Understanding which genes control which traits in an organism remains one of the central challenges in biology. Despite significant advances in data collection technology, our ability to map genes to traits is still limited. This genome-to-phenome (G2P) challenge spans several problem domains, including plant breeding, and requires methods capable of reasoning over high-dimensional, heterogeneous, and biologically structured data. Current datasets and data repositories, however, are not well-equipped for this task. Current studies do not link gene expression and trait data, and most focus on very specific traits, limiting the breadth of possible correlations. To address this gap, we present the novel Gene-Graph Regression for Arabidopsis Functional Traits (GRAFT) dataset, a curated multi-modal dataset linking gene expression profiles with phenotypic trait measurements in Arabidopsis thaliana, a model organism in plant biology. GRAFT supports tasks such as phenotype prediction and interpretable graph learning. In addition, we benchmark conventional regression and explanatory baselines, including a biologically-informed hypergraph baseline, to validate gene-trait associations. To the best of our knowledge, this is the first dataset to provide multimodal gene information and heterogeneous trait or phenotype data for the same Arabidopsis thaliana specimens. With GRAFT, we aim to foster research to accurately understand the relationship between genotypes and phenotypes using gene information, higher-order gene pairings, and trait data from multiple sources.

Stress is an adaptive response coordinated by neural and physiological systems. While acute stress can enhance survival, chronic stress drives structural brain changes, cognitive dysfunction, and increased psychiatric risk. At the cellular level, chronic stress shifts the excitatory-inhibitory (E/I) balance of prefrontal pyramidal neurons toward inhibitory dominance, yet the mechanisms underlying these alterations are still unknown. We here investigate possible mechanisms causing inhibitory dominance using recurrent neuronal networks trained on a working memory task. Chronic stress is modelled as a modulation in synaptic strength or neuronal activity, systematically comparing eight candidate operators against three experimentally motivated signatures of stress-induced prefrontal dysfunction: inhibitory dominance, excitatory hypofunction, and impaired task performance. These signatures are all recovered by a single stress mechanism, stronger inhibitory-to-excitatory synapses. Contrasting naive networks with resilient networks trained under the stress mechanism, we find that resilience training not only preserves task performance under stress, but also confines the network to the same dynamical subspace and energetic regime with and without stress. This resilience comes at a cost: resilient networks generalise less well when the task requires longer memory than seen during training, indicating that resilient networks find a specialised solution tuned to the trained regime. This trade-off between resilience and generalization performance persists across stress magnitude and network size, offering a computational analogue of the shift toward rigid, habit-like behaviour reported in animal following chronic stress.

Efficient evaluation of sequence data likelihoods and their high-dimensional gradients on phylogenetic trees improves inference under both maximum-likelihood and Bayesian frameworks. Here, we present BEAGLE 4.1, a high-performance library for statistical phylogenetics that incorporates new algorithms to evaluate these gradients on phylogenetic trees. We also provide new hardware implementations for both likelihoods and gradients supporting ARM NEON intrinsics and optimized matrix multiplication units -- called tensor cores -- on NVIDIA graphics processing units (GPUs). We benchmark the performance scaling of the library across a number of patterns and taxa on multi-core CPUs and GPUs, and compare the speedup afforded by NVIDIA and AMD GPUs as well as performance scaling with an increasing number of GPUs. We show that multi-core CPU implementations provide up to a fourfold speedup over single-threaded CPU implementations and up to an tenfold speedup for nucleotide and codon models, respectively, with performance generally improving as the number of taxa and site patterns increases. GPUs outperform multi-threaded CPU implementations for a realistic number of patterns, even for nucleotide models with a small state-space size of 4, while for codon models they provide substantially higher performance gains even for a single pattern or four taxa. Tensor cores on GPUs provide up to 2-fold speedup relative to standard CUDA cores for codon models. Using NEON instructions on ARM CPUs affords up to a $\sim 1.3$-fold speedup over non-SIMD implementation with the speedup going down to 1.1-fold at 8 CPU threads. We provide these new algorithms to evaluate the gradient and efficient hardware implementations for both likelihood and gradient calculations through BEAGLE 4.1, such that they can be readily integrated into phylogenetic software packages.

