Papers

Fresh from arXiv, typeset properly. Highlighted words trade on the market.

Quantitative Biology

q-bio Watch Quantitative Biology

Wednesday 23 September

Quantitative Methods

Prediction bias in biological ageing markers

Yiqun Lin, Ariel Yuhan Ong, Matthew Yu Heng Wong et al. (+52 more)

Biological ageing markers have attracted growing interest, with models estimating age from organ imaging or blood biomarkers. An estimated age above chronological age or the age-specific population expectation is assumed to reflect accelerated ageing and poorer health. Previous research has supported this assumption through positive associations between disease and age gaps or acceleration. However, in this study, we identified a widespread health-dependent prediction bias in ageing markers that affects their key interpretation and application. Specifically, we investigated five ageing markers derived from retinal images, brain MRI, chest radiographs, abdominal CT, and blood tests, and evaluated them using association analyses. We observed the well-recognised phenomenon of regression to the mean (RTM) in the four organ-image based markers, whereby estimated ages were shifted towards the mean age of the training cohort. More importantly, we revealed that the strength of RTM varied with health status, with stronger RTM in unhealthy than in healthy individuals. This differential RTM introduced a health-dependent prediction bias that persisted after calibration and systematically altered associations across age subgroups, suggesting that whole cohort associations may not reflect those observed within individual age subgroups. Additionally, we showed that the tested ageing markers, including PhenoAge derived from blood biomarkers, had limited ability to distinguish health status at the individual level. These findings call for careful interpretation of biological ageing markers and their use in clinical studies, and highlight the need for further development and validation before these ageing markers can reliably inform individual health assessments.

Read the full abstract

All authors Yiqun Lin, Ariel Yuhan Ong, Matthew Yu Heng Wong, Yilan Wu, Wenyi Hu, Shubhank Shobhit Verma, Broder Poschkamp, Lie Ju, Akshay Narayan, Sophie A. Martin, Maitrei Kohli, Fares Antaki, Alexander C. Heatley, Josef Huemer, Meng Wang, Ke Zou, Zheyuan Wang, Eden Ruffell, Dominic Williamson, Justin Engelmann, Hyunmin Kim, Takahiro Ninomiya, Rahul A. Jonas, Yansong Liu, Zijie Cheng, Hanyuan Zhang, Hejie Cui, Jiayang Xu, Hualiang Wang, Zuozhu Liu, Bin Pu, Chubo Liu, Kenli Li, Xinpeng Ding, Guokai Zhang, Weiru Wang, Jiaxiang Wang, Huazhu Fu, Zongyuan Ge, Xujia Liu, Mohammad Eslami, Milen Raytchev, Tobias Elze, Michael G. Morley, Neil P. Oxtoby, Daniel C. Alexander, Anthony P. Khawaja, Ching-Yu Cheng, Lisa Zhuoting Zhu, Yih Chung Tham, James H. Cole, Carol Y. Cheung, Pearse A. Keane, Siegfried K. Wagner, Yukun Zhou

Populations and Evolution · Cross-listed from math.CO

Characterisations of Planar Galled Networks

Hexuan Liu, Taoyang Wu and Guan-Ru Yu

Rooted phylogenetic networks are widely used to represent the evolution of species that have undergone reticulate processes. However, these networks can be highly non-planar, making them more difficult to visualise and interpret than evolutionary trees. In this paper, we investigate planarity properties of galled networks, an important subclass of phylogenetic networks. We show that all planar galled networks are necessarily upward planar. Furthermore, by leveraging recent results on planar phylogenetic networks, we provide three characterisations for each of the outerplanar and terminal planar galled network classes in terms of forbidden vertex configurations, forbidden directed subgraphs, and forbidden structures in their associated underlying undirected graphs. These results contribute to a deeper understanding of the structural properties of galled networks and may inform future methods for their construction and visualisation.

Read the full abstract

Quantitative Methods · Cross-listed from cs.LG

SpaFactor: Lightweight Spatial Context-Aware Gene Program Modeling for Histology-to-Transcriptomics Inference

Shiting Ruan, Xitong Ling, Qiming He et al. (+6 more)

Spatial transcriptomics (ST) profiles gene expression within tissue architecture, but its cost and experimental complexity limit routine use. Predicting spatial expression from routinely available hematoxylin and eosin (HE) images therefore offers a scalable alternative. However, conventional methods often fit high-dimensional gene outputs as independent targets, overlooking the biological coordination among genes while remaining vulnerable to high-dimensional noise and overfitting. Existing attempts to address this limitation often rely on computationally heavy graph networks or complex auxiliary supervision. We therefore introduce SpaFactor, a lightweight and efficient low-rank morphology-program-gene factorization framework. At the input, SpaFactor efficiently fuses the visual representation of the central spot with multiscale local and regional neighborhood context, yielding a histologic representation that captures cellular morphology and microenvironmental heterogeneity. For modeling, a residual MLP stably learns a nonlinear mapping from the tissue microenvironment to low-dimensional latent gene programs. These activities are decoded through shared gene loadings into coordinated multi-gene expression predictions. Across five public cohorts, SpaFactor achieves the best aggregate performance, with particularly clear improvements for spatially variable genes, and more faithfully recovers biologically organized spatial patterns. These results demonstrate that lightweight joint modeling of tissue context and gene programs can improve both predictive accuracy and biological fidelity.

