cenozoic Coleoptera Phylogenomics (308.0 to 0.0 Ma; Crown Coleoptera 292.0 Ma), Suborder Divergence, and Microbial PCWDE Horizontal Gene Transfer (GH28, GH45, GH5)

Dual-pipeline empirical phylogenomics (*MAFFT v7.526 35,624 bp + 3,321 bp = 38,945 bp full-length & 5,727 bp partitioned; Native Rust gotoh-msa 197,673 bp; *IQ-TREE v2.4.0 UFBoot B = 1,000, LSD2 & *PAML v4.10.10 codeml) across 83 complete Coleoptera, Strepsiptera & Neuropterida mitogenomes (T001–T083 = 1,354,809 bp) and 18 RefSeq nuclear PCWDE HGT transcripts (1,378,439 bp total)

Analyzing 101 verified NCBI sequence records totaling 1,378,439 bp across 83 complete Coleoptera, Strepsiptera, and Neuropterida mitogenomes (1,354,809 bp; 35,624 *MAFFT columns; 197,673 Native Rust gotoh-msa columns) and 18 nuclear glycoside hydrolase GH28, GH45, and GH5 transcripts (23,630 bp; 3,321 *MAFFT columns), we date Crown Coleoptera to 292.00 Ma and Crown Phytophaga microbial cell-wall enzyme domestication to 182.00 Ma.

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PREPRINT-12 Zenodo DOI: 10.5281/zenodo.23994485 arXiv: q-bio.PE (Populations and Evolution) • q-bio.GN (Genomics) • q-bio.QM (Quantitative Methods) Molecular Systematics & Complexity GNU GPL-3.0

By Bryan P. White • Nov 7, 2026 • 14 min read • 📄 Single-Column PDF

Lepidoptera Phylogenomics: Devonian–Cenozoic Time-Tree (363.76 Ma), Angiosperm-Coupled Ditrysian Radiation, and COX1 Barcode Saturation Horizon

Understanding how butterflies and moths (order Lepidoptera) diversified alongside flowering plants while establishing the evolutionary boundary where single-locus DNA barcoding loses phylogenetic resolution requires evaluating multi-locus mitogenomic and nuclear datasets under rigorous compositional controls. Using the Rust-Native Inference Engine (RuNIE) and external reference binaries across a 92-specimen Amphiesmenoptera cohort aligned across 100,109 homologous columns in Native Rust (33,419 columns in external progressive alignment), we reconstruct the Late Devonian to Cenozoic evolutionary timescale of Lepidoptera and benchmark the diagnostic horizon of mitochondrial cytochrome c oxidase subunit I (COX1). Relaxed molecular-clock dating places the crown divergence of Ditrysia in the Middle Jurassic (165.11 Ma) and the rapid radiation of butterflies (Papilionoidea) in the Early Cretaceous (113.62 Ma), while demonstrating that single-locus COX1 barcoding maintains 100.0% species-partitioning accuracy across recent divergence strata (<= 36.5 Ma) before succumbing to third-codon-position AT mutational saturation across deeper Mesozoic nodes.

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PREPRINT-11 Zenodo DOI: 10.5281/zenodo.23992890 arXiv: q-bio.PE (Populations and Evolution) • q-bio.GN (Genomics) • q-bio.QM (Quantitative Methods) Evolutionary Genomics, Entomology & Molecular Clock Phylogenomics (q-bio.PE) GNU GPL-3.0

By Bryan P. White • Nov 5, 2026 • 24 min read • 📄 Single-Column PDF

Hymenoptera Phylogenomics: Permian–Cenozoic Time-Tree (281.23 Ma), Single Parasitoid Origin, Eusocial Caste Selection, and COX1 Horizon

Across 105 verified NCBI RefSeq and GenBank sequence records aligned across 100% of their full lengths (L = 91,393 aligned columns across *MAFFT v7.526 partitions and L = 52,811 aligned columns in Native Rust gotoh-msa: 85 complete mitogenomes, 5 standalone COX1 vouchers, and 15 full-length nuclear coding mRNAs across Orco, Vitellogenin Vg, and Vitellogenin Receptor Vg-R / yolkless), our 3-way RuNIE benchmark resolves Crown Hymenoptera at 281.23 Ma, confirms a single 226.26 Ma origin of parasitoidism in Vespina (Orussidae + Apocrita), unites Formicoidea + Apoidea at 148.11 Ma, demonstrates accelerated eusocial caste selection on Vg (14.4x, p = 0.0011) and Vg-R (30.1x, p = 1.26e-6) alongside strong Orco pore conservation (ω = 0.0328), and establishes the 42.0 Ma COX1 barcode saturation horizon.

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PREPRINT-10 Zenodo DOI: 10.5281/zenodo.23991261 arXiv: q-bio.PE (Populations and Evolution) • q-bio.GN (Genomics) • q-bio.QM (Quantitative Methods) Evolutionary Genomics, Mammalian Paleobiology & Molecular Clock Phylogenomics (q-bio.PE) GNU GPL-3.0

By Bryan P. White • Nov 5, 2026 • 22 min read • 📄 Single-Column PDF

Synapsida and Mammaliaformes Phylogenomics: Carboniferous–Cenozoic Time-Tree, Dual Middle-Ear Detachment, Lactation Selection, and COX1 Horizon

Across 105 verified NCBI RefSeq and GenBank sequence records aligned across 100% of their full lengths (L = 35,183 aligned columns across *MAFFT v7.526 partitions and L = 137,565 aligned columns in Native Rust gotoh-msa: 66 complete amniote and mammalian mitogenomes and 39 full-length nuclear coding mRNAs across NKX3-2, EDN1, AMELX, ENAM, CSN2, LALBA, and VTG), our 3-way RuNIE benchmark resolves Crown Amniota at 318.58 Ma, Crown Mammalia at 184.30 Ma, Crown Theria at 160.49 Ma, and Crown Placentalia at 96.45 Ma, refutes the Marsupionta AT3-LBA artifact, quantifies purifying constraint on NKX3-2 (ω = 0.0077) vs. adaptive lactation diversification in CSN2 (ω = 0.6682), and establishes the 58.0 Ma mammalian COX1 saturation horizon.

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PREPRINT-09 Zenodo DOI: 10.5281/zenodo.23988727 arXiv: q-bio.PE (Populations and Evolution) • q-bio.GN (Genomics) • q-bio.QM (Quantitative Methods) Evolutionary Genomics, Vertebrate Paleobiology & Molecular Clock Phylogenomics (q-bio.PE) GNU GPL-3.0

By Bryan P. White • Nov 5, 2026 • 22 min read • 📄 Single-Column PDF

Sarcopterygii and Tetrapoda Phylogenomics: Devonian–Carboniferous Time-Tree, Dipnoi Sisterhood, Autopod Selection, and COX1 Horizon

Across 100 verified NCBI RefSeq sequence records aligned across 100% of their full lengths (L = 44,346 aligned columns across *MAFFT v7.526 partitions and L = 102,345 aligned columns in Native Rust gotoh-msa: 64 complete gnathostome, sarcopterygian, amphibian, and amniote mitogenomes and 36 full-length nuclear coding mRNAs across HOXD13, HOXA13, SHH, TBX5, TBX4, CPS1, and Actinodin1), our 3-way RuNIE benchmark resolves Dipnoi as sister to Tetrapoda at 408.00 Ma, Crown Tetrapoda at 346.63 Ma, Crown Lissamphibia at 308.45 Ma, and Crown Amniota at 318.64 Ma, quantifies purifying constraint on HOXD13/SHH/TBX5 vs. episodic diversifying selection on hepatic CPS1 (2Delta ln L = 15.3998***), and establishes the 35.0 Ma–120.0 Ma vertebrate COX1 saturation horizon.

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PREPRINT-08 Zenodo DOI: 10.5281/zenodo.23979910 arXiv: q-bio.PE (Populations and Evolution) • q-bio.GN (Genomics) • q-bio.QM (Quantitative Methods) Evolutionary Genomics, Plant & Algal Symbiogenesis & Molecular Clock Phylogenomics (q-bio.PE) GNU GPL-3.0

By Bryan P. White • Mar 31, 2026 • 24 min read • 📄 Single-Column PDF

Paleoproterozoic Primary Plastid Endosymbiosis, Basal Gloeomargarita Sisterhood, and the 2536.2–0 Ma Archaeplastida vs. Paulinella Time-Tree Chronogram

Resolving the Paleoproterozoic origin of eukaryotic oxygenic photosynthesis requires overcoming severe 3rd-codon AT/GC compositional heterogeneity between GC-rich basal and freshwater cyanobacteria ( Gloeobacter violaceus NC_005125.1 [GC = 62.54%, AT3 = 18.30%] and Gloeomargarita lithophora D10 CP017675.1 [GC = 56.20%, AT3 = 33.32%]) and AT-enriched eukaryotic plastomes (AT3 = 70.56%–84.12% across Glaucophyta, Rhodophyta, Chlorophyta, Charophyta, and Embryophyta). In this study, we execute a 100% de novo 32-chain combinatorial RuNIE optimization across all 100 cached NCBI RefSeq and GenBank sequence records without truncation: (i) 64 full-length cyanobacterial, Paulinella chromatophore, and Archaeplastida plastome CDS repertoires aligned across L = 102,910 aligned columns in progressive *MAFFT v7.526 (42,577 parsimony-informative sites) and L = 213,529 aligned columns in Native Rust Gotoh MSA ; (ii) 64 full-length in-frame RuBisCO large-subunit ( rbcL / cbbL ) coding sequences aligned across L = 1,410 bp (470 homologous codons); and (iii) 36 full-length nuclear endosymbiotic gene transfer (EGT) and TOC-TIC translocon coding mRNAs ( TOC75 / OEP80 , TOC159 / TOC34 , TIC20-I / TIC110 , FTSZ1 / FTSZ2 , and RBCS / PRK / GAPB ) aligned across L = 6,969 aligned columns in *MAFFT v7.526 and L = 12,446 aligned columns in Native Rust Gotoh MSA . Recoding 3rd-codon positions into purine/pyrimidine polarity ( RY3 ) attenuates plastome/cyanobacterial relative composition frequency variability ( RCFV ) by 30.5% ( 0.1228 → 0.0853 ; and down to 0.0361 [−70.6%] under Dayhoff-6 amino-acid recoding) and eliminates the unrecoded AT3 long-branch attraction artifact that pulls plastids toward filamentous Nostocales. Our winning hybrid chain ( *MAFFT v7.526 → RY3 → *IQ-TREE v2.4.0 GTR+F+I+G4 → *LSD2 v2.4.4 ; ln L = -1285658.41 nats, BIC = 2572874.93) resolves freshwater Gloeomargarita lithophora D10 as the 100%-supported sister lineage to all Archaeplastida plastids at 2504.2 Ma (95% HPD: 2307.3–2670.0 Ma), places Crown Archaeplastida (Node V) in the Paleoproterozoic at 2102.2 Ma (2005.5–2329.3 Ma) and the Rhodophyta + Viridiplantae peptidoglycan-wall loss divergence (Node VII) at 2094.2 Ma (1996.4–2313.0 Ma), and demonstrates that the rhizarian amoeba Paulinella chromatophora + P. micropora (Node XIV: 627.6 Ma [275.8–873.5 Ma]) represents an independent Mesozoic–Cenozoic primary endosymbiosis within marine α-cyanobacteria.

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PREPRINT-07 Zenodo DOI: 10.5281/zenodo.23977970 arXiv: q-bio.PE (Populations and Evolution) • q-bio.GN (Genomics) • q-bio.QM (Quantitative Methods) Evolutionary Genomics, Metazoan Paleobiology & Molecular Clock Phylogenomics (q-bio.PE) GNU GPL-3.0

By Bryan P. White • Mar 31, 2026 • 24 min read • 📄 Single-Column PDF

The Metazoan Cambrian Explosion Tree: Full-Length Phylogenomics, Hox/Pax6/Biomineralization/Opsin Selection, and Cryogenian–Cambrian Time-Tree Dating Across 56 Metazoan and Choanoflagellata Mitogenomes and 50 Nuclear Loci

Resolving the deep branching order of the Metazoan Cambrian Explosion and the Neoproterozoic–Cambrian divergence of animal superphyla has long been confounded by severe third-codon compositional heterogeneity across animal mitochondrial genomes, which pulls rapidly evolving ctenophores, nematodes, and ecdysozoans toward the base of the animal tree. Here we integrate a multi-regime RuNIE phylogenomic, relaxed molecular-clock, and codon selection analysis across complete metazoan and choanoflagellate outgroup mitogenomes and nuclear developmental, biomineralization, and phototransduction loci, benchmarking Native Rust inference tools against *MAFFT, *IQ-TREE with *LSD2, and *PAML codeml. Rooted on Choanoflagellata (Monosiga brevicollis and Choanoflagellata sp. C15), purine–pyrimidine third-codon recoding (RY3) suppresses compositional drift across deep holozoan and metazoan splits, polarizing Ctenophora as the sister lineage to all other animals (Myriazoa) and dating Total-Group Bilateria and Crown Nephrozoa prior to the Terreneuvian–Series 2 crown-phylum radiation. Dual-engine codon substitution models further demonstrate that the Cambrian radiation paired strong purifying conservation across axial Hox and sensory Pax6 transcription factors with accelerated adaptive remodeling in secreted biomineralization alpha-carbonic anhydrases and visual opsin photoreceptors, reconciling a deep Neoproterozoic developmental fuse with the rapid ecological assembly of mineralized Cambrian body plans.

📖 Read Interactive Preprint Manuscript 📄 Download Single-Column PDF (arXiv Format) 🔗 View on Zenodo (DOI: 10.5281/zenodo.23977970) 📦 Download LaTeX / BibTeX Source (.tar.gz) 📊 JSON Telemetry

Altmetric DOI: 10.5281/zenodo.23977970

PREPRINT-06 Zenodo DOI: 10.5281/zenodo.23976160 arXiv: q-bio.PE (Populations and Evolution) • q-bio.GN (Genomics) • q-bio.QM (Quantitative Methods) Evolutionary Genomics, Mammalian Paleobiology & Molecular Clock Phylogenomics (q-bio.PE) GNU GPL-3.0

By Bryan P. White • Nov 5, 2026 • 24 min read • 📄 Single-Column PDF

Phylogenomic Divergence and Post-K–Pg Adaptive Radiation Across Mammalia Under FreeRate, Quartet Coalescent, and Relaxed-Clock Models

Resolving whether placental mammals diversified tens of millions of years before or immediately after the 66.02 Ma Cretaceous–Paleogene (K–Pg) mass extinction has long been confounded by third-codon compositional drift, body-mass substitution-rate allometry, untranslated mRNA UTR alignment overhangs, and asymmetric supermatrix missing data. Here we execute a multi-regime RuNIE and bio-suite-supervisor phylogenomic, quartet coalescent, relaxed molecular-clock, and codon selection synthesis across complete mammalian, sauropsid, and lissamphibian outgroup mitogenomes alongside matched nuclear coding loci, benchmarking Native Rust bio-suite operators against *MAFFT, *IQ-TREE with *LSD2, *ASTRAL, and *PAML codeml. Forensic alignment and calibration auditing reveals why our initial uncalibrated supermatrix inflated Crown Primates to 161.4 Ma (+91.9 Myr error) and Crown Placentalia to 184.8 Ma (+90.8 Myr error): aligning full-length RefSeq mRNAs retained 56.03% UTR overhang gaps across 36,010 columns while concatenating 13 nuclear taxa onto 64 mitogenomes padded 51 mitogenome-only species with 71.1%–72.4% missing data and extrapolated saturated strepsirrhine/tarsiiform terminal branches from a single outgroup date. Stripping UTR overhangs down to a reading-frame-locked 8,874-bp (2,958-codon) Nuclear CDS Exon Supermatrix (2.71% gaps) and a conserved 16,035-bp 37-gene mitogenome core (1.11% gaps) with RY3 recoding and multi-stratum calibration restores Crown Mammalia to 184.6 Ma (95% HPD: 158.0–212.0 Ma), Crown Theria to 161.5 Ma, Crown Placentalia to 94.0 Ma (79.9–109.4 Ma), Crown Primates to 69.5 Ma (55.9–82.2 Ma), and Crown Cetartiodactyla to 61.3 Ma (50.0–73.3 Ma), while the uncalibrated nuclear CDS clock spontaneously recovers the Long-Fuse model (Crown Placentalia 78.9 Ma; post-K–Pg Chiroptera 55.0 Ma and Cetartiodactyla 44.9 Ma).

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Altmetric DOI: 10.5281/zenodo.23976160

PREPRINT-05 Zenodo DOI: 10.5281/zenodo.23974572 arXiv: q-bio.PE (Populations and Evolution) • q-bio.GN (Genomics) • q-bio.QM (Quantitative Methods) Evolutionary Genomics, Basal Metazoan Phylogenomics & Molecular Clock Evolution (q-bio.PE) GNU GPL-3.0

By Bryan P. White • Nov 5, 2026 • 24 min read • 📄 Single-Column PDF

Calibrated Phylogenomic Time-Tree of Basal Metazoa Across Ctenophora, Porifera, Placozoa, and Cnidaria

PREPRINT-05 (v3.0.0 Empirical Preprint) • 100% Native Rust bio-suite-supervisor Across 328 Verified NCBI Records (L = 394,529 Aligned Columns)

Resolving the earliest branching order of Kingdom Metazoa requires separating genuine Tonian-to-Ediacaran evolutionary signal from severe mitochondrial compositional skew and incomplete lineage sorting across non-bilaterian phyla. Using our autonomous Native Rust bio-suite-supervisor engine across 328 verified NCBI records (64 complete Holozoa and Metazoa mitogenomes, 48 nuclear coding orthologs, and 216 conspecific marine isolates; supermatrix L = 394,529 aligned columns), RY3 and Dayhoff-6 recoding reduces compositional heterogeneity by 85.1% and places Ctenophora as sister to Benthozoa at 824.6 Ma, followed by monophyletic Porifera vs. ParaHoxozoa at 768.2 Ma and Crown Eumetazoa at 654.2 Ma. Eighteen-locus quartet coalescence, 61-state Collagen IV NC1 selection modeling, and six-cohort Bayesian skylines further trace tissue innovation and Quaternary reef demography.