Obesity is a global health crisis associated with metabolic disorders such as type 2 diabetes and cardiovascular disease. This study employed single-cell RNA sequencing to reconstruct the developmental trajectory of human adipocytes from adipose tissue samples. Our analysis identified 15 transcriptionally distinct cell clusters, including 7 transitional states, revealing the dynamic process of adipocyte differentiation. We detected 16 functionally active signaling pathways mediating cellular communication between adipocytes and their progenitors. Among these, insulin-like growth factor (IGF) and fibroblast growth factor (FGF) pathways emerged as the most prominent networks, showing consistent activity across differentiation stages (p<0.05). The study revealed depot-specific differences, with visceral adipocytes undergoing additional extracellular matrix remodeling absent in subcutaneous differentiation. Spatial analysis further showed that IGF signaling was particularly active in perivascular niches, while FGF activity dominated in mature adipocyte zones. These results provide the first comprehensive map of human adipocyte development, highlighting IGF and FGF pathways as potential therapeutic targets. The identified signaling networks offer new insights for developing interventions to promote healthy adipose expansion or inhibit pathological fat accumulation. This work advances our fundamental understanding of adipose tissue biology while providing clinically relevant data for metabolic disorder treatments.

Continuous attractor neural networks (CANNs) are the canonical computational framework for how the brain encodes continuous variables such as spatial position, head direction, and movement direction, and explain the activity of hippocampal place cells, entorhinal grid cells, and head-direction cells. CANN research, however, is fragmented: most results rest on lab-specific implementations, general-purpose simulators lack CANN-specific abstractions, and the path from spike trains to attractor geometry in real recordings lacks a standardized toolkit. Here, we present a comprehensive open-source toolkit that unifies the full CANN research workflow. It combines three tightly integrated components: 1) canns, a Python library on BrainPy/JAX that provides standardized 1D/2D CANNs, spike-frequency-adaptation variants, grid cell networks, hierarchical path-integration models, and brain-inspired attractor architectures, together with curated datasets, task generators, an analyzer module and trainer modules for biologically plausible plasticity; 2) canns-lib, a Rust acceleration backend delivering hundreds-of-times speedups for spatial-navigation workloads and modest gains for Ripser-based persistent homology; 3) ASA (Attractor Structure Analyzer), a PySide6 pipeline applying persistent homology and cohomology to experimental neural recordings to detect ring-like and toroidal attractor signatures in real data. The toolkit ships with full-detail reproducible pipelines that recover recent CANN results including SFA-driven anticipative tracking, theta sweeps in head-direction/place/grid systems, and hierarchical path integration.

Ancestral sequence reconstruction (ASR) is a powerful approach for studying molecular evolution and the emergence of protein function. Yet most ASR methods assume that sites evolve independently, neglecting the epistatic constraints that shape protein structure, stability, and function. This simplification affects both ancestral inference and its evaluation: maximum-a-posteriori reconstructions may over-concentrate probability into a single over-idealized sequence, whereas independent posterior sampling can generate implausible or poorly functional ancestors. Here, we introduce a coevolution-aware ASR framework that combines standard phylogenetic inference with Direct Coupling Analysis (DCA), thereby preserving site-wise ancestral uncertainty while enforcing residue-residue constraints learned from extant protein families. To benchmark the method, we develop a controlled forward-evolution framework based on a DCA evolutionary sampler, allowing reconstructed ancestors to be compared with known ground-truth sequences generated under realistic epistatic constraints. Applied to beta-lactamases and DNA-binding domains, the approach improves reconstruction when ancestral states are epistatically constrained, and yields ensembles of candidate ancestors that are both phylogenetically consistent and statistically compatible with natural protein families. This framework bridges the gap between single-sequence MAP reconstruction and unconstrained posterior sampling, providing a practical route toward ancestral reconstructions that better reflect the coupled nature of protein evolution.

