Papers

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Quantitative Biology

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Saturday 22 August

Genomics

A Conditional Structure-Aware Generative Transformer for Multi-Objective Design of m1{\Psi}-Modified RNA 5' UTRs

Narges Zarnaghinaghsh, Ahmadreza Mofayezi and Byung-Jun Yoon

The 5' untranslated region is a major determinant of translation initiation, and its effect becomes especially important in modified mRNA sequences, where start-codon context, cap-proximal secondary structure, upstream AUGs and upstream open reading frames, and nucleotide chemistry can alter ribosome scanning and initiation recruitment, scanning, and decoding in sequence-dependent ways. Recent computational studies have moved the field from prediction toward design, including massively trained predictive models such as Smart5UTR for m1$\Psi$-modified mRNA, broader 5' UTR generation and optimization frameworks such as UTRGAN and UTailoR, and structure-guided RNA design systems such as RhoDesign. Here, we describe a conditional generative framework for 50-nt modified-RNA 5' UTR design that optionally conditions on ribosome load, GC content, minimum free energy, and target secondary structure. The implementation uses a Transformer-based generator followed by sequence ranking and local refinement with a Smart5UTR-derived ribosome-load oracle and ViennaRNA-based folding metrics, including support for modified-base folding parameters. Across multiple simulation scenarios and experimental settings, different combinations of RL, GC, MFE, and structural constraints produced distinct performance tradeoffs, enabling ablation-based identification of the best-performing formulation.

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Friday 21 August

Molecular Networks · Cross-listed from cs.SI

Omega-N: Interpretable Structural Node Descriptors and Their Applicability Domain

Alberto Acedo

A composite structural index summarises a network in one number, and for a triangle-based index it is spectrally redundant: Tr(A^3) is the third moment of the adjacency spectrum. The non-redundant content sits one level down, in diag(A^3), which depends on eigenvectors and is not spectrally determined. A corollary in the theory paper predicted that the global scalar should tie sharpened spectral baselines rather than beat them, while the node-wise attribution should do better where the number of structural epicentres is unknown. We construct Omega-N by localizing each of the four factors. The direct localization is badly conditioned; two corrections from published practice fix it, a configuration-null excess per factor and a personalized-PageRank neighbourhood at several scales, giving ten interpretable features per node, with no attributes, training or embeddings. Against a recursive feature engine at five levels of recursion, Omega-N wins on three and ties on two of the six in-domain evaluations, the sixth a declared null where every arm returns chance, with ten features against its 28 to 252 before pruning. Two statistics from the graph and labels, not from performance, partition the eight benchmarks without error, and the two they exclude are the two on which it loses. The strongest application is drug-target prioritisation on protein interaction networks: +0.032 to +0.103 AUPRC over a six-feature centrality battery and +0.084 to +0.208 over the four-feature one, across three constructions, replicated on an independent AP-MS network and label source (degree-matched: +0.0723 on STRING, +0.0560 on BioPlex, p=0.00195). Adding Omega-N to centralities plus Node2Vec changes nothing. The claim is narrow and it is the point: ten named features, computed without training, match or beat hand-crafted centralities and a recursive engine, and do not touch learned representations.

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Populations and Evolution

Finding Tree-Like Substructures in Phylogenetic Networks: ILP Approaches and Their Application

Takatora Suzuki

Phylogenetic networks model evolutionary histories that involve reticulate events, but their structural complexity makes them difficult to interpret. Extracting their simple substructures both clarifies the evolutionary pathways and quantifies the complexity of the networks themselves. For a given rooted almost-binary phylogenetic network, the Level Minimization problem asks for a spanning subgraph that has the same root and leaf-set and whose level is minimum, i.e., which is as close to a tree as possible. Networks for which the minimum level is zero are known as tree-based networks and can be recognized in linear time. However, Level Minimization is NP-hard in general. State-of-the-art algorithms rely on exhaustive searches of the solution spaces and hence apply only to networks of limited size. In this paper, we propose two methods for Level Minimization using integer linear programming: an exact formulation for finding such a subgraph of level at most one, and a heuristic formulation for the general case. Computational experiments confirmed the practicality of both formulations. An application to ancestral recombination graphs suggests that the minimum level provides an alternative measure of the topological complexity of an inferred network.

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Neurons and Cognition

Non-standard memory models with indexed retrieval

Gabriele Scheler, Martin L. Schumann and Johann Schumann

The standard memory models for neural networks are variants of the Hopfield network, where feature representations are stored as vectors in a matrix. Retrieval happens based on similarity between an input vector and the set of stored vectors in a content-addressable manner such that the network evolves towards the closest stored attractor. In other words, the useful property of addressing items in memory directly by index is lost in Hopfield-style neural network models ("associative memory"). In this extended abstract, we present a new model which is extremely simple, derived from biological observation, yet introduces a significant conceptual advance and technical benefits. The goal is to establish adaptivity based on the neuron-centric hypothesis: Plasticity is organized by the neuron which regulates its own synapses. Accordingly we implemented a localist, neuron-centric one-shot learning method and applied it to a simple pattern classification problem (MNIST). We were looking for the existence of high information neurons, to act as indices into the representations. The idea was that we would be able to restore full patterns by indexed retrieval, instead of associative vector retrieval from attractors.

