pwc-archive/papers-with-abstracts huggingface dataset:
Gumbel-Softmax Flow Matching with Straight-Through Guidance for Controllable Biological Sequence Generation
Authors: Sophia Tang, Yinuo Zhang, Alexander Tong, Pranam Chatterjee
Date: 2025-03-21 00:00:00

Flow matching in the continuous simplex has emerged as a promising strategy for DNA sequence design, but struggles to scale to higher simplex dimensions required for peptide and protein generation. We introduce Gumbel-Softmax Flow and Score Matching, a generative framework on the simplex based on a novel Gumbel-Softmax interpolant with a time-dependent temperature. Using this interpolant, we introduce Gumbel-Softmax Flow Matching by deriving a parameterized velocity field that transports from smooth categorical distributions to distributions concentrated at a single vertex of the simplex. We alternatively present Gumbel-Softmax Score Matching which learns to regress the gradient of the probability density. Our framework enables high-quality, diverse generation and scales efficiently to higher-dimensional simplices. To enable training-free guidance, we propose Straight-Through Guided Flows (STGFlow), a classifier-based guidance method that leverages straight-through estimators to steer the unconditional velocity field toward optimal vertices of the simplex. STGFlow enables efficient inference-time guidance using classifiers pre-trained on clean sequences, and can be used with any discrete flow method. Together, these components form a robust framework for controllable de novo sequence generation. We demonstrate state-of-the-art performance in conditional DNA promoter design, sequence-only protein generation, and target-binding peptide design for rare disease treatment.
Advances in Protein Representation Learning: Methods, Applications, and Future Directions
Authors: Viet Thanh Duy Nguyen, Truong-Son Hy
Date: 2025-03-20 00:00:00
Tasks: Drug Discovery, Representation Learning

Proteins are complex biomolecules that play a central role in various biological processes, making them critical targets for breakthroughs in molecular biology, medical research, and drug discovery. Deciphering their intricate, hierarchical structures, and diverse functions is essential for advancing our understanding of life at the molecular level. Protein Representation Learning (PRL) has emerged as a transformative approach, enabling the extraction of meaningful computational representations from protein data to address these challenges. In this paper, we provide a comprehensive review of PRL research, categorizing methodologies into five key areas: feature-based, sequence-based, structure-based, multimodal, and complex-based approaches. To support researchers in this rapidly evolving field, we introduce widely used databases for protein sequences, structures, and functions, which serve as essential resources for model development and evaluation. We also explore the diverse applications of these approaches in multiple domains, demonstrating their broad impact. Finally, we discuss pressing technical challenges and outline future directions to advance PRL, offering insights to inspire continued innovation in this foundational field.
Non-Canonical Crosslinks Confound Evolutionary Protein Structure Models
Authors: Romain Lacombe
Date: 2025-03-09 00:00:00
Tasks: Protein Structure Prediction

Evolution-based protein structure prediction models have achieved breakthrough success in recent years. However, they struggle to generalize beyond evolutionary priors and on sequences lacking rich homologous data. Here we present a novel, out-of-domain benchmark based on sactipeptides, a rare class of ribosomally synthesized and post-translationally modified peptides (RiPPs) characterized by sulfur-to-$\alpha$-carbon thioether bridges creating cross-links between cysteine residues and backbone. We evaluate recent models on predicting conformations compatible with these cross-links bridges for the 10 known sactipeptides with elucidated post-translational modifications. Crucially, the structures of 5 of them have not yet been experimentally resolved. This makes the task a challenging problem for evolution-based models, which we find exhibit limited performance (0.0% to 19.2% GDT-TS on sulfur-to-$\alpha$-carbon distance). Our results point at the need for physics-informed models to sustain progress in biomolecular structure prediction.
Tumor-associated CD19$^+$ macrophages induce immunosuppressive microenvironment in hepatocellular carcinoma
Authors: Junli Wang, Wanyue Cao, Jinyan Huang, Yu Zhou, Rujia Zheng, Yu Lou, Jiaqi Yang, Jianghui Tang, Mao Ye, Zhengtao Hong, Jiangchao Wu, Haonan Ding, Yuquan Zhang, Jianpeng Sheng, Xinjiang Lu, Pinglong Xu, Xiongbin Lu, Xueli Bai, Tingbo Liang, Qi Zhang
Date: 2025-03-22 00:00:00