Read the full abstract

All authors Shiting Ruan, Xitong Ling, Qiming He, Ziyou Yan, Huaitian Yuan, Tian Guan, Ying Xiao, Xu Guan, Yonghong He

Genomics · Cross-listed from cs.AI

BaseCamp — An Agentic AI Framework for Automating DNA Sequencing Data Pipelines

Eranga Bandara, Xueping Liang, Asanga Gunaratna et al. (+19 more)

DNA sequencing pipelines, spanning quality control, alignment, variant calling, and annotation, are now reliably executed by workflow management systems that orchestrate established bioinformatics tools at scale. What remains manual is the decision layer surrounding that execution: selecting quality thresholds appropriate to a sample and platform, adjudicating borderline variant calls, diagnosing anomalies, and determining which findings warrant expert review. These decisions are repetitive, judgment-intensive, inconsistent across operators, and frequently undocumented. This paper introduces BaseCamp, a novel agentic AI framework for automating the decision layer of DNA sequencing pipelines. The framework decomposes the pipeline into six specialized AI agents, covering sample intake and quality control, alignment, variant calling, annotation, cross-stage monitoring, and reporting. Critically, BaseCamp agents do not perform sequence analysis: established tools execute alignment, calling, and annotation, while the agents select among them, configure them, interpret their output, and decide what follows. This confines language model reasoning to the judgment layer where it is reliable and preserves the reproducibility existing tooling guarantees. Agent reasoning is powered by a consortium of fine-tuned, domain-specialized large language models coordinated by a central reasoning LLM, executing locally so no sequencing data leaves the operating environment, under human-in-the-loop orchestration. Evaluation shows agent-generated configurations are concordant with expert practice, that an explicit filtering ledger renders inspectable what filtering otherwise removes without trace, and that cross-stage anomaly detection surfaces conditions execution monitoring misses. BaseCamp offers a generalizable blueprint for agentic automation of scientific data pipelines.

Read the full abstract

All authors Eranga Bandara, Xueping Liang, Asanga Gunaratna, Tharaka Hewa, Abdul Rahman, Peter Foytik, Safdar H. Bouk, Sachini Rajapakse, Isurunima Kularathna, Pramoda Karunarathna, Chalani Rajapakse, Ng Wee Keong, Kasun De Zoysa, Amin Hass, Wathsala Herath, Ross Gore, Ravi Mukkamala, Nihal Siriwardanagea, Gihan Siriwardanagea, Aruna Withanage, Nilaan Loganathan, Sachin Shetty

Populations and Evolution

Scarlet Fever Dynamics in 19th and 20th Century London

Kevin H. Zhao and David J. D. Earn

Weekly scarlet fever (SF) mortality records for London, UK, from 1842 to 1939, together with notified case records from 1901 to 1939, reveal a strong annual epidemic pattern with peak prevalence in the autumn. In addition to the annual epidemic pattern, this long time series reveals a cyclical envelope with a period that lengthened over the decades. In particular, the period of the envelope increased from about four years to about eight years between 1880 and 1920, coinciding with a dramatic decline in pre-antibiotic-era SF deaths (from about 2300/yr to about 80/yr). We quantify the spectral features of the SF time series using a wavelet transform, and attempt to explain why the frequency structure changed over time, using a mechanistic mathematical model of SF transmission dynamics. We estimate the parameters of the model in part from the literature and in part by fitting mortality and incidence jointly. We use a mechanistic transition analysis (considering both attractors and transients in model solutions) to relate the observed changes in frequency structure to our estimated changes in model parameters. We find that almost all spectral evolution in the time series can be explained by changes in the effective reproduction number and the amplitude of seasonal forcing. These changes reflect observed variation in birth rates together with inferred changes in transmission, which may in part have arisen through pathogen evolution.

Read the full abstract

Genomics

Motif-Vocab: StatisticallyCalibrated Transcription-Factor-Identity Tokenization forGenomic Language Models

Liangyu Li and Michael White

Tokenization is a central design choice in genomic language models, yet most deoxyribonucleic acid (DNA) tokenizers use characters, fixed-length k-mers, or frequency-derived subwords without explicitly using prior information about the specificity of DNA-binding regulatory factors. We introduce Motif-Vocab, a biologically informed tokenizer that scans both DNA strands for statistically calibrated motif matches, emits transcription-factor (TF) identity tokens, and applies nucleotide, $k$-mer, or byte-pair encoding (BPE) to unmatched sequence. Motif-specific null distributions put position-weight matrices (PWMs) of different lengths and degeneracy on a common significance scale; deterministic overlap rules make the representation reproducible. In controlled Bidirectional Encoder Representations from Transformers (BERT) pretraining on two billion base pairs, real motif libraries outperform randomized-motif controls on 54 of 55 in-scope downstream tasks. On a motif-disjoint recognition task derived from DART-Eval Task 2, TF-specific tokens improve macro-F1 by 0.040 over a position-matched generic motif token and by 0.033 over a matched no-motif tokenizer (95% bootstrap confidence interval: 0.027–0.038). Motif tokens also receive stronger attribution and produce larger occlusion effects than shuffled controls. Dense no-motif tokenizers remain strong general-purpose baselines, including a near-tie on the five-task BERT-base panel. Thus, Motif-Vocab is not a universal accuracy replacement; it is a targeted, interpretable inductive bias for motif-sensitive genomic modeling.

Read the full abstract

Quantitative Methods

Circadian Derived Features for Early Discrimination Across Insomnia Severity Levels: At Least 8 Weeks of Monitoring Are Needed for Clinically Meaningful Assessment

Soheil Saghafi, Thomas C. Neylan, Qiao Li et al. (+2 more)

Background: Wearable devices provide continuous, objective measures of daily activity and offer promise for assessing sleep disorders. However, the minimum monitoring duration needed to differentiate insomnia severity remains unclear. We investigated when wearable-derived behavioral features become informative for distinguishing Insomnia Severity Index (ISI) categories and examined the contribution of Activity Count (AC) and circadian-derived features. Methods: We analyzed wearable data from 2,305 participants in the Advancing Understanding of Recovery after Trauma (AURORA) study. Separate binary classifiers were developed for four ISI categories across six follow-up periods using AC and circadian-derived feature sets. Performance was evaluated using accuracy, F1-score, precision, recall, and AUROC. Results: Classification improved with longer monitoring. Across ISI categories, approximately eight weeks was the earliest time point at which wearable-derived features consistently achieved informative discrimination, with modest improvements thereafter. Participants without clinically significant insomnia were easiest to identify, reaching an AUROC of 0.693. Circadian-derived features performed comparably to, and in several cases better than, AC features, suggesting that the temporal organization of daily activity provides information beyond overall activity volume. Conclusions: Approximately eight weeks of longitudinal wearable monitoring may represent a practical minimum for differentiating ISI-defined insomnia categories. Longer monitoring provided only incremental improvements. Circadian behavioral features show promise as digital biomarkers for objective insomnia assessment.