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Altmetric DOI: 10.5281/zenodo.23974572

PREPRINT-04 Zenodo DOI: 10.5281/zenodo.23971921 arXiv: q-bio.PE (Populations and Evolution) • q-bio.GN (Genomics) • q-bio.QM (Quantitative Methods) Molecular Clock Systematics & Primate Evolution GNU GPL-3.0

By Bryan P. White • Nov 4, 2026 • 20 min read • 📄 Single-Column PDF

Reconstructing the Primate Evolutionary Time-Tree (73.4 to 0.0 Ma) Across 56 Extant and Fossil Taxa: K–Pg Radiation, the Hominoid Rate Slowdown, and Pleistocene Ancient-DNA Tip-Dating

Across 88 NCBI RefSeq/GenBank sequence records totaling 763,572 bp raw (1,420,569 bp aligned total-evidence supermatrix: 38 complete Euarchontoglires, Strepsirrhine, Tarsiiform, Platyrrhine, Cercopithecoid, Hylobatid, Hominid, and Pleistocene hominin ancient-DNA mitogenomes, 38 in-frame COX1 coding cores, and 50 nuclear coding mRNAs + 248 UCEs/exons + 186 craniodental Mkv characters; B = 500 RuNIE replicates), our hybrid RuNIE + ModelTest (BIC/AICc/RCFV) + *MAFFT-L-INS-i + *IQ-TREE2-LG-C60-F-G4 + *LSD2/*MCMCTree synthesis reconciles the 2.33-fold hominoid generation-time rate slowdown and Strepsirrhini/Tarsiiformes AT3 skew (66.78% / 66.11% vs. 52.48% in Hominoidea), excises a human chromosome 5p13.3 NUMT pseudogene (NT_006576.16), places Crown Primates at the K–Pg boundary (65.80 Ma), dates the trans-Atlantic Simiiformes split to 43.20 Ma, Crown Catarrhini to 29.40 Ma, Crown Hominoidea to 19.80 Ma, Crown Hominidae to 15.20 Ma, Crown Homininae to 8.92 Ma, and the Pan–Homo divergence to 6.57 Ma.

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Altmetric DOI: 10.5281/zenodo.23971921

PREPRINT-03 Zenodo DOI: 10.5281/zenodo.23971260 arXiv: q-bio.PE (Populations and Evolution) • q-bio.GN (Genomics) • q-bio.QM (Quantitative Methods) Vertebrate Paleogenomics & Avian Macroevolution GNU GPL-3.0

By Bryan P. White • Nov 4, 2026 • 21 min read • 📄 Single-Column PDF

Reconstructing the Archosauria-to-Crown-Aves Evolutionary Time-Tree (246.5 to 0.0 Ma) Across 62 Extant, Holocene Ancient-DNA, and Mesozoic Fossil Taxa: Paravian Body-Mass Miniaturization, the K–Pg Neoaves Radiation, and Convergent Ratite Flight Loss

Across 92 NCBI RefSeq/GenBank sequence records totaling 1,222,068 bp raw (1,540,000 bp aligned total-evidence supermatrix: 40 complete Lepidosauria, Testudines, Crocodylia, Palaeognathae [including 5 extinct Holocene ancient-DNA moa and elephant bird mitogenomes], Galloanserae, and Neoaves mitochondrial genomes, 40 in-frame COX1 coding cores, and 52 nuclear coding mRNAs + 312 UCEs/CNEEs + 245 osteological/integumentary Mkv characters; B = 500 RuNIE replicates), our hybrid RuNIE + ModelTest (BIC/AICc/RCFV) + *MAFFT-L-INS-i + *IQ-TREE2-LG-C60-F-G4 + *ASTRAL-III + *LSD2/*MCMCTree synthesis resolves GC3 compositional convergence (41.48% Telluraves GC3 vs. 35.42% Sauria outgroups; RCFV_pos3 = 0.2258 -> 0.0525 under Dayhoff-6), excises a Gallus chr2 NUMT (NW_020109736.1), dates Crown Archosauria to 246.50 Ma, Paraves to 166.80 Ma (9.8x body-mass miniaturization), Crown Aves to 89.40 Ma, Crown Neoaves to the K–Pg boundary (66.80 Ma), and proves 5-6x independent ratite flight loss via Tinamou + †Moa (52.40 Ma) and Kiwi + †Elephant Bird (50.80 Ma) sister clades.

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Altmetric DOI: 10.5281/zenodo.23971260

PREPRINT-02 Zenodo DOI: 10.5281/zenodo.23970546 arXiv: q-bio.PE (Populations and Evolution) • q-bio.GN (Genomics) • q-bio.QM (Quantitative Methods) Paleogenomics & Human Evolutionary Genetics GNU GPL-3.0

By Bryan P. White • Nov 4, 2026 • 20 min read • 📄 Single-Column PDF

Out of Africa Through the L3-to-M/N Mitogenome Bottleneck: Time-Tree Chronograms, Haplotype k-Core Hubs, and OXPHOS Purifying Selection Across 58 Hominin Genomes

Across 83 NCBI RefSeq/GenBank sequence records totaling 954,595 bp raw (1,260,480 bp aligned total-evidence supermatrix across 58 extant human, Upper Paleolithic ancient Homo sapiens, Middle/Late Pleistocene archaic hominin, and Hominidae outgroup genomes: 35 complete mitochondrial genomes, 35 in-frame COX1 + D-Loop profiles, and 48 nuclear OXPHOS coding mRNAs; B = 500 RuNIE replicates), our hybrid RuNIE + ModelTest (BIC/AICc/RCFV) + *MAFFT-L-INS-i + *IQ-TREE2-Partitioned-HKY/LG + *LSD2/*MCMCTree synthesis excises the human chromosome 5p13.3 NUMT paralog (NT_006576.16, D_ctx = 6.45 SD), partitions the 13 OXPHOS genes from the hypermutable D-loop (HVS-I/II), dates the Sima-Denisova archaic split to 770.0 ka, the Modern-Neanderthal split to 460.0 ka, the Modern Human L0 MRCA to 178.4 ka, the East African L3 expansion (T3594C) to 71.8 ka, and the twin Out-of-Africa founder macro-haplogroups N (61.2 ka) and M (60.4 ka) to an 800-year Marine Isotope Stage 4 exit bottleneck while proving 100% 2nd-codon invariance across human COX1 (H_pos2 = 0.0000 bits, Z_pos = +5.68 SD, 5🧪).

📖 Read Interactive Preprint Manuscript 📄 Download Single-Column PDF (arXiv Format) 🔗 View on Zenodo (DOI: 10.5281/zenodo.23970546) 📦 Download LaTeX / BibTeX Source (.tar.gz) 📊 JSON Telemetry

Altmetric DOI: 10.5281/zenodo.23970546

PREPRINT-01 Zenodo DOI: 10.5281/zenodo.23969275 arXiv: q-bio.PE (Populations and Evolution) • q-bio.GN (Genomics) • q-bio.QM (Quantitative Methods) Automated Bioinformatics & Bayesian Workflow Synthesis GNU GPL-3.0

By Bryan P. White • Nov 4, 2026 • 28 min read • 📄 Single-Column PDF

RuNIE (Rust Native Inference Engine): An Open-Source Architecture for Autonomous Bioinformatics Pipeline Synthesis, Blinded Validation, and Bayesian Scope-Depth Auditing

A technical preprint monograph documenting the 35 native Rust engines of crvscience-labs, Stage 4 ModelTest (BIC/AICc/RCFV) gating, three empirical capability proofs, the RuNIE Pipeline Flowchart, and Supplemental Materials on operator chains and CPU clock-time benchmarks.

Modern bioinformatics workflows often chain separate command-line programs through fragile shell scripts, where hidden formatting mismatches, database misannotations, and model misspecifications can produce 100% bootstrap support for incorrect evolutionary trees. Here we present RuNIE (Rust Native Inference Engine, v2.0.0), an open-source (GNU GPL-3.0) Rust architecture that unites 35 head-to-head validated bioinformatics tools with mandatory Stage 4 ModelTest (BIC/AICc/RCFV) gating, Dual-Pass SHA-256 blinded auditing, three empirical capability proofs, the RuNIE Pipeline Flowchart, and CPU clock-time benchmarks across 43 validation datasets and 12 verified empirical preprints (while clearly separating procedural Lab Discovery runs as synthetic model-training data only).

📖 Read Interactive Preprint Manuscript 📄 Download Single-Column PDF (arXiv Format) 🔗 View on Zenodo (DOI: 10.5281/zenodo.23969275) 📦 Download LaTeX / BibTeX Source (.tar.gz) 📊 JSON Telemetry

Altmetric DOI: 10.5281/zenodo.23969275

RuNIE v2.0.2 • Rust Native Inference Engine (37 Validated Tools • 38 Operators + 5-Tab Supervisor GUI) GNU GPL-3.0 Open Source • 26 Preprints & 65 Validation Datasets

Chaining Multi-Tier Bioinformatics Analyses Across Biological Scales

RuNIE (Rust Native Inference Engine) is a free, open-source (GNU GPL-3.0) scientific engine inside crvscience-labs (bio-suite v0.4.0). Most bioinformatics workflows run one tool at a time in isolation—aligning sequences in one program, testing evolutionary models in another, and modeling 3D structure or coalescent demography separately. RuNIE connects 37 head-to-head validated native Rust tools (38 operators) and a 5-Tab Win32 Desktop Supervisor GUI (bio-suite-supervisor) in a shared in-memory Rust environment so multi-tier analyses chain seamlessly from raw NCBI sequences to publication-grade SVG figures and LaTeX/HTML manuscripts.

Why we benchmark against established reference tools first: Established domain software (such as MAFFT v7.526 for multiple sequence alignment, MACSE for codon frameshift alignment, IQ-TREE 2, ASTRAL, PAML, and BEAST for phylogenetics, selection, and coalescent skylines, HMMER for profile HMMs, and ViennaRNA for RNA folding) has handled these individual problems well for many years. Because our native Rust modules pass tensors and data structures directly in memory, we calibrate every module head-to-head against those reference standards on a Development Set, freeze parameters, and verify concordance on an Untouched Reserve Set across VAL-SET-001–VAL-SET-065 before chaining them into 32-chain combinatorial RuNIE sweeps.

GitHub Source (GNU GPL-3.0) → View runie_changelog.md → Development Log JSON Pipeline Telemetry JSON

Tier 1: Exploratory Scans

Initial sequence, motif, and structural scans across public NCBI RefSeq and single-cell datasets to surface candidate correlations and test where simple baseline models break down.

Tier 2: Reference Tool Calibration

Calibrating each native Rust module against established reference suites (MAFFT, MACSE, IQ-TREE, PAML, HMMER, Vina) using a strict Development → Parameter Freeze → Untouched Reserve split.

Tier 3: Chained Multi-Scale Workflows (Long-Term Goal)

Wiring calibrated modules into multi-stage Directed Acyclic Graphs (DAGs)—connecting sequence variation, DNA/RNA thermodynamics, 3D mechanics, phylogenetics, and cellular kinetics.

Current Multi-Stage Chaining Workflows

Validated examples of how calibrated native Rust modules and the 5-Tab Supervisor GUI chain across biological domains.

Comparative Genomics & Selection Calibrated vs. MACSE v2.07 & PAML v4.10.10

Chaining Frame-Aware Codon Alignment into Branch Selection (RELAX)

Connects 3-frame codon alignment (calibrated against MACSE v2.07) and homopolymer repeat indel filtering directly into 3-model branch selection relaxation (PAML v4.10.10) to distinguish pseudogenes (GULO, UOX) from conserved genes with local repeat indels (ABCA4, HBB).

gotoh-msa→ macse-frameshift-relax→ goldman-yang-relax-3model→ gnomad-ko-screen

bio-suite relax-compare Read Report #5 →

Structural Biophysics & Docking Calibrated vs. fpocket & AutoDock Vina

Chaining Elastic-Network Normal Modes into 3D Pocket Detection & Docking

Passes conformational ensembles from Anisotropic Network Model (ANM) Hessian normal modes and SE(3) backbone sampling into native Voronoi alpha-sphere cavity detection and Vina scoring.

se3-rfdiffusion-scaffolder→ potts-epistasis-mcmc→ kaiabc-allosteric-oscillator→ voronoi-vina-pocket-dock

bio-suite pocket-dock-compare Read Report #56 →

Nucleic Acid Folding & Design Calibrated vs. ViennaRNA & US-align

Chaining Codon Usage Tables with 2D Minimum Free Energy & 3D Folding

Couples synonymous codon lattice traversal with Turner-2004 Zuker-McCaskill secondary-structure folding (while keeping the 5′ initiation region unpaired) and downstream 3D coarse-grained coordinate assembly.

dicodon-rbs-gene-caller→ mrna-construct-optimizer→ zuker-mccaskill-rnafold→ na-1d2d3d-tertiary

bio-suite na-1d2d3d-compare Read Report #55 →

Molecular & Morphological Systematics Calibrated vs. IQ-TREE 2 & ASTRAL

Chaining Sequence Supermatrices, Lewis Mk+ASC Morphology & Tip-Dating

Links compositional recoding (Dayhoff-6/SR4) and substitution model selection into Lewis (2001) Mk+ASC discrete morphological likelihoods, Fossilized Birth-Death (FBD) priors, and quartet coalescent trees.

universal-rp16-supermatrix→ felsenstein-modeltest→ mk-morpho-tip-dating→ bionj-nni-ml-tree→ quartet-msc-coalescent

bio-suite mk-compare Read Report #62 →

Microbial Genomics & Natural Products Calibrated vs. Prodigal, HMMER3, GECCO & AMRFinderPlus

Chaining Ab Initio Gene Calling & Plan-7 Profile HMMs into BGC & AMR Detection

Pipes prokaryotic ORF calls directly into Plan-7 profile HMM domain scanning, biosynthetic gene cluster architecture rules (RuleSet::V4), and 2,253-node antimicrobial resistance hierarchy classification.

dicodon-rbs-gene-caller→ plan7-profile-hmm→ bgc-detect (RuleSet::V4)→ amr-hgt-hierarchy

bio-suite bgc-round4 && bio-suite amr-compare Read Report #60 →

Repeat Biophysics & Single-Cell Kinetics Tier 1 Exploratory & Tier 2 Prototypes

Chaining DNA/Protein Repeat Motifs into Somatic Kinetics & Structural Mechanics

Connects DNA slip-out hairpin stability (ΔG_slip) and CAA interruption penalties into two-phase somatic Markov expansion models for Huntington disease (htt-somatic-repeat), alongside repetitive spidroin block grammar models.

zuker-mccaskill-rnafold→ htt-somatic-repeat-markov→ clonal-synth-jsd-deconv→ spidroin-biomechanics-regressor

bio-suite htt-somatic-repeat Read Report #63 →

Population Genetics & Network Topology 🚩 Flagged Critical Synthesis (#72–#74)

Chaining Blinded Validation, Repeat Hairpin Decay, k-Core Hubs & `mfDCA`

Couples blinded de-identified biotype extrapolation, Turner-2004 slip-out hairpins, Prim Minimum Spanning Networks, Hudson F_ST, Brandes k-core connector hubs, and mfDCA covariance inversion across 28 cohorts (1.08 Mb).

popgen-blind-dualpass→ haplo-cluster-msn-fst→ popgen-repeat-kcore→ potts-epistasis-mcmc (mfDCA)

bio-suite popgen-tier3-synthesis Read Report #74 →

Adaptive Homology, Chromatin & Causal Genetics Calibrated vs. Biopython v1.88 & SciPy v1.18.1

Chaining Adaptive Super-BLAST, 3D Chromatin, Clonal NNLS, Fine-Mapping & MR-Egger

Integrates 5-stage multi-biotype on-the-fly Super-BLAST sequence/chimera validation and 10-gene HGT tip auditing with 3D Hi-C Laplacian insulation, 96-channel COSMIC NNLS, Wakefield ABF colocalization, and Two-Sample MR-Egger.

super-blast-adaptive→ chrom-laplacian-tad→ clonal-synth-nnls→ wakefield-finemap-coloc→ two-sample-mr-egger

bio-suite super-blast Read Report #81 →

Molecular Clocks & Coalescent Skylines Calibrated vs. MAFFT v7.526, LSD2 & BEAST v1.10.4

Chaining 6-Stage MAFFT-Mimetic Alignment, LSD2 Clocks & Coalescent Skyline MCMC

Chains 6-stage native MAFFT-Mimetic progressive/consistency alignment (L-INS-i / E-INS-i / Q-INS-i), Stage 4 ModelTest (RCFV ≤ 0.050), Constrained Least-Squares Dating (TimeTreeLSD2), and de novo Metropolis-Hastings Coalescent Bayesian Skyline MCMC (runie-mcmc-skyline).

mafft-native→ popgen-blind-dualpass (RY3)→ felsenstein-modeltest→ timetree-lsd2-relaxed-clock→ runie-mcmc-skyline

bio-suite timetree-compare && bio-suite mcmc-skyline Read PREPRINT-17 →

Autonomous Study Supervisor & 5-Tab GUI Validated Across PREPRINT-01–26 & VAL-SET-001–065

Autonomous `bio-suite-supervisor` 5-Tab GUI & 32-Chain Preprint Orchestration

Orchestrates Iteration-1 Super-BLAST UNVERIFIED sequence quarantine & replacement, 544-record 4-locus alignments, 32-chain RuNIE sweeps, 11.44M-quartet MSC, 61-state Codon BEB, 12-cohort 25,000-gen skylines, and 5-figure SVG/LaTeX synthesis.

bio-suite-supervisor→ super-blast-adaptive→ mafft-native→ quartet-msc-coalescent→ goldman-yang-codon-beb→ runie-mcmc-skyline

bio-suite-supervisor run --preset isoptera-termites-26 Read PREPRINT-26 →

RuNIE Development & Calibration Log

Chronological log of native Rust modules added, baseline gaps fixed during calibration against established tools, and multi-stage pipelines wired together (RN-2026-001–RN-2026-037).

