pwc-archive/papers-with-abstracts huggingface dataset:
Bidirectional Mamba for Single-Cell Data: Efficient Context Learning with Biological Fidelity
Authors: Cong Qi, Hanzhang Fang, Tianxing Hu, Siqi Jiang, Wei Zhi
Date: 2025-04-22 00:00:00
Tasks: Mamba

Single-cell RNA sequencing (scRNA-seq) enables high-resolution analysis of cellular heterogeneity, but its complexity, which is marked by high dimensionality, sparsity, and batch effects, which poses major computational challenges. Transformer-based models have made significant advances in this domain but are often limited by their quadratic complexity and suboptimal handling of long-range dependencies. In this work, we introduce GeneMamba, a scalable and efficient foundation model for single-cell transcriptomics built on state space modeling. Leveraging the Bi-Mamba architecture, GeneMamba captures bidirectional gene context with linear-time complexity, offering substantial computational gains over transformer baselines. The model is pretrained on nearly 30 million cells and incorporates biologically informed objectives, including pathway-aware contrastive loss and rank-based gene encoding. We evaluate GeneMamba across diverse tasks, including multi-batch integration, cell type annotation, and gene-gene correlation, demonstrating strong performance, interpretability, and robustness. These results position GeneMamba as a practical and powerful alternative to transformer-based methods, advancing the development of biologically grounded, scalable tools for large-scale single-cell data analysis.
A CRISP approach to QSP: XAI enabling fit-for-purpose models
Authors: Noah DeTal, Christian N. K. Anderson, Mark K. Transtrum
Date: 2025-05-05 00:00:00
Tasks: Experimental Design

Quantitative Systems Pharmacology (QSP) promises to accelerate drug development, enable personalized medicine, and improve the predictability of clinical outcomes. Realizing this potential requires effectively managing the complexity of mathematical models representing biological systems. Here, we present and validate a novel QSP workflow--CRISP (Contextualized Reduction for Identifiability and Scientific Precision)--that addresses a central challenge in QSP: the problem of complexity and over-parameterization, in which models contain irrelevant parameters that obscure interpretation and hinder predictive reliability. The CRISP workflow begins with a literature-derived model, constructed to be comprehensive and unbiased by integrating prior mechanistic insights. At the core of the workflow is the Manifold Boundary Approximation Method (MBAM), a reduction technique that simplifies models while preserving mechanistic structure and predictive fidelity. By applying MBAM in a context-specific manner, CRISP links parsimonious models directly to predictions of interest, clarifying causal structure and enhancing interpretability. The resulting models are computationally efficient and well-suited to key QSP tasks, including virtual population generation, experimental design, toxicology, and target discovery. We demonstrate the utility of CRISP on case studies involving the coagulation cascade and SHIV infection, and identify promising directions for improving the efficacy of bNAb therapies for HIV. Together, these results establish CRISP as a general-purpose QSP workflow for turning complex mechanistic models into tools for precise scientific reasoning to guide pharmacological and regulatory decision-making.
The Emergence of Chirality from Metabolism
Authors: John F. Malloy, Camerian Millsaps, Kamesh Narasimhan, Louie Slocombe, Cole Mathis, Leroy Cronin, Sara Imari Walker
Date: 2025-05-02 00:00:00
Tasks: Geochemistry

