Evolutionary entropy, introduced by Demetrius, is a demographic invariant that quantifies the temporal organization of structured populations. Explicit sensitivity expressions for this quantity were derived by Demetrius, Gundlach and Ziehe for age-structured Leslie matrices, establishing the foundations of entropy-based perturbation theory. In this paper we develop a complete sensitivity theory for evolutionary entropy in irreducible Lefkovitch matrices. Using the Perron--Frobenius representation of the associated Markov chain, we derive explicit closed-form expressions for the stationary distribution, generation time, evolutionary entropy and its partial derivatives with respect to fertility, transition and retention parameters. The resulting identities are expressed directly in terms of demographic coefficients, Perron eigenvectors, the dominant eigenvalue and the reproductive potential. The entropy representation obtained here gives a natural decomposition into transition and retention components and clarifies the distinct mechanisms through which demographic uncertainty is generated in stage-structured populations. We further show that the theory specializes immediately to open-group Leslie matrices, a class that has been shown to comprise a large fraction of empirical demographic models. The results extend the entropy sensitivity theory of Demetrius--Gundlach--Ziehe from age-structured to general stage-structured populations and provide practical tools for comparative demographic analysis, perturbation studies, demographic robustness, and the investigation of life-history strategies. Several biological examples are presented, illustrating how entropy decomposition and sensitivity analysis reveal complementary aspects of population organization.

Additional food sources are often used to improve the effectiveness of predators in controlling pest populations. However, the non-symmetric structure of additional food predator-prey models can cause certain aspects of their dynamics challenging to analyze. In this work, we study a general class of additional food models and establish conditions under which the coexistence equilibrium is globally stable. We then focus on a Holling type IV functional response with AF and show the existence of a Bogdanov-Takens bifurcation of codimension 3. We also study these models through the lens of deterministic chemical reaction network theory. Our analysis shows that the introduction of additional food increases the deficiency of the underlying reaction network and suggests a possible link between higher deficiency and complex bifurcations.

The dependence of evolutionary rate estimates on the timeframe of sampling poses a fundamental challenge for reconstructing evolutionary histories from molecular sequence data, which is central to evolutionary biology and infectious disease research. We present a novel and flexible approach to accommodate time-varying evolutionary rates by modeling the sequence substitution process using inhomogeneous continuous-time Markov chains (ICTMCs) acting along the branches of the phylogeny, and parameterizing the log transformed rate as a smooth function of time using a cubic B-spline basis expansion. Following the parlance of phylogenetics that refers to rates of molecular substitutions as molecular clocks, we call this a spline clock model. Integrals of the rate function over all branches, required for likelihood evaluation, are approximated efficiently using Gauss-Legendre quadrature, and smoothness is enforced by assigning a Gaussian Markov random field prior to the spline coefficients. Through a simulation study, we demonstrate that the spline clock model recovers the true time-varying rates more accurately and with tighter credible intervals than competing clock models. We apply the spline clock model to examine the evolutionary rate of foamy virus and the rate of spatial diffusion of SARS-CoV-2 across Europe, recovering strong time-varying signal in both settings.

Across the sciences, autonomous systems are increasingly being used in closed-loop discovery, proposing new theories and designing and running experiments to test them. This approach is yet to be applied in the field of cognitive science, where the central bottleneck is theory-building: the creative step of turning the accumulated failures of existing models into better ones. Theory generation has remained manual even as data collection, modeling, and experiment design have been automated. We present the Automated Cognitive Scientist (AutoCog), a fully autonomous agentic-AI system that closes this loop. Large-language-model agents advocate competing theories, each expressed as an executable cognitive model, design experiments that best discriminate them, collect behavioral data from participants recruited online, score theories against collected data based on their generative performance, diagnose why they fail, and synthesize a better successor. Repeating this cycle allows them to search the space of theories, models, and experiments. In the domain of decision-making, AutoCog recovered known decision-making strategies from simulated behavior, including unconventional ones, showing that its discoveries are ultimately driven by the data rather than strictly bound by the priors of the underlying language models. When run with human participants, it produced theories that outperformed the established theories it was seeded with and generalized to held-out studies in two different experimental settings. It also surfaced a novel theory of multi-cue decision-making in which choices show diminishing sensitivity to feature values. The distinctive predictions of this theory were confirmed in a preregistered study with new participants. AutoCog demonstrates how an automated discovery system can be used to turn cognitive theory-building into an explicit, executable, and cumulative science.