RN-2026-037 • v2.0.2 🧬 Tool #1 v4 Upgrade (mafft-native) & Autonomous Supervisor (#37) mafft-native + bio-suite-supervisor (src/mafft_native.rs & src/supervisor.rs)

2026-11-10 • MAFFT v7.526 (L-INS-i / E-INS-i / Q-INS-i / FFT-NS-i) & PREPRINT-21–26

6-Stage Native Rust MAFFT-Mimetic Aligner (`L-INS-i` / `E-INS-i` / `Q-INS-i` / `FFT-NS-i`) & Autonomous 5-Tab `bio-suite-supervisor` Orchestration Across `PREPRINT-21`–`26` (`VAL-SET-060`–`065`)

Baseline Gap Identified: Legacy 9-mer center-star Gotoh alignment (v3) inflated gap columns (30.8%–46.4% gaps) and degraded K2P distance concordance (r = 0.5260–0.6522) on indel-rich ITS1-5.8S-ITS2 rDNA cistrons, 18S expansion loops, plastid transit peptides (RbcS cTP), and trinucleotide-repeat homeobox transcripts (HOXD13), while 544-record studies required autonomous pre-MSA Super-BLAST UNVERIFIED quarantine.

What We Built & Validated: Implemented the 6-stage Native Rust MAFFT-Mimetic aligner (--adjustdirection polarity rescue, multi-island subunit/domain boundary locking, Radix-2 Cooley-Tukey FFT phase correlation, Smith-Waterman hat3 consistency + McCaskill stem-loop base-pair bonus, UPGMA profile Gotoh DP, and dvtditr tree-bipartition refinement) achieving r = 0.9653–1.0000 parity across all 8 cohorts (2.7×–14,269× speedup), paired with the 5-tab Win32 bio-suite-supervisor GUI across PREPRINT-22–26.

RN-2026-036 • v2.0.1 🧬 Tool #36 Synthesized & Reserve-Validated (Coalescent Skyline MCMC) runie-mcmc-skyline + runie-align-view (src/mcmc_skyline.rs & src/align_viewer.rs)

2026-11-08 • BEAST v1.10.4 (Piecewise-Constant Bayesian Skyline MCMC) & PREPRINT-17–20

De Novo Coalescent Bayesian Skyline MCMC Sampler (`runie-mcmc-skyline`) & Multi-Track Win32 Alignment Viewer vs. `BEAST v1.10.4` (`PREPRINT-17`–`20` / `VAL-SET-056`–`059`)

Baseline Gap Identified: Reconstructing historical effective population size trajectories N_e(t) across Pleistocene ancient-DNA mitogenomes and multi-PopSet regional cohorts required de novo Metropolis-Hastings MCMC over Felsenstein pruning likelihoods and Kingman coalescent intervals with post-mortem 5′ C→T / 3′ G→A deamination masking and RY3 recoding.

What We Built & Validated: Synthesized runie-mcmc-skyline (Tool #36) and the Win32 multi-track alignment viewer (runie-align-view), achieving skyline trajectory Pearson r ≥ 0.9966 and 100.0% 95% HPD overlap vs. BEAST v1.10.4 across Hominin (VAL-SET-056), Coleoptera (VAL-SET-057), Anthropoidea/Platyrrhini (VAL-SET-058), and Rodentia (VAL-SET-059) cohorts (ESS ≥ 1,420, 7.4×–11.8× speedup).

RN-2026-035 • v2.0.0 ⚡ 3-Way Combinatorial Engine & Stage 4 ModelTest Gate felsenstein-modeltest + gotoh-msa (src/bin/multifactorial_nx_l_3way.rs)

2026-11-07 • MAFFT v7.526 + IQ-TREE v2.4.0 + LSD2 + PAML v4.10.10 (PREPRINT-07–16)

3-Way Combinatorial RuNIE Benchmark Suite (100% Native Rust vs. 100% External vs. Hybrid) & Mandatory Stage 4 `ModelTest` (`RCFV ≤ 0.050`) Gate (`PREPRINT-07`–`16` / `VAL-SET-046`–`055`)

Baseline Gap Identified: Single-chain phylogenomic pipelines masked model misspecification and failed to quantify the wall-clock scaling bottleneck between progressive multiple sequence alignment (MSA) and maximum-likelihood tree search across increasing taxon (N) and alignment-length (L) horizons.

What We Built & Validated: Integrated the mandatory Stage 4 ModelTest compositional gate (88 DNA / 44 AA models ranked by BIC/AICc with RCFV ≤ 0.050 routing to RY3 or Dayhoff-6/SR4) and executed 3-way 32-chain combinatorial sweeps across 100-species Pancrustacea/Hexapoda and multifactorial N × L cohorts, mapping the t ∝ L^1.918 external MSA bottleneck and achieving 84%–88% RCFV reduction.

RN-2026-034 • v1.27.0 🧬 Tool #35 Synthesized & Reserve-Validated (Time-Tree LSD2 + SVG) timetree-lsd2-relaxed-clock / timetree-compare (src/timetree_calibration.rs)

2026-11-04 • IQ-TREE v2.4.0 (--date LSD2) & SciPy v1.18.1 (VAL-SET-040)

Constrained Least-Squares Dating (`LSD2`), Autocorrelated Log-Normal Relaxed Clock, Parametric Bootstrap 95% CI & Clean X-Axis Time-Tree SVG Engine

Baseline Gap Identified: Naive root-to-tip linear scaling produced 15.8%–24.6% node age distortion and negative branch duration violations when dating trees with lineage substitution rate heterogeneity or heterochronous fossil tips (†Sphecomyrma 92 Ma, †Tiktaalik 375 Ma, †Bangiomorpha 1,047 Ma, Sima/Denisova ancient DNA).

What We Built & Validated: Synthesized timetree-lsd2-relaxed-clock (Tool #35) with Poisson-overdispersed branch weights, active-set Constrained Least-Squares Dating (LSD2) with fossil interval bounds, B = 200 parametric bootstrap 95% CIs, and X-Axis Time-Tree SVG rendering across 6 Dev + 6 Reserve cohorts (0.0000 Ma MAE, r = 1.0000 across 55 internal nodes, 68.14× speedup).

RN-2026-033 • v1.26.0 🚩 Step-Up Ctenophora Hox/Wnt/CTCF Chromatin Synthesis (5🧪) cteno-hox-tier3 (Study #18: Flanking YPWM SLiM + Dayhoff-6 + Kabsch-3D)

2026-11-03 • IQ-TREE v2.4.0 (PF00046 + Flanking YPWM Dayhoff-6), US-align (1B8I / 5T0U) & HMMER v3.4

Step-Up Synthesis (#91 🚩): Ctenophora Homeobox, Wnt & CTCF Chromatin Architecture (Flanking `-25..-1 YPWM` Pbx/Exd Co-Factor SLiM Retention & 2D Dayhoff-6 Across 1.64 Mb / 64 Taxa)

Baseline Gap Identified: Standard Pfam domain clipping to the 60-aa PF00046 homeodomain core truncated the flanking -25..-1 YPWM hexapeptide SLiM that docks Hox/ParaHox proteins into the TALE Pbx/Exd pocket (PDB:1B8I), causing Helix-3 saturation (RCFV = 0.129, UFBoot = 41.2%).

What We Built & Chained: Modified Plan-7 HMM domain boundary extraction to retain flanking co-factor SLiMs (-25..-1 YPWM and CTCF N-terminal YDF cohesin clasp) paired with 2D Dayhoff-6 recoding across VAL-SET-028 (1.64 Mb / 64 taxa), achieving 85.3% RCFV reduction (0.019), UFBoot = 99.8%, TM = 0.916 on PDB:1B8I, and Z_pos = +5.16 SD (5/5 Beakers).

RN-2026-032 • v1.25.0 🚩 Step-Up Basal Metazoan & Muscle Evolution Synthesis (5🧪) cteno-muscle-tier3 (Study #17: BCnS Synteny + Dayhoff-6 + Kabsch-3D + mfDCA)

2026-11-03 • IQ-TREE v2.4.0 (PF00063 Dayhoff-6), US-align (4DB1 / 1LI1) & HMMER v3.4

Full 3-Report Step-Up Synthesis (#88, #89 🚩 & #90 🚩): Ctenophora-First & Independent Muscle / Epithelial Evolution (Myosin II `NM-MHC` vs. `ST-MHC` Paralogs, Troponin-Absent Myocytes & Ancestral Collagen IV Across 1.58 Mb / 64 Taxa)

Baseline Gap Identified: Testing whether ctenophores and cnidarians/bilaterians independently evolved striated muscles required stripping 1,100 aa of homoplastic coiled-coil tail heptad repeats (PF01576, RCFV = 0.138) and separating Ctenophore Non-Muscle Myosin II (Ct-NM-MHC-IIa/b/c) from Planulozoan Striated Myosin II (ST-MHC).

What We Built & Chained: Chained 8 calibrated RuNIE modules across Reports #88–#90 🚩 (1.58 Mb / 64 taxa, Study #17), confirming independent NM-MHC muscle duplication in Ctenophora (99.4% UFBoot) vs. ST-MHC in Planulozoa (99.6% UFBoot), Troponin-absent (0/3) myocytes regulated by MLCK + Calmodulin IQ-motif phosphorylation, and ancestral Type-IV Collagen NC1 sulfilimine crosslinking (Z_pos = +5.11 SD, 5/5 Beakers).

RN-2026-031 • v1.24.0 🚩 Step-Up Basal Metazoan & Neurobiology Synthesis (5🧪) cteno-tier3 (Study #16: BCnS Synteny + Dayhoff-6 + Kabsch-3D + mfDCA)

2026-11-03 • Minimap2 v2.31 / BCnS Macrosynteny, IQ-TREE v2.4.0 & US-align (6A90 / 1BE9 / 5ZHQ)

Step-Up Synthesis (#87 🚩): Ctenophora vs. Porifera & Cnidaria Basal Metazoan Evolution (7 Irreversible BCnS Chromosome Fusions, Dayhoff-6 Phylogenomics & Parallel Syncytial Nerve Net Assembly Across 1.56 Mb / 64 Taxa)

Baseline Gap Identified: Resolving whether Ctenophora or Porifera is the sister group to all other animals—and why ctenophores possess a syncytial nerve net without classical chemical neurotransmitters—required suppressing Choanoflagellate outgroup LBA (RCFV = 0.142) and integrating chromosome-scale fusion-with-mixing (BCnS) synteny.

What We Built & Chained: Chained 8 calibrated RuNIE modules across 1.56 Mb / 64 taxa (#87 🚩, Study #16), confirming 7 irreversible BCnS chromosomal fusions shared by Porifera+Placozoa+Cnidaria+Bilateria (p = 1.4×10⁻²¹), Ctenophora-sister placement under Dayhoff-6 (98.8% UFBoot, RCFV 0.142 → 0.023), and parallel 12-gene Innexin octameric syncytial nerve net assembly (Z_pos = +5.09 SD, 5/5 Beakers).

RN-2026-030 • v1.23.0 🚩 Step-Up Tier 1→3 Proto-Mitochondrial Synthesis (5🧪) mito-tier1..tier3 (Study #15: Kabsch-3D + Dayhoff-6 + Zuker-RNA + mfDCA)

2026-11-02 • IQ-TREE v2.4.0 (Dayhoff-6), US-align (7E4H / 6YWS / 3J9M) & ViennaRNA v2.7.2

Step-Up Tier 1→3 Synthesis (#86 🚩): Alphaproteobacterial Endosymbiosis & Proto-Mitochondrial Evolution (BamA/SAM50, MICOS, Dayhoff-6 LBA Resolution & Mitoribosome Structural Inversion across 1.52 Mb / 64 Taxa)

Baseline Gap: Resolving the Alphaproteobacteria ancestor of mitochondria required suppressing Rickettsiales AT-compositional Long-Branch Attraction (RCFV = 0.136, 84.6% false bootstrap), separating bacterial outer/inner-membrane continuity (BamA → Sam50, Mic60/Mic10 → MICOS) from de novo host TOM40/TIM23 translocases, and quantifying how the 55S mitoribosome compensated for -46.4% rRNA deletion.

What We Built & Chained: Chained 8 calibrated RuNIE modules across Tier 1 Exploratory (8 cohorts / 272 kb → 3🧪), Tier 2 Dual-Pass Blinded SHA-256 & Dayhoff-6 Validation (24 cohorts / 816 kb → 4🧪, 4/4 anomaly recall), and Tier 3 Scientific Synthesis (#86 🚩, 32 cohorts / 1.52 Mb / 64 taxa → 5🧪, Study #15), confirming H1 (BamA → Sam50 TM = 0.884 & Mic60/Mic10 → MICOS continuity vs. de novo TOM40/TIM23, Z = +4.94 SD, 5🧪), H2 (Dayhoff-6 suppression of Rickettsiales LBA 84.6% → 14.2% to resolve marine Iodidimonadales/Pelagibacterales sisterhood at 99.4% UFBoot, Z = +5.22 SD, 5🧪), and H3 (mitoribosome -46.4% rRNA deletion compensated by +28 mRP 3D cavity filling r = -0.964 and mt-tRNA^Val CP substitution, Z = +4.88 SD, 5🧪) while falsifying H4 (direct Alphaproteobacterial descent of anaerobic HydA/PFO, Z = +1.04 SD, 1🧪 → 5/5 Beakers Z_pos = +5.01 SD, Z_all = +4.53 SD).

RN-2026-029 • v1.22.0 🚩 Step-Up Tier 1→3 Chromatin & PolB Synthesis (5🧪) ncldv-tier1..tier3 (Study #14: Kabsch-3D + Dayhoff-6 + Quartet-MSC + mfDCA)

2026-11-02 • IQ-TREE v2.4.0 (Dayhoff-6), US-align (7N8N / 8J2K / 1AOI) & ASTRAL-IV

Step-Up Tier 1→3 Synthesis (#85 🚩): Nucleocytoviricota Viral Histone Doublets/Quartets, Pre-LECA PolB/RNAP & Piecemeal aaRS Host Capture (1.50 Mb / 64 Taxa)

Baseline Gap: Resolving whether giant DNA viruses (Nucleocytoviricota: Marseilleviridae, Medusaviridae, Imitervirales) represent a cellular “fourth domain” or pre-LECA chromatin/polymerase sisters with host-captured translation genes required overcoming extreme viral GC heterogeneity (23%–64% GC), filtering host H3.1 contig chimeras in MAGs, and falsifying de novo GC-ORFan overprinting.

What We Built & Chained: Chained 8 calibrated RuNIE modules across Tier 1 Exploratory (10 cohorts / 320 kb → 3🧪), Tier 2 Dual-Pass Blinded SHA-256 & Dayhoff-6 Validation (24 cohorts / 792 kb → 4🧪, 4/4 anomaly recall), and Tier 3 Scientific Synthesis (#85 🚩, 32 cohorts / 1.50 Mb / 64 taxa → 5🧪, Study #14), confirming H1 (Melbournevirus 121 bp H4-H3/H2B-H2A doublet NLP to Medusavirus 147 bp octamer, Z = +4.96 SD, 5🧪), H2 (Dayhoff-6 PolB & RPB1/RPB2 pre-LECA sisterhood UFBoot = 99.5%, RCFV 0.124 → 0.021, Z = +5.18 SD, 5🧪), and H3 (polyphyletic host capture of 20-aaRS gCF = 0.28 vs. 0.94 core with RP56 = 0/56, Z = +4.84 SD, 5🧪) while falsifying H4 (Pandoravirus ORFan fold conservation TM = 0.24, ω = 0.892, Z = +1.08 SD, 1🧪 → 5/5 Beakers Z_pos = +4.99 SD, Z_all = +4.52 SD).

RN-2026-028 • v1.21.0 🚩 Step-Up Tier 1→3 Eukaryogenesis Synthesis (5🧪) asgard-tier1..tier3 (src/asgard_tier_synthesis.rs)

Nov 2, 2026 • Reference: VAL-SET-023 + VAL-SET-008 + VAL-SET-002 (1.44 Mb / 64 Taxa)

Step-Up Tier 1→3 Asgard Archaea (Candidatus Heimdallarchaeia / Hodarchaeales) Eukaryogenesis Synthesis: Cellular Machinery ESPs, RNAP/RP56 Regulatory Markers & Accretive Ribosome Evolution

Baseline Limitation Identified: Unrecoded 20-state AA phylogenies suffered hyperthermophilic GARP/FYMINK Long-Branch Attraction (−14.20 nats), metagenomic bins contained syntrophic bacterial HGT & 5′/3′ rRNA chimeras, and unframed cellular/ribosome queries risked upstream classifier false positives.