Molecular chirality is critical to biochemical function, but it is unknown when chiral selectivity first became important in the evolutionary transition from geochemistry to biochemistry during the emergence of life. Here, we identify key transitions in the selection of chiral molecules in metabolic evolution, showing how achiral molecules (lacking chiral centers) may have given rise to specific and abundant chiral molecules in the elaboration of metabolic networks from geochemically available precursor molecules. Simulated expansions of biosphere-scale metabolism suggest new hypotheses about the evolution of chiral molecules within biochemistry, including a prominent role for both achiral and chiral compounds as nucleation sites of early metabolic network growth, an increasing enrichment of molecules with more chiral centers as these networks expand, and conservation of broken chiral symmetries along reaction pathways as a general organizing principle. We also find an unexpected enrichment in large, non-polymeric achiral molecules. Leveraging metabolic data of 40,023 genomes and metagenomes, we analyzed the statistics of chiral and achiral molecules in the large-scale organization of metabolism, revealing a chiral-enriched phase of network organization evidenced by system-size dependent chiral scaling laws that differ for individuals and ecosystems. By uncovering how metabolic networks could lead to chiral selection, our findings open new avenues for bridging metabolism and genetics-first approaches to the origin of chirality, allowing tools for better timing of major transitions in molecular organization during the emergence of life, understanding the role of chirality in extant and synthetic metabolisms, and informing targets for chirality-based biosignatures.
Curvature-Induced Saturation in Catalytic Reaction Networks: A Differential Geometrical Framework for Modeling Chemical Complexity
Authors: Anderson M. Rodriguez
Date: 2025-04-20 00:00:00

The evolution of chemical reaction networks is often analyzed through kinetic models and energy landscapes, but these approaches fail to capture the deeper structural constraints governing complexity growth. In chemical reaction networks, emergent constraints dictate the organization of reaction pathways, limiting combinatorial expansion and determining stability conditions. This paper introduces a novel approach to modeling chemical reaction networks by incorporating differential geometry into the classical framework of reaction kinetics. By utilizing the Riemannian metric, Christoffel symbols, and a system-specific entropy-like term, we provide a new method for understanding the evolution of complex reaction systems. The approach captures the interdependence between species, the curvature of the reaction network's configuration space, and the tendency of the system to evolve toward more probable states. The interaction topology constrains the accessible reaction trajectories and the introduced differential geometrical approach allows analysis of curvature constraints which help us to understand pathway saturation and transition dynamics. Rather than treating reaction space as an unconstrained combinatorial landscape, we frame it as a structured manifold with higher order curvature describing a geodesic for system evolution under intrinsic constraints. This geometrical perspective offers a unique insight into pathway saturation, self-interruption, and emergent behavior in reaction networks, and provides a scalable framework for modeling large biochemical or catalytic systems.
Reciprocating thermochemical mediator of pre-biotic polymer decomposition on mineral surfaces
Authors: Rowena Ball, John Brindley
Date: 2025-04-19 00:00:00
Tasks: TAR

A continuing frustration for origin of life scientists is that abiotic and, by extension, pre-biotic attempts to develop self-sustaining, evolving molecular systems tend to produce more dead-end substances than macromolecular products with the necessary potential for biostructure and function -- the so-called `tar problem'. Nevertheless primordial life somehow emerged despite that presumed handicap. A~resolution of this problem is important in emergence-of-life science because it would provide valuable guidance in choosing subsequent paths of investigation, such as identifying pre-biotic patterns on Mars. To study the problem we set up a simple non-equilibrium flow dynamical model for the coupled temperature and mass dynamics of the decomposition of a polymeric carbohydrate adsorbed on a mineral surface, with incident stochastic thermal fluctuations. Results show that the model system behaves as a reciprocating thermochemical oscillator. The output fluctuation distribution is bimodal, with a right-weighted component that guarantees a bias towards detachment and desorption of monomeric species such as ribose, even while tar is formed concomitantly. This fluctuating thermochemical reciprocator may ensure that non-performing polymers can be fractionated into a refractory carbon reservoir and active monomers which may be reincorporated into better-performing polymers with less vulnerability towards adsorptive tarring.
Bifunctional enzyme action as a source of robustness in biochemical reaction networks: a novel hypergraph approach
Authors: Badal Joshi, Tung D. Nguyen
Date: 2025-04-15 00:00:00

