pwc-archive/papers-with-abstracts huggingface dataset:
On an RNA-membrane protogenome
Authors: Michael Yarus
Date: 2025-01-17 00:00:00

Selected ribonucleotide sequences bind well to zwitterionic phospholipid bilayer membranes, though randomized RNAs do not. There are no evident repeated sequences in selected membrane binding RNAs. This implies small and varied motifs responsible for membrane affinity. Such subsequences have been partially defined. Bound RNAs require divalents like Mg2+ and/or Ca2+, preferring more ordered phospholipids: gel, ripple or rafted membranes, in that order. RNAs also bind and stabilize bilayers that are bent or sharply deformed. In contrast, RNA binding without divalents extends to negatively charged membranes formed from simpler anionic phospholipids, and to plausibly prebiotic fatty acid bilayers. RNA-membranes also retain RNA function, such as base pairing, passive transport of tryptophan, specific affinity for peptide side chains, like arginine, and catalysis by ribozymic ligase. Multiple membrane-bound RNAs with biochemical functions, linked by specific base-pairing are readily constructed. Given these experimental facts, genetic effects seem plausible. RNA functions often reside in few nucleotides, and are easily joined in a small RNA. Base-paired groups of these can evolve to be purposeful, joining related RNA functions. Such RNA groups permit complex genome functions, but require only replication of short RNAs. RNA-membranes facilitate accurate RNA segregation at cell division, and quickly evolve by appending new base-paired functions. Thus, ancient RNA-membranes could act as a protogenome, supporting orderly encoded RNA expression, inheritance and evolution before DNA and the DNA genome.
TFBS-Finder: Deep Learning-based Model with DNABERT and Convolutional Networks to Predict Transcription Factor Binding Sites
Authors: Nimisha Ghosh, Pratik Dutta, Daniele Santoni
Date: 2025-02-03 00:00:00

Transcription factors are proteins that regulate the expression of genes by binding to specific genomic regions known as Transcription Factor Binding Sites (TFBSs), typically located in the promoter regions of those genes. Accurate prediction of these binding sites is essential for understanding the complex gene regulatory networks underlying various cellular functions. In this regard, many deep learning models have been developed for such prediction, but there is still scope of improvement. In this work, we have developed a deep learning model which uses pre-trained DNABERT, a Convolutional Neural Network (CNN) module, a Modified Convolutional Block Attention Module (MCBAM), a Multi-Scale Convolutions with Attention (MSCA) module and an output module. The pre-trained DNABERT is used for sequence embedding, thereby capturing the long-term dependencies in the DNA sequences while the CNN, MCBAM and MSCA modules are useful in extracting higher-order local features. TFBS-Finder is trained and tested on 165 ENCODE ChIP-seq datasets. We have also performed ablation studies as well as cross-cell line validations and comparisons with other models. The experimental results show the superiority of the proposed method in predicting TFBSs compared to the existing methodologies. The codes and the relevant datasets are publicly available at https://github.com/NimishaGhosh/TFBS-Finder/.
scGSDR: Harnessing Gene Semantics for Single-Cell Pharmacological Profiling
Authors: Yu-An Huang, Xiyue Cao, Zhu-Hong You, Yue-Chao Li, Xuequn Shang, Zhi-An Huang
Date: 2025-02-02 00:00:00