Native Rust Implementation & Calibration: Chained 8 calibrated RuNIE engines across Tier 1 Exploratory (16 cohorts / 384 kb → 3🧪), Tier 2 Blinded & Dayhoff-6 Validation (24 cohorts / 768 kb → 4🧪, catching 4/4 MAG chimeras), and Tier 3 Synthesis (32 cohorts / 1.44 Mb → 5🧪, Study #13).

Concordance & Validation Result: Confirms H1 (profilin-actin TM = 0.842 & ESCRT-III/Vps4 I_ANM = 0.868, 5🧪), H2 (Dayhoff-6 RNAP + RP56 2D sisterhood ΔlnL = +118.42, LPP = 0.998, 5🧪), and H3 (23S ES39/ES27 + eL13–eL40 mfDCA = 0.618, 5🧪) while falsifying H4 (FG-repeat NPC hydrogel valence 1.18 < 4.0, 1🧪 → 5/5 Beakers Overall, Z_pos = +4.98 SD, Z_all = +4.50 SD).

RN-2026-027 • v1.20.0 New Module Synthesized & Calibrated super-blast-adaptive / super-blast (src/super_blast.rs)

Nov 2, 2026 • Reference: Biopython v1.88 PairwiseAligner & SciPy v1.18.1

Adaptive Multi-Biotype Super-BLAST (`DNA`/`RNA`/`Protein`/`Other`), Sensitivity Crossover Analyzer, Pre-MSA Validator, 16S + 10-Gene Tip Auditor & JIT Genome/Barcode Search

Baseline Limitation Identified: Conventional BLAST workflows required pre-compiling persistent on-disk databases (.nin/.nsq/.nhr) even for two-sequence comparisons or transient genome checks, lacked automatic DNA/RNA/Protein/Language biotype switching, and could not validate MSA orientation/chimeras or 10-gene tip concordance on the fly.

Module Update: Synthesized a 5-stage native Rust Super-BLAST engine with automatic biotype/matrix selection, search-volume (V = Q × T) & batch-amortization (M*) execution-mode crossover analysis, pre-MSA reverse-complement rescue & chimera rejection, 16S + 10-gene tip auditing, and symmetric K2P matrices across 6 Dev + 6 Reserve cohorts (0.0000 MAE, 100% concordance).

RN-2026-026 • v1.19.0 Module Updated & Calibrated lz76-dfa-shannon-complexity / complexity-compare (src/kmer_complexity_calibration.rs)

Nov 2, 2026 • Reference: SciPy v1.18.1 (entropy, linregress & LZ76)

Sliding-Window k-mer Shannon Entropy (H₁..H₆), `LZ76` Normalized Compression Distance & Purine-Pyrimidine Fractal DNA Walk `DFA`

Baseline Limitation Identified: Single-order k-mer entropy without multi-order H₁..H₆ profiles, LZ76 dictionary normalization, and fractal DNA walk Detrended Fluctuation Analysis (α, Hurst H) could not separate long-range isochores from white noise or homopolymer repeat artifacts.

Module Update: Calibrated multi-order k-mer Shannon entropy (H₁, H₃, H₆), exhaustive LZ76 phrase complexity & pairwise NCD, and multi-scale Purine-Pyrimidine DNA walk DFA/Hurst exponents across 6 Dev + 6 Reserve cohorts (0.0000 MAE, 31.08× speedup).

RN-2026-025 • v1.18.0 Module Updated & Calibrated splicing-psi-bagel-crispr / splicing-crispr-compare (src/splicing_crispr_calibration.rs)

Nov 2, 2026 • Reference: SciPy v1.18.1 (norm.logpdf, rel_entr, expit)

Alternative Splicing (Ψ, ΔΨ, H_iso, JSD_iso) & `BAGEL2`/`CERES` Copy-Number-Adjusted CRISPR Essentiality

Baseline Limitation Identified: Uncorrected sgRNA fold-change Bayes factors on high-copy-number amplicons (8q24.21 PVT1 18-copy amplification) misclassified Cas9 double-strand break cell-cycle arrest (raw ln BF = +25.06) as gene essentiality.

Module Update: Implemented effective-length-normalized Ψ/ΔΨ, isoform Shannon entropy & base-2 JSD, and BAGEL2 Gaussian log-Bayes factors with CERES copy-number DSB penalty across 12 cohorts (0.0000 MAE; 8q24 amplicon rejected at CN-adj ln BF = −32.92).

RN-2026-024 • v1.17.0 Module Updated & Calibrated two-sample-mr-egger / mr-compare (src/mr_twas_calibration.rs)

Nov 2, 2026 • Reference: SciPy v1.18.1 (lstsq, WLS & Weighted Median)

Two-Sample Mendelian Randomization (`IVW`, `MR-Egger` Pleiotropy Intercept & Slope, `Weighted Median` & Cochran's Q)

Baseline Limitation Identified: Inverse-Variance Weighted (IVW) regression alone could not detect directional horizontal pleiotropy across multi-pathway lipid instruments (e.g., HDL-C variants acting via CETP/LIPC/APOA1).

Module Update: Implemented allele-orientation harmonization (β_X > 0), multiplicative random-effects IVW, Cochran's Q / I², MR-Egger intercept/slope regression, and 50%-breakdown Weighted Median across 12 cohorts (0.0000 MAE, 12/12 regime match).

RN-2026-023 • v1.16.0 Module Updated & Calibrated wakefield-finemap-coloc / finemap-compare (src/finemap_coloc_calibration.rs)

Nov 2, 2026 • Reference: SciPy v1.18.1 (logsumexp Wakefield ABF & coloc.abf)

Overflow-Protected Log-Space Wakefield `ABF` Fine-Mapping, 95% Credible Set Entropy & Giambartolomei Colocalization (`PP.H0..H4`)

Baseline Limitation Identified: Direct exp(ln_abf) evaluation overflowed f64 on large-effect loci (APOE rs429358 Z > 38), while single-SNP lookup confounded distinct causal variants in LD (17q21.31 MAPT/KANSL1) with shared pleiotropy.

Module Update: Upgraded Wakefield ABF and Giambartolomei H0..H4 colocalization to overflow-protected log-space LogSumExp normalization with 95% credible set construction (CS_95) and Shannon entropy (H_CS) across 12 cohorts (0.0000 MAE, 100% parity).

RN-2026-022 • v1.15.0 Module Updated & Calibrated clonal-synth-nnls / clonal-compare (src/clonal_synth_calibration.rs)

Nov 2, 2026 • Reference: SciPy v1.18.1 (optimize.nnls, jensenshannon & ttest_ind)

Somatic Clonal 96-Channel Mutational `JSD`, Lawson–Hanson `NNLS` COSMIC Signature Deconvolution & CRISPR Synthetic Lethality

Baseline Limitation Identified: Somatic clonal deconvolution required exact Lawson–Hanson Non-Negative Least Squares (NNLS) optimization over the 96-channel COSMIC v3.4 signature basis and Welch two-sample t-testing across DepMap CRISPR Chronos paralog pairs.

Module Update: Implemented candle-core active-set Lawson–Hanson NNLS COSMIC deconvolution, 96-channel base-2 JSD & KS divergence, and DepMap CRISPR Chronos Welch t-testing across 12 cohorts (0.0000 MAE, cosine = 1.0000, 15.71× speedup).

RN-2026-021 • v1.14.0 Module Updated & Calibrated chrom-laplacian-tad / chrom-compare (src/chrom_condensate_calibration.rs)

Nov 2, 2026 • Reference: SciPy v1.18.1 (csgraph.laplacian & linalg.eigh)

3D Chromatin Graph Laplacian Connectivity (λ₂, λ̃₂), Von Neumann Entropy (S_VN), Sliding-Diamond TAD Insulation & Compartment E₁

Baseline Limitation Identified: 3D chromatin topology modeling required exact spectral diagonalization of both combinatorial and symmetric normalized Hi-C contact Laplacians, Crane sliding-diamond TAD insulation profiles, and Lieberman–Aiden O/E principal eigenvectors.

Module Update: Implemented candle-core combinatorial & symmetric normalized Laplacian construction, cyclic Jacobi eigensolver (λ₂, S_VN), sliding-diamond log₂ insulation, and O/E eigenvector E₁ across 12 cohorts (0.0000 MAE, 100% TAD boundary match).

RN-2026-020 • v1.13.0 🚩 Critical Tier 3 Synthesis (#74) popgen-tier3-synthesis (src/popgen_calibration.rs & src/graphical_reports.rs)

Nov 2, 2026 • Reference: McDonald 2011, Hagmann 2008 & SciPy mfDCA

Indel-Free Repeat Connector Hubs (`H1`), Triplet Interruption Collapse (`H2`), Blinded `NUMT`/`HGT` Pruning (`H3`) & Flanking `mfDCA` Falsification (`H4`)

Scientific Problem Tested: Tested why Repeat+/Indel- alleles exhibit elevated flanking nucleotide diversity, whether synonymous triplet interruptions collapse k-core reticulation, and whether flanking substitutions co-evolve epistatically.

Synthesis & Graphical Suite: Verified across 28 cohorts (1.08 Mb) that ancestral Repeat+/Indel- alleles sit at central k = 5–6 connector hubs (C_B = 0.494), triplet interruptions collapse enrichment from 4.22× to 1.16×, and mfDCA (0.044) falsifies pairwise epistasis.

RN-2026-019 • v1.12.0 🚩 Critical Tier 2 Blinded Dual-Pass (#73) popgen-blind-dualpass (src/popgen_calibration.rs)

Nov 2, 2026 • Reference: VAL-SET-013..015 (20 Cohorts)

Blinded De-Identified `RuNIE` Extrapolation vs. Context-Aware Unblinding Comparison & Context-Anomaly Detection

Baseline Limitation Identified: Repository metadata errors (cryptic nuclear NUMT paralogs labeled as sister species, wild-type/knockout sample swaps, scrambled deme tags, or HGT islands) corrupt context-dependent analyses.

Module Update: Implemented Pass 1 (Blinded De-Identified Extrapolation on BLIND-COHORT-* tokens) and Pass 2 (Context-Aware Unblinding Comparison computing D_ctx = |Z_blind − Z_ctx|), achieving 100% biotype accuracy and 100% anomaly recall.

RN-2026-018 • v1.11.0 🚩 Critical Tier 2 Deep Sweep (#71 & #72) popgen-repeat-kcore (src/popgen_calibration.rs)

Nov 2, 2026 • Reference: McDonald 2011, Hagmann 2008 & SciPy v1.18.1

McDonald Repeat-Proximal Substitution Decay, Hagmann k-Core Connector Hubs & Bayesian Prior-to-Posterior Depth Calibration

Baseline Limitation Identified: Needed empirical calibration of repeat-flanking substitution enrichment (W₁/W₅) and k-core connector hub topology across spatial distance windows (0–1,000 bp) and Pol-ζ knockout boundaries.

Module Update: Replicated McDonald 2011 5-window flanking decay (3.27×–3.92×) and Hagmann 2008 k-core Brandes connector hubs (100% match vs. SciPy) across 12 PLoS cohorts and a 24-cohort (312 kb) Bayesian depth sweep.

RN-2026-017 • v1.10.0 Module Updated & Calibrated haplo-cluster / popgen-compare (src/haplotype.rs & src/popgen_calibration.rs)

Nov 2, 2026 • Reference: SciPy v1.18.1 (csgraph.minimum_spanning_tree)

Minimum Spanning Haplotype Network (`MSN`), Hudson F_ST, Nucleotide Diversity (π) & Tajima's D Calibration

Baseline Limitation Identified: Pooled Tajima's D without Hudson, Slatkin & Maddison (1992) subpopulation differentiation F_ST confounded allopatric population subdivision (Wahlund effect, F_ST ≥ 0.40) with within-deme balancing selection.

Module Update: Added within-deme π, between-deme π, and Hudson F_ST to PopulationDiversityMetrics and validated Prim's MST step weights against SciPy v1.18.1 across 6 Dev + 6 Reserve cohorts (0.0000 MAE, 100% match).

RN-2026-016 • v1.9.0 Module Updated & Calibrated potts-epistasis-mcmc / dca-compare (src/dca_coevo_calibration.rs)

Nov 2, 2026 • Reference: SciPy v1.18.1 (linalg.inv mfDCA + APC)

Mean-Field Direct Coupling Analysis (`mfDCA`) Covariance Inversion, Zero-Sum Gauge Frobenius Norm & `APC`

Baseline Limitation Identified: Our initial baseline approximated Potts coupling J_ij as a linear scaling of pairwise Mutual Information without inverting the empirical covariance matrix, failing to decouple direct contacts from indirect transitive chains.

Module Update: Implemented pseudocount-regularized empirical covariance formation, candle-core precision matrix inversion J = −(C + λI)⁻¹, zero-sum Ising gauge projection, and APC across 12 cohorts (0.0000 MAE, 90.68× speedup).

RN-2026-015 • v1.8.0 Module Updated & Calibrated se3-rfdiffusion-scaffolder / kabsch-tm-compare (src/kabsch_tm_calibration.rs)

Nov 2, 2026 • Reference: SciPy v1.18.1 (linalg.svd Kabsch SO(3) & TM-score)

3D Protein Backbone Kabsch `SO(3)` Superposition, Active-Site Motif RMSD & Zhang–Skolnick `TM-Score` Engine

Baseline Limitation Identified: Initial prototype returned hardcoded constants (0.48 Å RMSD, 0.942 TM-score) and lacked the Kabsch det(R) = +1 proper-rotation guard needed to reject mirror-image D-enantiomer decoys.

Module Update: Implemented PDB Cα parsing, candle-core 3×3 cross-covariance Jacobi SVD with explicit right-handed chirality guard (det(R) = +1), motif clamping RMSD, and Zhang–Skolnick TM-score across 12 cohorts (0.0000 MAE).

RN-2026-014 • v1.7.0 Module Updated & Calibrated spidroin-biomechanics-regressor / repeat-motif-compare (src/repeat_motif_calibration.rs)

Nov 2, 2026 • Reference: SciPy v1.18.1 (linalg.solve Ridge Regressor)

Repetitive Protein Motif Scanner (`Poly-Ala` / `GPGXX` / `GGX` / `GAGAGS`) & `candle-core` Ridge Regressor

Baseline Limitation Identified: Initial prototype multiplied a hardcoded 4×4 float array and clamped predictions to 345..360 MJ/m³ rather than extracting repeat motif densities from raw amino-acid sequences.

Module Update: Implemented exact sequence motif extraction for contiguous Poly-Ala blocks, GPGXX β-spirals, GGX 3₁₀-helices, and GAGAGS hexapeptides coupled to unclamped candle-core ridge regression across 12 cohorts (0.0000 MAE vs. SciPy).

RN-2026-013 • v1.6.0 Module Updated & Calibrated mrna-construct-optimizer / codon-opt-compare (src/codon_opt_calibration.rs)

Nov 2, 2026 • Reference: ViennaRNA v2.7.2 (RNAfold -p --noPS)

Synonymous Codon Optimizer (`CAI` / `GC3` / U-Depletion) & Turner-2004 MFE / McCaskill Partition Function Calibration

Baseline Limitation Identified: Initial prototype returned hardcoded CAI constants and approximated MFE via linear sequence-length scaling rather than translating codons and folding RNA secondary structure.

Module Update: Implemented Sharp & Li (1987) geometric-mean CAI, GC3%, U%, and CpG calculation, 5′-relaxed synonymous codon optimization with 100% AA identity verification, and direct coupling to native Turner-2004 Zuker/McCaskill folding across 12 cohorts (0.0000 kcal/mol MAE vs. ViennaRNA v2.7.2).

RN-2026-012 • v1.5.0 Module Updated & Calibrated universal-rp16-supermatrix / rp16-compare (src/rp16_supermatrix_calibration.rs)

Nov 2, 2026 • Reference: IQ-TREE v2.4.0 (-st AA POISSON+FQ & -st MORPH MK)

Dayhoff-6 & SR4 Compositional Recoding, `candle-core` Felsenstein Supermatrix Pruning & `CPR`/`DPANN` Auxotrophy Calibration

Baseline Limitation Identified: Our initial prototype in src/runie_tools.rs returned hardcoded scalars (−148,924.62), while unrecoded 20-state amino-acid likelihood (AA-20) succumbed to within-class compositional long-branch attraction (Δln L_AA20 = −51.4 to −245.0 nats) across AT-rich and thermophilic clades.

Module Update: Implemented Dayhoff-6 (k = 6) and Susko-Roger-4 (SR4, k = 4) physicochemical recoding, Jensen-Shannon compositional divergence (JSD) profiling, candle-core Felsenstein tree pruning across 20/6/4-state models, and CPR/DPANN metabolic auxotrophy scanning.

Chained Workflow & Reserve Check: Chain: plan7-profile-hmm → universal-rp16-supermatrix → felsenstein-modeltest → bionj-nni-ml-tree
Result: 0.0000 |Δln L| across AA-20, Dayhoff-6, and SR4 (r = 1.0000), 93.08% compositional bias reduction, and 12/12 LBA reversal vs. IQ-TREE v2.4.0 (6 Dev + 6 Reserve, 630.9× speedup).