Read the full abstract

All authors Soheil Saghafi, Thomas C. Neylan, Qiao Li, Samuel A. McLean, Gari D. Clifford

Quantitative Methods · Cross-listed from stat.CO

Penguin data reanalyzed via Computational Taxonomy

Thomas B. Michelon, Avidane C. Caballero and Hsieh Fushing

We employ Computational Taxonomy (CT) to reanalyze the penguin data set penguins_lter by validating and addressing two biological issues: Sexual Size Dimorphism (SSD) and mate-selection criteria. Via Scientific Data Analysis (SDA) computing, CT constructs a Taxonomic Hierarchy by splitting Species first and then Sex, without involving Island, to achieve less complexity. This Taxonomic Hierarchy validates SSD as a branch comparison: (Species, Sex = Male)-vs-(Species, Sex = Female), upon which SDA explores all potential pieces of associative information from all covariate feature-sets, including interacting effects from order-2 to order-4, and then confirms them via their idiosyncratic reliability checks. The collective of confirmed information pieces are displayed on a heatmap platform to manifest underlying dynamics of SSD with explicit block-structured heterogeneity found within males and females. SSD dynamics is explained through mechanistic dependence pertaining to one chief factor consisting of up to 8 feature-sets: Body-Mass coupled by combinations of {Culmen-length,Culmen-depth, Flipper-length}, and two minor factors consisting of low-order combinations of {Culmen-length,Culmen-depth, Flipper-length}. Such Intra-Sex heterogeneity invalidates all Logistic regression modeling on SSD in the original paper. Further, we explore potential mate-selection criteria through the data-frame of Nest-ID within-species homogeneity.

Read the full abstract

Neurons and Cognition

AI-Driven Neural Surrogates for In Silico Design of Cognitive-Affective Neuromodulation Targets

Marco Rothermel, Madleen Stenger, Soroush Daftarian et al. (+7 more)

In neuropsychiatry, the primary goal is often not only to decode brain activity but to change it, for example to lessen a negative affective bias or an overly salient memory. Motivated by control theory, we develop an AI-driven neural-surrogate framework that proposes candidate representational changes and tests their predicted perceptual effects from snapshots of stimulus-evoked fMRI activity, without physical stimulation. The framework combines fMRI decoding, deep generative modeling, and constrained latent-space steering. Valence and memorability are used only as worked examples. Using more than 36,000 image-fMRI observations from four deeply sampled Natural Scenes Dataset participants, subject-specific models recovered coarse generative structure from visually responsive cortex (two-way identification, 0.79-0.88; chance, 0.5). Graded perturbations were reconstructed as images and evaluated with automated scorers and human ratings from 7,200 trials by 18 participants. In the primary VDVAE model, valence shifted from -0.61 to +1.03 SD and memorability from -1.34 to +1.45 SD; a later Versatile Diffusion refinement reduced or altered these effects. Across five perturbation levels, human valence ratings moved in the predicted direction under the linear time-correction model (mean slope, 0.038 SD per unit of alpha; 95 percent CI, 0.003-0.074; positive in 16 of 18 participants). Perceived memorability did not change reliably. Baseline agreement with the automated assessor was suggestive for valence (r = 0.30) and weak for memorability (r = 0.10). Extreme perturbations drifted from the original stimulus, so intended change must be weighed against loss of fidelity. These findings provide a falsifiable upstream method for designing and behaviorally testing candidate representational targets for future neuromodulation in psychiatry, while marking the limits of the present static approximation.

Read the full abstract

All authors Marco Rothermel, Madleen Stenger, Soroush Daftarian, Svenja Jule Francke, Bita Shariatpanahi, José C. García Alanis, Mohammad-Ali Nikouei Mahani, Stefan G. Hofmann, Tim Hahn, Hamidreza Jamalabadi

Quantitative Methods

Topological Inference for Organoids

Haochen Yang, Byung Ho Lee, Anne Grapin-Botton et al. (+2 more)

The reproducibility of organ morphology and the extent to which computational models can predict morphogenesis remain difficult to quantify, particularly for organs with complex networks of fluid-filled lumina. Here, we combine Topological Data Analysis (TDA), biophysical simulation, and Bayesian inference to study lumen morphogenesis in pancreatic organoids. Lumen formation is governed by physical processes that are challenging to measure directly, including cell proliferation and luminal osmotic pressure. We simulate organoid development using a phase-field model and address the inverse problem of inferring these parameters from either time-lapse images or single morphological snapshots. Since lumen architectures vary substantially in size, structure, and connectivity, conventional geometric descriptors provide only a partial representation of their morphology. We therefore represent each organoid using SampEuler, a topological descriptor derived from the Euler Characteristic Transform (ECT). We first show that SampEuler captures morphological information encoded by established morphometrics. We then perform parameter inference using an approximate Bayesian computation (ABC) rejection framework with the SampEuler Wasserstein distance. Using synthetic organoids with known ground-truth parameters, our approach accurately recovers the osmotic pressure and the proliferation rate while revealing a compensatory trade-off between the two processes. Applied to experimental data from ten pancreatic organoids, the inferred posterior distributions are consistent with biological expectations. Together, these results establish a non-destructive, image-based pipeline for estimating otherwise inaccessible physical parameters governing lumen formation and highlight the potential of topological representations for linking complex biological morphology to mechanistic models.