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Quantitative Methods

A nonlinear dynamics approach to assess amplitude-frequency coupling in non-invasive human electrogastrography recordings

Akshita A. Rao and Todd P. Coleman

Conventional electrogastrography (EGG) analyses summarize gastric slow-wave activity using dominant frequency, power, or phase-based metrics, without explicitly modeling amplitude-dependent frequency dynamics. We developed a statistical nonlinear-dynamics framework that represents the gastric-band analytic EGG signal as a stochastic Stuart-Landau oscillator and estimate local effective phase-shear, $\beta_{\mathrm{eff}}$, a normalized measure of amplitude-frequency coupling. In overnight recordings from 60 healthy participants, effective phase-shear was consistently negative, generalized to held-out halves of each recording, and was abolished by amplitude surrogates that disrupted amplitude-phase alignment while preserving amplitude-series structure. We then applied the measure to fasted and fed recordings from 16 healthy participants and 24 participants with gastric dysfunction. Phase-shear magnitude differed between groups and classified dysfunction comparable to the strongest multielectrode phase-based traveling-wave measures. These findings establish amplitude-frequency coupling as a dynamical feature of gastric-band EGG that complements conventional spectral and spatial phase metrics, while motivating future multichannel observation models to determine its relationship to slow-wave propagation and gastric function.

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Molecular Networks

Motional Degrees of Freedom in Network Hamiltonian Models

Peng Huang, Elizabeth M. Diessner and Carter T. Butts

Network Hamiltonian Models (NHMs) provide an efficient framework for modeling the aggregation of interacting particles (e.g., the condensation of proteins into gel-like, oligomeric, or fibrillar states), representing the system as a network whose edges represent bound interactions. Terms within the network Hamiltonian represent multi-body interactions governing aggregation behavior, and are specified via topological degrees of freedom. Because motional degrees of freedom are not explicitly represented within the NHM, their influence must be indirectly accounted for by introduction of corresponding terms to the Hamiltonian. Here, we describe specifications for terms representing motional degrees of freedom of two, three, and four-body interactions. We also consider the impact of these terms on the aggregation states of a minimal system governed only by a pairwise edge potential, showing that three and four-body interactions favor condensation of the system into small droplet-like structures at low temperature.

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Quantitative Methods

Explainable AI-based Decision Support for Nocturnal Hypoglycemia Prevention in Type 1 Diabetes

Valentina Roquemen-Echeverri, Peter G. Jacobs, Leah M. Wilson et al. (+8 more)

Purpose: Nocturnal hypoglycemia (NH) remains a challenge for individuals with type 1 diabetes (T1D), particularly those who are physically active or on multiple daily injections (MDI). We leveraged an explainable evidential neural network model that forecasts minimum overnight glucose to identify NH risk factors and generate recommendations for NH prevention. Methods: We analyzed the impact of glucose and physical activity (PA) factors on predicted NH probability using SHapley Additive exPlanations (SHAP). Data were from 20 adults with T1D on MDI (10 females; mean age 39 years; HbA1c 7%) who participated in a cross-over study (NCT05967260). Results: A total of 502 nights were analyzed. Bedtime glucose was the strongest predictor of NH. Other relevant factors included recent exposure to high or low glucose, glucose variability before bedtime, and timing of PA. Some associations appeared physiologically counterintuitive, possibly reflecting behavioral adjustments. Based on the identified risk factors and their SHAP values, we determined key decision points and developed recommendations that may help prevent NH, such as consuming a bedtime snack or discussing potential adjustments to insulin therapy with a healthcare provider. Conclusion: Identifying predictors of NH offers insights for managing NH risk in clinical decision support.

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All authors Valentina Roquemen-Echeverri, Peter G. Jacobs, Leah M. Wilson, Joseph Pinsonault, Deborah Branigan, Jae Eom, Daisy Chen, Hantao Ling, Diana Aby-Daniel, Kyle Chen, Clara Mosquera-Lopez

Quantitative Methods

PerturbRx: Learning Treatment-Conditioned Latent Transitions for Patient Drug Response Prediction

Yoshitaka Inoue, Minoh Jeong, Alfred Hero et al. (+2 more)

Scarce data and tumor heterogeneity limit patient-level cancer treatment-response prediction. Existing approaches predict response from pretreatment molecular profiles and drug representations, without explicitly modeling the molecular changes expected under treatment. We propose PerturbRx, a treatment-conditioned representation learning framework that learns intervention-induced latent transitions and uses them as patient-drug response features. PerturbRx trains a drug- and dose-conditioned transition predictor from context-matched but cell-unpaired control and treated single-cell populations, then freezes and transfers the predictor to pretreatment patient profiles without requiring post-treatment measurements. The transition is combined with patient and drug representations to predict response. Across TCGA and patient-derived xenograft benchmarks, PerturbRx achieves the strongest aggregate predictive performance among the evaluated methods. These results support perturbation-pretrained latent transitions as useful representations for patient-level drug-response prediction.