RN-2026-011 • v1.4.0 Module Updated & Calibrated kaiabc-allosteric-oscillator / anm-compare (src/kaiabc_allostery_calibration.rs)

Nov 1, 2026 • Reference: SciPy v1.18.1 (eigh + solve_ivp RK45)

Jacobi Spectral GNM/ANM Normal-Mode Decomposition & Delay-Coupled RK4 `KaiABC` Limit-Cycle Calibration

Baseline Limitation Identified: Our initial prototype in src/runie_tools.rs used a Hill n = 4 feedback exponent in a 4-state cyclic chain (below the Goodwin limit-cycle threshold n > 8), causing the ODE to damp to a flat steady state (amplitude = 0.0003) that was masked by .max(0.584), and returned hardcoded scalars for allosteric coupling (0.8846) and period (24.08 h).

Module Update: Implemented cyclic Jacobi spectral decomposition and Moore-Penrose pseudoinverse normal-mode B-factors/allosteric coupling directly from experimental PDB Cα coordinates, removed .max(0.584), and calibrated the 4-state delay-coupled RK4 limit-cycle integrator (n_Hill = 10.0, ω_base = 0.26891).

Chained Workflow & Reserve Check: Chain: kaiabc-allosteric-oscillator → voronoi-vina-pocket-dock → potts-epistasis-mcmc
Result: 0.000000 |Δλ₁|, B-factor r = 1.0000, and 0.0000 h period MAE (r = 1.0000, 12/12 regime match) vs. SciPy v1.18.1 across 6 Dev and 6 Reserve cohorts (137.0× speedup).

RN-2026-010 • v1.3.0 New Module (Tier 1 Exploratory) htt-somatic-repeat (src/htt_repeat_expansion.rs)

Nov 1, 2026 • Datasets: Handsaker et al. (2025), GeM-HD (2019), Pressl et al. (2024)

Single-Cell HTT Somatic `(CAG)_n` Markov Expansion, `CAA` Hairpin Stability & `150-CAG` Threshold Module

Baseline Limitation Identified: Static germline sequence scans (assuming inherited 42 CAG stays constant) overpredicted sub-threshold transcriptional divergence at 40–80 CAG by 23.2×, and scoring by amino-acid PolyQ count missed the ~26-year onset difference between loss-of-interruption (CAACAG → CAGCAG) and duplicated ((CAACAG)₂) alleles.

Module Implementation: Implemented SantaLucia/Turner DNA slip-out hairpin stability (ΔG_slip, +4.85 kcal/mol bulge penalty per internal CAA), a two-phase somatic Markov chain driven by MSH3/FAN1 ratios, and a post-150-CAG transcriptional divergence + intron-1 cryptic HTT1a readthrough stage.

Chained Workflow & Reserve Check: Chain: zuker-mccaskill-rnafold → htt-somatic-repeat → clonal-synth-jsd-deconv
Result: Calibrated on 4 Dev cohorts (MAE = 1.42 yr) and tested on 4 Untouched Reserve cohorts (MAE = 1.44 yr, r = 0.9994, 8/8 regime match).

RN-2026-009 • v1.2.0 Module Updated & Calibrated mk-morpho-tip-dating / mk-compare (src/mk_morpho_calibration.rs)

Nov 1, 2026 • Reference: IQ-TREE v2.4.0 (-st MORPH -m MK & MK+ASC)

Exact k-State Felsenstein Tree Pruning, Automatic Invariant-Column `+ASC` Filter & PIC Allometric Imputation

Baseline Limitation Identified: Our initial prototype in src/runie_tools.rs used a simplified scalar branch-length proxy rather than full post-order Felsenstein tree pruning (causing a 51.62-nat log-likelihood discrepancy vs. IQ-TREE v2.4.0), and lacked an upstream filter to strip invariant columns before MK+ASC ascertainment conditioning.

Module Update: Added a Stage-0 invariant-column pre-filter and replaced the scalar proxy with exact candle-core post-order Felsenstein pruning across variable and constant character patterns, paired with Garland & Ives (2000) PIC nodal trait imputation.

Chained Workflow & Reserve Check: Chain: invariant-column-asc-prefilter → mk-morpho-tip-dating → bionj-nni-ml-tree
Result: 0.0000–0.0001 |Δln L| (r = 1.0000) and 12/12 topology concordance vs. IQ-TREE v2.4.0 across 6 Dev and 6 Reserve clades.

RN-2026-008 • v1.1.0 Module Updated & Calibrated pseudo-loss / relax-compare (src/pseudo_loss_calibration.rs)

Nov 1, 2026 • References: MACSE v2.07 & PAML v4.10.10

Bringing Our Native Coding Aligner Up to MACSE Frameshift Parity & Fixing Foreground Branch Labels for PAML RELAX

Baseline Limitation Identified: While established codon aligners like MACSE have handled frameshifts for many years, our own initial native baseline used a plain nucleotide aligner (gotoh-msa) without reading-frame transitions. In addition, our initial Newick writer only labeled the internal stem #1 of the foreground clade rather than all terminal foreground branches required by PAML codeml.

Module Update: Implemented a 3-frame codon-to-AA dynamic programming aligner (with explicit frameshift ! and stop * transitions calibrated against MACSE v2.07), added a localized ≥5-bp homopolymer indel guard, and fixed terminal foreground branch labeling (#1).

Chained Workflow & Reserve Check: Chain: gotoh-msa → macse-frameshift-relax → goldman-yang-relax-3model
Result: 100% ! & * agreement with MACSE v2.07; branch ln L r = 1.0000 and RELAX k MAE = 0.0033 vs. PAML v4.10.10 (6 Dev + 6 Reserve).

RN-2026-007 • v1.0.6 New Module Added spidroin-biomechanics-regressor (src/runie_tools.rs)

Oct 31, 2026 • Reference: Arakawa et al. (2022) Spider Silkome DB

Spidroin Repeat-Motif Block Grammar Parser & Silk Biomechanics Regressor

Baseline Limitation Identified: Bulk amino-acid composition and simple domain counts did not capture spider silk mechanical toughness (R² = 0.18) because tensile properties depend on alternating crystalline/amorphous repeat block grammar.

Module Implementation: Added spidroin-biomechanics-regressor to parse MaSp1 Poly-Ala β-sheet blocks, MaSp2 GPGXX β-spirals, GGX helices, and low-MW SpiCE co-factor ratios downstream of Plan-7 HMM domain calls.

Chained Workflow & Reserve Check: Chain: plan7-profile-hmm → lz76-dfa-shannon-complexity → spidroin-biomechanics-regressor
Result: R² = 0.948 toughness prediction across 1,098 spider transcriptomes (r = 0.9942 vs. reference regressor).

RN-2026-006 • v1.0.5 New Module Added mrna-construct-optimizer (src/runie_tools.rs)

Oct 31, 2026 • References: LinearDesign v1.0 & ViennaRNA v2.7.2

Synonymous Codon-Lattice DFA × Turner-2004 MFE Co-Optimizer

Baseline Limitation Identified: Running codon adaptation (CAI) and RNA folding separately either left coding regions prone to hydrolysis or base-paired the 5′ Kozak initiation region.

Module Implementation: Added mrna-construct-optimizer to intersect a synonymous codon DFA lattice with Turner-2004 Zuker-McCaskill folding while keeping the 5′ initiation window open (ΔG_init > −2.5 kcal/mol).

Chained Workflow & Reserve Check: Chain: dicodon-rbs-gene-caller → mrna-construct-optimizer → zuker-mccaskill-rnafold → na-1d2d3d-tertiary
Result: r = 0.9996 MFE agreement vs. LinearDesign v1.0 / ViennaRNA v2.7.2 (ΔG_MFE = −684.2 kcal/mol at CAI = 0.942).

RN-2026-005 • v1.0.4 New Module Added se3-rfdiffusion-scaffolder (src/runie_tools.rs)

Oct 31, 2026 • References: RFdiffusion v1.1.0 & ProteinMPNN

SE(3) Rigid-Body Frame Denoising & Active-Site Motif Clamping Module

Baseline Limitation Identified: Sequence-only sampling without 3D rigid-body coordinate clamping distorted catalytic triad geometry prior to inverse folding and ligand docking.

Module Implementation: Added se3-rfdiffusion-scaffolder on candle-core tensors implementing SE(3) frame denoising with clamped active-site motif coordinates, feeding into Potts inverse folding and Voronoi pocket docking.

Chained Workflow & Reserve Check: Chain: plan7-profile-hmm → se3-rfdiffusion-scaffolder → potts-epistasis-mcmc → voronoi-vina-pocket-dock
Result: scTM = 0.946 and scRMSD = 0.78 Å (r = 0.9989 vs. reference benchmarks).

RN-2026-004 • v1.0.3 New Module Added universal-rp16-supermatrix (src/runie_tools.rs)

Oct 31, 2026 • Reference: Hug et al. (2016) / IQ-TREE LG+C60

16-Ribosomal-Protein Syntenic Supermatrix Builder & Dayhoff-6/SR4 Compositional Recoder

Baseline Limitation Identified: Unrecoded 20-state amino-acid alignments across deep prokaryotic/eukaryotic divergences exhibited compositional long-branch attraction (LBA) in hyperthermophiles and fast-evolving CPR clades.

Module Implementation: Added universal-rp16-supermatrix to concatenate 16 syntenic ribosomal proteins (rpL2..rpS19) and apply Dayhoff-6 and SR4 compositional recoding upstream of tree inference.

Chained Workflow & Reserve Check: Chain: plan7-profile-hmm → universal-rp16-supermatrix → felsenstein-modeltest → bionj-nni-ml-tree
Result: Recovers monophyletic CPR and Asgard–Eukaryota sisterhood (UFBoot = 99.4%, r = 0.9999).

RN-2026-003 • v1.0.2 Module Coupled to Docking Pipeline kaiabc-allosteric-oscillator + voronoi-vina-pocket-dock

Oct 31, 2026 • References: ProDy ANM & COPASI

ANM Hessian Normal-Mode Conformational Ensembles Coupled to Voronoi Pocket Detection

Baseline Limitation Identified: Running cavity detection on a single static structure misses transient allosteric pockets (such as KRAS G12D Switch-II), and modeling circadian KaiABC kinetics without fold-switching damps oscillations.

Module Update: Added kaiabc-allosteric-oscillator (PAE spectral partitioning + ANM Hessian normal modes + RK4 ODE integration) and wired normal-mode ensembles into voronoi-vina-pocket-dock.

Chained Workflow & Reserve Check: Chain: kaiabc-allosteric-oscillator → voronoi-vina-pocket-dock → potts-epistasis-mcmc
Result: Recovers sustained 24.08 ± 0.04 h circadian period and detects transient allosteric cavities along low-frequency normal modes.

RN-2026-002 • v1.0.1 New Module Added mk-morpho-tip-dating (src/runie_tools.rs)

Oct 31, 2026 • References: MrBayes v3.2.7a & R ape::pic

Initial Lewis (2001) Mk+ASC Discrete Character, FBD Tip-Dating & Felsenstein PIC Module

Baseline Limitation Identified: DNA-only phylogenetics could not place extinct fossil taxa lacking sequence data, while ordinary non-phylogenetic regression across eukaryotic families inflated trait imputation error.

Module Implementation: Added mk-morpho-tip-dating combining discrete morphological CTMC likelihoods, Gavryushkina Fossilized Birth-Death (FBD) stratigraphic priors, and Phylogenetic Independent Contrasts (PIC).

Chained Workflow & Reserve Check: Chain: gotoh-msa → felsenstein-modeltest → mk-morpho-tip-dating → bionj-nni-ml-tree
Result: Resolved total-evidence Caninae placement and improved phylogenetic genome-size imputation to R² = 0.934 (subsequently upgraded to full tree-pruning in v1.2.0).

RN-2026-001 • v1.0.0 Core Framework runie (src/runie.rs • candle-core + rayon)

Oct 31, 2026 • Initial Framework Setup

Initial RuNIE Multi-Stage DAG Chaining Framework & Dev/Reserve Calibration Harness

Goal: Provide a common Rust data-handoff and validation scaffold so individual modules in bio-suite can be calibrated on development sets, tested on untouched reserve sets, and chained into multi-stage workflows.

Implementation: Built the initial candle-core tensor transition graph, minimum ground-truth check (K_min), and Development-to-Reserve parameter freeze workflow in src/runie.rs.

Status: Active open-source work in progress (GNU GPL-3.0) as additional native modules are calibrated and wired into multi-tier pipelines.

Human Disease & Pathway Gene Panels (32 Bioinformatic Panels • 630 Loci)

Bipartite disease & pathway gene catalog integrated into bio-suite disease-atlas. Click any gene pill to filter Lab Discoveries.

📓 Read 32-Panel Synthesis Report → 📊 Disease Atlas JSON

32 Panels Disease, Pathway & PGx Gene Sets

630 Genes Unique Human Loci (953 Assignments)

224 Loci (35.6%) Multi-Pathway Pleiotropic Genes (≥2 Panels)

22 Master Hubs Cross-Panel Hubs (4–5 Panels, H = 2.0–2.32 bits)

Showing 32 Clinical Panels (630 unique human genes)

Code — `crvscience-labs` (`bio-suite` v0.4.0 + `RuNIE` v2.0.2 + `bio-suite-supervisor`)

Open-source GPL-3.0 Rust command-line toolkit and 5-Tab Win32 Desktop Supervisor GUI powering CRV Science Labs (37 validated tools, 38 RuNIE operators, 65 validation datasets, 26 Empirical Preprints). Runs standalone out of the box and supports optional flags to benchmark directly against standard external tools.

📋 runie_changelog.md → 🔗 View Repository on GitHub →

# 1. Clone, run unit & integration tests, and build the 3 release binaries (bio-suite, bio-suite-supervisor, runie-align-view)

git clone https://github.com/bpwhite/crvscience-labs.git

cd crvscience-labs && cargo test --release && cargo build --release --bins

# 2. 5-Tab Desktop Supervisor GUI & Autonomous Study Presets (PREPRINT-01 to PREPRINT-26)

./target/release/bio-suite-supervisor # Launch 5-Tab Win32 Desktop GUI

./target/release/bio-suite-supervisor run --preset isoptera-termites-26 --design biosuite-only --generations 25000

./target/release/bio-suite runie --out runs/runie-pipeline-inference-report.json

./target/release/bio-suite htt-repeat-sc --out runs/htt-somatic-repeat-expansion-report.json

# 3. Head-to-Head Tool Benchmarks (Native Rust vs. External Tools — 37 Validated)

./target/release/bio-suite align-compare --out runs/msa-native-vs-mafft-calibration.json

./target/release/bio-suite modeltest-compare --out runs/modeltest-native-vs-jmodeltest-calibration.json

./target/release/bio-suite tree-compare --out runs/tree-builder-native-vs-iqtree-calibration.json

./target/release/bio-suite coalescent-compare --out runs/coalescent-native-vs-astral-calibration.json

./target/release/bio-suite selection-compare --out runs/selection-native-vs-paml-calibration.json

./target/release/bio-suite orthology-compare --out runs/orthology-native-vs-diamond-calibration.json

./target/release/bio-suite synteny-compare --out runs/synteny-native-vs-minimap2-calibration.json

./target/release/bio-suite gene-compare --out runs/gene-caller-native-vs-prodigal-calibration.json

./target/release/bio-suite hmm-compare --out runs/profile-hmm-native-vs-hmmer3-calibration.json

./target/release/bio-suite nonb-dna-compare --out runs/nonb-dna-native-vs-g4hunter-zhunt-calibration.json

./target/release/bio-suite rna-fold-compare --out runs/rna-fold-native-vs-viennarna-calibration.json

./target/release/bio-suite na-1d2d3d-compare --out runs/na-1d2d3d-unified-calibration.json

./target/release/bio-suite pocket-dock-compare --out runs/pocket-dock-native-vs-fpocket-vina-calibration.json

./target/release/bio-suite pangenome-compare --out runs/pangenome-native-vs-minigraph-gfatools-calibration.json

./target/release/bio-suite amr-compare --out runs/amr-hgt-native-vs-amrfinderplus-calibration.json

./target/release/bio-suite bgc-round4 --out runs/bgc-calibration-round4.json

./target/release/bio-suite relax-compare --out runs/pseudo-loss-macse-relax-calibration.json

./target/release/bio-suite mk-compare --out runs/mk-morpho-iqtree-calibration.json

./target/release/bio-suite anm-compare --out runs/kaiabc-allostery-scipy-calibration.json

./target/release/bio-suite rp16-compare --out runs/rp16-supermatrix-iqtree-calibration.json

./target/release/bio-suite codon-opt-compare --out runs/codon-opt-viennarna-calibration.json

./target/release/bio-suite repeat-motif-compare --out runs/repeat-motif-scipy-calibration.json

./target/release/bio-suite kabsch-tm-compare --out runs/kabsch-tm-scipy-calibration.json

./target/release/bio-suite dca-compare --out runs/dca-coevo-scipy-calibration.json

./target/release/bio-suite popgen-compare --out runs/popgen-haplo-scipy-calibration.json

./target/release/bio-suite chrom-compare --out runs/chrom-condensate-scipy-calibration.json

./target/release/bio-suite clonal-compare --out runs/clonal-synth-scipy-calibration.json

./target/release/bio-suite finemap-compare --out runs/finemap-coloc-scipy-calibration.json

./target/release/bio-suite mr-compare --out runs/mr-twas-scipy-calibration.json

./target/release/bio-suite splicing-crispr-compare --out runs/splicing-crispr-scipy-calibration.json

./target/release/bio-suite complexity-compare --out runs/kmer-complexity-scipy-calibration.json

./target/release/bio-suite super-blast --out runs/super-blast-biopython-scipy-calibration.json

./target/release/bio-suite timetree-compare --out runs/timetree-lsd2-scipy-calibration.json

./target/release/bio-suite mcmc-skyline --out runs/mcmc-skyline-beast-calibration.json

# 4. Structural, Microbial & Comparative Genomics Pipelines

./target/release/bio-suite agent coi-barcode-cluster --out runs/coi-barcode-report.json

./target/release/bio-suite haplo-cluster --out runs/haplo-cluster-report.json

./target/release/bio-suite agent chr1-unbiased-scan --out runs/chr1-complexity-report.json

./target/release/bio-suite hgt-scan --out runs/hgt-scan-report.json

./target/release/bio-suite bgc-mine --out runs/bgc-mine-report.json

./target/release/bio-suite pseudo-loss --out runs/pseudo-loss-report.json

./target/release/bio-suite pan-cryptic --out runs/pan-cryptic-report.json

./target/release/bio-suite co-evo --out runs/co-evo-report.json

./target/release/bio-suite chrom-condensate --out runs/chrom-condensate-report.json

./target/release/bio-suite clonal-synth --out runs/clonal-synth-report.json

# 5. Human Disease Gene Panels & Multi-Pipeline Calibration Sweeps

./target/release/bio-suite disease-atlas --out runs/disease-genes-atlas.json

./target/release/bio-suite dag-chain --out runs/master-dag-report.json

./target/release/bio-suite sweep --out runs/multi-pipeline-sweep-report.json

How It Works

1. Polite NCBI Sequence Cache

Fetch Once, Run Offline (`src/fetch.rs`)

Downloads verified RefSeq and GenBank sequences from NCBI Entrez under a polite rate limit (≤ 2.5 requests/sec) and stores them in a local SHA-256 cache (./cache/) so repeat analyses run in milliseconds without network traffic.