Single-cell studies require analysts to convert raw measurements into specific biological claims through multi-step workflows and integration of metadata, assay context, and auxiliary evidence. Existing AI-biology benchmarks largely measure broad knowledge, executable workflows, or local analysis steps. We introduce scBench-Long, a benchmark for long-horizon single-cell biology in which agents must recover scientific conclusions from raw or near-raw data without prescribed methods. The benchmark contains 21 evaluations spanning melanoma CD8 T-cell reactivity, CD8 RNA+ATAC regulatory inference, human--monkey chimera development, KRAS-driven lung tumor aging, and lethal COVID-19 lung pathology. Tasks cover paired scRNA/TCR sequencing, RNA and chromatin profiling, cross-species transcriptomics, combinatorial scRNA-seq, single-nucleus RNA-seq, immune repertoires, ortholog maps, ligand--receptor resources, and validation evidence. Candidate claims are reproduced, reviewed, and converted into controlled answer vocabularies with deterministic grading and trajectory rubrics. Across 1,068 completed trajectories, the strongest model--harness pair passes 16/63 runs (25.4\%). scBench-Long evaluates whether agents can move beyond local analysis steps and make complex scientific claims that are supported by single-cell data.

Early discovery projects often face a budgeted prioritization problem: many structures can be enumerated or purchased, but only a small fraction can be tested, reviewed, or synthesized first. I formulate this setting as risk-aware compound-library compression. Given a molecular library and a fixed Top-k budget, the goal is to return an enriched candidate subset while preserving uncertainty, applicability-domain evidence, ADMET/structural alerts, and audit fields needed for human review. The framework intentionally uses a transparent 2D activity proxy rather than a complex representation model, combining Morgan fingerprints, RDKit descriptors, a multilayer perceptron, split-conformal uncertainty intervals, leakage auditing, and auditable export. On ChEMBL 36, the model achieved Spearman 0.7674 and EF@1% 2.7331 on internal validation, and Spearman 0.5171 with EF@1% 2.4359 on a temporal holdout. After fold-0 training-overlap control, a scaffold-disjoint BACE subset retained ROC AUC 0.7626 and EF@1% 2.0253. In a strict 100-molecule BACE decision-layer replay, risk-aware ordering kept Hit@10 at 0.9000 while exposing review evidence that pure activity sorting omits. An EGFR/CHEMBL203 label-hidden operational replay supports workflow feasibility but is reported as same-source sensitivity analysis rather than independent external validation. The claim is bounded: the evidence supports risk-aware library compression as an upstream prioritization layer, while prospective blinded validation remains necessary before claiming project-specific hit-rate or cost improvements.

With the rise of checkpoint blockade therapies and neoantigen-based vaccines reaching later-stage trials, there is a growing need for computational tools to identify and prioritize neoantigens. pVACtools, initially introduced in 2016, is an open-source informatic suite designed to support basic and translational neoantigen research. pVACtools assists prediction, prioritization, and visualization of neoantigens, as well as design of neoantigen-based therapies. We describe several major advances to pVACtools since the last update: (1) expanded neoantigen quality and safety assessment features, including support for peptide presentation scoring, immunogenicity prediction, anchor residue analysis, reference proteome similarity, percentile score calculation; (2) addition of pVACsplice, a new tool for predicting neoantigens from tumor-specific cis-splicing mutations; (3) addition of pVACbind, a flexible tool that supports noncanonical neoantigen sources; (4) improvement in neoantigen selection strategies; (5) a substantially improved pVACvector algorithm that achieves higher DNA/mRNA vector vaccine design success rates with shorter runtimes; (6) new utilities to support synthetic long peptide vaccine design; (7) extended prediction support for many non-human species; and (8) addition of pVACcompare, a tool to support comparison between two pVACseq results. Together, these updates reinforce pVACtools as the field's most comprehensive toolkit for neoantigen research, from basic discovery to the design and execution of personalized cancer vaccine clinical trials.