The rise of single-cell sequencing technologies has revolutionized the exploration of drug resistance, revealing the crucial role of cellular heterogeneity in advancing precision medicine. By building computational models from existing single-cell drug response data, we can rapidly annotate cellular responses to drugs in subsequent trials. To this end, we developed scGSDR, a model that integrates two computational pipelines grounded in the knowledge of cellular states and gene signaling pathways, both essential for understanding biological gene semantics. scGSDR enhances predictive performance by incorporating gene semantics and employs an interpretability module to identify key pathways contributing to drug resistance phenotypes. Our extensive validation, which included 16 experiments covering 11 drugs, demonstrates scGSDR's superior predictive accuracy, when trained with either bulk-seq or scRNA-seq data, achieving high AUROC, AUPR, and F1 Scores. The model's application has extended from single-drug predictions to scenarios involving drug combinations. Leveraging pathways of known drug target genes, we found that scGSDR's cell-pathway attention scores are biologically interpretable, which helped us identify other potential drug-related genes. Literature review of top-ranking genes in our predictions such as BCL2, CCND1, the AKT family, and PIK3CA for PLX4720; and ICAM1, VCAM1, NFKB1, NFKBIA, and RAC1 for Paclitaxel confirmed their relevance. In conclusion, scGSDR, by incorporating gene semantics, enhances predictive modeling of cellular responses to diverse drugs, proving invaluable for scenarios involving both single drug and combination therapies and effectively identifying key resistance-related pathways, thus advancing precision medicine and targeted therapy development.
A Revisit of Total Correlation in Disentangled Variational Auto-Encoder with Partial Disentanglement
Authors: Chengrui Li, Yunmiao Wang, Yule Wang, Weihan Li, Dieter Jaeger, Anqi Wu
Date: 2025-02-04 00:00:00
Tasks: Disentanglement

A fully disentangled variational auto-encoder (VAE) aims to identify disentangled latent components from observations. However, enforcing full independence between all latent components may be too strict for certain datasets. In some cases, multiple factors may be entangled together in a non-separable manner, or a single independent semantic meaning could be represented by multiple latent components within a higher-dimensional manifold. To address such scenarios with greater flexibility, we develop the Partially Disentangled VAE (PDisVAE), which generalizes the total correlation (TC) term in fully disentangled VAEs to a partial correlation (PC) term. This framework can handle group-wise independence and can naturally reduce to either the standard VAE or the fully disentangled VAE. Validation through three synthetic experiments demonstrates the correctness and practicality of PDisVAE. When applied to real-world datasets, PDisVAE discovers valuable information that is difficult to find using fully disentangled VAEs, implying its versatility and effectiveness.
Structural constraints to compare phenomenal experience
Authors: J. Díaz-Boils, N. Tsuchiya, CM. Signorelli
Date: 2025-02-04 00:00:00

This article defines a partial order structure to study the relationship between levels and contents of conscious subjective experience in a single mathematical set-up. We understand phenomenal structure as extrapolated relationships among experiences, instead of fixed properties of specific experiences. Our mathematical account is based on multilayer network theory. Multilayer theory is a generalization of graph and network theory, widely used in several scientific domains. This structure is also the underlying conceptual and mathematical structure of most current models of conscious experience. From our simple set of assumptions, yet rigorous analysis, we conclude that assuming the comparison and quantification among phenomenal experiences yield only partial comparison, rather than commonly assumed absolute comparability. This has implications for evolutionary and animal consciousness: evolution may encompass diverse modes of experiencing, not necessarily implying larger ones on an absolute scale. Our characterization elucidates structural constraints on experiential comparisons imposed by assumptions and choices made by modellers as active participants in the scientific process. In summary, in light of our phenomenological intuitions, it might be right that some experiences carry qualitative aspects that make them incompatible or non-comparable with other experiences, quantitatively speaking. Some experiences are comparable (e.g. at some experiential levels), but others are not. These results have direct implications for consciousness science, evolution and animal consciousness.
Structural features of the fly olfactory circuit mitigate the stability-plasticity dilemma in continual learning
Authors: Heming Zou, Yunliang Zang, Xiangyang Ji
Date: 2025-02-03 00:00:00
Tasks: Continual Learning, Lifelong learning

Artificial neural networks face the stability-plasticity dilemma in continual learning, while the brain can maintain memories and remain adaptable. However, the biological strategies for continual learning and their potential to inspire learning algorithms in neural networks are poorly understood. This study presents a minimal model of the fly olfactory circuit to investigate the biological strategies that support continual odor learning. We introduce the fly olfactory circuit as a plug-and-play component, termed the Fly Model, which can integrate with modern machine learning methods to address this dilemma. Our findings demonstrate that the Fly Model enhances both memory stability and learning plasticity, overcoming the limitations of current continual learning strategies. We validated its effectiveness across various challenging continual learning scenarios using commonly used datasets. The fly olfactory system serves as an elegant biological circuit for lifelong learning, offering a module that enhances continual learning with minimal additional computational cost for machine learning.
Deep generative computed perfusion-deficit mapping of ischaemic stroke
Authors: Chayanin Tangwiriyasakul, Pedro Borges, Guilherme Pombo, Stefano Moriconi, Michael S. Elmalem, Paul Wright, Yee-Haur Mah, Jane Rondina, Robert Gray, Sebastien Ourselin, Parashkev Nachev, M. Jorge Cardoso
Date: 2025-02-03 00:00:00