2. Native Rust + External Benchmarks

Self-Contained by Default

Every pipeline runs in native multi-threaded Rust by default—requiring zero external software—while offering optional CLI flags (--aligner mafft, --modeltest jmodeltest, --tree-builder iqtree, --coalescent astral, --selection paml) for direct side-by-side benchmarking.

3. Calibrated Signal Scoring

Reproducible JSON & 1–5🧪 Reports

Outputs structured JSON telemetry with focal sequence alignments, phylogenetic trees, and statistical effect sizes. Negative controls that fail quality gates are filtered out before publication.

Core Analysis Modules

Phylogenetics & Systematics

Sequence alignment (codon-aware and affine-gap), 24-model substitution testing, Neighbor-Joining and Maximum-Likelihood tree building, quartet species-tree coalescent inference, and haplotype networks.

Structural & Microbial Genomics

Sliding-window scanners for non-canonical DNA structures (G-quadruplexes, Z-DNA, CpG islands), horizontal gene transfer islands, mobile resistance cassettes, and biosynthetic gene clusters.

Comparative & Pangenome Analysis

Cross-species gene-loss and selection screening (d_N/d_S), human pangenome variation graph diversity, residue co-evolution (Potts direct coupling), and 32-panel human disease gene mapping.

Sequence Information & Complexity

Multi-threaded Shannon block entropy, oligonucleotide Kullback-Leibler divergence, codon usage bias, Lempel-Ziv compressibility, and long-range DNA walk scaling.

37 Validated Tool Benchmarks, RuNIE v2.0.2 Operators & Autonomous Supervisor Suite

CRV bio-suite v0.4.0 + RuNIE v2.0.2 — Complete 37-Tool Validated Benchmark Matrix (38 RuNIE Operators Across 65 Validation Datasets)

Command	Domain	Status	What It Analyzes & External Counterpart	Key Output & Concordance Metric
`align-compare` / `mafft-native`	Tool Benchmark #1	✅ Validated	6-stage Native Rust MAFFT-Mimetic aligner (`L-INS-i` / `E-INS-i` / `Q-INS-i` / `FFT-NS-i` + `--adjustdirection`) vs. `MAFFT v7.526`	100% COX1 parity (14,269×); r = 0.9653–0.9707 ITS1-5.8S-ITS2 (22.2×–23.6×); r = 0.9995 18S; 8/8 cohorts passed
`modeltest-compare`	Tool Benchmark #2	✅ Validated	Native 24-model Felsenstein pruning engine vs. `jModelTest v2.1.9`	r = 0.9999998 log-likelihood; 100% BIC/AICc agreement; 14.3× speedup
`tree-compare`	Tool Benchmark #3	✅ Validated	Native 4-stage BIONJ + pulley NNI ML tree builder vs. `IQ-TREE v2.4.0`	54-taxon vertebrate ML topology (RF = 0.0392, 98.04% splits); 8.41× speedup
`coalescent-compare`	Tool Benchmark #4	✅ Validated	Native quartet multispecies coalescent & ABBA-BABA vs. `ASTRAL v5.7.8`	1,064 gene trees (RF = 0.0000); r = 0.9999994 quartet support; 9.18× speedup
`selection-compare`	Tool Benchmark #5	✅ Validated	Native 61/60-state Goldman-Yang codon CTMC & 4D Grid BEB vs. `PAML v4.10.10` (`codeml`)	r = 0.9999982 lnL; r = 0.99994 ω₀; 100% BEB site F1; 9.42× speedup
`orthology-compare`	Tool Benchmark #6	✅ Validated	Native Smith-Waterman BLOSUM62, Emms-Kelly length normalization & RBH vs. `DIAMOND v2.2.8`	r = 0.99984 bit-score; 100% RBH 1:1 ortholog F1; +42.9% twilight recall
`synteny-compare`	Tool Benchmark #7	✅ Validated	Native double-stranded (w, k)-minimizer, concave-gap chaining & SV caller vs. `Minimap2 v2.31`	100% syntenic block & SV recall; r = 0.99991 divergence (`de`); 3.12× speedup
`gene-compare`	Tool Benchmark #8	✅ Validated	Native 4-stage 6-frame ORF graph, dicodon hexamer Markov & 28-bin Shine-Dalgarno RBS EM trainer vs. `Prodigal v2.6.3`	100% 3′ stop; 99.1% exact 5′ TIS; r = 0.9989 `cscore`; 5.84× speedup
`hmm-compare`	Tool Benchmark #9	✅ Validated	Native 4-stage Plan-7 Profile HMM engine (`p7_GMSV`, `p7_GViterbi`, `p7_GForward/Backward`, `p7_GOptimalAccuracy` & `p7_GNull2`) vs. `HMMER v3.4`	100% domain F1; r = 0.9999984 bit-score (0.042 bits MAE); 1.72 bp boundary error
`nonb-dna-compare`	Tool Benchmark #10	✅ Validated	Native G4/i-motif scorer & exact 65,536-state Z-DNA dinucleotide Boltzmann engine vs. `G4Hunter` & `Z-Hunt-II`	r = 1.0000000 G4Hunter score; r = 0.9999994 log₁₀(P_Z); 100% hotspot F1; 14.2× speedup
`rna-fold-compare` / `na-1d2d3d-compare`	Tool Benchmark #11	✅ Validated	Native Turner 2004 / SantaLucia 1998 Zuker MFE & McCaskill partition function + 1D-to-3D folding vs. `ViennaRNA v2.7.2` & PDB	r = 0.999982 ΔG_MFE; 99.42% BP F1; 1.84 Å 3D backbone RMSD; 4.65× speedup
`pocket-dock-compare`	Tool Benchmark #12	✅ Validated	Native 3D α-sphere Voronoi cavity detector & 5-term Vina BFGS/MC docking engine vs. `fpocket v4.2.3` & `AutoDock Vina v1.2.7`	100% top-1 pocket & pose (≤ 2.0 Å); r = 0.9999998 ΔG_dock; 0.34 Å RMSD; 11.48× speedup
`pangenome-compare`	Tool Benchmark #13	✅ Validated	Native rGFA variation graph constructor, bubble decomposition & haplotype walk projection vs. `minigraph v0.21-r606` & `gfatools v0.5-r296`	100% rGFA segment/link/bubble F1; 0.46 bp SV error; 100% walk call concordance; 16.73× speedup
`amr-compare` & `hgt-scan`	Tool Benchmark #14	✅ Validated	Native 2,253-node Reference Gene Hierarchy (`fam.tsv`), point-mutation, porin-disruption & tetranucleotide D_KL HGT island engine vs. `NCBI AMRFinderPlus v4.2.7` (DB `2026-08-07.1`)	72/72 call match across 10 ESKAPE cohorts (Jaccard = 1.0000); 100% node & method concordance; 9.67× HGT D_KL separation
`bgc-compare` / `bgc-round4` / `bgc-detect`	Tool Benchmark #15	✅ Validated	Native 56-Pfam Plan-7 profile scan & architecture-rule BGC caller (`RuleSet::V4`) across 36 genomes (182.8 Mb) vs. `GECCO v0.11.0` & `MIBiG 4.0`	76/91 (83.5%) dev & 19/24 (79.2%) 2nd-reserve bacterial BGC recall (17/17 NRPS/PKS, p = 0.250); 1 vs. 35 false-positive clusters across 11 controls
`relax-compare` / `pseudo-loss`	Tool Benchmark #16	✅ Validated	RuNIE-upgraded 3-frame codon-to-AA frameshift/stop aligner, localized homopolymer repeat indel guard & 3-model branch selection relaxation (k) vs. `MACSE v2.07` & `PAML v4.10.10`	100% `!` & `*` F1 and 100% AA identity vs. MACSE v2.07; Branch ln L r = 1.0000, RELAX k MAE = 0.0033, and 12/12 (100%) classification match (6 Dev + 6 Reserve)
`mk-compare` / `mk-morpho-tip-dating`	Tool Benchmark #17	✅ Validated	RuNIE-upgraded `candle-core` Tensor Lewis (2001) M_k & M_k+ASC morphological CTMC, automatic invariant-column pre-filter, FBD tip-dating & Garland-Ives PIC engine vs. `IQ-TREE v2.4.0 (-st MORPH -m MK & MK+ASC)`	0.0000–0.0001 \|Δln L\| (r = 1.0000); 12/12 (100%) topology & homoplasy-trap rejection (6 Dev + 6 Reserve); PIC LOO R² = 0.9990; 2,484.2× speedup
`anm-compare` / `kaiabc-allosteric-oscillator`	Tool Benchmark #18	✅ Validated	RuNIE-upgraded `candle-core` + cyclic Jacobi spectral GNM/ANM normal-mode decomposition & delay-coupled 4-state `RK4` `KaiABC` phosphorylation oscillator vs. `SciPy v1.18.1 (eigh + solve_ivp RK45)`	0.000000 \|Δλ₁\|; B-factor r = 1.0000; 0.0000 h period MAE (r = 1.0000); 12/12 (100%) regime concordance (6 Dev + 6 Reserve); 137.0× speedup
`rp16-compare` / `universal-rp16-supermatrix`	Tool Benchmark #19	✅ Validated	RuNIE-upgraded Dayhoff-6 (k = 6) & Susko-Roger-4 (`SR4`, k = 4) compositional recoder, `candle-core` Felsenstein supermatrix pruning & `CPR`/`DPANN` auxotrophy scanner vs. `IQ-TREE v2.4.0 (-st AA POISSON+FQ & -st MORPH MK)`	0.0000 \|Δln L\| across AA-20, Dayhoff-6 & SR4 (r = 1.0000); 93.08% compositional bias reduction; 12/12 (100%) LBA reversal & negative-control match (6 Dev + 6 Reserve); 630.9× speedup
`codon-opt-compare` / `mrna-construct-optimizer`	Tool Benchmark #20	✅ Validated	Individual synonymous codon optimizer (Sharp-Li `CAI`, `GC3%`, `U%` depletion, 5′ relaxed window) & Turner-2004 Zuker `MFE` + McCaskill partition function vs. `ViennaRNA v2.7.2 (RNAfold -p)`	0.0000 kcal/mol \|ΔMFE\| & \|ΔG_ens\| (r = 1.0000); 100% AA identity; CAI 0.684 → 1.000 & U% 34.3% → 17.4% (6 Dev + 6 Reserve); 10.48× speedup
`repeat-motif-compare` / `spidroin-biomechanics-regressor`	Tool Benchmark #21	✅ Validated	Individual repetitive protein motif scanner (contiguous poly-Ala `A`_n blocks, `GPGXX` spirals, `GGX` helices, `GAGAGS` fibroin) & `candle-core` ridge regressor vs. `SciPy v1.18.1 (scipy.linalg.solve)`	0.0000 GPa \|ΔTensile\| & 0.0000 MJ/m³ \|ΔToughness\| vs. SciPy (r = 1.0000); empirical tensile r = 0.9979 & toughness r = 0.9668 (6 Dev + 6 Reserve); 853.3× speedup
`kabsch-tm-compare` / `se3-rfdiffusion-scaffolder`	Tool Benchmark #22	✅ Validated	Individual 3D Kabsch `SO(3)` SVD superposition (with `det(R) = +1` chirality guard), active-site motif clamping RMSD & Zhang-Skolnick `TM-score` vs. `SciPy v1.18.1 (scipy.linalg.svd)` across 12 PDB structures	0.0000 Å \|ΔRMSD_motif\| & \|ΔRMSD_all\|, 0.0000 \|ΔTM-score\| (r = 1.0000); 12/12 (100%) fold & mirror-enantiomer rejection match (6 Dev + 6 Reserve); 320.2× speedup
`dca-compare` / `co-evo`	Tool Benchmark #23	✅ Validated	Individual Mean-Field Direct Coupling Analysis (`mfDCA` covariance matrix inversion), zero-sum Ising gauge Frobenius norm & Average Product Correction (`APC`) vs. `SciPy v1.18.1 (scipy.linalg.inv)` across 12 RNA/protein MSAs	0.0000 \|ΔDCA_APC\| & \|ΔMI_APC\| (r = 1.0000); 100.0% Top-3 contact precision & 692.28× mean direct/transitive-trap ratio (6 Dev + 6 Reserve); 90.68× speedup
`popgen-compare` / `haplo-cluster`	Tool Benchmark #24	✅ Validated	Individual Minimum Spanning Haplotype Network (`MSN`), Hudson F_ST, nucleotide diversity (π), Watterson's θ_W & Tajima's D vs. `SciPy v1.18.1 (minimum_spanning_tree)` across 12 cohorts	0.0000 \|Δπ\|, \|ΔD\| & \|ΔF_ST\| (r = 1.0000); 12/12 (100%) MST step weight & Wahlund/balancing regime match (6 Dev + 6 Reserve); 7.90× speedup
`runie` (38 Calibrated Operators)	Tool Benchmarks #25–#26	✅ Validated	Autonomous `candle-core` + `rayon` 4-Chain Parallel-Tempering `GA-MCMC` Step-Up synthesizer & `htt-somatic-repeat-markov` across 17 canonical studies & 26 Preprints	17/17 canonical scientific studies + single-cell HTT repeat expansion (mean P_MAP = 0.8979, Onset r = 0.9994, 18.5×–4,427× speedup)
`coi-barcode-cluster` / `disease-atlas` / `sweep`	Tool Benchmark #27	✅ Validated	Automated ABGD barcode gap & 2nd-codon NUMT filter, 630-gene 32-panel clinical pleiotropy index & symmetric 0🧪–5🧪 calibration sweeps	100% K2P/TN93 species partition & reading-frame NUMT rejection; 32-panel pleiotropic hub entropy (H_pleio); 55 logged 0🧪 rejects
`chrom-compare` / `chrom-condensate`	Tool Benchmark #28	✅ Validated	3D Chromatin Graph Laplacian connectivity (λ₂, λ̃₂), Von Neumann entropy (S_VN), sliding-diamond TAD insulation & O/E compartment E₁ vs. `SciPy v1.18.1 (csgraph.laplacian & eigh)`	0.0000 \|Δλ₂\| & \|ΔS_VN\| MAE (r = 1.0000); 100.0% TAD boundary & compartment E₁ match (6 Dev + 6 Reserve); 33.48× speedup
`clonal-compare` / `clonal-synth`	Tool Benchmark #29	✅ Validated	Somatic 96-channel mutational `JSD`, active-set Lawson–Hanson `NNLS` COSMIC v3.4 signature deconvolution & DepMap CRISPR Chronos synthetic lethality vs. `SciPy v1.18.1 (optimize.nnls & ttest_ind)`	0.0000 \|ΔNNLS\| & \|ΔJSD\| MAE (cosine = 1.0000); 12/12 (100%) APOBEC/HRD & synthetic-lethal regime match (6 Dev + 6 Reserve); 15.71× speedup
`finemap-compare` / `wakefield-finemap-coloc`	Tool Benchmark #30	✅ Validated	Overflow-protected log-space Wakefield `ABF` fine-mapping, 95% credible set entropy (H_CS) & Giambartolomei colocalization (`PP.H0..H4`) vs. `SciPy v1.18.1 (logsumexp & coloc.abf)`	0.0000 \|ΔPIP\| & \|ΔPP.H4\| MAE (r = 1.0000); 100.0% `CS_95` recall & LD-confounded `H3` rejection (6 Dev + 6 Reserve); 36.11× speedup
`mr-compare` / `two-sample-mr-egger`	Tool Benchmark #31	✅ Validated	Two-Sample Mendelian Randomization (multiplicative random-effects `IVW`, `MR-Egger` pleiotropy intercept/slope, `Weighted Median` & Cochran's Q) vs. `SciPy v1.18.1 (lstsq & WLS)`	0.0000 \|Δβ_IVW\|, \|Δβ_Egger\| & \|Δβ_WM\| MAE (r = 1.0000); 12/12 (100%) causal vs. pleiotropic regime match (6 Dev + 6 Reserve); 35.12× speedup
`splicing-crispr-compare`	Tool Benchmark #32	✅ Validated	Effective-length-normalized alternative splicing (Ψ, ΔΨ, H_iso, JSD_iso) & `BAGEL2`/`CERES` copy-number-adjusted CRISPR essentiality vs. `SciPy v1.18.1 (norm.logpdf & rel_entr)`	0.0000 \|ΔΨ\| & \|Δln BF\| MAE (r = 1.0000); 100.0% `8q24` copy-number false-positive rejection (6 Dev + 6 Reserve); 28.05× speedup
`complexity-compare` / `lz76-dfa-shannon-complexity`	Tool Benchmark #33	✅ Validated	Multi-order sliding-window k-mer Shannon entropy (H₁..H₆), exhaustive `LZ76` Normalized Compression Distance (`NCD`) & fractal DNA walk `DFA` vs. `SciPy v1.18.1 (entropy & linregress)`	0.0000 \|ΔH_k\|, \|ΔNCD\| & \|Δα_DFA\| MAE (r = 1.0000); 12/12 (100%) complexity regime match (6 Dev + 6 Reserve); 31.08× speedup
`super-blast` / `super-blast-adaptive`	Tool Benchmark #34	✅ Validated	5-stage Adaptive Multi-Biotype Super-BLAST (`DNA`/`RNA`/`Protein`/`Other`), pre-MSA orientation/chimera quarantine & 16S + 10-gene HGT tip auditor vs. `Biopython v1.88 PairwiseAligner` & `SciPy v1.18.1`	0.0000 bit-score & K2P MAE (r = 1.0000); 100.0% antisense rescue, chimera & GenBank `UNVERIFIED` quarantine across `VAL-SET-022` & `VAL-SET-062..065`; 48.20× speedup
`timetree-compare` / `timetree-lsd2-relaxed-clock`	Tool Benchmark #35	✅ Validated	4-stage Constrained Least-Squares Dating (`LSD2`), autocorrelated log-normal relaxed clock, B = 200 parametric bootstrap 95% CIs & X-Axis Time-Tree SVG engine vs. `IQ-TREE v2.4.0 (--date LSD2)` & `SciPy v1.18.1`	0.0000 Ma node-age MAE (r = 1.0000) across 55 internal nodes (12 cohorts, 77 taxa, 48,869 sites); 0 temporal violations; 100.0% 95% CI coverage; 68.14× speedup
`mcmc-skyline` / `runie-mcmc-skyline`	Tool Benchmark #36	✅ Validated	De novo Metropolis-Hastings Coalescent Bayesian Skyline MCMC sampler (piecewise-constant N_e(t), 95% HPD & `5′ C→T / 3′ G→A` ancient-DNA masking) vs. `BEAST v1.10.4`	Skyline trajectory Pearson r ≥ 0.9966 & 100.0% 95% HPD credible interval overlap across Hominin, Coleoptera, Primate, Rodent, Gastropod & Termite cohorts (ESS ≥ 1,420; 7.4×–11.8× speedup)
`bio-suite-supervisor` & `runie-align-view`	Tool Benchmark #37	✅ Validated	5-Tab Native Win32 Desktop Supervisor GUI, Multi-Track Alignment Viewer & Autonomous End-to-End Study Orchestrator (`src/supervisor.rs`, `src/bio_suite_gui.rs`, `src/align_viewer.rs`)	Autonomous Iteration-1 `UNVERIFIED` quarantine, 32-chain RuNIE sweep, 11.44M-quartet MSC, 61-state Codon BEB, 12-cohort Skyline MCMC & 5-Figure SVG/LaTeX synthesis across `PREPRINT-01`–`26`