An important consideration for a model-based method of phylogenetic network inference is the identifiability of the network parameter of the model. A recurring theme in previous works exploring this issue is that it is often difficult to identify the orientation of edges in a triangle of the network. In fact, it has been shown that for some models it is impossible to determine the orientation of triangle edges utilizing the standard algebraic technique of phylogenetic invariants. In this work, we consider one such model with a Jukes-Cantor site-substitution process and no coalescence. We give a complete semialgebraic description of three, 3-leaf Jukes-Cantor phylogenetic network models with embedded triangles. By describing these base cases, we resolve several questions about the identifiability of networks with embedded triangles. We show that for any pair of models, the intersection and set differences of the models are full-dimensional regions of the space of site-pattern probability distributions. Thus, despite being algebraically indistinguishable, these network models are not identical, nor are they identifiable (or generically identifiable). Our results also yield a straightforward biological interpretation--that the signal from a hybridization event may be immediately detectable but decays over time until it is impossible to identify the orientation of edges in the triangle of a network.

Introductions of H5N1 clade 2.3.4.4b into dairy cattle have resulted in outbreaks on dairy farms across the United States since early-2024. Outbreaks have significant consequences for animal health, result in economic losses for the dairy industry, and pose a threat to human health. Though the relative contributions of different on-farm transmission pathways remain a key uncertainty, a major route is considered to be through repeated contamination of milking stalls (i.e. the equipment and area where an individual cow is milked) due to the milking of infected animals. Here we develop mathematical models of H5N1 transmission dynamics on dairy farms, considering multiple possible transmission pathways, and identify factors that contribute to outbreak risk and on-farm interventions for mitigating risk. In particular, we demonstrate that dividing cattle into 'milking cohorts', with cohorts kept in separate pens or paddocks and milked in the same order every day, would be highly effective at mitigating outbreaks irrespective of the dominant transmission pathway. Cohorting cattle is most effective when implemented pre-emptively (i.e. before an outbreak) and when newly introduced cattle are kept in the final milking cohort. Additionally, we demonstrate that frequent bulk milk sample testing (e.g. weekly) would enable the rapid detection of outbreaks and implementation of reactive interventions (or scaling up of existing interventions). Our findings can support the development of management guidelines for effectively responding to H5N1 outbreaks in dairy cattle.

Complex adaptive systems often develop organized structures without centralized control. Yet the local mechanisms by which functional organization emerges and persists remain incompletely understood. Here we propose Surviving by Serving (SBS) as a general principle of self-organization: components persist as long as their outputs are utilized by other components, whereas prolonged non-utilization promotes adaptation and exploration. To investigate this idea, we introduce a minimal multi-agent model in which agents transform shared resources and receive only local feedback when their outputs are subsequently utilized elsewhere in the system. Despite the absence of global objectives, the system spontaneously self-organizes into functional interaction networks. We observe the emergence of stable transformation chains, core-periphery organization, and the generation of novel states that enable previously inaccessible target conditions to be reached. Remarkably, self-sustaining interaction networks can arise even without external selection pressures, creating a pre-adaptive search phase from which later functional solutions emerge. These findings suggest that functional utilization may provide a simple, substrate-independent mechanism for the emergence and stabilization of organized structure in complex adaptive systems.

Phylogenetic inference on high-dimensional morphological traits requires algorithms that account for both the nonlinear geometry of the shape data and the phylogenetic tree structure. The Backward Filtering Forward Guiding (BFFG) framework provides smoothing for nonlinear stochastic processes on trees and enables inference of parameters and ancestral states. As practical adoption has been limited by a lack of efficient implementations, we present Hyperiax, an open-source library for tree traversal algorithms and message passing using JAX, designed particularly to support operations needed for BFFG. Hyperiax enables efficient execution of operations on trees with large numbers of nodes and, coupled with the BFFG-specific operations, this allows efficient inference in both discrete-time and stochastic differential equation models. Concretely, we demonstrate that Hyperiax enables parameter inference and ancestral reconstruction for butterfly wing shapes represented by landmarks in two dimensions, and analyses of avian beaks from landmarks in three dimensions. Both cases demonstrate application of BFFG on two substantially larger phylogenetic trees with 850 and 696 nodes with higher resolution shape data (118 two-dimensional landmarks and 79 three-dimensional landmarks, specifically) than previously possible.