Read the full abstract

All authors Haochen Yang, Byung Ho Lee, Anne Grapin-Botton, Heather A. Harrington, Helen M. Byrne

Quantitative Methods · Cross-listed from cs.LG

Pheno-GS: Phenoscape-scale Geodesic Sinkhorn

Alistair Wilkinson, Christopher J. Tape and Smita Krishnaswamy

High-throughput single-cell data is now collected across large patient cohorts. Understanding patient-level heterogeneity from cellular-level data motivates phenoscaping: embedding each single-cell distribution as a "datapoint," with distances given by optimal transport (OT). Computing geometry-aware OT at this scale, between all pairs of patient datasets, remains an open challenge, since existing methods either rely on Euclidean ground metrics that distort manifold structure or fail under sparse, unevenly sampled, or large-scale data. We present Pheno-GS (Phenoscape-scale Geodesic Sinkhorn), which computes accurate, scalable geodesic transport distances under noisy, unbalanced, large-scale settings via three components: ($1$) graph connectivity regularization for well-defined geodesics on sparse/disconnected manifolds; ($2$) an unbalanced OT formulation via KL marginal penalties; and ($3$) a batched matrix algorithm computing all pairwise distances in one heat diffusion (over $200 \times$ faster than Geodesic Sinkhorn for $500$ distributions). We validate Pheno-GS on synthetic benchmarks and a CyTOF perturbation dataset.

Read the full abstract

Quantitative Methods · Cross-listed from cs.LG

Discover, Falsify, Revise: Auditing Input-Use Claims from Source Code to Predictive Contribution in Agent-Discovered Cell Models

Mengran Li, Bo Li, Chengyang Zhang et al. (+3 more)

AI virtual cells aim to predict cellular responses to specified interventions, yet held-out predictive performance alone does not establish use of the supplied perturbation information. This prediction-claim gap matters in agentic model discovery, where language-model agents generate and revise predictors using score-based feedback. We introduce CELLAUDIT, which audits input-use claims by asking whether an input can enter the cited computation, whether fitted predictions depend on it, and whether that dependence improves prediction of observed response. On a paired morphology-transcriptomics perturbation benchmark (BBBC047), an agent-selected predictor attains a mean held-out Global Pearson correlation coefficient (PCC) of 0.3153 but remains invariant to compound replacement; a control-profile-only predictor reaches 0.3142. Source inspection identifies a compound-query pathway blocked by singleton key-value attention, and the invariance persists after refitting with disjoint control wells. In a stratified audit of 48 candidates across two linked tasks, 47 change predictions under compound replacement on both held-out folds, but only 20 show target-loss gains with intervals above zero on both folds. On BBBC047, falsification-guided revisions recover positive mean compound contributions while retaining gains over the control-profile-only baseline. In matched sci-Plex searches, audit-enriched feedback yields higher held-out performance and larger mean compound and dose contributions across five trajectories, although paired intervals span zero. Refitting fixed designs on an independently acquired cohort shows predictive generalization need not imply generalization of input-use claims: dose contribution persists, whereas support for compound identity does not. CELLAUDIT adds a falsification layer to agentic model discovery, moving from generate-score-revise toward discover-falsify-revise.

Read the full abstract

All authors Mengran Li, Bo Li, Chengyang Zhang, Yang Yan, Jinfeng Xu, Zhenchao Tang

Tissues and Organs · Cross-listed from cs.SD

Physiologically Informed Digital Auscultation for Pneumonia Detection in Long-term Care Residents

Nicholas Rasmussen, Oleg Zaslavsky, Zih-Ling Wang et al. (+5 more)

Pneumonia is difficult to diagnose in older long-term care residents; multimorbidity and atypical presentations obscure signs, motivating operationally efficient objective testing. We analyzed multi-channel digital stethoscope recordings from 185 Japanese residents (73 pneumonia, 112 symptomatic without), using radiologist-confirmed chest X-rays and clinician diagnoses as supervisory signals that train convolutional neural networks, multimodal fusion, and channel-based variants with time-domain Grad-CAM interpretability. Models were evaluated with repeated patient-level cross-validation showing models with X-ray supervision outperformed clinician supervision (F1 0.729, accuracy 0.783 vs. F1 0.637, accuracy 0.711). Additionally, a three-channel selection protocol maintained performance (F1 0.736; accuracy 0.803), with two mid-thoracic sites ranking highest and Grad-CAM attention overlapping adventitious sounds. These findings indicate automated multi-channel lung-sound analysis can aid long-term care pneumonia diagnosis, with X-ray supervision being more reliable than clinical, and fewer channels preserving performance while lowering acquisition times.

Read the full abstract

All authors Nicholas Rasmussen, Oleg Zaslavsky, Zih-Ling Wang, Hongyu Yu, Joelle Fathi, Kaibao Nie, Amil Khanzada, Tomoko Ito

Other Quantitative Biology · Cross-listed from cs.CV

Benchmarking Active Spot Selection for Cost-Efficient Spatial Transcriptomics

Zheyu Zhu, Junchao Zhu, Fengbei Liu et al. (+7 more)

Spatial transcriptomics (ST) measures gene expression in tissue context, but dense capture grids can be costly and may repeatedly sample morphologically similar regions. Most active learning strategies were developed for categorical labels and independent samples. We conduct a retrospective pool-based benchmark of active learning versus uniform Random sampling for ST, where expression vectors are high-dimensional and continuous and candidates are spatially correlated. Using two fully profiled public ST cohorts, we mask candidate expression vectors and simulate multi-round selection with uncertainty-based Monte Carlo dropout (MC-dropout) and temporal output discrepancy (TOD), and diversity-based CoreSet and TypiClust-inspired selection. We compare 160 completed configurations at 5%, 10%, 30%, and 50% of the fold-wide training spot pool under patient-level cross-validation, with a separate full-label reference. Within each budget, strategies share the selection schedule, morphology-to-expression predictor, and optimization protocol. We assess mean per-gene within-slide Pearson correlation coefficient (PCC), expression-cluster agreement, and Moran's I fidelity. On HER2-positive breast cancer, pooled mean PCC differences from Random across the four active strategies were -0.0176, -0.0117, +0.0056, and +0.0057 at 5%, 10%, 30%, and 50%, respectively. On cutaneous squamous cell carcinoma (cSCC), three strategies were below Random at 5%, and all four were below Random at 10%. On HER2-positive breast cancer, CoreSet and MC-dropout had lower PCC but higher expression-cluster agreement than Random at the two smallest budgets; this pattern did not reproduce on cSCC. Under the reported fixed training horizons, the evaluated active strategies do not consistently improve on Random at small budgets, and rankings depend on the evaluation measure.