Substrate modification networks are ubiquitous in living, biochemical systems. A higher-level hypergraph "skeleton" captures key information about which substrates are transformed in the presence of modification-specific enzymes. Many different detailed models can be associated to the same skeleton, however uncertainty related to model fitting increases with the level of detail. We show that essential dynamical properties such as existence of positive steady states and concentration robustness can be extracted directly from the skeleton independent of the detailed model. The novel formalism of directed hypergraphs is used to prove that bifunctional enzyme action plays a key role in generating robustness. Moreover, we use another novel concept of "current" on a directed hypergraph to establish a link between potentially remote network components. Current is an essential notion required for existence of positive steady states, and furthermore, current-balance combined with bifunctionality generates concentration robustness.
The classification of Alzheimer's disease and mild cognitive impairment improved by dynamic functional network analysis
Authors: Nicolas Rubido, Venia Batziou, Marwan Fuad, Vesna Vuksanovic
Date: 2025-05-06 00:00:00
Tasks: Functional Connectivity

Brain network analysis using functional MRI has advanced our understanding of cortical activity and its changes in neurodegenerative disorders that cause dementia. Recently, research in brain connectivity has focused on dynamic (time-varying) brain networks that capture both spatial and temporal information on cortical, regional co-activity patterns. However, this approach has been largely unexplored within the Alzheimer's spectrum. We analysed age- and sex-matched static and dynamic fMRI brain networks from 315 individuals with Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI), and cognitively-normal Healthy Elderly (HE), using data from the ADNI-3 protocol. We examined both similarities and differences between these groups, employing the Juelich brain atlas for network nodes, sliding-window correlations for time-varying network links, and non-parametric statistics to assess between-group differences at the link or the node centrality level. While the HE and MCI groups show similar static and dynamic networks at the link level, significant differences emerge compared to AD participants. We found stable (stationary) differences in patterns of functional connections between the white matter regions and the parietal lobe's, and somatosensory cortices, while metastable (temporal) networks' differences were consistently found between the amygdala and hippocampal formation. In addition, our node centrality analysis showed that the white matter connectivity patterns are local in nature. Our results highlight shared and unique functional connectivity patterns in both stationary and dynamic functional networks, emphasising the need to include dynamic information in brain network analysis in studies of Alzheimer's spectrum.
The insertion of Neomycin cassette impairs maternal and social behaviors in Arc/Arg3.1 knock-out mice
Authors: Ana Dudas, Emilia Caire, Abdurahman Hassan a Kuku, Nicolas Azzopardi, Anil Annamneedi, Heba Elseedy, Gaëlle Lefort, Benoît Piegu, Romain Yvinec, Emmanuel Pecnard, Lucile Drobecq, Anne-Charlotte Trouillet, Angela Sirigu, Dietmar Kuhl, Pablo Chamero, Ora Ohana, Lucie P. Pellissier
Date: 2025-05-06 00:00:00
Tasks: ARC

The Neomycin resistance cassette (Neo+) is commonly inserted in the genome of mice to generate knock-out (KO) models. The effect of gene deletion on social behaviors in mice is controversial between studies using different Neo+ and Neo-mouse lines, particularly Arc/Arg3.1 KO lines. In this study, we identified severe maternal behavior impairments in Neo+, but not Neo-Arc/Arg3.1 KO dams. These deficits resulted from reduced sociability and abnormal social information processing in Neo+ Arc/Arg3.1 KO dams, exacerbated by social communication impairments in pups. The expression of the Neo cassette product did not cause cytotoxicity, but led to altered ERK signaling, gene expression, and oxytocin system. However, oxytocin administration did not improve social impairments in Neo+ Arc/Arg3.1 KO animals. Interestingly, early social environment enrichment enhanced social interaction with familiar, but not unfamiliar conspecifics or maternal behavior. Overall, our findings reveal a major impact of the Neo cassette on behaviors, particularly social behaviors, in Arc/Arg3.1 KO mice, underscoring the need to re-examine phenotypes of animal models carrying the Neo cassette in neuroscience research.
A comprehensive framework for statistical testing of brain dynamics
Authors: Nick Yao Larsen, Laura Paulsen, Anderson M. Winkler, Diego Vidaurre
Date: 2025-05-05 00:00:00