Tumor-associated macrophages are a key component that contributes to the immunosuppressive microenvironment in human cancers. However, therapeutic targeting of macrophages has been a challenge in clinic due to the limited understanding of their heterogeneous subpopulations and distinct functions. Here, we identify a unique and clinically relevant CD19$^+$ subpopulation of macrophages that is enriched in many types of cancer, particularly in hepatocellular carcinoma (HCC). The CD19$^+$ macrophages exhibit increased levels of PD-L1 and CD73, enhanced mitochondrial oxidation, and compromised phagocytosis, indicating their immunosuppressive functions. Targeting CD19$^+$ macrophages with anti-CD19 chimeric antigen receptor T (CAR-T) cells inhibited HCC tumor growth. We identify PAX5 as a primary driver of up-regulated mitochondrial biogenesis in CD19$^+$ macrophages, which depletes cytoplasmic Ca$^{2+}$, leading to lysosomal deficiency and consequent accumulation of CD73 and PD-L1. Inhibiting CD73 or mitochondrial oxidation enhanced the efficacy of immune checkpoint blockade therapy in treating HCC, suggesting great promise for CD19$^+$ macrophage-targeting therapeutics.
Combining multiplexed functional data to improve variant classification
Authors: Atlas of Variant Effects Alliance, :, Jeffrey D. Calhoun, Moez Dawood, Charlie F. Rowlands, Shawn Fayer, Elizabeth J. Radford, Abbye E. McEwen, Clare Turnbull, Amanda B. Spurdle, Lea M. Starita, Sujatha Jagannathan, Ken, Ruth Davee Department of Neurology, Northwestern Feinberg School of Medicine, Chicago, Illinois, Human Genome Sequencing Center, Department of Molecular, Human Genetics, Medical Scientist Training Program, Baylor College of Medicine, Houston, TX, Brotman Baty Institute for Precision Medicine, Department of Genome Sciences, WA, Wellcome Sanger Institute, Hinxton, CB10 1SA, Department of Pediatrics, University of Cambridge, Level 8, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK., Department of Laboratory Medicine, Pathology, University of Washington, Seattle, Division of Genetics, Epidemiology, The Institute of Cancer Research, London, UK, Population Health Program, QIMR Berghofer Medical Research Institute, Herston, Faculty of Medicine, The University of Queensland, Brisbane, QLD, 4006, Australia, Department of Biochemistry, Molecular Genetics, USA., RNA Bioscience Initiative, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
Date: 2025-03-24 00:00:00
Tasks: Classification

With the surge in the number of variants of uncertain significance (VUS) reported in ClinVar in recent years, there is an imperative to resolve VUS at scale. Multiplexed assays of variant effect (MAVEs), which allow the functional consequence of 100s to 1000s of genetic variants to be measured in a single experiment, are emerging as a source of evidence which can be used for clinical gene variant classification. Increasingly, there are multiple published MAVEs for the same gene, sometimes measuring different aspects of variant impact. Where multiple functional consequences may need to be considered to get a more complete understanding of variant effects for a given gene, combining data from multiple MAVEs may lead to the assignment of increased evidence strength which could impact variant classifications. Here, we provide guidance for combining such multiplexed functional data, incorporating a stepwise process from data curation and collection to model generation and validation. We illustrate the potential of this approach by showing the integration of multiplexed functional data from four MAVEs for the gene TP53. By following these steps, researchers can maximize the value of MAVEs, strengthen the functional evidence for clinical variant classification, reclassify more VUS, and potentially uncover novel mechanisms of pathogenicity for clinically relevant genes.
Application of Single-cell Deep Learning in Elucidating the Mapping Relationship Between Visceral and Body Surface Inflammatory Patterns
Authors: Haixiang Huang, Bingbing Shen, Zhenwei Zhang, Jianming Yue, Lu Mei, Qiusheng Chen
Date: 2025-03-21 00:00:00