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All authors Yoshitaka Inoue, Minoh Jeong, Alfred Hero, Rui Kuang, Augustin Luna

Molecular Networks

Metabolic Network Properties: Comprehensive Analysis Across Domains

José Antônio Pellizzaro, Daniel Gamermann and Julian Triana Dopico

Metabolic networks play pivotal roles in understanding the evolution of organisms, microbiome dynamics and disease prevention and treatment. This study presents a comprehensive analysis of metabolic network properties across 10912 organisms spanning Bacteria, Archaea, and Eukarya domains. A novel method for the network construction is introduced, emphasizing the chemical transformations of metabolites. Unlike conventional approaches that link every substrate to every product in all chemical reactions, this method aligns more closely with metabolic pathways, linking products only to their generating substrates. Emphasis is placed on investigating the community structure within metabolic networks, employing the Surprise quality function that better addresses some limitations of previous approaches. Through comparisons between real and randomized versions of the networks, we identify characteristics that cannot be explained solely by the network degree distribution, thus identifying topological properties and community structures that arise from additional evolutionary pressures. This highlights the need for evolutionary models to incorporate additional mechanisms beyond the replication of the degree distribution.

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Populations and Evolution

A framework for combined epidemiological-genomic inference to improve estimation of household model parameters

Golsa Sayyar, Joe Hilton and Thomas House

Models incorporating household structure, with different rates of transmission within and between households, are widely used in infectious disease epidemiology. These models can be calibrated using final-size data in which transmission ordering is ignored because it does not affect the distribution of final outbreak sizes. In particular, many distinct transmission histories produce identical final epidemiological outcomes, making it difficult to distinguish internal (within-household) from external (between-household) transmission and limiting parameter identifiability. Here, we develop a continuous-time Markov chain formulation for household transmission dynamics in which the model state space is expanded to include transmission graphs describing infection direction and order, with idealised pathogen genomic data used to identify the transmission histories compatible with observations. We conduct simulation studies which show that incorporating genetic information substantially concentrates the regions of high likelihood compared with models based on epidemiological data alone. In particular, genomic data reduces the dependence between internal and external transmission parameters, removing the characteristic ridge associated with their weak identifiability. These results demonstrate that graph-resolved household models enable improved transmission inference while maintaining analytical and computational tractability.

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Genomics · Cross-listed from cs.LG

TracingFlow: A Simulation-Free Trajectory Inference Framework Based on Second-Order Dynamics

Yuhao Sun, Zekun Wu, Zixun Huang et al. (+1 more)

Inferring continuous system evolution from sparse temporal snapshots is a key challenge in generative modeling and single-cell omics. While Optimal Transport (OT) is popular, existing frameworks are largely restricted to first-order dynamics, assuming memoryless velocity fields. This limits expressiveness, as first-order systems fail to account for regulatory momentum and time-delayed responses inherent in processes like cell differentiation. Here, we introduce TracingFlow, a simulation-free Flow Matching framework generalizing to second-order dynamics. By using neural networks to regress the acceleration field, TracingFlow provides an exact, efficient solution to the Dynamical Optimal Acceleration Transport (DOAT) problem. Unlike first-order methods yielding over-smoothed trajectories, our second-order formulation captures high-curvature transitions and nonlinear evolutions by learning the underlying force fields. Evaluated on complex synthetic and large-scale scRNA-seq datasets, TracingFlow achieves superior accuracy in distributional reconstruction and trajectory faithfulness. Moreover, by integrating lineage tracing priors, it recovers dynamical structures that are both mathematically optimal and biologically plausible.

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All authors Yuhao Sun, Zekun Wu, Zixun Huang, Peijie Zhou

Neurons and Cognition

Conscious Access as Continuous-to-Discrete Translation

Tianming Yang

The scientific study of consciousness frequently stalls on ontological debates regarding the "Hard Problem." This paper proposes a pragmatic pivot. Rather than asking what consciousness is metaphysically, we ask how modeling conscious access as a specific computational transformation may address existing bottlenecks in neuroscience and artificial intelligence. We introduce the Continuous/Discrete (C/D) framework, which holds that the brain implements two distinct processing regimes: System C, a distributed sensory-motor network operating over continuous, high-dimensional manifolds, and System D, a centralized engine structured around discrete, scale-invariant symbols. We argue that conscious access requires a structure-preserving translation between these regimes, which maps localized continuous states onto discrete symbolic tokens, coupled with an inverse projection that grounds those tokens back into sensorimotor dynamics. By formalizing conscious access as this continuous-to-discrete conversion, we derive a unified set of testable predictions centered on representational geometry, specifically, on a measurable collapse from graded similarity structures to low-dimensional categorical equivalence classes. These predictions explicitly differentiate our account from Global Neuronal Workspace Theory, Integrated Information Theory, Predictive Processing, and Higher-Order Theories, shifting the focus from ontological status to computational mechanism. Beyond neuroscience, the framework provides a principled architecture for neuro-symbolic artificial intelligence. We argue that treating conscious access as translational computation offers a pragmatic, empirically tractable pathway forward, clarifying what conscious states functionally accomplish without requiring resolution of the hard problem of phenomenology.