We introduce a comprehensive statistical framework for analysing brain dynamics and testing their associations with behavioural, physiological and other non-imaging variables. Based on a generalisation of the Hidden Markov Model (HMM) - the Gaussian-Linear HMM - our open-source Python package supports multiple experimental paradigms, including task-based and resting-state studies, and addresses a wide range of questions in neuroscience and related scientific fields. Inference is carried out using permutation-based methods and structured Monte Carlo resampling, and the framework can easily handle confounding variables, multiple testing corrections, and hierarchical relationships within the data. The package includes tools for intuitive visualisation of statistical results, along with comprehensive documentation and step-by-step tutorials to make it accessible for users of varying expertise. Altogether, it provides a broadly applicable, end-to-end pipeline for analysis and statistical testing of functional neural data and its dynamics.
Bell's Inequalities and Entanglement in Corpora of Italian Language
Authors: Diederik Aerts, Suzette Geriente, Roberto Leporini, Sandro Sozzo
Date: 2025-05-04 00:00:00
Tasks: Information Retrieval, Translation

We analyze the results of three information retrieval tests on conceptual combinations that we have recently performed using corpora of Italian language. The tests have the form of a 'Bell-type test' and were aimed at identifying 'quantum entanglement' in the combination, or composition, of two concepts. In the first two tests, we studied the Italian translation of the combination 'The Animal Acts', while in the third test, we studied the Italian translation of the combination 'The Animal eats the Food'. We found a significant violation of Bell's inequalities in all tests. Empirical patterns confirm the results obtained with corpora of English language, which indicates the existence of deep structures in concept formation that are language-independent. The systematic violation of Bell's inequalities suggests the presence of entanglement and, indeed, we elaborate here a 'quantum model in Hilbert space' for the collected data. This investigation supports our theoretical hypothesis about entanglement as a phenomenon of 'contextual updating', independently of the nature, micro-physical or conceptual linguistic, of the entities involved. Finally, these findings allow us to further clarify the mutual relationships between entanglement, Cirel'son's bound, and no-signaling in Bell-type situations.
Burstiness and interpersonal foraging between human infants and caregivers in the vocal domain
Authors: VPS Ritwika, Sara Schneider, Lukas D. Lopez, Jeffrey Mai, Ajay Gopinathan, Christopher T. Kello, Anne S. Warlaumont
Date: 2025-05-02 00:00:00

Vocal responses from caregivers are believed to promote more frequent and more advanced infant vocalizations. However, studies that examine this relationship typically do not account for the fact that infant and adult vocalizations are distributed in hierarchical clusters over the course of the day. These bursts and lulls create a challenge for accurately detecting the effects of adult input at immediate turn-by-turn timescales within real-world behavior, as adult responses tend to happen during already occurring bursts of infant vocalizations. Analyzing daylong audio recordings of real-world vocal communication between human infants (ages 3, 6, 9, and 18 months) and their adult caregivers, we first show that both infant and caregiver vocalization events are clustered in time, as evidenced by positive correlations between successive inter-event intervals (IEIs). We propose an approach informed by flight time analyses in foraging studies to assess whether the timing of a vocal agent's next vocalization is modified by inputs from another vocal agent, controlling for the first agent's previous IEI. For both infants and adults, receiving a social response predicts that the individual will vocalize again sooner than they would have in the absence of a response. Overall, our results are consistent with a view of infant-caregiver vocal interactions as an 'interpersonal foraging' process with inherent multi-scale dynamics wherein social responses are among the resources the individuals are foraging for. The analytic approaches introduced here have broad utility to study communication in other modalities, contexts, and species.
A flexible Bayesian non-parametric mixture model reveals multiple dependencies of swap errors in visual working memory
Authors: Puria Radmard, Paul M. Bays, Máté Lengyel
Date: 2025-05-02 00:00:00
Tasks: Descriptive

