pwc-archive/papers-with-abstracts huggingface dataset:
Neural Heterogeneity Enables Adaptive Encoding of Time Sequences
Authors: Raphaël Lafond-Mercier, Leonard Maler, Avner Wallach, André Longtin
Date: 2025-05-20 00:00:00

Biological systems represent time from microseconds to years. An important gap in our knowledge concerns the mechanisms for encoding time intervals of hundreds of milliseconds to minutes that matter for tasks like navigation, communication, storage, recall, and prediction of stimulus patterns. A recently identified mechanism in fish thalamic neurons addresses this gap. Representation of intervals between events uses the ubiquitous property of neural fatigue, where firing adaptation sets in quickly during an event. The recovery from fatigue by the next stimulus is a monotonous function of time elapsed. Here we develop a full theory for the representation of intervals, allowing for recovery time scales and sensitivity to past stimuli to vary across cells. Our Bayesian framework combines parametrically heterogeneous stochastic dynamical modeling with interval priors to predict available timing information independent of actual decoding mechanism. A compromise is found between optimally encoding the latest time interval and previous ones, crucial for spatial navigation. Cellular heterogeneity is actually necessary to represent interval sequences, a novel computational role for experimentally observed heterogeneity. This biophysical adaptation-based timing memory shapes spatiotemporal information for efficient storage and recall in target recurrent networks.
Exploring Holography in Neuro-Vascular Dynamics
Authors: Christian Kerskens
Date: 2025-05-19 00:00:00

The holonomic brain theory, originally formulated to account for the need of non-local memory encoding in cognitive systems, could gain new theoretical traction when integrated with holographic principles from physics, most notably the AdS/CFT correspondence. Recent findings in neuroscience suggest that conformal field theories (CFTs), emerging at critical points across spatiotemporal scales in neural dynamics, are essential for brain function. Concurrently, black-brane geometries, long studied in gravitational physics, can find unexpected analogues in the interplay of active matter dynamics and the brain s neuroanatomical organization. Motivated by these parallels, we posit a generalized holographic framework and interrogate its validity through the fluid/gravity duality; a correspondence linking hydrodynamic equations to gravitational spacetime metrics. In this work, we explore the holographic principles at the Navier-Stokes regime, demonstrating that holography can model key neurophysiological mechanisms: cerebral autoregulation (the brain s hemodynamic self-stabilization) and neurovascular coupling (the dynamic neuron-bloodflow interplay). This work bridges holography, active matter physics, and neuroscience, proposing a unified framework to decode the brain s multiscale organization, its resilience to perturbations, and its computational capabilities. By grounding neurovascular physiology in gravitational duals, we open pathways to reinterpret brain function through the lens of emergent spacetime geometry.
Automatic detection of abnormal clinical EEG: comparison of a finetuned foundation model with two deep learning models
Authors: Aurore Bussalb, François Le Gac, Guillaume Jubien, Mohamed Rahmouni, Ruggero G. Bettinardi, Pedro Marinho R. de Oliveira, Phillipe Derambure, Nicolas Gaspard, Jacques Jonas, Louis Maillard, Laurent Vercueil, Hervé Vespignani, Philippe Laval, Laurent Koessler, Ulysse Gimenez
Date: 2025-05-13 00:00:00
Tasks: EEG

Electroencephalography (EEG) is commonly used by physicians for the diagnosis of numerous neurological disorders. Due to the large volume of EEGs requiring interpretation and the specific expertise involved, artificial intelligence-based tools are being developed to assist in their visual analysis. In this paper, we compare two deep learning models (CNN-LSTM and Transformer-based) with BioSerenity-E1, a recently proposed foundation model, in the task of classifying entire EEG recordings as normal or abnormal. The three models were trained or finetuned on 2,500 EEG recordings and their performances were evaluated on two private and one public datasets: a large multicenter dataset annotated by a single specialist (dataset A composed of n = 4,480 recordings), a small multicenter dataset annotated by three specialists (dataset B, n = 198), and the Temple University Abnormal (TUAB) EEG corpus evaluation dataset (n = 276). On dataset A, the three models achieved at least 86% balanced accuracy, with BioSerenity-E1 finetuned achieving the highest balanced accuracy (89.19% [88.36-90.41]). BioSerenity-E1 finetuned also achieved the best performance on dataset B, with 94.63% [92.32-98.12] balanced accuracy. The models were then validated on TUAB evaluation dataset, whose corresponding training set was not used during training, where they achieved at least 76% accuracy. Specifically, BioSerenity-E1 finetuned outperformed the other two models, reaching an accuracy of 82.25% [78.27-87.48]. Our results highlight the usefulness of leveraging pre-trained models for automatic EEG classification: enabling robust and efficient interpretation of EEG data with fewer resources and broader applicability.
An Affective-Taxis Hypothesis for Alignment and Interpretability
Authors: Eli Sennesh, Maxwell Ramstead
Date: 2025-05-03 00:00:00