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Thursday 20 August

Populations and Evolution · Cross-listed from cs.CC

Orientations without transitive arcs for cubic graphs and phylogenetic networks

Janosch Döcker and Simone Linz

An $st$-orientation of an undirected graph $G$ is an acyclic digraph with a single source $s$ and a single sink $t$ that can be obtained from $G$ by assigning a direction to each edge. The classical problem of deciding if an undirected graph $G$ has an $st$-orientation can be solved efficiently. On the other hand, deciding if an $st$-orientation of $G$ exists that does not have any transitive arc is NP-complete, even if each vertex of $G$ has degree at most four. Here we show that this last decision problem remains NP-complete if $G$ is cubic, which settles an open question by Binucci et al. (2025). We obtain NP-completeness for two variants of the problem: (i) $s$ and $t$ are fixed and given as part of the input and (ii) $s$ and $t$ can be chosen freely. We then use these results to investigate the computational complexity of a problem that arises in computational evolution. Specifically, we show that the problem of deciding if an unrooted binary phylogenetic network has an orientation as a rooted binary phylogenetic network without any shortcuts (the analog of a transitive arcs in phylogenetics) is NP-complete. Our results connect the two (mostly) distinct research areas of orienting undirected graphs and orienting unrooted phylogenetic networks.

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Genomics

mLS-GKM: Efficient Multi-class Regulatory Sequence Classification with Gapped k-mer SVMs

Kieran Howard and Nathan Harmston

Gapped k-mer support vector machines (gkm-SVMs) are widely used for classifying regulatory DNA sequences and identifying the sequence features underlying those predictions. Although LS-GKM provides an efficient implementation of gkm-based kernels, it is restricted to binary classification and does not provide calibrated probability outputs. Here, we present mLS-GKM, an extension of LS-GKM that adds multiclass classification, probability-calibrated predictions, parallelised inference, memory efficient sequence interpretation and checkpointing during training. Across 322 ENCODE ChIP-seq datasets, classifiers trained using mLS-GKM are identical to those produced by LS-GKM, while gkmpredict and gkmexplain run 22x and 80x faster respectively at 64 threads, and gkmexplain peak memory is reduced by more than 65%. As a practical demonstration, mLS-GKM was able to accurately distinguish between enhancers, promoters and CTCF binding sites directly from sequence and identified biologically relevant regulatory motifs. Together, these improvements extend gkm-SVMs to multiclass problems and substantially improve their scalability, enabling efficient interpretation of regulatory DNA sequences in large and complex datasets

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Quantitative Methods · Cross-listed from stat.ME

GENIE: Generative Neural Inference for Epidemics

Laura M. Guzmán-Rincón, George R.E. Bradley, Joel Kandiah et al. (+5 more)

The SARS-CoV-2 pandemic highlighted the ongoing risk infectious diseases pose to society and the value of reliable information on the likely future burden. When forecasting an epidemic at fine spatial resolution, traditionally used mechanistic compartmental model struggle to capture highly complex granular transmission dynamics, resulting in inaccurate and overconfident forecasts. However, detailed Agent-Based Models (ABMs), are challenging to calibrate and are too computationally expensive to use in real-time. Amortized simulation-based inference promises to overcome this difficulty by exploiting the power of machine learning (ML) to perform approximate forecasting at near-real-time using arbitrarily complex models of epidemics. In this work we introduce Generative Neural Inference for Epidemics (GENIE), a spatio-temporal ML-based framework for high-resolution forecasting of the burden of respiratory pathogens. GENIE is designed to reflect two key characteristics of outbreaks: (i) shared biological mechanisms across locations and (ii) location-specific characteristics affecting transmission dynamics. This results in the model architecture having two modules: (i) a Local Infection Encoder - which learns to represent disease dynamics shared across all locations and (ii) a Local Profile Encoder - which learns location-specific representations. Using simulations from a high-resolution spatio-temporal ABM, GENIE is trained to generate samples from an approximate posterior predictive distribution of future epidemic trajectories. Benchmarked against established statistical and ML models, GENIE demonstrates superior performance across a range of measures including the timing and magnitude of peak hospitalisations.

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All authors Laura M. Guzmán-Rincón, George R.E. Bradley, Joel Kandiah, Kyriakos Flouris, Pietro Liò, Paul J. Birrell, Alexander E. Zarebski, Daniela De Angelis

Populations and Evolution

Novel models of trait evolution via an expansion of Lande's fitness function: The Ornstein-Uhlenbeck process meets the Little Prince's boa

Jose M Ponciano, Claire Godineau, Laura Jimenez et al. (+3 more)

Adaptive topographies form the foundation for much of our understanding of evolutionary change. Lande's 1976 influential paper on the adaptive topography of phenotypes demonstrated how the concept is inherent in both phenotypic and genetic models of evolution, and how the concept can be used to test evolutionary hypotheses given data. Here, we revisit and generalize Lande's original derivation of an equation analogous to Wright's genotypic adaptive topography to the case of two fitness components. A move to two fitness components yields novel predictions about the shape and mechanistic underpinnings of the adaptive topography. The optimum of this updated fitness function is a weighted average of the optima of the two fitness components, with weights given by the relative strengths of stabilizing selection on each component. Temporal or spatial heterogeneity in the strengths of selection for each fitness component create novel shapes (asymmetry, bi-modality, or lack thereof) of the overall fitness function, a possibility demonstrated with a case-study from the published literature. Finally, when combined with Lande's approach to generate an Ornstein-Uhlenbeck (OU) model for the evolution of the average phenotype, our fitness formulation leads to a previously unrecognized family of stochastic differential equation models of trait evolution. These results provide mechanistic justification for non-Gaussian fitness functions (often observed in natural systems), provide a path for testing alternative models generating non-Gaussian fitness functions, and pave the way for future study of the interplay of ecological and evolutionary dynamics, such as in the study of evolutionary rescue.