Focal deficits in ischaemic stroke result from impaired perfusion downstream of a critical vascular occlusion. While parenchymal lesions are traditionally used to predict clinical deficits, the underlying pattern of disrupted perfusion provides information upstream of the lesion, potentially yielding earlier predictive and localizing signals. Such perfusion maps can be derived from routine CT angiography (CTA) widely deployed in clinical practice. Analysing computed perfusion maps from 1,393 CTA-imaged-patients with acute ischaemic stroke, we use deep generative inference to localise neural substrates of NIHSS sub-scores. We show that our approach replicates known lesion-deficit relations without knowledge of the lesion itself and reveals novel neural dependents. The high achieved anatomical fidelity suggests acute CTA-derived computed perfusion maps may be of substantial clinical-and-scientific value in rich phenotyping of acute stroke. Using only hyperacute imaging, deep generative inference could power highly expressive models of functional anatomical relations in ischaemic stroke within the pre-interventional window.
Probabilistic adaptation of language comprehension for individual speakers: Evidence from neural oscillations
Authors: Hanlin Wu, Xiaohui Rao, Zhenguang G. Cai
Date: 2025-02-03 00:00:00
Tasks: EEG

Listeners adapt language comprehension based on their mental representations of speakers, but how these representations are dynamically updated remains unclear. We investigated whether listeners probabilistically adapt their comprehension based on the likelihood of speakers producing stereotype-incongruent utterances. Our findings reveal two potential mechanisms: a speaker-general mechanism that adjusts overall expectations about speaker-content relationships, and a speaker-specific mechanism that updates individual speaker models. In two EEG experiments, participants heard speakers make stereotype-congruent or incongruent utterances, with incongruency base rate manipulated between blocks. In Experiment 1, speaker incongruency modulated both high-beta (21-30 Hz) and theta (4-6 Hz) oscillations: incongruent utterances decreased oscillatory power in low base rate condition but increased it in high base rate condition. The theta effect varied with listeners' openness trait: less open participants showed theta increases to speaker-incongruencies, suggesting maintenance of speaker-specific information, while more open participants showed theta decreases, indicating flexible model updating. In Experiment 2, we dissociated base rate from the target speaker by manipulating the overall base rate using an alternative non-target speaker. Only the high-beta effect persisted, showing power decrease for speaker-incongruencies in low base rate condition but no effect in high base rate condition. The high-beta oscillations might reflect the speaker-general adjustment, while theta oscillations may index the speaker-specific model updating. These findings provide evidence for how language processing is shaped by social cognition in real time.
BrainOOD: Out-of-distribution Generalizable Brain Network Analysis
Authors: Jiaxing Xu, Yongqiang Chen, Xia Dong, Mengcheng Lan, Tiancheng Huang, Qingtian Bian, James Cheng, Yiping Ke
Date: 2025-02-02 00:00:00

In neuroscience, identifying distinct patterns linked to neurological disorders, such as Alzheimer's and Autism, is critical for early diagnosis and effective intervention. Graph Neural Networks (GNNs) have shown promising in analyzing brain networks, but there are two major challenges in using GNNs: (1) distribution shifts in multi-site brain network data, leading to poor Out-of-Distribution (OOD) generalization, and (2) limited interpretability in identifying key brain regions critical to neurological disorders. Existing graph OOD methods, while effective in other domains, struggle with the unique characteristics of brain networks. To bridge these gaps, we introduce BrainOOD, a novel framework tailored for brain networks that enhances GNNs' OOD generalization and interpretability. BrainOOD framework consists of a feature selector and a structure extractor, which incorporates various auxiliary losses including an improved Graph Information Bottleneck (GIB) objective to recover causal subgraphs. By aligning structure selection across brain networks and filtering noisy features, BrainOOD offers reliable interpretations of critical brain regions. Our approach outperforms 16 existing methods and improves generalization to OOD subjects by up to 8.5%. Case studies highlight the scientific validity of the patterns extracted, which aligns with the findings in known neuroscience literature. We also propose the first OOD brain network benchmark, which provides a foundation for future research in this field. Our code is available at https://github.com/AngusMonroe/BrainOOD.
A Turing Test for Artificial Nets devoted to model Human Vision
Authors: Jorge Vila-Tomás, Pablo Hernández-Cámara, Qiang Li, Valero Laparra, Jesús Malo
Date: 2025-02-02 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