Recent breakthroughs in synaptic-resolution network connectomics have revealed that brain circuits feature fine-scale structural connectivity, such as pairs of correlated synaptic couplings known as second-order motifs. Large-scale recordings of neuronal activity in networks containing nonlinear neurons reveal macroscopic heterogeneous population dynamics throughout the brain. These findings rekindle the inquiry into this intriguing question: Can microscale synaptic structures contribute to macroscopic heterogeneous dynamics and computations in ways that canonical brain circuit models cannot? To answer this question, we create random RNNs with various cell types, nonlinear non-negative neural responses, and arbitrary marginal and second-order correlated synaptic statistics. We derive mean-field low-rank equations for P-population networks in which the pre- and postsynaptic neuronal population identities determine the synaptic and motif strengths. Our framework requires 2P latent dynamic variables with P variables describing mean population activity and P variables capturing within-population variability. Theoretical and simulational results demonstrate that chain motifs induce correlations in synaptic variability, enabling microscopic fluctuations to be integrated and influence mesoscopic mean population dynamics. We apply this framework to reverse engineer network connectivity that recapitulates the heterogeneous activity across the population in the mouse primary visual cortex. By bridging the gap between synaptic organization and nonlinear heterogeneous population dynamics, our results offer a principled approach and testable predictions regarding the relationship between fine-scale connectivity, heterogeneous dynamics, and functional computations.

Evolutionary intermediates connect observed proteins, but the sequence of steps that produced them is rarely recoverable from extant data alone. Here we ask what can, and cannot, be inferred about such intermediates from the endpoints. Using generative sequence landscapes as controlled models of protein-family evolution, we benchmark data-driven reconstruction against ground-truth simulated trajectories. We find that the best point prediction is not necessarily the most faithful evolutionary reconstruction: maximum-likelihood intermediates can be residue-wise accurate yet statistically atypical, whereas conditional sampling better captures the ensemble of plausible histories. Predictability is limited by the topology of the landscape. Constrained, low-mutability regions preserve information about the path, while permissive high-mutability regions open many alternative routes and erase path-specific memory. We also show that sequence divergence alone is an insufficient measure of elapsed evolutionary time; incorporating endpoint mutability provides a more reliable way to place intermediates in the landscape. These results recast intermediate reconstruction as a calibrated probabilistic problem. Rather than seeking a single "true" sequence, data-driven models should identify when endpoints contain evolutionary information, and return realistic ensembles.

Background: Both between- and within-subject variations in circadian timing matter for health. If lifestyle changes could be used to regulate circadian timing, they would offer accessible and scalable routes to chronotherapy, but this link remains unclear under real-life conditions. Here, we explore how lifestyle 'traits' (such as typical wake time) and 'states' (day-to-day deviations from traits, such as waking up later than typical) explain between- and within-subject variation in acrophase (peak time) of the circadian rhythm of heart rate (CRHR). Methods: We collected free-living wearable data (smartwatch, continuous glucose monitor) from healthy volunteers for up to 4 weeks. The CRHR was derived from activity-adjusted heart rate, and acrophase was defined as time-of-day at daily CRHR peak. Sleep, food, and physical activity 'factors' were calculated and split into traits and states. Using a linear mixed-effects model, we tested how traits and states associate with between- and within-subject acrophase variance. Findings: Data from 105 healthy volunteers (66 female, age = 42.5 $\pm$ 15.7 years) spanning ~2000 days (18.8 $\pm$ 8.30 days each) were analysed. Traits were substantially more influential than states, explaining 42.3% versus 0.9% of total acrophase variance. Accordingly, traits explained 86.5% of between-subject variance, whereas states explained only 1.8% of within-subject variance. Sleep, food and physical activity factors contributed both jointly and uniquely, and lifestyle timing mattered most. Interpretation: Between-subject lifestyle traits explained acrophase better than within-subject lifestyle states. This asymmetry, alongside the considerable overlap between factors, supports sustained, holistic, timing-focused lifestyle adjustments as chronotherapy targets, testable through future interventional studies.