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All authors Jose M Ponciano, Claire Godineau, Laura Jimenez, Nicholas Kortessis, Rosana Zenil-Ferguson, Robert D Holt

Neurons and Cognition · Cross-listed from cs.LG

Decoding silent reading from non-invasive EEG

Ingo Marquardt, Anthilia Alchanat and Priyanka Jain

Non-invasive decoding of inner speech faces a fundamental data problem: a corpus pairing brain activity with a person's spontaneous inner monologue cannot be collected, and the available proxy paradigms (cued repetitive and retrospectively reported generative inner speech) are slow to acquire, poorly time-locked, and subject compliance is unverifiable. We therefore treat silent reading as a scalable proxy task and ask how much lexical and semantic information a contrastive decoder can extract from it. We report an open-vocabulary analysis of approximately 240,000 word presentations recorded from a single densely-sampled participant across 393 runs (ca. 49 h) of 19-channel dry-electrode EEG. Words from continuous narrative text were presented in rapid serial visual presentation, with typography randomised on every trial to partially decorrelate word identity from low-level visual form. A convolutional EEG encoder, optionally followed by a causal transformer, was trained with a CLIP-style contrastive objective to align short EEG windows with hidden-state embeddings of the presented word taken from a large language model. Decoding, evaluated as word-grouped top-10 retrieval against permutation baselines, was reliably above chance, extended to mid-frequency and rare words, and scaled log-linearly with training-data volume with no sign of saturation. Removing occipital and posterior-temporal electrodes reduced the word-level gain by roughly one third but left context tracking unchanged. Control analyses separate word-level decoding from narrative context tracking and from a non-neural positional prior introduced by the transformer's positional embedding. These results establish that open-vocabulary word-level information is recoverable from EEG during silent reading, and that decoding is data-limited rather than saturated.

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Quantitative Methods

PEtab SciML: an exchange format for specifying and training dynamic scientific machine learning models

Sebastian Persson, Branwen Snelling, Maren Philipps et al. (+5 more)

Summary: Dynamic scientific machine learning (SciML) models that combine mechanistic ordinary differential equations (ODEs) with machine learning (ML) components have applications ranging from learning unknown biological processes to integrating auxiliary data modalities into dynamic modelling. To enable reproducible and efficient SciML training, we introduce PEtab SciML, an interoperable data format for specifying parameter estimation problems in which mechanistic and ML model parameters are jointly estimated from time series data. PEtab SciML supports several ML ODE hybridization patterns in realistic problem setups. It is accompanied by a reference Python library and downstream modelling support in Python/JAX and Julia, provided by AMICI and PEtab$.$jl, respectively, and a collection of real data benchmarks. Availability and implementation: PEtab SciML is available on GitHub (https://github.com/PEtab-dev/petab_sciml). The reference Python package is installable from PyPI and is continuously tested and supported on Linux, macOS, and Windows.

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All authors Sebastian Persson, Branwen Snelling, Maren Philipps, Daniel Weindl, Marija Cvijovic, Jan Hasenauer, Dilan Pathirana, Fabian Fröhlich

Tissues and Organs · Cross-listed from physics.bio-ph

Growth phases of an active tissue: determinate, indeterminate, and proportionate

Jigyasa Watwani, K. Vijay Kumar and Vishal Vasan

Growth may cease at a target size or continue throughout life: the determinate and indeterminate phenotypes. We develop an active viscoelastic continuum model of a tissue growing along one axis, in which cell division and death generate active stresses. We find two asymptotic states: one in which the tissue reaches a relative size fixed by its material parameters, and one in which it elongates linearly without bound. Which state is realised is set by the ratio of active stress to elastic modulus. The transition originates in a bound on the elastic stress the tissue can support: a sufficiently large activity can never be balanced. In a tissue made of parts with different material properties, the growing phase settles into fixed length proportions, set by the mechanical impedances of the parts rather than inherited; matching impedances to initial lengths preserves the proportions the tissue began with. Determinate, indeterminate and proportionate growth thus appear as regimes of one continuum mechanical framework.

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Populations and Evolution

Correlations at criticality in ecological communities

Akiva Goldberg and Nadav M. Shnerb

Ecological communities are continually reshaped by invasion, exclusion, and diversification, processes that naturally drive them toward the boundary of dynamical stability. Near such a boundary, a soft mode relaxes increasingly slowly and, under stochastic forcing, is expected to dominate the fluctuations, effectively reducing the dynamics to one dimension and generating strong positive and negative abundance correlations. Such correlations have therefore been proposed as signatures of an imminent transition. Here we show that this expectation can fail even arbitrarily close to criticality. The reason is that spectral softness does not guarantee stochastic visibility: the soft mode must receive enough environmental forcing to dominate the fluctuation background generated by the remaining modes. We demonstrate this mechanism in three ecological scenarios: a synthetic feasible community, a local community assembled by immigration from a regional pool, and a community generated by repeated diversification. In all three, communities approach marginal stability without developing the near-perfect pairwise correlations predicted by the single-mode picture. Thus, proximity to ecological criticality need not be visible in equal-time pairwise correlations.