Read the full abstract

All authors Zheyu Zhu, Junchao Zhu, Fengbei Liu, Tianyuan Yao, Gelei Xu, John Cannon, Haichun Yang, Yuankai Huo, Mert R. Sabuncu, Ruining Deng

Quantitative Methods

BreCol: Benchmarking Classical and Deep-Learning Methods for Microbiome-Based Cancer Detection

Jeffrey M. Dick

DNA sequencing of the gut microbial community shows promise for cancer detection, but questions remain about the generalizability of results across studies. We propose BreCol, a benchmark of 2,040 16S rRNA gene sequencing runs across 26 studies spanning breast cancer, colorectal cancer, and healthy cohorts. Train-test splits are made within pre-2023 studies, while holdout evaluation uses studies from 2023 onward, reflecting temporal separation from training data. Classical models reach test/holdout AUCs of 0.77/0.60 for cancer diagnosis and 1.00/0.83 for cancer type prediction. We train the models on both cancer types simultaneously and find that colorectal cancer is often easier to detect than breast cancer. We also evaluate two deep learning models: HyenaDNA, a long-range sequence model that pools hidden states for classification, and SetBERT, a transformer that produces contextualized embeddings over sets of reads. Both deep learning models underperform the best classical methods on holdout data, though tuning training set size and the classification head yields modest gains. Our classical pipeline uses unsupervised clustering to derive features from tetramer frequencies, preserving within-run compositional signal and achieving near state-of-the-art performance without relying on taxonomic assignments. BreCol data and associated code are publicly available.

Read the full abstract

Biomolecules · Cross-listed from physics.bio-ph

Spatially Resolved Nucleated Polymerization: A Free-Boundary Model of Protein Aggregation in Concentrated Solutions

Pouria A. Mistani

Kinetic models of protein aggregation describe populations by size, not by spatial organization or morphology. We extend Lumry-Eyring nucleated polymerization to a model in which the monomer is a density field and each aggregate is a region bounded by a level set. Growth is a flux condition on the available sites of a surface. Condensation is a reaction between the bonding sites of two surfaces in contact, at a rate set by the bond rate and the contact geometry. The availability of those sites is a field on the interface, and its equilibrium value follows from Wertheim's perturbation theory. The collision efficiency and the Fuchs stability ratio are therefore computed, not fitted. In a well-mixed limit the model's spatial averages satisfy the rate equations term by term; the monomer fraction agrees to eight parts in ten thousand, a difference that arises from equating aggregate size with volume. The condensation kernel's exponent is $0.5806\pm0.0013$ against the $0.600\pm0.010$ fitted to a monoclonal antibody. The computed stability ratio reproduces thirteen of fourteen published conditions at twelve $k_BT$, but only with the bond rate at the top of its range. In a many-body box, aggregates merge at $2.2$ to $3.8$ times the two-body rate.

Read the full abstract

Tuesday 22 September

Neurons and Cognition · Cross-listed from cs.NE

The Computational Value of Sensory-Aligned Receptive Fields Depends on Neuronal Expressivity

Agnese Adorante, Aaron Spieler and Anna Levina

Biological sensory neurons have selective receptive fields organized along meaningful stimulus coordinates, such as frequency, motion direction, or retinotopic position. Such structure may arise from efficient coding and biological constraints on activity, connectivity, and wiring, as computational studies of simple neurons have shown across modalities. This raises a question: do structured receptive fields confer a computational advantage beyond resource efficiency itself, and does this advantage persist when individual neurons are highly expressive? We address this question in recurrent networks of Expressive Leaky Memory neurons, where we can independently vary neuronal complexity and the organization of feed-forward receptive fields. Across auditory and event-based visual classification tasks, receptive fields aligned with a task-relevant sensory coordinate improve test accuracy relative to budget-matched random receptive fields. This advantage disappears when sensory coordinates are scrambled, or when receptive fields follow task-irrelevant coordinates, showing that the benefit comes from alignment with task geometry rather than restricted connectivity alone. Increasing neuronal complexity reduces the performance advantage of structured receptive fields. Finally, generic synaptic sparsity regularization induces input selectivity and partially recovers performance, but remains substantially below explicitly structured receptive fields, suggesting that sparsity alone is insufficient to recover the full computational benefit of task-aligned receptive fields. Together, our results show that appropriate receptive fields can serve as a computational prior beyond sparsity itself, and that their value depends on the computational expressivity of individual neurons.

Read the full abstract

Cell Behavior · Cross-listed from cond-mat.soft

Entropy-Driven Initiation and Cytoskeletal Viscoelasticity in Endocytosis: An Onsager Variational Framework

Jinjie Liu, Zhongcan Ouyang and Hao Wu

Receptor-mediated endocytosis requires a particle to approach the cell membrane to within a few nanometers before ligand–receptor binding can occur. Existing continuum models often start from an already established contact and do not explicitly describe how crowding particles on the extracellular side influence the distribution of the particle near the membrane. We examine entropic depletion forces as one possible nonspecific contribution to this initial approach. For ideal depletants, the Asakura–Oosawa excluded-volume construction gives an exact depletion potential for the planar geometry before contact. The potential and force vanish continuously at the onset of excluded-volume overlap. This interaction provides a possible contribution to membrane proximity before specific binding, while its extension to curved wrapping geometries requires additional approximation. Within a reduced continuum model, we combine depletion attraction, ligand–receptor binding, membrane deformation, and cytoskeletal viscoelastic dissipation. The viscoelastic contact is formulated through a hereditary integral and a standard linear solid. The kinetic model gives a conditional minimum ligand density for complete engulfment, a finite particle-size window, and a stiffness-dependent upper limit. When the stationary radius lies inside the domain of finite positive wrapping times, the estimated wrapping time has a minimum at a radius that decreases with increasing binding energy density. At fixed viscosity and other independent parameters, the same time approximation predicts slower wrapping as cell stiffness increases. The two positive roots defining the size window merge at a limiting parameter value, which characterizes closure of the admissible size interval. Depletion attraction is interpreted as one possible contribution to particle-membrane association