Accurate assessment of tumor proportion score (TPS) in non-small cell lung cancer (NSCLC) is critical for treatment planning and prognosis. Key challenges include the tedious manual work required to annotate each slide, combined with the limited number of experts certified for this task. Multiple instance learning (MIL) has proven to be an effective approach for predicting TPS scores at the slide level; however, existing methods struggle with non-expressive (zero class) images. Our approach involves two models: (1) an embedding-extraction and multiclass-classification network that captures the histopathological features of individual patches, and (2) a MIL model that aggregates these embeddings to predict zero-inflated beta (ZIBeta) parameters representing the overall TPS probability distribution for the entire slide. Using only slide-level TPS scores as labels, we demonstrate how this end-to-end framework can leverage a novel distribution-based architecture to improve prediction accuracy and explainability. ZIBeta modeling significantly outperforms baseline linear and ridge regression while capturing expected accuracy through distribution concentration.

In this paper, we study reactive strategies in repeated additive games between two players with finitely many actions. Reactive strategies condition only on the opponent's previous action, making them one of the simplest ways players can respond to past interactions. Additive games include important models of cooperation, such as the donation game and games with a punishment option. We show that, for this class of games and strategies, the conditions for symmetric Nash equilibria reduce to a system of linear equalities and inequalities in the strategy parameters, allowing us to characterise all such equilibria. We establish a one-to-one correspondence between non-empty subsets S of the action set and equilibrium classes, which we call S-supporting equilibria. These are equilibria that use exactly the actions in S when playing against themselves. As a special case, we recover the well-known equalizer strategies as the equilibria supported on the entire action set. To assess which equilibrium classes are most evolutionarily relevant, we complement our analytical characterisation with simulations of social learning dynamics. We find that their prevalence is determined by two factors: how likely they are to be generated and how robust they are against invasion.

Artificial intelligence is transforming biodiversity monitoring by enabling automated analysis of ecological imagery collected from camera traps, drones, satellites, underwater platforms, and other sensing systems. These tools can expand the scale and speed of conservation assessments, yet many computer vision models remain difficult to inspect, making it challenging to determine whether predictions are based on ecologically meaningful signals or on spurious correlations, sampling biases, and other artifacts that may undermine conservation decisions. We argue that explainable artificial intelligence (XAI) should become a standard component of ecological model validation because conservation practitioners increasingly depend on understanding not only whether a model is accurate, but why it is accurate. We provide practical guidance for applying XAI to three common ecological computer vision tasks: image classification, object detection, and image segmentation. To illustrate how XAI can support ecological model auditing, refinement, and deployment, we present two case studies using aerial imagery: harbor seal detection and cetacean anatomical segmentation. These examples demonstrate how explanation methods can identify biologically meaningful cues, reveal false positives driven by background and shape confounds, uncover edge and occlusion effects, and guide data collection, augmentation, and retraining strategies. More broadly, they show how explainability can help assess whether model reasoning aligns with ecological understanding. We conclude by identifying key challenges and opportunities. By making model behavior more transparent and scientifically interrogable, XAI can help ensure that AI-supported ecological evidence is more reliable, understandable, and actionable for biodiversity conservation.

Protein language models are standard priors for biological sequence generation, but steering them toward explicit distributional design targets remains largely unexplored. We study a constrained protein generation problem in which sequences must match a desired amino-acid (AA) composition profile while preserving plausible sequence statistics and diversity. The motivating application is synthetic feed protein design, where the AA composition of dietary proteins directly determines their nutritional value. We propose a two-stage pipeline in which domain-adaptive fine-tuning (FT) on an in-domain protein dataset is followed by iterative reward-weighted FT via reinforcement learning (RL) anchored against the FT model as a frozen reference. We evaluate the pipeline on two AA compositions and find that FT brings the average composition close to the target, while the subsequent RL enforces specific sequence constraints that FT alone cannot satisfy. We additionally evaluate the design choices of the proposed composition reward term against two baselines and an ablated variant, isolate the contribution of each training stage, and verify that AA composition alignment is achieved without degrading sequence quality.