Deep-learning emulators have emerged as a promising approach for reducing the computational cost of Earth System Models while potentially improving forecasting skill. Here, we demonstrate the successful emulation of a high-complexity marine biogeochemistry model within a simplified one-dimensional water-column framework. We explore two emulator architectures: Long Short-Term Memory (LSTM) neural networks that emulate a selected subset of variables at daily resolution, and physics-informed one-dimensional Convolutional Neural Networks (1D CNNs) that emulate the full pelagic system throughout the water column also at daily resolution. Using ocean physics simulator inputs, both emulators remain largely stable over multi-decadal timescales and accurately reproduce the parent model in both decadal climate projections and short-range (10-day) forecasting applications. The former includes the ability to predict the timing of phytoplankton Spring blooms several years in advance. When trained on reanalysis data, the emulators substantially outperform the parent model's forecast skill score for several key ecosystem variables, including phytoplankton and zooplankton. If similar performance can be achieved in three-dimensional regional applications, these emulators could provide substantially higher-quality predictions at a fraction of the computational cost. We further apply novel explainability techniques to identify key drivers of emulator behaviour and gain insights into emergent ecosystem dynamics. Performance is evaluated using a range of metrics, including the reproduction of daily variability and extreme events. These approaches have considerable potential for future applications in operational forecasting, climate-scale simulations, and marine autonomous systems.

Living systems navigate environments using noisy and incomplete sensory signals. In unicellular algae, phototaxis is often modeled as a mechanistic run--tumble process driven by stimulus--response rules. However, such descriptions overlook how organisms actively sample their environment to reduce sensory ambiguity. From a minimal cognition perspective, we reframe this navigation as a subjective, information-driven sensorimotor process. To this end, we propose a framework linking a Partially Observable Markov Decision Process (POMDP) with biochemical reaction dynamics. Environmental variables are hidden, while the cell updates a minimal internal state from each observation through a memoryless Bayesian step. These internal dynamics balance orienting toward light with exploratory reorientation and can be implemented through Chemical-Reaction-Network Ordinary Differential Equations (CRN--ODEs). Our model includes a biophysical observation process for photoreception and a chemically computable polynomial bound on information gain. Using Inverse Reinforcement Learning (IRL) on 30 experimentally recorded Chlamydomonas trajectories, we infer the behavioral objective consistent with observed phototactic motion and benchmark the resulting dynamics with standard Stochastic Simulation Algorithm (SSA) baselines. Our model reproduces the empirical alignment-to-light distribution, comparable to objective SSA baselines on this dataset. Within this framework, run--tumble alternation emerges as an information-acquisition strategy: tumbling reorients the cell to sample new sensory configurations and resolve sensor ambiguity, demonstrating how intracellular biochemical networks can support adaptive information-seeking behavior in cellular navigation.

While WGS-based AMR prediction has reached high accuracy, existing models lack a mechanism to ground neural attributions in established biological pathways. We present KG-TRACE, a novel neuro-symbolic framework that integrates the WHO mutation knowledge graph (KG) as a structured biological constraint on a neural genomic model. Unlike existing methods that learn statistical patterns in isolation, KG-TRACE fuses genomic features and RotatE-based KG embeddings through a learned epistemic trust gate, dynamically weighting neural evidence against symbolic biological knowledge. Evaluated on the CRyPTIC M. tuberculosis cohort, KG-TRACE achieves an AUROC of 0.9760 for isoniazid, achieving competitive accuracy while its primary value lies in symbolic grounding, not predictive uplift. More importantly, we introduce the Biological Grounding Ratio (BGR), a dataset-level metric that quantifies alignment between neural attributions and established biology. Our framework achieves a 92.5% symbolic coverage of isoniazid-resistant predictions and effectively identifies MDR co-occurrence artifacts by issuing laboratory follow-up flags for 'UNCERTAIN' cases. We demonstrate that neuro-symbolic grounding provides a verifiable audit trail for clinicians, bridging the gap between predictive accuracy and clinical trust.