AI alignment is a field of research that aims to develop methods to ensure that agents always behave in a manner aligned with (i.e. consistently with) the goals and values of their human operators, no matter their level of capability. This paper proposes an affectivist approach to the alignment problem, re-framing the concepts of goals and values in terms of affective taxis, and explaining the emergence of affective valence by appealing to recent work in evolutionary-developmental and computational neuroscience. We review the state of the art and, building on this work, we propose a computational model of affect based on taxis navigation. We discuss evidence in a tractable model organism that our model reflects aspects of biological taxis navigation. We conclude with a discussion of the role of affective taxis in AI alignment.
Silence is Not Consensus: Disrupting Agreement Bias in Multi-Agent LLMs via Catfish Agent for Clinical Decision Making
Authors: Yihan Wang, Qiao Yan, Zhenghao Xing, Lihao Liu, Junjun He, Chi-Wing Fu, Xiaowei Hu, Pheng-Ann Heng
Date: 2025-05-27 00:00:00
Tasks: Decision Making, Diagnostic, Question Answering, Visual Question Answering (VQA)

Large language models (LLMs) have demonstrated strong potential in clinical question answering, with recent multi-agent frameworks further improving diagnostic accuracy via collaborative reasoning. However, we identify a recurring issue of Silent Agreement, where agents prematurely converge on diagnoses without sufficient critical analysis, particularly in complex or ambiguous cases. We present a new concept called Catfish Agent, a role-specialized LLM designed to inject structured dissent and counter silent agreement. Inspired by the ``catfish effect'' in organizational psychology, the Catfish Agent is designed to challenge emerging consensus to stimulate deeper reasoning. We formulate two mechanisms to encourage effective and context-aware interventions: (i) a complexity-aware intervention that modulates agent engagement based on case difficulty, and (ii) a tone-calibrated intervention articulated to balance critique and collaboration. Evaluations on nine medical Q&A and three medical VQA benchmarks show that our approach consistently outperforms both single- and multi-agent LLMs frameworks, including leading commercial models such as GPT-4o and DeepSeek-R1.
An Eye for a Treat: Human Gazing Modulates Begging by Free-ranging Dogs
Authors: Sourabh Biswas, Srijaya Nandi, Tuhin Subhra Pal, Aesha Lahiri, Anamitra Roy, Hindolii Gope, Kalyan Ghosh, Anindita Bhadra
Date: 2025-05-23 00:00:00

Interspecific communication plays a critical role in mediating human-animal interactions, particularly in contexts involving access to anthropogenic resources. This study investigates the influence of human gazing on the begging strategies of free-ranging dogs in urban and peri-urban environments. Begging behaviour, commonly observed in dogs seeking food from humans, offers insights into their behavioural flexibility and cognitive attunement to human social cues. We observed 650 adult dogs in both solitary and group settings to assess how social context shapes the expression of begging behaviour in free-ranging dogs. Our findings indicate that solitary dogs beg more frequently than those in groups, and that female dogs exhibit higher rates of begging, predominantly through passive strategies. Moreover, dogs modulate their active begging in response to subtle variations in human gazing and food availability. These results suggest that passive begging is influenced by stable situational factors such as sex and group composition, while active begging is more responsive to immediate environmental cues, including human attentional state. Collectively, our findings highlight the social competence and behavioural plasticity of free-ranging dogs in navigating interspecies interactions, and contribute to a broader understanding of how communicative strategies evolve in response to social and ecological pressures.
CFD-Based Quantification of Hemodynamic Variables in Cerebral Aneurysms: How Hemodynamics Shape Aneurysm Fate
Authors: Reza Bozorgpour, Pilwan Kim
Date: 2025-05-05 00:00:00
Tasks: Variable Selection