Strategic behaviour in queueing systems has been studied extensively in the behavioural queueing literature, but almost exclusively for systems that admit closed-form expressions for the cost or utility experienced by a strategic user. Evolutionary game theory offers a mature framework for analysing populations whose individual payoffs depend on the composition of the population itself, and would in principle apply to a much wider class of queueing systems; its application has, however, been constrained by the same closed-form requirement. We introduce Discrete Event Population Updates (DEPU), a general algorithmic framework that couples a single long run of a discrete event simulation (DES) directly to an evolutionary population update rule, removing that constraint. We present two implementations: Discrete Event Replicator Dynamics (DERD), which follows an Euler discretisation of the replicator dynamics equation, and Discrete Event Moran Replacement (DEMR), which maintains a finite population updated via Moran-style copying events. Both are applied to a multi-server jockeying model for which no closed-form fitness expressions are available. On the jockeying model considered, DEPU reaches comparable precision tens of times faster than the standard practice of nesting short simulations inside an outer evolutionary loop, and because each operating point then costs only a single simulation run it also makes systematic parameter sweeps tractable. This brings the toolkit of evolutionary dynamics within reach of any system a modeller can build in a discrete event simulator.

Subjective cognitive decline (SCD) doubles dementia risk. This study investigates how self-perceived cognitive worsening shapes neural dynamics during naturalistic speech perception. EEG was collected from 60 cognitively normal older adults as they listened to speech varied in prosodic contexts, categorized by expressive style (scrambled, descriptive, dialogue, exciting). Encoding models mapping three speech representations -- acoustic, subsyllabic segmentation and phonotactic features -- to ongoing EEG signals were built. Cortical tracking strength (CTS) showed that models fitted with subsyllabic linguistic features outperformed acoustic ones. Crucially, greater SCD severity was associated with weaker CTS of (1) subsyllabic linguistic but not acoustic features, and (2) prosodically flat speech (scrambled and descriptive). Thus, the CTS of higher-level linguistic features while listening to prosodically flat speech may serve as a potential neural marker for early-stage cognitive decline.

The NIH Human BioMolecular Atlas Program (HuBMAP) Data Portal (this https URL) serves as a comprehensive repository for multimodal, multi-scale spatial and single-cell data from healthy human tissues. As of June 2026, the portal hosts 9,232 public datasets from 25 data types spanning 29 organ classes across 498 donors. Portal infrastructure and user interfaces support data search and discovery, visualization, and analysis directly in web browsers. These capabilities include metadata- and data-driven search, collaborative Workspaces with access to high-performance compute, and interactive Vitessce visualizations across non-spatial, 2D, and 3D spatial datasets. Data-type-specific uniform processing pipelines and rigorous quality control processes ensure comparability of results across laboratories, organs, and donors, while externally processed community-contributed datasets provide complementary perspectives. Here we describe portal functionality, infrastructure, and design, and highlight its role as a platform for large-scale spatial single-cell research across diverse data types, organs, and scales.

Ecological interaction networks are rarely homogeneous: species naturally form communities with distinct interaction structures, resulting in block-structured variance and correlation profiles in the interaction matrix. We study the equilibrium properties of generalized Lotka-Volterra systems whose interaction matrices are random and non-symmetric with variance and correlation profiles. Based on recent advances in approximate message passing (AMP) for heterogeneous and correlated random matrices, we derive a set of self-consistent fixed-point equations that, in the large-$n$ limit, characterize the equilibrium abundance distribution. In particular, we show that this limiting distribution is an explicit mixture of truncated Gaussians, driven by the variance and correlation profiles. We then illustrate the ecological implications of this result through three applications involving two interacting communities. First, we show that local changes in the correlation profile within a single community induce system-wide responses in species persistence, revealing the non-local nature of persistence dynamics. Second, we find that communities dominated by mutualistic or competitive interactions are more robust to increasing inter-community coupling, whereas communities structured by predator-prey interactions are more prone to collapse. Third, we demonstrate that asymmetric interaction variance alone, in the complete absence of correlation, can generate feedback loop between communities.