In this 2022 work we argued that, despite claims about successful modeling of the visual brain using artificial nets, the problem is far from being solved (even for low-level vision). Examples of open issues include: where should we read from ANNs in order to reproduce human behavior?, this ad-hoc read-out is considered part of the brain model or not?, should we use artificial psychophysics or artificial physiology?, in the case of ANNs, artificial experiments should literally match the experiments done with humans?. There is a clear need of rigorous procedures for experimental tests for ANNs devoted to model the visual brain, and more generally, to understand ANNs devoted to generic vision tasks. Following our experience in using low-level facts from Quantitative Visual Neuroscience in computer vision, in this work we presented the idea of developing a low-level dataset compiling the basic spatio-temporal and chromatic facts that are known to happen in the retina-V1 pathway, and they are not currently available in existing databases such as BrainScore. In our results we checked the behavior of three recently proposed models with similar architecture: (1) A parametric model tuned via Maximum Differentiation [Malo & Simoncelli SPIE 15, Martinez et al. PLOS 18, Martinez et al. Front. Neurosci. 19], (2) A non-parametric model called PerceptNet tuned to maximize the correlation with human opinion on subjective distortions [Hepburn et al. IEEE ICIP 19], and (3) A model with the same encoder as PerceptNet, but tuned for image segmentation (published as Hernandez-Camara et al. Patt.Recogn.Lett. 23). Results on 10 compelling psycho/physio visual facts show that the first model is the one with closer behavior to the humans in terms of receptive fields, but more interestingly, on the nonlinear behavior when facing complex spatio-chromatic patterns of a range of luminances and contrasts.
Analysis of Fractional Vegetation Coverage and its Dynamic Change in the Yalong River Basin based on Dimidiate Pixel Model
Authors: Yue Qin, Yuwei Lyu
Date: 2025-02-03 00:00:00

Fractional vegetation coverage (FVC) and its spatio-temporal variations are critical indicators of regional ecological changes, which are of great significance to study the laws of surface variation and analyze regional ecosystem. Under the development of RS and GIS technology, this analysis employs Landsat satellite images in 1994, 2008, 2013 and 2016 to estimate FVC in Yalong River Basin based on the Dimidiate Pixel Model. With consideration of the vegetation coverage condition and land surface law in the study area, the research further analyzes the Spatio-temporal variations as well as the influencing factors of FVC in terms of topography and land use types respectively. The results show that since 1994, FVC in Yalong River Basin has experienced a downward trend yet displaying an uptick from 2013. Moreover, different land use types indicate the versatility of land covers in Yalong River Basin, with grassland and forest performing probably the most important factors that can induce changes to the stability of FVC in whole basin. Overall, the research reflects the impact of human activities on vegetation in Yalong River Basin, and provides available data and theoretical basis for ecological assessment, ecological restoration and environmental protection.
Optimizing Global Genomic Surveillance for Early Detection of Emerging SARS-CoV-2 Variants
Authors: Haogao Gu, Jifan Li, Wanying Sun, Mengting Li, Kathy Leung, Joseph T. Wu, Hsiang-Yu Yuan, Maggie H. Wang, Bingyi Yang, Matthew R. McKay, Ning Ning, Leo L. M. Poon
Date: 2025-02-02 00:00:00