Read the full abstract

Neurons and Cognition

Deep Learning in Infant Functional Neuroimaging: Challenges, Advances, and Future Directions

Dan Hu, Jiale Cheng, Weiran Xia et al. (+5 more)

Infancy is a critical developmental window characterized by rapid functional brain reorganization, during which large-scale networks emerge, individualized connectome signatures continue to form, and early deviations may shape long-term cognitive and clinical outcomes. Functional MRI (fMRI) offers an opportunity to study these processes in vivo, yet extracting developmentally meaningful information from it remains challenging due to comparatively short scan duration, structured motion artifacts, variable scan states, and rapid brain maturation. Amid these challenges, deep learning has expanded the capacity of computational neuroimaging by learning robust representations from noisy, high-dimensional data, integrating complex spatial and temporal information, and capturing the nonlinear and rapidly evolving organization of the developing brain. Here, we review recent advances in deep learning for infant functional neuroimaging, synthesizing progress across input representation formatting, population and individualized brain mapping, longitudinal trajectory forecasting, robust and explainable model evaluation, and biological translation. Collectively, these methodological advances mark a paradigm shift in infant functional neuroimaging from descriptive, group-level analyses toward reliable, individualized, and developmentally grounded models. Future progress will depend on larger and more diverse longitudinal datasets, developmentally appropriate model designs, rigorous and standardized evaluation, and integration of computational predictions with biological mechanisms towards clinically meaningful outcomes. Addressing these priorities will help establish deep learning as a robust framework for understanding early functional brain development, identifying developmental variation at the individual level, and ultimately supporting earlier and precise assessment of neurodevelopmental risk.

Read the full abstract

All authors Dan Hu, Jiale Cheng, Weiran Xia, Kangfu Han, Matthew Wu, Li Wang, Weili Lin, Gang Li

Biomolecules

In Vivo Length Distributions as Mechanistic Fingerprints of Pathological Protein Aggregation

Matthew W. Cotton, David Klenerman and Georg Meisl

Modern imaging techniques can resolve individual pathological protein aggregates in postmortem human samples, providing detailed measurements of aggregate size distributions that are inaccessible with conventional bulk approaches. These distributions represent mechanistic fingerprints of the microscopic processes that generated the observed pathology, but extracting this mechanistic information requires a quantitative theoretical framework. Here, we develop the mathematical tools needed to interpret aggregate length distributions in living systems, where aggregate growth competes with active removal. We show that, across a class of models, the length distribution of sufficiently large aggregates approaches a geometric decay. Crucially, the decay rate is determined by the balance between aggregate elongation and removal, providing a direct quantitative readout of these competing processes from a single time point measurement. This enables mechanistic comparisons between healthy and diseased human samples without requiring longitudinal measurements of aggregate dynamics. We further analyse how additional aggregation and removal processes modify the observed length distributions. Together, these results establish the mathematical foundations and tools to use aggregate length distributions as an experimentally accessible route for inferring microscopic aggregation dynamics directly from human tissue.

Read the full abstract

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

Emotions as intrinsic colored noise in biological systems

V.I. Yukalov and E.P. Yukalova

The idea that emotions in biological systems are analogous to intrinsic colored noise is advanced and justified. A model describing the dynamics of operation of biological networks under the influence of colored noise is suggested. The agents of a biological network can be represented either by biological species, such as humans and animals, or by neurons of the brain, or by the nodes of a neural network. Operational actions, or decisions, in a biological noisy network are based not only on the evaluation of utility of alternatives, but also on the agents emotions. The model is probabilistic, with the choice of alternatives characterized by the related probabilities. At the initial step, the agents make decisions individually and then start exchanging information with each other and imitating the actions of other agents, thus forming a biological network of interacting agents. Numerical simulations are accomplished for a heterogeneous society consisting of three groups of agents, one group possessing long-range memory, the other group, short-range memory, and the third group of super-rational agents acting strictly on the basis of utility, being deprived of emotions. Dynamics of opinions in different groups can be smooth, oscillatory, or chaotic. Altogether, eight types of operation are found, depending on the dynamics of group decisions. Under strong imitation effect, there appears chaotic motion in the evolution of decision choice. It is shown how the Ellsberg paradox can be resolved and how the exchange of information influences the dynamics of this paradox.

Read the full abstract

Tissues and Organs

Simulation and Analysis of Solute Transport in Multi-Lymphangion Lymphatic Vessels

Timo Jakumeit, Thiha Aung, Robert Schober et al. (+1 more)

The lymphatic system (LS), a body-wide network of vessels and lymphoid organs governing fluid homeostasis and immune surveillance, has so far not been investigated as a domain for diagnostic and therapeutic molecular communications (MC) applications, despite several properties that make it a promising, complementary alternative to the cardiovascular system. These favorable properties include slower flow, simpler and less dense molecular fluid composition, and direct anatomical access to lymph nodes. Realizing this potential, however, requires a quantitative understanding of how solutes propagate through the LS, a problem that, unlike lymph flow itself, remains largely unaddressed in the literature. As a first step towards narrowing this gap, we develop a particle-based simulation (PBS) framework of solute transport through a three-dimensional chain of valve-separated, concatenated vessel segments, called lymphangions. We simulate the spatiotemporal evolution of solute concentration and qualitatively validate the resulting transport dynamics against existing in vivo measurements of fluorescent tracer propagation in multi-lymphangion lymphatic vessels. Our simulations show that the valve-gated nature of solute transport in lymphatic vessels leads to bursty solute concentrations over time, a characteristic that can also be observed in vivo. Additionally, we find that, within one pumping period, peak timing is dictated by the valves' synchronizing effect rather than the particle release time, while diffusivity and receiver placement determine peak sharpness. Overall, the proposed PBS framework provides a first quantitative basis for solute transport modeling in the LS and several concrete application scenarios for MC in this underexplored domain. Supplementary video material illustrating the PBS is publicly available on Zenodo [DOI: 10.5281/zenodo.21888066].