Cerebral aneurysms are pathological dilations of intracranial arteries that can rupture with devastating consequences, including subarachnoid hemorrhage, stroke, and death. Accumulating evidence indicates that local hemodynamic forces play a critical role in aneurysm initiation, growth, and rupture. Computational fluid dynamics (CFD) and imaging-based techniques have enabled the extraction of various hemodynamic variables to characterize these flow conditions. However, the literature is highly fragmented, with different studies adopting distinct sets of metrics such as wall shear stress (WSS), oscillatory shear index (OSI), wall shear stress gradient (WSSG), relative residence time (RRT), or endothelial cell activation potential (ECAP) making it difficult to compare results or establish standardized methodologies. This paper provides the first comprehensive catalog of hemodynamic variables used in cerebral aneurysm studies to date. By systematically identifying and organizing these parameters based on their physical basis and frequency of use, this work offers a consolidated reference to guide future research. The goal is to support consistent variable selection, enhance reproducibility, and facilitate the design of more robust studies linking vascular biomechanics to aneurysm pathophysiology. This review aims to serve as a foundational resource for researchers and clinicians seeking to incorporate hemodynamic modeling into cerebral aneurysm analysis and risk assessment.
Infection dynamics for fluctuating infection or removal rates regarding the number of infected and susceptible individuals
Authors: Seong Jun Park, M. Y. Choi
Date: 2025-05-28 00:00:00

In general, the rates of infection and removal (whether through recovery or death) are nonlinear functions of the number of infected and susceptible individuals. One of the simplest models for the spread of infectious diseases is the SIR model, which categorizes individuals as susceptible, infectious, recovered or deceased. In this model, the infection rate, governing the transition from susceptible to infected individuals, is given by a linear function of both susceptible and infected populations. Similarly, the removal rate, representing the transition from infected to removed individuals, is a linear function of the number of infected individuals. However, existing research often overlooks the impact of nonlinear infection and removal rates in infection dynamics. This work presents an analytic expression for the number of infected individuals considering nonlinear infection and removal rates. In particular, we examine how the number of infected individuals varies as cases emerge and obtain the expression accounting for the number of infected individuals at each moment. This work paves the way for new quantitative approaches to understanding infection dynamics.
Response to comment on Mutualism weaken the latitudinal diversity gradient among oceanic islands
Authors: Camille Delavaux, Thomas Crowther, James Bever, Evan Gora
Date: 2025-05-27 00:00:00
Tasks: Articles, Diversity

In our original paper (Delavaux et al. 2024; https://www.nature.com/articles/s41586-024-07110-y), we find that the latitudinal diversity gradient (LDG) in plant species richness is reduced on oceanic islands worldwide. Moreover, we find that plants that associate with mutualists, including pollinators, AM fungi, and N-fixing bacteria, disproportionately contribute to this reduction. To make this assertion, we fit models of continental diversity to absolute latitude and compared mainland diversity estimated by these models to the observed diversity of individual islands. The critique of our work by Pitcher and Hartig (arXiv:2411.15105) questions our results through a reanalysis of the data. In their reanalysis, they add longitude as a predictor of richness on islands. However, we have several concerns about this reanalysis. In particular, we question (1) the biological/physical basis of predicting species richness with longitude as well as modelling non-linear relationships potentially leading to spurious relationships and (2) the justification for how this comment calculated island deficits from the data. Additionally, (3) described analyses and provided code (https://github.com/MaximilianPi/IslandLDG) are in many cases insufficient to understand or meaningfully engage with this work, precluding productive discussion about cited analytical concerns. Given these major issues with the reanalysis, we reject the conclusions from this critique that our prior results are due to statistical artifacts. We therefore do not have reason to question our original conclusion that the latitudinal diversity gradient is weakened by mutualisms among oceanic islands worldwide.
Pan-tropical plant functional trait variation from space
Authors: David Schimel, Andres Baresch, Adam Chlus, Phil Townsend, Fabian Schneider, Gaia Vaglio Laurin, Ben Poulter
Date: 2025-05-25 00:00:00
Tasks: Diversity