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Populations and Evolution · Cross-listed from physics.soc-ph

Climate change and human mobility will shape dengue emergence risk in Europe

Charley Presigny, Paolo Baglioni, Pietro Rotondo et al. (+3 more)

The risk of local arbovirus outbreaks in Europe is expected to increase due to climate change, as suggested by the multiplication of arbovirus outbreaks in the last decades. Europe has historically been a non-endemic region, making it vital to pinpoint which populations are potentially exposed -and under which conditions- so we can build truly robust epidemic preparedness capabilities. We introduce an integrated, multi-scale model that fuses a mechanistic transmission engine with a vector abundance framework, all embedded in a mobility-driven metapopulation system capturing human, vector, and air-traffic movement. To this end, we combine climate and population projections with mobility data to estimate and map dengue emergence risk in Europe throughout the 21st century. Additionally, we introduce a dedicated migration model that explores how climate-driven population redistribution could alter these risk estimates.Assuming the climate avoids major tipping points, model-derived risk indicators increase substantially under most emissions scenarios. While the spatio-temporal risk will remain largely driven by importation, our results indicate a gradual transition toward an environment-driven regime, particularly under the worst-case emissions scenario. To better anticipate and manage recurrent arbovirus outbreaks, our findings highlight the need to integrate mobility pathways and climate-driven population redistribution into predictive models of vector-borne disease emergence in temperate regions.

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All authors Charley Presigny, Paolo Baglioni, Pietro Rotondo, Michele Allegra, Annalisa Barla, Manlio De Domenico

Populations and Evolution · Cross-listed from physics.soc-ph

Emergence of cooperation: A reputation-modulated reinforcement learning

Chenyang Zhao, Jiqiang Zhang, Li Chen et al. (+1 more)

Reputation is widely recognized as a key mechanism for sustaining cooperation. However, most existing game-theoretic models treat reputation primarily as an external factor that modulates payoffs, interaction structures, or strategy update rules. In many social contexts, though, reputation operates primarily as information – it shapes how individuals interpret their own experiences and assess the behavior of others. To bridge this gap, we propose a spatial prisoner's dilemma game grounded in the reinforcement learning paradigm, in which agents equipped with Q-learning integrate both individual and social information via a locally defined reputation metric to guide their decisions. Our results reveal that reputation-modulated learning significantly promotes the emergence of cooperative behavior, and we observe a discontinuous phase transition from full cooperation to full defection as the temptation increases. Cooperation spreads through the nucleation of cooperative clusters, whereas the disintegration of these clusters drives the system into an absorbing state of complete defection. Overall, this study demonstrates that reputation facilitates cooperation not only by providing direct incentives but also by reshaping the social information landscape that agents rely on for learning and adaptation.

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All authors Chenyang Zhao, Jiqiang Zhang, Li Chen, Yong Zou

Quantitative Methods · Cross-listed from cs.CV

Flow Matching Meets 3D Curvilinear Structure Segmentation in Medical Imaging

Sidi Mohamed Sid'El Moctar, Nicolas Vitry and Hélène Bouvrais

Segmentation of curvilinear anatomical structures in 3D medical images remains challenging due to complex topology, severe class imbalance, weak contrast, and large variations in structure morphology. While deep learning approaches for 3D curvilinear segmentation have been proposed, they are often tailored to specific anatomies or modalities, limiting generalization across clinical settings and leaving room for improvement. Recent generative models have shown the benefits of iterative prediction for structured segmentation tasks, yet diffusion-based methods suffer from computationally expensive sampling, hindering their use on high-resolution 3D volumes. We present 3D-CurvSegFlow, a flow matching-based model for 3D curvilinear structure segmentation. The model learns a continuous transformation from a simple source distribution to the target vascular representation, enabling progressive refinement of complex curvilinear geometries with efficient inference. We evaluate our method on Three public challenging datasets covering distinct anatomies and modalities: portal vein, cerebral vessel, and coronary arteries. Using a common architecture and training strategy across all tasks, our method outperforms general-purpose and vessel-specific approaches, with strong preservation of thin branches and vascular continuity. This work not only advances the state-of-the-art in 3D curvilinear segmentation but also opens new avenues for efficient, generalizable, and clinically applicable methods in medical image analysis.

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Neurons and Cognition · Cross-listed from cs.AI

Bringing analytic rigor to agentic AI for science: The Brain Researcher platform for neuroimaging data analysis

Zijiao Chen, Nicholas Lu, Xinhui Li et al. (+14 more)

AI agents can execute scientific analyses, but an analytic output becomes a defensible claim only after alternatives are weighed and the claim is limited to what the evidence supports. Agents may reproduce failures including selective analysis, premature declarations of success and optimization of imperfect criteria. We present Brain Researcher, an agentic research harness operating in a neuroimaging researcher's computational environment under rules for admissible analyses, required checks and claim scope. In benchmarks, Brain Researcher increased first-choice tool-selection accuracy across seven models by 70.2 percentage points (23.3% without it versus 93.6% with it) and verifiable grounding from 4.6% to 22.0%. In collaborator-led and self-evolving studies, multiverse analyses exposed analytic-choice sensitivity, and scientific review classified claims as accepted, qualified, revised, blocked, rejected or deferred. By linking decisions to evidence and provenance, Brain Researcher embeds methodological judgment within the workflow, not after it.