Read the full abstract

All authors Timo Jakumeit, Thiha Aung, Robert Schober, Maximilian Schäfer

Populations and Evolution

Mutation Order and Selection Shape Intratumor Heterogeneity in Tumor Evolution

Yichen Chu, Xiaoxia Sheng, Xuanming Zhang et al. (+1 more)

Cancer progression often requires multiple driver mutations, but the same drivers may be acquired in different orders. How these pathways jointly shape tumor clonal structure remains unclear. We develop a multitype branching-process model in which malignant transformation requires two driver mutations, distinguishing malignant cells by mutation order and the independent transformation event that founded their clone. Under a successive exponential approximation, we establish point-process limits for pathway-specific clone sizes and derive a closed-form expression for the limiting expected Simpson's index of the combined malignant population. When both mutation orders yield malignant cells with the same net growth rate, the index decomposes into effective pathway weights, determined by mutation rates and birth-death dynamics at preceding stages, and within-pathway concentration terms, determined by intermediate-to-malignant growth-rate ratios. A driver's effect on heterogeneity thus depends critically on when it is acquired. A strong driver acquired early expands the intermediate lineage and increases the supply of independent malignant founders, whereas the same driver acquired last strengthens the growth and age advantage of early-founded malignant clones. Under additive fitness effects, these opposing mechanisms can produce a non-monotone relationship between selective advantage and clonal concentration. Threshold-like non-additive fitness effects can generate highly concentrated malignant populations, while order-dependent terminal fitness causes the faster-growing pathway to dominate asymptotically. These results show how mutation order, mutational accessibility, selection, and epistasis jointly determine lineage-level intratumor heterogeneity.

Read the full abstract

All authors Yichen Chu, Xiaoxia Sheng, Xuanming Zhang, Zicheng Wang

Quantitative Methods · Cross-listed from cs.CV

RootQuantV2: Adapting a Vision Foundation Model for Root-Trait Regression from Minirhizotron Imagery

Kinjalk Parth, Sebastian Varela and Andrew D. B. Leakey

A lack of high-throughput phenotyping solutions for root traits in field-grown crops has severely constrained understanding and improvement of below-ground traits and processes. Minirhizotrons are the standard non-destructive root-phenotyping method in field environments. Computer vision solutions are needed to allow automated trait estimation at scale, but training data is scarce and human annotations are often inaccessible because they reside in proprietary software that only exports per-image scalar totals of root length and surface area. Nevertheless, large numeric archives of these root traits already exist. RootQuant showed that the traits can be predicted directly from the whole image by regression, thus removing manually traced masks from the pipeline; RootQuantV2 takes that idea further by replacing RootQuant's CNN backbone with a self-supervised ViT. We adapt a frozen DINOv3 ViT-L/16 with a hybrid parameter-efficient scheme. Training only 11.9M parameters (3.78% of the model), RootQuantV2 achieves length and area $R^2$ of 0.950 and 0.930, respectively, while lowering length/area RMSE by 24.3%/20.7% over RootQuant. RootQuantV2 thus repurposes legacy numeric archives for high-throughput, automated root trait estimation.

Read the full abstract

Monday 21 September

Biomolecules · Cross-listed from cs.CV

Combinatorial Network-Based Manifold Topological Deep Learning for Image Analysis

Alice Wachira, Xiang Liu, Zhe Su et al. (+3 more)

Medical image analysis remains fundamentally challenging because of the intricate geometric and topological structures present in medical data. Conventional convolutional neural networks model images as regular Euclidean grids, limiting their ability to preserve geometric relationships and higher-order structural information. Recently, manifold topological deep learning (MTDL) has emerged as a promising paradigm that integrates deep learning with geometric and topological representations. Nevertheless, existing methods have not yet fully exploited discrete manifold structures within combinatorial complex neural networks. To bridge this gap, we introduce CNMTDL, a MTDL framework that integrates Hodge decomposition with a combinatorial attention mechanism. In our approach, medical images are represented as discrete manifolds and decomposed into three Hodge components. Features extracted from these components are concatenated and embedded into a combinatorial complex architecture, enabling enhanced higher-order message passing between $0$-cells and $2$-cells through attention-based blocks. We evaluate CNMTDL on six two-dimensional and three-dimensional datasets from the MedMNIST v2 benchmark, demonstrating its effectiveness for medical image analysis.

Read the full abstract

All authors Alice Wachira, Xiang Liu, Zhe Su, Yiying Tong, Ge Wang, Guo-Wei Wei

Neurons and Cognition

Physics-constrained inference of somatic dynamics from dendritic recordings with sparse somatic supervision in weakly coupled two-compartment neuron model

Abdeltif Oujbara, Benjamin Ambrosio and M.A. Aziz-Alaoui

Somatic membrane potential is the primary determinant of neuronal output, yet it remains inaccessible in many experimental setups where only dendritic recordings are available. Reconstructing somatic dynamics from distal measurements is a challenging inverse problem, particularly when the soma and dendrites are weakly coupled, as dendritic signals represent a filtered and attenuated version of somatic activity. To address this, we use a physics-informed neural network (PINN) constrained by a two-compartment Hodgkin–Huxley model. The network is trained on dense dendritic voltage recordings and the known injected current, complemented by a small number of somatic voltage samples (at most 5% of the time points), and it reconstructs the full somatic trajectory while adjusting a selected set of somatic maximal conductances. On synthetic data from four stimulation protocols, we quantify how the reconstruction depends on the amount of somatic supervision. Without somatic samples, the present formulation returns a smooth trajectory in which the action potentials are absent and the subthreshold level is biased, with a root-mean-square error of 10–14~mV; 1% of the somatic time points is enough to recover every spike; and 5% brings the root-mean-square error to about 2~mV, spikes included, with relative errors below 0.1% on the sodium and delayed-rectifier conductances. We also compare the PINN with an unscented Kalman filter constrained by the same model and assimilating the same observations, and we assess robustness to measurement noise and to random initialization. The reconstructions reported here therefore rely on sparse somatic anchoring in addition to the dendritic recordings. The results delimit what can be inferred in this synthetic, weakly supervised two-compartment setting and identify the amount of somatic information required by the present formulation.