This work presents a unified framework for the parametrization and comparison of microbial survival models under UVC exposure. Four dose-response models - the single-target, multi-target, linear-quadratic, and two-stage decay models - are analyzed with respect to their mathematical structure, parameter identifiability, and biological interpretability, while ensuring physically meaningful parameter estimates. To address the limited size and lack of replication in published datasets, parameter uncertainty is quantified using a parametric bootstrap approach under multiplicative dose uncertainty. Model comparison combines goodness-of-fit in logarithmic survival space, the Akaike Information Criterion corrected for small samples, and identifiability considerations. Application to data for 32 microorganisms shows that no single model is universally optimal, highlighting that reliable model selection requires combining statistical performance with physical and biological consistency, and providing a robust basis for UVC survival analysis and disinfection modeling.

Chronic Kidney Disease (CKD) affects nearly 10\% of the global population and often progresses to end-stage renal failure. Accurate prognosis prediction is vital for timely interventions and resource optimization. We present a transformer-based framework for predicting CKD progression using multi-modal electronic health records (EHR) from the Seoul National University Hospital OMOP Common Data Model. Our approach (\textbf{ProQ-BERT}) integrates demographic, clinical, and laboratory data, employing quantization-based tokenization for continuous lab values and attention mechanisms for interpretability. The model was pretrained with masked language modeling and fine-tuned for binary classification tasks predicting progression from stage 3a to stage 5 across varying follow-up and assessment periods. Evaluated on a cohort of 91,816 patients, our model consistently outperformed CEHR-BERT, achieving ROC-AUC up to 0.995 and PR-AUC up to 0.989 for short-term prediction. These results highlight the effectiveness of transformer architectures and temporal design choices in clinical prognosis modeling, offering a promising direction for personalized CKD care.

Democratic backsliding is often framed as a contest between pro-democratic defenders and anti-institutional norm-breakers. That framing can miss a third behavior, a public that withholds punishment from norm-breakers while penalizing those who confront them. We study a minimal three-strategy evolutionary game, with institutional defenders, anti-institutional disruptors, and this non-punishing public evolving under replicator dynamics. We grant defenders a head-to-head advantage over disruptors and ask whether it guarantees their long-run success. It does not. Two payoff regimes, differing only in how the public and disruptors interact, produce two failure modes. In an exploitation regime, the public is harmed by disruptors yet withholds sanction, so the three strategies exhibit cyclic dominance. When the losses around the cycle outweigh the gains, every interior trajectory approaches a boundary heteroclinic cycle in which disruptors repeatedly resurge. In an accommodation regime, the public and disruptors each gain from their interaction. When the public's gain is large enough, every interior trajectory converges to a stable public-disruptor coalition that excludes defenders. A pro-democratic advantage is therefore not enough. Weak sanction and penalized confrontation can leave anti-institutional disruption recurring or entrenched.

Contact tracing (CT) is a frontline measure against emerging epidemics, yet in practice it is never complete. The quantitative impact of missing information -- such as untraced cases or unnotified contacts -- on the effectiveness of CT remains insufficiently understood. Using a stochastic agent-based model with sociodemographics from metropolitan areas in South Korea, we simulate how different forms of information loss affect epidemic spreading dynamics. We construct information-loss scenarios based on two types: infector-omission (IO), the omission of infected individuals from the tracing process, and contact-omission (CO), the omission of specific contact events even when the infected individuals themselves are identified. The sensitivity of epidemic dynamics to increasing omission rates differs markedly between the two types: IO produces substantially stronger and more abrupt changes in transmission structure and epidemic outcomes, whereas CO produces more gradual effects. Notably, CT effectiveness breaks down beyond a city-specific threshold -- an IO rate of approximately 4% in Seoul but about 10% in less populous Busan -- underscoring that CT strategies must be tailored to regional population and mobility structure. Both IO and CO scenarios also lead to an increase in the transmission network diameter as information loss grows, indicating that a small network diameter reflects effective contact tracing that limits the depth of transmission chains. Collectively, our results offer threshold estimates and practical guidance for designing robust CT systems in the real world.