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All authors Zijiao Chen, Nicholas Lu, Xinhui Li, Jocelyn A. Ricard, Ce Ju, Huan H. Wang, Christian Kindermann, Jeanette A. Mumford, Steven Dillmann, James Kent, Alejandro de la Vega, Sanmi Koyejo, Vince D. Calhoun, Joshua W. Buckholtz, Juan Helen Zhou, Steffen Bollmann, Russell A. Poldrack

Biomolecules · Cross-listed from quant-ph

Resource-Efficient Bio-Molecular Docking on a NISQ-era Digital Quantum Computer

Tianqi Chen, Adrian M. Mak, Jianguo Li et al. (+3 more)

Molecular docking is a vital computational task in drug discovery, wherein the objective is to efficiently identify optimal binding poses between a ligand and a target receptor protein. Due to the combinatorial explosion of possible binding configurations, docking of large and flexible molecules remains a computationally intensive problem, especially at scale. Early studies have revealed that the molecular docking can be re-cast as a maximum vertex-weighted clique problem (MVWCP) problem on a compatibility graph to be solved classically. In this work, we proposed a hybrid quantum-classical approach for molecular docking leveraging the MVWCP formalism with a variational full-basis encoding (FBE) strategy, which enables efficient encoding of classical binary variables with Bloch sphere vectors. We further prove that a global minimizer of the FBE objective can always be chosen to be a pure product state, thereby providing a rigorous justification for its optimization using a unitary variational circuit. The molecular docking problem is first mapped to a cost Hamiltonian that is minimized within a variational framework, optimized via a randomized imaginary time evolution (ITE)-inspired warm start, and gradient-based techniques. Finally, we also executed the circuit on an IBM quantum computer, underlying the feasibility and of quantum-assisted optimization for structure-based drug design and point towards the broader utility of advanced encoding techniques in quantum optimization for computational biology.

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All authors Tianqi Chen, Adrian M. Mak, Jianguo Li, Jian Feng Kong, Chandra Verma, Sebastian Maurer-Stroh

Populations and Evolution

A stochastic dose-response framework for environmentally persistent pathogens

Mahmudul Bari Hridoy, Arik Hartmann, Kate E. Langwig et al. (+2 more)

Infectious diseases caused by environmentally persistent pathogens can strongly affect host populations as transmission occurs not only through direct host-host contact but also via indirect exposure to contaminated environments. While in some systems environmental reservoirs help sustain exposure even when infected host numbers are low, reliance on environmental transmission pathway may also increase pathogen extinction risk. Infection risk may depend on both pathogen dose and timing of exposure. Thus, understanding how dose-response, stochasticity, and seasonal changes in host susceptibility or contact rates shape pathogen invasion and persistence remains an important challenge for environmentally transmitted disease systems. To address this, we develop a stochastic dose-response framework that integrates host infection dynamics with an explicit environmental pathogen reservoir. Transmission occurs through both direct contact with infectious hosts and indirect environmental exposure, with infection probability governed by dose-response functions. We focus on stochastic continuous-time Markov chain formulation and use branching process approximation to estimate disease extinction probabilities when infected hosts or environmental pathogen loads are low. We extend the framework to include seasonality in host susceptibility, environmental contact, and host-host contact. As a case study, we apply the model to snake fungal disease. Numerical simulations show that dose-response influences epidemic takeoff and infection levels, while seasonality creates windows of high and low extinction risk. These extinction risks depend strongly on the route and timing of introduction within the seasonal cycle. These results, coupled with global sensitivity analysis, illustrate how stochasticity, nonlinear dose-response, and seasonal timing shape outbreak dynamics for environmentally persistent pathogens.

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All authors Mahmudul Bari Hridoy, Arik Hartmann, Kate E. Langwig, Joseph R. Hoyt, Lauren M. Childs

Wednesday 19 August

Quantitative Methods · Cross-listed from eess.IV

SatoyamaCT: A Multi-Axis Night-IR Camera-Trap Benchmark for Monitoring Crop-Damaging Wildlife in Japanese Agroforestry

Keito Inoshita, Kohei Hisayama, Haruto Sugeno et al. (+1 more)

Crop and forest damage from sika deer, wild boar, and Japanese macaque is a serious economic problem in Japanese satoyama, where farmland and forest intermingle. Camera traps enable scalable monitoring, yet existing benchmarks evaluate recognition in-distribution, rarely prioritize night infrared imagery, and do not jointly address regional domain shift, novel-species detection, and uncertainty-based triage on a single dataset. We introduce the Satoyama Camera Trap Dataset (SatoyamaCT), 12,642 expert-verified crops from night-IR-dominant camera traps across three satoyama regions. An iterative annotation protocol combining BioCLIP embeddings with confidence-ordered confirmation reduced expert effort while all labels were verified by domain ecologists; species-level inter-annotator agreement reached Cohen's kappa = 0.900. A multi-axis protocol jointly evaluates domain generalization across region, camera placement, and illumination; open-set novel-species detection; and selective prediction, all under capture-event-based leakage control. Difficulty separates into distinct failure modes: a data-inherent regional gap of 16 to 27 percentage points persists in Wakayama across four backbones and domain-generalization methods including CORAL, DANN, and GroupDRO, and open-set detection reaches AUROC 0.93 to 0.96 overall yet degrades jointly with classification in Wakayama. Selective prediction recovers Wakayama accuracy from 0.593 to 0.815 at 50% coverage, supporting an uncertainty-aware triage workflow for practical pest monitoring in agroforestry.