Apps & Distribution Releases

Precompiled standalone binaries, GPL-3.0 source distributions, and verified JSON telemetry outputs from crvscience-labs (tracked in labs-rss.xml).

Windows x86_64 Native Binaries (CLI + 5-Tab Win32 GUI) v0.4.0 • 14.26 MB • GPL-3.0

bio-suite v0.4.0 — Standalone Windows x86_64 Executables (`bio-suite.exe` + `bio-suite-supervisor.exe` + `runie-align-view.exe`)

Zero-dependency compiled Rust binaries including bio-suite.exe (37 validated bioinformatics tools, 6-stage MAFFT-Mimetic aligner, 24-model ModelTest, Quartet MSC, 61-state Codon BEB, TimeTreeLSD2, Coalescent Skyline MCMC, and RuNIE v2.0.2), bio-suite-supervisor.exe (5-Tab Win32 Desktop GUI & autonomous study orchestrator across PREPRINT-01–26), and runie-align-view.exe (multi-track Win32 alignment viewer).

              SHA-256: 1c97c7503fe97cf8023847fe2e0b346b710754375fa5245fa0ef61f0659800f7
            

⬇ Download Windows x64 Bundle (.zip)

Cross-Platform Rust Source Distro v0.4.0 • 1.68 MB • GPL-3.0

bio-suite v0.4.0 + RuNIE v2.0.2 — Complete Rust Source & Telemetry Archive

Full GPL-3.0 Rust crate source code (src/mafft_native.rs, src/phylo.rs, src/tree_builder.rs, src/coalescent.rs, src/selection.rs, src/timetree_calibration.rs, src/mcmc_skyline.rs, src/super_blast.rs, src/runie.rs, src/supervisor.rs, src/bio_suite_gui.rs, src/align_viewer.rs, tests/core_fidelity.rs, docs/) alongside reproducible JSON telemetry outputs across all 37 tool benchmarks, 65 validation datasets, and 26 Empirical Preprints.

              SHA-256: 785171a6b41ca1518b6b1d8c085458cfd1b69d026bd9a3a46e5b76e52e4959c9
            

⬇ Download Source Distro (.zip) 📋 RuNIE v2.0.2 Changelog & 38-Operator Ledger JSON 🗂️ Validation Datasets Registry (VAL-SET-001–065) JSON 📊 #35 TimeTreeLSD2 Molecular Clock & Relaxed Clock vs. SciPy v1.18.1 JSON 📊 #34 Adaptive Multi-Biotype Super-BLAST vs. Biopython v1.88 & SciPy JSON 🔬 Tier 3 Synthesis: Repeat+/Indel− k-Core Connector Hubs & CAA/CAT Interruption Collapse (28 Cohorts / 1.08 Mb) JSON 📊 Tier 2 Blinded vs. Context-Aware Validation (20 Cohorts: 100% Biotype & 4/4 Anomaly Recall) JSON 📊 Tier 2 Deep Sweep (Bayesian 4–5🧪 Prior → 4🧪 Posterior, 24 Cohorts / 312 kb) JSON 📊 Tier 2 Validation: PopGen/HaploMap Repeat-Indel (McDonald 2011) & k-Core (Hagmann 2008) JSON 📊 #24 Haplotype MSN, Hudson F_ST & Tajima's D vs. SciPy v1.18.1 JSON 📊 #23 Mean-Field DCA & APC Co-Evolution vs. SciPy v1.18.1 JSON 📊 #22 3D Kabsch SO(3) & TM-Score vs. SciPy v1.18.1 JSON 📊 #21 Repeat Motif & Spidroin Regressor vs. SciPy v1.18.1 JSON 📊 #20 Codon Optimizer & MFE vs. ViennaRNA v2.7.2 JSON 📊 #19 RP16 Dayhoff-6/SR4 vs. IQ-TREE v2.4.0 JSON 📊 #18 KaiABC Allostery & Oscillator vs. SciPy JSON 🧠 HTT Single-Cell Somatic Repeat Expansion JSON 🧠 RuNIE 9-Study Inference JSON 📊 #17 Mk+ASC Morphology & Tip-Dating vs. IQ-TREE v2.4.0 JSON 📊 #16 Pseudogene & RELAX vs. MACSE/PAML JSON 📊 #58 AMR & HGT vs. AMRFinderPlus v4.2.7 JSON 📊 #59 BGC Rules vs. GECCO v0.11.0 JSON 📊 #60 BGC Calibration & Reserve Genomes JSON 📊 #52 Profile HMM vs. HMMER 3.4 JSON 📊 #51 Gene Calling vs. Prodigal JSON 📊 #50 Synteny vs. Minimap2 JSON 📊 #47 Orthology vs. DIAMOND JSON 📊 #46 Codon Selection vs. PAML JSON 📊 #45 Coalescent vs. ASTRAL JSON 📊 #44 ML Tree vs. IQ-TREE JSON 📊 #42 ModelTest vs. jModelTest JSON 📊 #41 Aligner vs. MAFFT JSON 📊 #40 Avian Systematics JSON 📊 #38 Disease Atlas JSON 📊 #15 Haplotype Cluster JSON 📊 #2 COI Barcode JSON

❓ Labs FAQ — Plain-Language & Technical Guide

Straightforward explanations of how our benchmarks work—from Development vs. Untouched Reserve Data and millisecond Rust speedups to RuNIE, homopolymer repeat indels, and 1🧪–5🧪 Beaker ratings.

Showing 10 of 10 questions

Reserve Data & Validation 1. What do you mean by "Untouched Reserve Data" vs. "Development Data"?

Summary: Development Data is the "practice set" we use while building and debugging a tool; Untouched Reserve Data is the "unseen test set" held aside until all code and parameters are frozen.

When you build or calibrate a bioinformatics tool, there is a common risk of overfitting ("teaching to the test"): if you tweak your code, thresholds, and equations while looking at all 12 datasets at once, you can accidentally tailor the rules just to make those specific 12 datasets pass—only for the tool to lose accuracy on a 13th dataset. We saw this firsthand in our early Biosynthetic Gene Cluster (BGC) work (Discovery #60), where rules tuned on an initial genome set dropped in recall when tested on new genomes.

Step 1 — Development Cohort (The Workbench): During development, we inspect errors and refine the Rust code on the Development set (e.g., DEV-01 to DEV-06).
Step 2 — Parameter Freeze (Pencils Down): Once the module matches the external reference software on the Development set, all weights, thresholds, and formulas are locked.
Step 3 — Untouched Reserve Cohort (The Unseen Test): We run the frozen module on the Reserve datasets (e.g., RES-01 to RES-06) that were not used during development to verify that it generalizes cleanly.

Speed & Architecture 2. How do your native Rust benchmarks finish in ~1–2 ms when external CLI suites take 3,000–5,000 ms?

Summary: On multi-cohort benchmark loops, roughly 95% of the external CLI runtime is spent spawning OS processes and writing temporary files to disk, whereas the in-memory Rust harness keeps all cohorts in CPU cache inside a single running process.

In Tool Benchmark #17 (bio-suite mk-compare vs. IQ-TREE v2.4.0, 2.31 ms vs. 5,738 ms) or Tool Benchmark #16 (relax-compare vs. MACSE/PAML), three factors explain the wall-clock difference:

Zero OS Process Spawns & Zero Disk I/O: To score 12 cohorts across 2 models (MK and MK+ASC), an external CLI harness has to spawn iqtree2.exe 24 separate times on Windows—loading DLLs, reading .phy/.nwk files, and writing 168 output files to disk (~230 ms of OS/disk overhead per launch). Our Rust engine evaluates all 12 cohorts in memory inside a single process via rayon::par_iter().
Closed-Form Analytical Math: For symmetric models like Lewis (2001) M_k, the transition probability matrix simplifies to a closed-form analytical exponential equation rather than general numerical matrix diagonalization, and scores both MK and MK+ASC in a single tree pass.
Important Caveat (Small-Matrix Loops vs. Large De Novo Searches): Avoiding subprocess and disk startup overhead yields large speedups when scoring fixed trees or chaining multiple stages in memory. On a single long-running de novo tree search where IQ-TREE launches once and runs in RAM for minutes, process startup overhead is negligible and the algorithmic/parallelism speedup is in the 5×–28× range.

RuNIE & Open Source 3. What is RuNIE (Rust Native Inference Engine) and what is its long-term goal?

Summary: RuNIE is a free, open-source (GNU GPL-3.0) workflow and inference module inside crvscience-labs—currently an early-stage work in progress—designed to chain multiple calibrated bioinformatics stages together across biological scales.

In most bioinformatics workflows, researchers have to glue together separate command-line tools in different languages using ad-hoc shell scripts. Our long-term goal with RuNIE is to build a unified Rust framework where calibrated native modules share typed in-memory data structures so analyses can be chained across Tier 1 (Exploratory), Tier 2 (Validation), and Tier 3 (Scientific) workflows:

Current Scope (Work in Progress): Benchmarking individual native Rust modules against established external tools (such as MAFFT, IQ-TREE, PAML, MACSE, HMMER3, and ViennaRNA) on Development and Untouched Reserve cohorts to ensure our baseline implementations match reference behavior.
Multi-Stage Chaining Goal: Connecting sequence/repeat mechanics, evolutionary selection, 2D/3D structural biophysics, and cellular/clinical phenotypes into reproducible multi-stage pipelines without intermediate file-parsing bottlenecks.
Public Development Log: Engineering notes and calibration fixes are tracked in RuNIE & Changelog and runie_changelog.md.

Indels, Fossils & Markers 4. How do codon-aware aligners handle frameshifts and homopolymer repeat indels?

Summary: Dedicated codon-aware aligners like MACSE have handled frameshifts for many years. When calibrating our own native Rust aligner against MACSE v2.07, we added 3-frame codon translation and a localized homopolymer guard so our native module matches reference behavior.

Our initial baseline Rust aligner only used nucleotide-level gap penalties, which shifted the downstream reading frame when encountering 1-bp deletions or single-base indels inside homopolymer tracts (such as a 6G run in ABCA4 or a 5A run in HBB). Established tools like MACSE solve this by aligning in 3-frame amino-acid space with explicit frameshift states (!).

In entry RN-2026-008, we updated our native Rust module in src/pseudo_loss.rs to align in 3-frame codon space and check downstream purifying selection (d_N/d_S ≪ 1), bringing our native implementation into 100% agreement with MACSE v2.07 and PAML v4.10.10 across the 12 test cohorts so it can be chained cleanly into multi-stage workflows.

Beakers, Tiers & Scope 5. What do the 3 Research Scope Tiers (Tier 1: Exploratory, Tier 2: Validation, Tier 3: Scientific) mean for datasets and validation sets?

Summary: The Tier Badge defines both the study scope and dataset provenance policy (seeking out new/unseen datasets vs. repeatedly replicated validation sets vs. combined scientific syntheses), while the 1🧪–5🧪 Beaker Rating reflects dataset scale and statistical power.

🧭 Tier 1: Exploratory: Seeks out and explores a mix of new, classic, or unseen datasets (datasets we had not previously evaluated in the suite), or uses an existing validation set in a new analytical way when it makes sense as part of a calibration sweep or tool benchmark.
🎯 Tier 2: Validation: Uses curated validation datasets (tracked in validation-datasets-registry.json, VAL-SET-001 through VAL-SET-065 across 65 Development/Reserve cohorts and 26 Empirical Preprints) that we have repeatedly replicated and validated—building out structured metadata around Development vs. Untouched Reserve splits, primary literature anchors, and cross-tool concordance as part of our pipeline validation process (such as Discovery #61 and Discovery #62).
🔬 Tier 3: Scientific: Combines a mix of new and established validation data—building on previous results by adding new datasets or testing new hypotheses—and must arrive at a new scientific interpretation, conclusion, or result.

Beakers, Tiers & Scope 6. How does the 1🧪–5🧪 Beaker Signal Rating work—and where do failed 0🧪 runs go?

Summary: Small datasets and known reference rediscoveries are rated 1🧪 so routine baselines are not overstated, while failed negative controls (0🧪) are excluded from publication and recorded in a calibration reject log.

0🧪 Excluded / Calibration Reject (zero-star-calibration-dump.json): Runs that fail quality gates—such as collapsed barcoding gaps (≤ 0.0), single-copy housekeeping operons lacking biosynthetic synthases, or shuffled negative controls. Logged to verify that our gates reject false positives.
🧪 1/5 Baseline / Review Note: Small test datasets (e.g., single-locus or 60–100 kb single-region scans), intragenomic paralog baselines, or known reference rediscoveries (MIBiG Jaccard ≥ 0.80).
🧪🧪 2/5 Technical Validation: Reproducible multi-locus (≥ 5 loci) or multi-clade benchmarks confirming established biological or structural parameters.
🧪🧪🧪 3/5 Comparative Synthesis: Cross-clade (≥ 12–25 taxa/cohorts or ≥ 250 kb) quantitative synthesis uncovering structured trade-offs across engines.
🧪🧪🧪🧪 4/5 Strong Emergent Signal & 🧪🧪🧪🧪🧪 5/5 Significant Biological Signal: Reserved for larger, well-powered datasets (≥ 250 kb to ≥ 1.0 Mb, ≥ 3 chained pipelines, Z ≥ 3.50–4.50 SD) with verified cross-engine concordance.

Indels, Fossils & Markers 7. Why do standard DNA barcodes (16S, 18S, ITS, COI) sometimes fail—and what is Ascertainment Bias (+ASC) in fossils?

Summary: Every genetic or anatomical marker has a resolution window. Outside that window, multi-copy paralogs, nuclear pseudogenes (NUMTs), or unrecorded invariant traits can distort phylogenetic inference unless paired with a secondary marker or model correction.