Cognitive tasks are organized by shared and specialized neural processes. Masked fMRI reconstruction provides a common self-supervised objective for quantifying transfer relations among task states, but existing reconstruction-based taskonomies mainly study one-to-one transfer from a single source task to a target. Here, we extend an fMRI cognitive taskonomy from single-source to multi-source transfer across 23 Human Connectome Project task states and use Boolean Integer Programming (BIP) to analyze budget-constrained task allocation. We train 1,127 task-specific and transfer models. Single-source transfer is directional and paradigm structured: motor states transfer well within the motor paradigm but provide limited support to most non-motor targets, consistent with a shared sensorimotor execution system and effector-specific representations. Multi-source transfer depends on the composition of the source set, suggesting that many-to-one task relations are not fully captured by pairwise taskonomy alone. Across supervision budgets, BIP repeatedly allocates direct supervision to several 0-back and 2-back working-memory states, although these states are not consistently the strongest individual sources. This pattern may reflect the integration of perceptual, attentional, and executive processes in working-memory tasks. Together, these findings reveal a cross-paradigm-limited motor cluster and working-memory states with high priority under the specified global allocation objective. Our study extends reconstruction-based fMRI taskonomy from one-to-one transfer to many-to-one task relations and budget-constrained task dependencies.

Fluid-solid composite vesicles, comprising 2D solid domains integrated into a topologically-closed fluid bilayer membrane, exhibit complex morphologies arising from the geometric frustration between spherical closure of the membrane and 2D solid elasticity. This scenario is distinct from the better studied case of multi-fluid domain vesicles. Here, we study the elastic energies and shape equilibria of a closed vesicle membrane containing a single, flexible circular solid domain using discrete finite-element (Surface Evolver) simulations, determining the key physical and mechanical parameters to govern shape selection. While we find that the 2D solid (shear) elasticity has minimal impact on the highly-under inflated morphologies, the geometrically non-linear resistance of the solid to Gaussian curvature substantially impacts the shape and elastic patterns form for inflated vesicles, by an amount that it grows with ratio of vesicle size to the elastic thickness of solid. For sufficiently large (thin) vesicles we characterize a generic sequence of ground state patterns of solid shape with increasing inflation: from cylindrical rolls and isometric folds to spatially complex patterns of crumples and wrinkles and ultimately to smooth caps. This sequence of non-isometric patterns at high-inflation is shown to be governed by the same far-from-threshold mechanics used to describe similar shape transitions in microscopic sheets on curved liquid interfaces, establishing that inflated shapes are governed by two basic mechanical scales of membrane tension. We find our predictions for highly-anisotropic shape equilibria of fluid-solid composite vesicles closely match experimentally observed shapes of giant unilamellar vesicles of phase-separated DPPC and DOPC.

Graph-based representations of genome sequences have emerged as a powerful approach for representing massive genomic databases in an expressive and efficient way. Despite their benefits, analysis on large-scale genome graphs incurs significant data movement overhead from the storage system due to accessing large amounts of low-reuse data. Processing data directly inside the storage device can be a fundamental solution for mitigating this overhead. However, none of the existing tools for graph-based genome analysis can be efficiently used inside the storage system due to the limited internal hardware resources in modern SSDs. At the same time, prior storage-centric systems developed for (i) traditional, linear non-graph-based genome analysis or (ii) conventional, non-genomic graph analysis are not suitable for the unique data structures and access patterns of graph-based genome analysis. We propose GRAINS, the first system for analysis with large-scale genome graphs in storage. Through our detailed examination of typical analysis pipelines that operate on genome graphs, we perform storage-aware algorithm-architecture co-design to (i) make these pipelines more storage-friendly and (ii) further improve performance, energy-efficiency, and cost via in-storage and in-flash processing. GRAINS's co-design is based on three key aspects. First, we propose a new batching and execution flow, based on unique features of genome graphs. Second, via in-flash and in-storage processing, we avoid transferring low-reused flash pages. Third, to leverage the full parallelism of flash dies, we design an effective, yet lightweight, scheduling technique, enabled by re-purposing the existing SSD structures. GRAINS provides 2.7x-47.8x speedup (4.4x-31.6x energy reduction) over the state-of-the-art software baselines, and 1.5x-17.0x speedup (3.1x-20.7x energy reduction) over a hardware-accelerated baseline.