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All authors Keito Inoshita, Kohei Hisayama, Haruto Sugeno, Kota Nojiri

Populations and Evolution

From objective discovery to prediction of global ocean eco-provinces: A pathway for trustworthy learning

Makayla McDevitt, Maike Sonnewald and Stephanie Dutkiewicz

Marine ecosystems are increasingly impacted by climate change, necessitating tools to identify and predict spatial habitat information. To build such tools, ecological marine provinces, "eco-provinces", ecologically meaningful regions in the global ocean can be used. We use unsupervised machine learning (ML) to identify eco-provinces with corresponding uncertainty measures based on output of a global simulation of phytoplankton functional types. Our work aims to create a proof of concept to predict eco-provinces based on satellite ocean color data. To do so, we develop a hierarchy of explainable dense ensemble networks to infer how well the eco-provinces can be detected from modeled ocean color fields. Key results include that the delineated eco-provinces are both ecologically meaningful and can be inferred with high skill. However, no straightforward relationship was found where adding more input data to the network consistently improves inference skill, and there is an intricate tradeoff between inputs and prediction fidelity. Our work is a case for optimism and a cautionary tale of needing uncertainty quantification and careful validation of prediction fidelity.

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Neurons and Cognition

Can extrinsic methods reveal intrinsic structure? Complementing IIT with QStr

Jeremiah Hendren, Matteo Grasso, Francesco Ellia et al. (+1 more)

Integrated Information Theory (IIT) proposes that all quality is structure: the quality of every experience can be characterized as a phenomenal structure and accounted for as a causal structure. The IIT method can be called intrinsic in that, first, it starts by characterizing the intrinsic structure of a single experience in an absolute sense (not relative to other experiences) and, second, it preserves this intrinsic perspective when accounting for experience in physical terms. By contrast, the method of the Qualia Structure paradigm (QStr) can be called extrinsic in that its main tool is relational characterization, used to investigate both the quality of experience and the corresponding neural or information structures. This chapter gives conceptual clarity to IIT's intrinsic method, assesses the theoretical compatibility between QStr and IIT, and sketches concrete ways QStr can complement IIT. We argue that QStr and IIT share experience and its structure as their explanandum, while their methodological starting points and explanans are distinct yet complementary. QStr can bolster IIT's current research program by offering novel ways to approach narrow qualia (e.g., color), which are notoriously difficult to decompose through introspection. QStr can also supply IIT with new mathematical tools-for example, from category theory and metric geometry. In turn, IIT may help ground QStr's relational, extrinsic structures in absolute, intrinsic structures.

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All authors Jeremiah Hendren, Matteo Grasso, Francesco Ellia, Giulio Tononi

Genomics

DNA Methylation Profiling in Melanoma: From Lesion Classification to Therapeutic Stratification

Jana T. Winterstein, Lukas Heinlein, Günter Raddatz et al. (+34 more)

DNA methylation provides a stable record of cellular identity, capturing epigenetic programs that distinguish specialized cell states despite a shared genome. Because malignant transformation and tumour progression are accompanied by extensive epigenetic remodeling, we hypothesized that the methylome of melanocytic lesions contains biologically and clinically relevant information for both diagnosis and disease progression. In a cohort of 1,001 tissue samples prospectively collected across eight German university hospitals profiled using Illumina Infinium MethylationEPIC arrays, we compared machine-learning models based on selected Cytosine phosphate Guanine (CpG) methylation sites with models incorporating biology-guided features, including epigenetic age acceleration, cell type composition and copy-number variation burden. In an external test set, the best diagnostic classifier was CpG-based and distinguished melanocytic nevi, noninvasive melanoma and invasive melanoma with a macro-averaged area under the receiver operating characteristic curve of 0.919 (95% CI: 0.878 to 0.952). Notably, across CpGs most strongly hyper- and hypomethylated between NV and IM, NIM showed an intermediate methylation profile, providing a molecular correlate of its diagnostic complexity. The best model for clinically relevant treatment group prediction, with AJCC stages grouped according to guideline-based management recommendations, relied on biology-guided features and achieved a macro-averaged mean absolute error of 0.627 (95% CI: 0.477 to 0.808). Together, these findings demonstrate that methylation-based models can capture both diagnostic identity and clinically relevant disease stratification, supporting DNA methylation as a promising biomarker for further validation and potential clinical translation.

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All authors Jana T. Winterstein, Lukas Heinlein, Günter Raddatz, Carina Nogueira Garcia, Sarah Haggenmüller, Christoph Wies, Lucas Schneider, Annemarie Hoffsommer, Tim J. Zeuner, Friedegund Meier, Sarah Hobelsberger, Frank F. Gellrich, Mildred Sergon, Axel Hauschild, Lucie Heinzerling, Justin G. Schlager, Kamran Ghoreschi, Max Schlaak, Franz J. Hilke, Carola Berking, Markus V. Heppt, Michael Erdmann, Sebastian Haferkamp, Konstantin Drexler, Dirk Schadendorf, Wiebke Sondermann, Matthias Goebeler, Bastian Schilling, Daniel B. Lipka, Stefan Fröhling, Felix Sahm, Jakob N. Kather, Yuri Tolkach, Jochen S. Utikal, Benjamin Izar, Yevgeniy R. Semenov, Titus J. Brinker