As a system of integrated homeostasis, life is susceptible to disruptions by visceral inflammation, which can disturb internal environment equilibrium. The role of body-spread subcutaneous fascia (scFascia) in this process is poorly understood. In the rat model of Salmonella-induced dysentery, scRNA-seq of scFascia and deep-learning analysis revealed Warburg-like metabolic reprogramming in macrophages (MPs) with reduced citrate cycle activity. Cd34+/Pdgfra+ telocytes (CPTCs) regulated MPs differentiation and proliferation via Wnt/Fgf signal, suggesting a pathological crosstalk pattern in the scFascia, herein termed the fascia-visceral inflammatory crosstalk pattern (FVICP). PySCENIC analysis indicated increased activity transcription factors Fosl1, Nfkb2, and Atf4, modulated by CPTCs signaling to MPs, downregulating aerobic respiration and upregulating cell cycle, DNA replication, and transcription. This study highlights scFascia's role in immunomodulation and metabolic reprogramming during visceral inflammation, underscoring its function in systemic homeostasis.
Machine Learning-Based Genomic Linguistic Analysis (Gene Sequence Feature Learning): A Case Study on Predicting Heavy Metal Response Genes in Rice
Authors: Ruiqi Yang, Jianxu Wang, Wei Yuan, Xun Wang, Mei Li
Date: 2025-03-20 00:00:00

This study explores the application of machine learning-based genetic linguistics for identifying heavy metal response genes in rice (Oryza sativa). By integrating convolutional neural networks and random forest algorithms, we developed a hybrid model capable of extracting and learning meaningful features from gene sequences, such as k-mer frequencies and physicochemical properties. The model was trained and tested on datasets of genes, achieving high predictive performance (precision: 0.89, F1-score: 0.82). RNA-seq and qRT-PCR experiments conducted on rice leaves which exposed to Hg0, revealed differential expression of genes associated with heavy metal responses, which validated the model's predictions. Co-expression network analysis identified 103 related genes, and a literature review indicated that these genes are highly likely to be involved in heavy metal-related biological processes. By integrating and comparing the analysis results with those of differentially expressed genes (DEGs), the validity of the new machine learning method was further demonstrated. This study highlights the efficacy of combining machine learning with genetic linguistics for large-scale gene prediction. It demonstrates a cost-effective and efficient approach for uncovering molecular mechanisms underlying heavy metal responses, with potential applications in developing stress-tolerant crop varieties.
ATP requirements for growth reveal the bioenergetic impact of mitochondrial symbiosis
Authors: William F. Martin
Date: 2025-03-21 00:00:00

Studies by microbiologists from the 1970s provided robust estimates for the energy supply and demand of a prokaryotic cell. The amount of ATP needed to support growth was calculated from the chemical composition of the cell and known enzymatic pathways that synthesize its constituents from known substrates in culture. Starting in 2015, geneticists and evolutionary biologists began investigating the bioenergetic role of mitochondria at eukaryote origin and energy in metazoan evolution using their own, widely trusted but hitherto unvetted model for the costs of growth in terms of ATP per cell. The more recent model contains, however, a severe and previously unrecognized error that systematically overestimates the ATP cost of amino acid synthesis up to 200 fold. The error applies to all organisms studied by such models and leads to conspicuously false inferences, for example that the synthesis of an average amino acid in humans requires 30 ATP, which no biochemistry textbook will confirm. Their ATP cost calculations would require that Escherichia coli obtains roughly 100 ATP per glucose and that mammals obtain roughly 240 ATP per glucose, propositions that invalidate evolutionary inferences so based. By contrast, established methods for estimating the ATP cost of microbial growth show that the first mitochondrial endosymbionts could have easily doubled the hosts available ATP pool, provided that genes for growth on environmental amino acids were transferred from the mitochondrial symbiont to the archaeal host and that the host for mitochondrial origin was an autotroph using the acetyl-CoA pathway.
Vision: looking and seeing through our brain's information bottleneck
Authors: Li Zhaoping
Date: 2025-03-24 00:00:00