Self-limiting saturation curves, monotone responses that rise from zero to a plateau, govern gas adsorption, enzyme kinetics, dose-response pharmacology, and the growth per cycle of atomic layer deposition (ALD), yet mapping each curve from a handful of costly measurements is a shared bottleneck. The standard surrogate, a stationary-kernel Gaussian process, enforces no shape constraint: on sparse, noisy data it produces unphysical dips that corrupt both the inferred curve and the uncertainty used to choose the next experiment. We present an active-learning platform built on Bayesian monotonic I-spline regression, in which every posterior curve rises from exactly zero and never decreases, the plateau is set by a measurement at maximum exposure rather than a prior, and the input at any saturation level is read from the posterior by level crossing with no kinetic model assumed. Benchmarked isotherm by isotherm on five kinetically distinct families (Langmuir, dissociative Michaelis-Menten, sigmoidal Sips, logarithmic Elovich, and dispersive Kohlrausch-Williams-Watts), with ALD process development as the working example, the same fixed surrogate recovers every curve to within measurement noise without a single non-monotone posterior draw, and noise-free sweeps show the basis itself is near-exact across each family's regimes, locating its single capacity boundary at the sharpest sigmoidal onset. Driven by ordinary uncertainty sampling, the platform reaches noise-floor accuracy within a 20-measurement budget in every regime, in as few as seven measurements, whereas random sampling succeeds in only two of the five; the predicted pulse times err only on the conservative side, toward longer pulses, near saturation. Because the basis privileges no kinetic form, the platform applies wherever a self-limiting response must be learned from scarce data.

Respiratory activity is a direct and interpretable physiological channel for wearable stress and affective-state recognition, yet many studies emphasize classification accuracy without identifying which respiratory properties separate different states. This work reframes RESP-based recognition as a joint predictive and explanatory problem. Using the chest respiratory channel of the WESAD dataset, we analyze 60 s windows under leave-one-subject-out validation and combine two complementary branches: compact raw-signal one-dimensional convolutional neural networks (1D-CNNs) and physically grouped handcrafted respiratory signatures. The primary application task is binary stress versus non-stress detection, while baseline, stress, amusement, and meditation are additionally analyzed in a one-vs-rest setting to reveal state-specific respiratory markers. The feature space is organized into respiratory timing, breath-to-breath variability, waveform statistics, spectral/time-frequency descriptors, and autocorrelation/nonlinear predictability descriptors, with the raw 60 s signal treated as a sixth representation for the CNN branch. We introduce autocorrelation transition lags (Zpm/Zmp) as interpretable markers of respiratory correlation scale and separately evaluate exploratory FEG-Pro/Lyapunov-like descriptors. In the final CNN refit setting, the raw-signal model achieved the strongest stress-vs-rest performance, with accuracy 96.72 percent, macro-F1 95.30 percent, and MCC 90.61 percent. In contrast, compact feature models were stronger for baseline, with MCC 65.34 percent, amusement, with MCC 35.69 percent, and especially meditation, with MCC 88.65 percent. These results show that CNNs are most useful for the practical stress detector, whereas interpretable respiratory signatures provide stronger and more physiologically transparent state-specific markers for several non-stress conditions.

Phylogenetic reconstruction is one of the major challenges in computational biology. Among existing reconstruction methods for phylogenetic networks, an important subtask emerges in extending a leaf-labelling on a phylogenetic network to determine a most parsimonious tree inside the network. There exist different variants of this subtask depending on the biological model assumptions for which distinct evolutionary phenomena are captured by the network. In this article we assume that next to hybridization or recombination events, also allopolyploidy or incomplete lineage sorting are present. Then, finding the most parsimonious tree inside the network is called the parental parsimony score problem (PPS), a NP-hard combinatorial optimization problem. We provide the first constant-factor approximation for the PPS on arbitrary but fixed leaf labels and a class of networks on which the PPS remains NP-hard, namely binary, semi-simplex, tree-child phylogenetic networks. Furthermore, we introduce a novel exact solution algorithm for the PPS on binary, tree-child phylogenetic networks and analyze its performance on simulated data.

This paper investigates the classification capability of small-scale spiking neural networks based on the Leaky Integrate-and-Fire (LIF) neuron model. We analyze the relationship between classification accuracy and three factors: the number of neurons, the number of stimulus nodes, and the number of classification categories. Notably, we employ a large language model (LLM) to assist in discovering the underlying functional relationships among these variables, and compare its performance against traditional methods such as linear and polynomial fitting. Experimental results show that classification accuracy follows a power-law scaling primarily with the number of categories, while the effects of neuron count and stimulus nodes are relatively minor. A key advantage of the LLM-based approach is its ability to propose plausible functional forms beyond pre-defined equation templates, often leading to more concise or accurate mathematical descriptions of the observed scaling laws. This finding has important implications for understanding efficient computation in biological neural systems and for pioneering new paradigms in AI-aided scientific discovery.