Bacterial 16S rRNA Paralog Collapse: A single E. coli cell carries seven 16S copies (rrsA–rrsH) that differ more from each other (0.0091) than E. coli differs from Salmonella (0.0078). Rescue: Switch to single-copy rpoB/gyrB (Discovery #29) or whole-contig tetranucleotide D_KL for plasmid resistance islands.
Vertebrate 18S Floor & Mitochondrial COX1 NUMTs: Nuclear 18S is 99.89% identical between mouse and rat, while COX1 PCR primers often co-amplify non-functional nuclear pseudogenes (NUMTs). Rescue: Pair full-length COX1 + MT-RNR2 (16S mtDNA) and filter out sequences where 2nd-codon entropy > 0.05 bits or d_N/d_S ≥ 0.10 (Discovery #30).
Morphological Ascertainment Bias (Mk+ASC): Paleontologists typically score only anatomical traits that vary across their fossil sample. Without Lewis (2001) +ASC conditioning (dividing by 1 − L_const) and invariant-column filtering, branch lengths inflate or software rejects constant columns (Discovery #62).

RuNIE & Open Source 8. Is CRV Science Labs & RuNIE free and open-source, and do I need Python, Conda, or Docker to run it?

Summary: Yes—crvscience-labs (including bio-suite v0.4.0, RuNIE v2.0.2, bio-suite-supervisor, and runie-align-view) is free and open-source under GNU GPL-3.0, and compiles to standalone Rust binaries with no Python, Conda, or Docker dependencies required.

All 37 validated native tools (6-stage MAFFT-Mimetic aligner, phylogenetic tree builders, Quartet MSC, Codon BEB, TimeTreeLSD2, Coalescent Skyline MCMC, profile HMM scanners, pocket dockers, and candle-core tensor routines) plus the 5-Tab Win32 Desktop Supervisor GUI are written in Rust. You can run the native pipelines and GUI offline with standalone binaries (bio-suite.exe, bio-suite-supervisor.exe, and runie-align-view.exe on Windows, or ./target/release/bio-suite on Linux/macOS), while optional CLI flags (like --tree-builder iqtree or --selection paml) allow side-by-side comparisons against installed external tools.

Speed & Architecture 9. How do I copy data tables into Excel/R or verify the exact NCBI RefSeq & PDB accessions?

Summary: Every data table has a 📋 Copy for Excel (TSV) button with atomic numeric columns, and every report links its raw JSON telemetry bundle and verified NCBI RefSeq / PDB accessions.

Click 📋 Copy for Excel (TSV) on any table in a Lab Discovery report to copy tab-separated values with one number per cell—ready to paste into Excel, R, or Python pandas. Every figure and table also includes a bottom Computational Tool Attribution footer (🔧 Generated by: ...) and a Provenance Ledger linking the CLI command and downloadable JSON artifact in /labs/releases/.

Reserve Data & Validation 10. Does CRV Science Labs perform wet-lab clinical trials, or computational bioinformatics?

Summary: We perform computational bioinformatics, comparative genomics, structural biophysics, and molecular systematics. Computational pipelines surface statistical correlations and candidate hypotheses to inform wet-lab and clinical researchers—not standalone causal or clinical proof.

We do not conduct wet-lab synthesis or clinical trials. When our pipelines analyze human knockout targets (PCSK9, ANGPTL3, HSD17B13), pharmacogenomic variants (MT-RNR2 m.1555A>G, CYP2D6), or somatic repeat expansions (HTT), our goal is to provide reproducible sequence and structural informatics that downstream wet-lab biologists and clinicians can evaluate experimentally.

1. Introduction: The Holocene Baseline and the Anthropocene Dilemma

1.1. Holocene Geoclimatic Stability and the Emergence of Agrarian Civilizations

1.2. The Mid-20th-Century Great Acceleration and Planetary-Scale Forcing

1.3. Beyond Reductionism: Earth as a Complex Non-Equilibrium Dissipative System

2. Mathematical Foundations, Basins of Attraction, and System Hysteresis

2.1. High-Dimensional Phase Space and Potential Well Dynamics

2.2. Critical Slowing Down and Loss of System Resilience

2.3. Multi-Century Hysteresis and Irreversible Biophysical Commitments

2.4. Upstream Placement of the Safe Operating Space

3. Quantitative Status of the Nine Planetary Boundaries

3.1. Climate Change (Core Boundary)

3.2. Biosphere Integrity (Core Boundary)

3.3. Biogeochemical Flows: Nitrogen and Phosphorus Cycles

3.4. Land-System Change

3.5. Freshwater Change: Blue and Green Water Dynamics

3.6. Ocean Acidification

3.7. Atmospheric Aerosol Loading

3.8. Stratospheric Ozone Depletion (A Model of Successful Stewardship)

3.9. Novel Entities

4. Coupled Tipping Cascades and Cross-Boundary Feedbacks

4.1. The Polar Cryosphere–Climate Feedback Cascade

4.2. Continental Evapotranspiration and Amazonian Savannization

4.3. Marine Synergies: The "Deadly Trio"

4.4. Saddle-Point Dynamics and Hothouse Earth Trajectories

5. Discussion: Planetary Stewardship and Systemic Governance

5.1. Transcending Carbon Tunnel Vision

5.2. Operationalizing the Safe and Just Operating Space (Doughnut Economics)

5.3. Five Strategic Pillars for Long-Term Planetary Stewardship

6. Foundational Sources (CRV Science Wiki Knowledge Base)

Foundational Sources (CRV Science Wiki)

Discussion & Key Takeaways

Lab Discoveries

📄 Preprints & Working Manuscripts 26 Preprint Monographs

Chaining Multi-Tier Bioinformatics Analyses Across Biological Scales

Current Multi-Stage Chaining Workflows

Chaining Frame-Aware Codon Alignment into Branch Selection (RELAX)

Chaining Elastic-Network Normal Modes into 3D Pocket Detection & Docking

Chaining Codon Usage Tables with 2D Minimum Free Energy & 3D Folding

Chaining Sequence Supermatrices, Lewis Mk+ASC Morphology & Tip-Dating

Chaining Ab Initio Gene Calling & Plan-7 Profile HMMs into BGC & AMR Detection

Chaining DNA/Protein Repeat Motifs into Somatic Kinetics & Structural Mechanics

Chaining Blinded Validation, Repeat Hairpin Decay, k-Core Hubs & mfDCA

Chaining Adaptive Super-BLAST, 3D Chromatin, Clonal NNLS, Fine-Mapping & MR-Egger

Chaining 6-Stage MAFFT-Mimetic Alignment, LSD2 Clocks & Coalescent Skyline MCMC

Autonomous bio-suite-supervisor 5-Tab GUI & 32-Chain Preprint Orchestration

RuNIE Development & Calibration Log

6-Stage Native Rust MAFFT-Mimetic Aligner (L-INS-i / E-INS-i / Q-INS-i / FFT-NS-i) & Autonomous 5-Tab bio-suite-supervisor Orchestration Across PREPRINT-21–26 (VAL-SET-060–065)

De Novo Coalescent Bayesian Skyline MCMC Sampler (runie-mcmc-skyline) & Multi-Track Win32 Alignment Viewer vs. BEAST v1.10.4 (PREPRINT-17–20 / VAL-SET-056–059)

3-Way Combinatorial RuNIE Benchmark Suite (100% Native Rust vs. 100% External vs. Hybrid) & Mandatory Stage 4 ModelTest (RCFV ≤ 0.050) Gate (PREPRINT-07–16 / VAL-SET-046–055)

Constrained Least-Squares Dating (LSD2), Autocorrelated Log-Normal Relaxed Clock, Parametric Bootstrap 95% CI & Clean X-Axis Time-Tree SVG Engine

Step-Up Synthesis (#91 🚩): Ctenophora Homeobox, Wnt & CTCF Chromatin Architecture (Flanking -25..-1 YPWM Pbx/Exd Co-Factor SLiM Retention & 2D Dayhoff-6 Across 1.64 Mb / 64 Taxa)

Full 3-Report Step-Up Synthesis (#88, #89 🚩 & #90 🚩): Ctenophora-First & Independent Muscle / Epithelial Evolution (Myosin II NM-MHC vs. ST-MHC Paralogs, Troponin-Absent Myocytes & Ancestral Collagen IV Across 1.58 Mb / 64 Taxa)

Step-Up Synthesis (#87 🚩): Ctenophora vs. Porifera & Cnidaria Basal Metazoan Evolution (7 Irreversible BCnS Chromosome Fusions, Dayhoff-6 Phylogenomics & Parallel Syncytial Nerve Net Assembly Across 1.56 Mb / 64 Taxa)

Step-Up Tier 1→3 Synthesis (#86 🚩): Alphaproteobacterial Endosymbiosis & Proto-Mitochondrial Evolution (BamA/SAM50, MICOS, Dayhoff-6 LBA Resolution & Mitoribosome Structural Inversion across 1.52 Mb / 64 Taxa)

Step-Up Tier 1→3 Synthesis (#85 🚩): Nucleocytoviricota Viral Histone Doublets/Quartets, Pre-LECA PolB/RNAP & Piecemeal aaRS Host Capture (1.50 Mb / 64 Taxa)

Step-Up Tier 1→3 Asgard Archaea (Candidatus Heimdallarchaeia / Hodarchaeales) Eukaryogenesis Synthesis: Cellular Machinery ESPs, RNAP/RP56 Regulatory Markers & Accretive Ribosome Evolution

Adaptive Multi-Biotype Super-BLAST (DNA/RNA/Protein/Other), Sensitivity Crossover Analyzer, Pre-MSA Validator, 16S + 10-Gene Tip Auditor & JIT Genome/Barcode Search

Sliding-Window k-mer Shannon Entropy (H1..H6), LZ76 Normalized Compression Distance & Purine-Pyrimidine Fractal DNA Walk DFA

Alternative Splicing (Ψ, ΔΨ, Hiso, JSDiso) & BAGEL2/CERES Copy-Number-Adjusted CRISPR Essentiality

Two-Sample Mendelian Randomization (IVW, MR-Egger Pleiotropy Intercept & Slope, Weighted Median & Cochran's Q)

Overflow-Protected Log-Space Wakefield ABF Fine-Mapping, 95% Credible Set Entropy & Giambartolomei Colocalization (PP.H0..H4)

Somatic Clonal 96-Channel Mutational JSD, Lawson–Hanson NNLS COSMIC Signature Deconvolution & CRISPR Synthetic Lethality

3D Chromatin Graph Laplacian Connectivity (λ2, λ̃2), Von Neumann Entropy (SVN), Sliding-Diamond TAD Insulation & Compartment E1

Indel-Free Repeat Connector Hubs (H1), Triplet Interruption Collapse (H2), Blinded NUMT/HGT Pruning (H3) & Flanking mfDCA Falsification (H4)

Blinded De-Identified RuNIE Extrapolation vs. Context-Aware Unblinding Comparison & Context-Anomaly Detection

McDonald Repeat-Proximal Substitution Decay, Hagmann k-Core Connector Hubs & Bayesian Prior-to-Posterior Depth Calibration

Minimum Spanning Haplotype Network (MSN), Hudson FST, Nucleotide Diversity (π) & Tajima's D Calibration

Mean-Field Direct Coupling Analysis (mfDCA) Covariance Inversion, Zero-Sum Gauge Frobenius Norm & APC

3D Protein Backbone Kabsch SO(3) Superposition, Active-Site Motif RMSD & Zhang–Skolnick TM-Score Engine

Repetitive Protein Motif Scanner (Poly-Ala / GPGXX / GGX / GAGAGS) & candle-core Ridge Regressor

Synonymous Codon Optimizer (CAI / GC3 / U-Depletion) & Turner-2004 MFE / McCaskill Partition Function Calibration

Dayhoff-6 & SR4 Compositional Recoding, candle-core Felsenstein Supermatrix Pruning & CPR/DPANN Auxotrophy Calibration

Jacobi Spectral GNM/ANM Normal-Mode Decomposition & Delay-Coupled RK4 KaiABC Limit-Cycle Calibration

Single-Cell HTT Somatic (CAG)n Markov Expansion, CAA Hairpin Stability & 150-CAG Threshold Module

Exact k-State Felsenstein Tree Pruning, Automatic Invariant-Column +ASC Filter & PIC Allometric Imputation

Bringing Our Native Coding Aligner Up to MACSE Frameshift Parity & Fixing Foreground Branch Labels for PAML RELAX

Spidroin Repeat-Motif Block Grammar Parser & Silk Biomechanics Regressor

Synonymous Codon-Lattice DFA × Turner-2004 MFE Co-Optimizer

SE(3) Rigid-Body Frame Denoising & Active-Site Motif Clamping Module

16-Ribosomal-Protein Syntenic Supermatrix Builder & Dayhoff-6/SR4 Compositional Recoder

Chaining Blinded Validation, Repeat Hairpin Decay, k-Core Hubs & `mfDCA`

Autonomous `bio-suite-supervisor` 5-Tab GUI & 32-Chain Preprint Orchestration

6-Stage Native Rust MAFFT-Mimetic Aligner (`L-INS-i` / `E-INS-i` / `Q-INS-i` / `FFT-NS-i`) & Autonomous 5-Tab `bio-suite-supervisor` Orchestration Across `PREPRINT-21`–`26` (`VAL-SET-060`–`065`)

De Novo Coalescent Bayesian Skyline MCMC Sampler (`runie-mcmc-skyline`) & Multi-Track Win32 Alignment Viewer vs. `BEAST v1.10.4` (`PREPRINT-17`–`20` / `VAL-SET-056`–`059`)

3-Way Combinatorial RuNIE Benchmark Suite (100% Native Rust vs. 100% External vs. Hybrid) & Mandatory Stage 4 `ModelTest` (`RCFV ≤ 0.050`) Gate (`PREPRINT-07`–`16` / `VAL-SET-046`–`055`)

Constrained Least-Squares Dating (`LSD2`), Autocorrelated Log-Normal Relaxed Clock, Parametric Bootstrap 95% CI & Clean X-Axis Time-Tree SVG Engine

Step-Up Synthesis (#91 🚩): Ctenophora Homeobox, Wnt & CTCF Chromatin Architecture (Flanking `-25..-1 YPWM` Pbx/Exd Co-Factor SLiM Retention & 2D Dayhoff-6 Across 1.64 Mb / 64 Taxa)

Full 3-Report Step-Up Synthesis (#88, #89 🚩 & #90 🚩): Ctenophora-First & Independent Muscle / Epithelial Evolution (Myosin II `NM-MHC` vs. `ST-MHC` Paralogs, Troponin-Absent Myocytes & Ancestral Collagen IV Across 1.58 Mb / 64 Taxa)

Adaptive Multi-Biotype Super-BLAST (`DNA`/`RNA`/`Protein`/`Other`), Sensitivity Crossover Analyzer, Pre-MSA Validator, 16S + 10-Gene Tip Auditor & JIT Genome/Barcode Search

Sliding-Window k-mer Shannon Entropy (H₁..H₆), `LZ76` Normalized Compression Distance & Purine-Pyrimidine Fractal DNA Walk `DFA`

Alternative Splicing (Ψ, ΔΨ, H_iso, JSD_iso) & `BAGEL2`/`CERES` Copy-Number-Adjusted CRISPR Essentiality

Two-Sample Mendelian Randomization (`IVW`, `MR-Egger` Pleiotropy Intercept & Slope, `Weighted Median` & Cochran's Q)

Overflow-Protected Log-Space Wakefield `ABF` Fine-Mapping, 95% Credible Set Entropy & Giambartolomei Colocalization (`PP.H0..H4`)

Somatic Clonal 96-Channel Mutational `JSD`, Lawson–Hanson `NNLS` COSMIC Signature Deconvolution & CRISPR Synthetic Lethality

3D Chromatin Graph Laplacian Connectivity (λ₂, λ̃₂), Von Neumann Entropy (S_VN), Sliding-Diamond TAD Insulation & Compartment E₁

Indel-Free Repeat Connector Hubs (`H1`), Triplet Interruption Collapse (`H2`), Blinded `NUMT`/`HGT` Pruning (`H3`) & Flanking `mfDCA` Falsification (`H4`)

Blinded De-Identified `RuNIE` Extrapolation vs. Context-Aware Unblinding Comparison & Context-Anomaly Detection

Minimum Spanning Haplotype Network (`MSN`), Hudson F_ST, Nucleotide Diversity (π) & Tajima's D Calibration

Mean-Field Direct Coupling Analysis (`mfDCA`) Covariance Inversion, Zero-Sum Gauge Frobenius Norm & `APC`

3D Protein Backbone Kabsch `SO(3)` Superposition, Active-Site Motif RMSD & Zhang–Skolnick `TM-Score` Engine

Repetitive Protein Motif Scanner (`Poly-Ala` / `GPGXX` / `GGX` / `GAGAGS`) & `candle-core` Ridge Regressor

Synonymous Codon Optimizer (`CAI` / `GC3` / U-Depletion) & Turner-2004 MFE / McCaskill Partition Function Calibration

Dayhoff-6 & SR4 Compositional Recoding, `candle-core` Felsenstein Supermatrix Pruning & `CPR`/`DPANN` Auxotrophy Calibration

Jacobi Spectral GNM/ANM Normal-Mode Decomposition & Delay-Coupled RK4 `KaiABC` Limit-Cycle Calibration

Single-Cell HTT Somatic `(CAG)_n` Markov Expansion, `CAA` Hairpin Stability & `150-CAG` Threshold Module

Exact k-State Felsenstein Tree Pruning, Automatic Invariant-Column `+ASC` Filter & PIC Allometric Imputation

Code — `crvscience-labs` (`bio-suite` v0.4.0 + `RuNIE` v2.0.2 + `bio-suite-supervisor`)

Fetch Once, Run Offline (`src/fetch.rs`)

bio-suite v0.4.0 — Standalone Windows x86_64 Executables (`bio-suite.exe` + `bio-suite-supervisor.exe` + `runie-align-view.exe`)