Our brain recognizes only a tiny fraction of sensory input, due to an information processing bottleneck. This blinds us to most visual inputs. Since we are blind to this blindness, only a recent framework highlights this bottleneck by formulating vision as mainly looking and seeing. Looking selects a tiny fraction of visual information for progression through the bottleneck, mainly by shifting gaze to center an attentional spotlight. Seeing decodes, i.e., recognizes, objects within the selected information. Since looking often occurs before seeing and evokes limited awareness, humans have the impression of seeing whole scenes clearly. According to the new framework, the bottleneck starts from the output of the primary visual cortex (V1) to downstream brain areas. This is motivated by the evidence-backed V1 Saliency Hypothesis (V1SH) that V1 creates a saliency map of the visual field to guide looking. Massive visual information loss downstream from V1 makes seeing vulnerable to ambiguity and illusions (errors). To overcome this, feedback from downstream to upstream areas such as V1 queries for additional relevant information. An integral part of this framework is the central-peripheral dichotomy (CPD) theory proposing that vision in the peripheral and central visual fields are specialized for looking (deciding where to shift the gaze) and seeing, respectively, and that the feedback query to aid seeing is mainly directed to the central visual field. This V1SH-Bottleneck-CPD framework predicts that the peripheral visual field, lacking feedback queries, is more vulnerable to illusions, and that such illusions become visible in the central visual field when the feedback query is compromised. We present theoretical predictions, experimental confirmations, a Feedforward-Feedback-Verify-and-reWeight (FFVW) algorithm for seeing through the bottleneck.
MammAlps: A multi-view video behavior monitoring dataset of wild mammals in the Swiss Alps
Authors: Valentin Gabeff, Haozhe Qi, Brendan Flaherty, Gencer Sumbül, Alexander Mathis, Devis Tuia
Date: 2025-03-23 00:00:00
Proceeding: CVPR 2025 1
Tasks: Scene Segmentation, Video Understanding

Monitoring wildlife is essential for ecology and ethology, especially in light of the increasing human impact on ecosystems. Camera traps have emerged as habitat-centric sensors enabling the study of wildlife populations at scale with minimal disturbance. However, the lack of annotated video datasets limits the development of powerful video understanding models needed to process the vast amount of fieldwork data collected. To advance research in wild animal behavior monitoring we present MammAlps, a multimodal and multi-view dataset of wildlife behavior monitoring from 9 camera-traps in the Swiss National Park. MammAlps contains over 14 hours of video with audio, 2D segmentation maps and 8.5 hours of individual tracks densely labeled for species and behavior. Based on 6135 single animal clips, we propose the first hierarchical and multimodal animal behavior recognition benchmark using audio, video and reference scene segmentation maps as inputs. Furthermore, we also propose a second ecology-oriented benchmark aiming at identifying activities, species, number of individuals and meteorological conditions from 397 multi-view and long-term ecological events, including false positive triggers. We advocate that both tasks are complementary and contribute to bridging the gap between machine learning and ecology. Code and data are available at: https://github.com/eceo-epfl/MammAlps
Feature Learning beyond the Lazy-Rich Dichotomy: Insights from Representational Geometry
Authors: Chi-Ning Chou, Hang Le, Yichen Wang, SueYeon Chung
Date: 2025-03-23 00:00:00
Tasks: image-classification, Image Classification, Out-of-Distribution Generalization

The ability to integrate task-relevant information into neural representations is a fundamental aspect of both biological and artificial intelligence. To enable theoretical analysis, recent work has examined whether a network learns task-relevant features (rich learning) or resembles a random feature model (or a kernel machine, i.e., lazy learning). However, this simple lazy-versus-rich dichotomy overlooks the possibility of various subtypes of feature learning that emerge from different architectures, learning rules, and data properties. Furthermore, most existing approaches emphasize weight matrices or neural tangent kernels, limiting their applicability to neuroscience because they do not explicitly characterize representations. In this work, we introduce an analysis framework based on representational geometry to study feature learning. Instead of analyzing what are the learned features, we focus on characterizing how task-relevant representational manifolds evolve during the learning process. In both theory and experiment, we find that when a network learns features useful for solving a task, the task-relevant manifolds become increasingly untangled. Moreover, by tracking changes in the underlying manifold geometry, we uncover distinct learning stages throughout training, as well as different learning strategies associated with training hyperparameters, uncovering subtypes of feature learning beyond the lazy-versus-rich dichotomy. Applying our method to neuroscience and machine learning, we gain geometric insights into the structural inductive biases of neural circuits solving cognitive tasks and the mechanisms underlying out-of-distribution generalization in image classification. Our framework provides a novel geometric perspective for understanding and quantifying feature learning in both artificial and biological neural networks.
Boundary Effects in Biological Planar Networks: Pentagons Dominate Marginal Cells
Authors: Kai Xu, Fei He
Date: 2025-03-24 00:00:00
Tasks: Mathematical Proofs