Read the full abstract

Neurons and Cognition

A theory of plasticity: capacity for change as inverse configurational constraint

Igor Branchi

Plasticity is invoked across the sciences to explain how systems can change, yet it is inferred from the very change it is meant to explain. A system may have many alternatives, realize none and still be plastic. Another may be driven far toward its only alternative, but the magnitude of that change does not establish its plasticity. What matters for plasticity is not how far the system moves but how strongly its present configuration constrains alternatives. Here I propose that plasticity, understood as a prospective property, is inverse configurational constraint: a system is plastic to the extent that its present configuration weakly constrains displacement toward alternatives within a declared representation. For systems sharing a functional partition, structural representation, and normalized architecture, increasing plasticity lowers every positive finite structural barrier separating the current state from its alternatives. With a fixed accessibility criterion, the accessible repertoire can expand but cannot shrink. In network representations, aggregate coupling is identified as configurational constraint, whose inverse defines plasticity. Barrier reduction and repertoire nesting are consequences, not components, of the definition. This connects two research traditions: quantifying connectivity without interpreting it as plasticity, and treating plasticity as a capacity for change without a prospective operational measure.

Read the full abstract

Populations and Evolution · Cross-listed from math.CO

Characterization of tree-child networks in terms of mu vectors

Toni Fuentes, Vincent Moulton, Katharina T. Huber et al. (+1 more)

We characterize tree-child phylogenetic networks in terms of their mu-representations. First, we give a structural characterization of tree-child networks by means of ordered tree-path decompositions. We then translate this decomposition into a set of purely vectorial conditions on finite subsets M in N^n. We prove that such a set M is the mu-representation of a tree-child phylogenetic network if and only if it is tree-child mu-compatible. This provides a feasibility criterion for tree-child mu-representations which can be used as a basis for reconstruction and further algorithmic applications. Note that this paper presents results arising from ongoing research on tree-child networks and that the results will be further developed and placed into proper context in subsequent versions.

Read the full abstract

All authors Toni Fuentes, Vincent Moulton, Katharina T. Huber, Joan Carles Pons

Quantitative Methods

Uncertainty Quantification-Incorporated Ensemble Model for Personalized Prediction of Severe Radiotherapy-Induced Immunosuppression Among Esophageal Cancer Patients

Baode Gao, Yan Chu, Zongsheng Hu et al. (+10 more)

Severe radiation-induced lymphopenia (RIL), specifically Grade 4 RIL (G4RIL), is a frequent immune toxicity and a strong predictor of poor survival for esophageal cancer patients undergoing concurrent chemoradiation therapy. This study developed an uncertainty-incorporated ensemble machine learning model to predict Absolute Lymphocyte Count (ALC) nadir using baseline clinical and dosimetric variables, supporting treatment decisions such as proton therapy selection and dose distribution optimization. A cohort of 1,491 esophageal cancer patients treated with photon or proton therapy between July 2001 and April 2022 was analyzed. A weighted ensemble model was constructed using clinical features and dosimetric variables, including the composite dosimetric score (CDS) derived from radiation dose distributions. To quantify individual-level predictive uncertainty, Cross-Residual Conformal Prediction (CRCP) was developed and evaluated against standard conformal prediction methods. Proton therapy patients maintained a significantly higher mean ALC nadir than photon patients (0.33 vs. 0.25 K/uL). The ensemble model achieved a mean absolute error of 0.0926 K/uL for ALC nadir prediction and an ROC-AUC of 0.78 for G4RIL classification. Baseline ALC, Planning Target Volume, and CDS were the most influential predictors. CRCP achieved competitive prediction interval widths with mathematically guaranteed coverage, offering a favorable balance between interval width and coverage accuracy. This framework enables risk-aware, personalized clinical decision-making in radiation oncology.

Read the full abstract

All authors Baode Gao, Yan Chu, Zongsheng Hu, Madison E. Grayson, Peter S.N. van Rossum, Clemens Grassberger, Heiko Enderling, Harald Paganetti, Ibrahim Chamseddine, Steven H. Lin, Brian P. Hobbs, Radhe Mohan, Yiqing Chen

Populations and Evolution

Stochastic Field Theory of HIV Latency: Instanton Dynamics and the Path to Viral Rebound

Jose de Jesus Bernal-Alvarado, David Delepine and Georges Delepine

The transition from clinical latency to active HIV infection is a stochastic process. Traditional deterministic models based on ordinary differential equations (ODEs) fail to capture the extinction boundary and the subsequent rebound of the virus because they neglect demographic noise in the small-population limit. We map the resulting Master Equation onto a coherent-state path integral using the Doi-Peliti second-quantization formalism and verify that the stochastic Hamiltonian reproduces the correct mean-field ODEs on the classical manifold $\bar{\phi}_i = 1$. In the semiclassical (large system-size) limit we derive eight coupled Hamilton-Jacobi equations, identify the virus-free and endemic fixed points, and obtain the basic reproduction number $R_0$ for the four-species network. For the analytically tractable case of single-virion bursting ($n=1$) we show that the stochastic Hamiltonian factorizes into two bilinear terms, exposing a non-trivial zero-energy surface that constitutes the instanton trajectory. A quasi-steady-state reduction then yields a first-order linear ODE whose closed-form solution gives the instanton action $S_{\rm inst}$ in terms of biological parameters. Calibrating to clinical data—infected-cell half-life $\delta_I^{-1}\approx 2\,\rm d$, viral burst size $N\approx 10^3$, clearance rate $\delta_V\approx 23\,\rm d^{-1}$, reactivation rate $\eta\approx 10^{-3}\,\rm d^{-1}$, and latent reservoir size $L_0\approx 10^6$ cells—we obtain a mean first-passage time (MFPT) to viral rebound of $\approx 6$ months for a typical patient, with a range of days to years depending on reservoir size. These predictions are qualitatively consistent with observed post-interruption rebound timescales.

Read the full abstract