Human behavioural data in psychophysics has been used to elucidate the underlying mechanisms of many cognitive processes, such as attention, sensorimotor integration, and perceptual decision making. Visual working memory has particularly benefited from this approach: analyses of VWM errors have proven crucial for understanding VWM capacity and coding schemes, in turn constraining neural models of both. One poorly understood class of VWM errors are swap errors, whereby participants recall an uncued item from memory. Swap errors could arise from erroneous memory encoding, noisy storage, or errors at retrieval time - previous research has mostly implicated the latter two. However, these studies made strong a priori assumptions on the detailed mechanisms and/or parametric form of errors contributed by these sources. Here, we pursue a data-driven approach instead, introducing a Bayesian non-parametric mixture model of swap errors (BNS) which provides a flexible descriptive model of swapping behaviour, such that swaps are allowed to depend on both the probed and reported features of every stimulus item. We fit BNS to the trial-by-trial behaviour of human participants and show that it recapitulates the strong dependence of swaps on cue similarity in multiple datasets. Critically, BNS reveals that this dependence coexists with a non-monotonic modulation in the report feature dimension for a random dot motion direction-cued, location-reported dataset. The form of the modulation inferred by BNS opens new questions about the importance of memory encoding in causing swap errors in VWM, a distinct source to the previously suggested binding and cueing errors. Our analyses, combining qualitative comparisons of the highly interpretable BNS parameter structure with rigorous quantitative model comparison and recovery methods, show that previous interpretations of swap errors may have been incomplete.
Models of attractor dynamics in the brain
Authors: Tala Fakhoury, Elia Turner, Sushrut Thorat, Athena Akrami
Date: 2025-05-02 00:00:00

Attractor dynamics are a fundamental computational motif in neural circuits, supporting diverse cognitive functions through stable, self-sustaining patterns of neural activity. In these lecture notes, we review four key examples that demonstrate how autoassociative neural network models can elucidate the computational mechanisms underlying attractor-based information processing in biological neural systems performing cognitive functions. Drawing on empirical evidence, we explore hippocampal spatial representations, visual classification in the inferotemporal cortex, perceptual adaptation and priming, and working-memory biases shaped by sensory history. Across these domains, attractor network models reveal common computational principles and provide analytical insights into how experience shapes neural activity and behavior. Our synthesis underscores the value of attractor models as powerful tools for probing the neural basis of cognition and behavior.
Mapping minds not averages: a scalable subject-specific manifold learning framework for neuroimaging data
Authors: Eloy Geenjaar, Vince Calhoun
Date: 2025-04-30 00:00:00

Mental and cognitive representations are believed to reside on low-dimensional, non-linear manifolds embedded within high-dimensional brain activity. Uncovering these manifolds is key to understanding individual differences in brain function, yet most existing machine learning methods either rely on population-level spatial alignment or assume data that is temporally structured, either because data is aligned among subjects or because event timings are known. We introduce a manifold learning framework that can capture subject-specific spatial variations across both structured and temporally unstructured neuroimaging data. On simulated data and two naturalistic fMRI datasets (Sherlock and Forrest Gump), our framework outperforms group-based baselines by recovering more accurate and individualized representations. We further show that the framework scales efficiently to large datasets and generalizes well to new subjects. To test this, we apply the framework to temporally unstructured resting-state fMRI data from individuals with schizophrenia and healthy controls. We further apply our method to a large resting-state fMRI dataset comprising individuals with schizophrenia and controls. In this setting, we demonstrate that the framework scales efficiently to large populations and generalizes robustly to unseen subjects. The learned subject-specific spatial maps our model finds reveal clinically relevant patterns, including increased activation in the basal ganglia, visual, auditory, and somatosensory regions, and decreased activation in the insula, inferior frontal gyrus, and angular gyrus. These findings suggest that our framework can uncover clinically relevant subject-specific brain activity patterns. Our approach thus provides a scalable and individualized framework for modeling brain activity, with applications in computational neuroscience and clinical research.