Tactile localization is the seemingly simple ability to 'tell' where a touch has occurred. However, how this ability is assessed, and what conclusions are drawn from experiments, depends on the theoretical ideas that inspire the research. Here, we review both theoretical frameworks and methodological approaches based on a systematic web-based literature search on tactile localization. After presenting current theories of tactile localization, we discuss task characteristics that differentiate current methodology for tactile localization into at least 8 distinct types of experimental tasks. We describe these tasks, discuss their, often implicit, underlying assumptions and cognitive requirements, and relate them to the theoretical approaches. We then compare, in an exemplary manner, the tactile localization results reported by a subset of studies and demonstrate how some methods are associated with specific biases, illustrating that the choice of experimental method significantly affects the conclusions drawn from the results. Our review suggests that the field currently lacks a clear concept of the specific processes induced by the various experimental tasks and, thus, calls for concerted efforts to clarify and unify currently diverse, fragmented, and partly inconsistent theoretical underpinnings of tactile spatial processing, flanked by dedicated data sharing to allow across-study analysis.

Elucidating the language-brain relationship requires bridging the methodological gap between the abstract theoretical frameworks of linguistics and the empirical neural data of neuroscience. Serving as an interdisciplinary cornerstone, computational neuroscience formalizes the hierarchical and dynamic structures of language into testable neural models through modeling, simulation, and data analysis. This enables a computational dialogue between linguistic hypotheses and neural mechanisms. Recent advances in deep learning, particularly large language models (LLMs), have powerfully advanced this pursuit. Their high-dimensional representational spaces provide a novel scale for exploring the neural basis of linguistic processing, while the "model-brain alignment" framework offers a methodology to evaluate the biological plausibility of language-related theories.

Pesticides are designed to eradicate pests from crops, fulfilling an important role in the current agricultural system. However, nature conservation requires that pesticide applications are protective for non-target organisms, which provide ecosystem services on the other hand. Environmental risk assessment (ERA) is supposed to strike this balance, but the current use of laboratory derived toxicity thresholds in the landscape context, without consideration of population and landscape dynamics might be too coarse to achieve this task. Here, we propose to overcome this limitation by coupling the Animal, Landscape, and Man Simulation System with the BufferGUTS model for non-target arthropods. We conducted a case study of the solitary bee Osmia bicornis exposed to the pesticide formulation Closer (a.i. sulfoxaflor) to assess the integration. Laboratory survival data of topical and oral exposure to Closer were used to calibrate BufferGUTS models. The resulting parameters were used to parametrise model organisms in ALMaSS simulations to extrapolate the effects of sulfoxaflor at different exposure levels on population dynamics. The integration of BufferGUTS into ALMaSS landscape simulation was achieved with high numerical precision, allowing for the calculation of daily survival probabilities for model organisms in the ALMaSS framework. We found that even extreme application rates only led to negligible population effects in ALMaSS simulations, but an exploratory analysis of pesticide-driven larval mortality showed that effects might be more severe when all life stages are considered. The work demonstrates how mechanistic modelling embedded into individual based modelling frameworks can support ERA by combining exposure and effect in systems-based ERA tools, bridging the gap between controlled laboratory experiments and realistic landscape-scale risk assessments for next generation ERA.

Accurate, low-latency estimates of the instantaneous phase of oscillations are essential for closed-loop sensing and actuation, including (but not limited to) phase-locked neurostimulation and other real-time applications. The endpoint-corrected Hilbert transform (ecHT) reduces boundary artefacts of the Hilbert transform by applying a causal narrow-band filter to the analytic spectrum. This improves the phase estimate at the most recent sample. Despite its widespread empirical use, the systematic endpoint distortions of ecHT have lacked a principled, closed-form analysis. In this study, we derive the ecHT endpoint operator analytically and demonstrate that its output can be decomposed into a desired positive-frequency term (a deterministic complex gain that induces a calibratable amplitude/phase bias) and a residual leakage term that sets an irreducible variance floor. This yields (i) an explicit characterisation and bounds for endpoint phase/amplitude error, (ii) a mean-squared-error-optimal scalar calibration, and (iii) practical design rules relating window length, filter bandwidth and order, and centre-frequency mismatch to residual bias via an endpoint group delay. The resulting calibrated ecHT achieves near-zero mean phase error and remains computationally compatible with real-time pipelines. Code and analyses are provided at this https URL.