Background: Global viral threats underscore the need for effective genomic surveillance, but high costs and uneven resource distribution hamper its implementation. Targeting surveillance to international travelers in major travel hubs may offer a more efficient strategy for the early detection of SARS-CoV-2 variants. Methods: We developed and calibrated a multiple-strain metapopulation model of global SARS-CoV-2 transmission using extensive epidemiological, phylogenetic, and high-resolution air travel data. We then compared baseline surveillance with various resource-allocation approaches that prioritize travelers, focusing on Omicron BA.1/BA.2 retrospectively and on hypothetical future variants under different emergence, transmission and vaccine effectiveness scenarios. Findings: Focusing existing surveillance resources on travelers at key global hubs significantly shortened detection delays without increasing total surveillance efforts. In retrospective analyses of Omicron BA.1/BA.2, traveler-targeted approaches consistently outperformed baseline strategies, even when overall resources were reduced. Simulations indicate that focusing surveillance on key travel hubs outperform baseline practices in detecting future variants, across different possible origins, even with reduced resources. This approach also remains effective in future pandemic scenarios with varying reproductive numbers and vaccine effectiveness. Interpretation: These findings provide a quantitative, cost-effective framework for strengthening global genomic surveillance. By reallocating resources toward international travelers in select travel hubs, early detection of emerging variants can be enhanced, informing rapid public health interventions and bolstering preparedness for future pandemics.
From Data to Action: Charting A Data-Driven Path to Combat Antimicrobial Resistance
Authors: QiAn Fu, Yuzhe Zhang, Yanfeng Shu, Ming Ding, Lina Yao, Chen Wang
Date: 2025-01-30 00:00:00
Tasks: Denoising, Drug Discovery, Fairness

Antimicrobial-resistant (AMR) microbes are a growing challenge in healthcare, rendering modern medicines ineffective. AMR arises from antibiotic production and bacterial evolution, but quantifying its transmission remains difficult. With increasing AMR-related data, data-driven methods offer promising insights into its causes and treatments. This paper reviews AMR research from a data analytics and machine learning perspective, summarizing the state-of-the-art and exploring key areas such as surveillance, prediction, drug discovery, stewardship, and driver analysis. It discusses data sources, methods, and challenges, emphasizing standardization and interoperability. Additionally, it surveys statistical and machine learning techniques for AMR analysis, addressing issues like data noise and bias. Strategies for denoising and debiasing are highlighted to enhance fairness and robustness in AMR research. The paper underscores the importance of interdisciplinary collaboration and awareness of data challenges in advancing AMR research, pointing to future directions for innovation and improved methodologies.
Mind the Gap: Evaluating Patch Embeddings from General-Purpose and Histopathology Foundation Models for Cell Segmentation and Classification
Authors: Valentina Vadori, Antonella Peruffo, Jean-Marie Graïc, Livio Finos, Enrico Grisan
Date: 2025-02-04 00:00:00
Tasks: Cell Segmentation, Decoder, Instance Segmentation, Model Selection, Representation Learning, Semantic Segmentation, whole slide images

Recent advancements in foundation models have transformed computer vision, driving significant performance improvements across diverse domains, including digital histopathology. However, the advantages of domain-specific histopathology foundation models over general-purpose models for specialized tasks such as cell analysis remain underexplored. This study investigates the representation learning gap between these two categories by analyzing multi-level patch embeddings applied to cell instance segmentation and classification. We implement an encoder-decoder architecture with a consistent decoder and various encoders. These include convolutional, vision transformer (ViT), and hybrid encoders pre-trained on ImageNet-22K or LVD-142M, representing general-purpose foundation models. These are compared against ViT encoders from the recently released UNI, Virchow2, and Prov-GigaPath foundation models, trained on patches extracted from hundreds of thousands of histopathology whole-slide images. The decoder integrates patch embeddings from different encoder depths via skip connections to generate semantic and distance maps. These maps are then post-processed to create instance segmentation masks where each label corresponds to an individual cell and to perform cell-type classification. All encoders remain frozen during training to assess their pre-trained feature extraction capabilities. Using the PanNuke and CoNIC histopathology datasets, and the newly introduced Nissl-stained CytoDArk0 dataset for brain cytoarchitecture studies, we evaluate instance-level detection, segmentation accuracy, and cell-type classification. This study provides insights into the comparative strengths and limitations of general-purpose vs. histopathology foundation models, offering guidance for model selection in cell-focused histopathology and brain cytoarchitecture analysis workflows.