The topological and geometrical features at the boundary zone of planar polygonal networks remain poorly understood. Based on observations and mathematical proofs, we propose that marginal cells in Pyropia haitanensis thalli, a two-dimensional (2D) biological polygonal network, have an average edge number of exactly five. We demonstrate that this number is maintained by specific division patterns. Furthermore, we reveal significant limitations of Lewis law and Aboav-Weaire law by comparing the topological and geometrical parameters of marginal cells and inner cells. We find strong boundary effects that are manifested in the distinct distributions of interior angles and edge lengths in marginal cells. Similar to inner cells, cell division tend to occur in marginal cells with large sizes. Our findings suggest that inner cells should be strictly defined based on their positional relationship to the marginal cells.
Simulating foot-and-mouth dynamics and control in Bolivia
Authors: Nicolas C. Cardenas, Diego Viali dos Santos, Daniel Magalhaaes Lima, Hernan Oliver Daza Gutierrez, Daniel Rodney Gareca Vaca, Gustavo Machado
Date: 2025-03-20 00:00:00

Examining the dissemination dynamics of foot-and-mouth disease (FMD) is critical for revising national response plans. We developed a stochastic SEIR metapopulation model to simulate FMD outbreaks in Bolivia and explore how the national response plan impacts the dissemination among all susceptible species. We explored variations in the control strategies, mapped high-risk areas, and estimated the number of vaccinated animals during the reactive ring vaccination. Initial outbreaks ranged from 1 to 357 infected farms, with control measures implemented for up to 100 days, including control zones, a 30-day movement ban, depopulation, and ring vaccination. Combining vaccination (50-90 farms/day) and depopulation (1-2 farms/day) controlled 60.3% of outbreaks, while similar vaccination but higher depopulation rates (3-5 farms/day) controlled 62.9% and eliminated outbreaks nine days faster. Utilizing depopulation alone controlled 56.76% of outbreaks, but had a significantly longer median duration of 63 days. Combining vaccination (25-45 farms/day) and depopulation (6-7 farms/day) was the most effective, eliminating all outbreaks within a median of three days (maximum 79 days). Vaccination alone controlled only 0.6% of outbreaks and had a median duration of 98 days. Ultimately, results showed that the most effective strategy involved ring vaccination combined with depopulation, requiring a median of 925,338 animals to be vaccinated. Outbreaks were most frequent in high-density farming areas such as Potosi, Cochabamba, and La Paz. Our results suggest that emergency ring vaccination alone can not eliminate FMD if reintroduced in Bolivia, and combining depopulation with vaccination significantly shortens outbreak duration. These findings provide valuable insights to inform Bolivia national FMD response plan, including vaccine requirements and the role of depopulation in controlling outbreaks.
Dynamics and control of maize infection by Busseola fusca: multi-seasonal modeling and biocontrol strategies
Authors: Clotilde Djuikem, Josué Tchouanti
Date: 2025-03-20 00:00:00

Maize production in sub-Saharan Africa faces significant challenges due to the maize stalk borer (Busseola fusca), a major pest that causes substantial yield losses. Chemical control methods have raised concerns about environmental impact and pest resistance, making biological control a promising alternative. In this study, we develop a multi-seasonal mathematical model using an impulsive system of differential equations to describe stalk borer population dynamics and evaluate pest control strategies. We analyze the stability of the pest-free solution using Floquet theory and study the effects of periodic predator releases on pest suppression. Numerical simulations illustrate the impact of cultural practice and predator release frequency. Moreover, our simulations show that, under good cultural practices, releasing predators once or three times a year is an effective biocontrol strategy. However, in cases of poor cultural practices, biocontrol has only a limited effect, and the best outcome is achieved when predators are released once a year at the beginning of the cropping season.