Plant functional trait variation in tropical forests is central to predicting ecosystem responses to change. Informaiton on traits is limited relative to the diversity of climate, landforms, disturbance regimes and species present. These traits are central to modeled predictions of ecosystem change. We used a new spaceborne imagining spectrometer from the Italian Space Agency to sample roughly 1% of the tropical moist forest biome for traits along the Leaf Economics Spectrum, as well as data to contrast them with adjacent biomes. we used these data to examine LES traits between tropical moist forests on three continents. Knowledge of trait variation and its environmental controls can inform models of ecosystem response in a changing environment, allowing biological detail in models of biophysical and biogeochemical processes.
A metapopulation model for the spread of cholera
Authors: Abdramane Annour Saad, Julien Arino, Patrick M Tchepmo Djomegni, Mahamat S Daoussa Haggar
Date: 2025-05-22 00:00:00
Tasks: Unity

We consider a metapopulation model of cholera describing explicitly the movement of individuals and contaminated water between locations as well as a simple vaccination mechanism. The global stability of the disease-free equilibrium point when the location-specific reproduction numbers are all less than unity is established. We then conduct some numerical investigation of the model.
Spatio-temporal agent-based modelling of malaria
Authors: Camelia R. Walker, Md Nurul Anwar, Leandra Brauninger, Jack Richards, Ricardo Ataide, Ngo Duc Thang, Nguyen Xuan Thang, Sara Canavati, Jennifer A. Flegg
Date: 2025-05-22 00:00:00

Plasmodium falciparum is responsible for the majority of malaria morbidity and mortality each year. Malaria transmission rates vary by location and time of year due to climate and environmental conditions. We show the impact of these factors by developing a stochastic spatiotemporal agent-based malaria model that captures the impact of spatially distributed interventions on malaria transmission. Our model uses spatiotemporal estimates of mosquito climatic suitability and household location data to model the interaction between human and mosquito agents. We apply our model to investigate how strategies for distributing interventions to households in Vietnam impact the disease burden. Our study shows that providing some level of protection to a wide range of households reduces malaria prevalence more compared to providing a strong level of protection to a limited number of households.
Towards improved pest management of the soybean aphid
Authors: Urvashi Verma, Margaret Lewis, Jordan Lehman, Rana D. Parshad
Date: 2025-05-21 00:00:00
Tasks: Management

The soybean aphid (\emph{Aphis glycines}) is an invasive insect pest that continues to cause large-scale damage to soybean crops in the North Central United States. The current manuscript proposes several mathematical models for the top-down bio-control of the aphid, as well as control via pesticides and neonicotinoids. The models are motivated empirically, and constructed based on laboratory experiments conducted to test control of aphids by Lacewing larvae, as well as by a parasitic wasp (\emph{Aphidius colemani}). The effectiveness of these models is compared by taking into account factors such as economic injury levels for soybeans, life history traits such as cannibalism amongst the predator, and intraguild predation between competing bio-control agents such as predators and parasitoids. The models predict multiple population peaks and transient chaotic dynamics when a predator and/or insecticides are used. It is observed that parasitoids, in conjunction with predators, are more efficient at stabilizing the population dynamics than insecticide use. They also suggest a combination of predators, parasitoids, and insecticides would be more efficient at suppressing aphid populations than using only predators or parasitoids. The models also qualitatively capture the features seen in long-time field data from 2000-2013. We discuss applications of our results to pest management strategies for soybean aphids in the context of a changing climate, as well as regime shifts.
Information-Theoretical Measures for Developmental Cell-Fate Proportioning Processes
Authors: Michael A. Ramirez-Sierra, Thomas R. Sokolowski
Date: 2025-05-29 00:00:00

Self-organization is a fundamental process of complex biological systems, particularly during the early stages of development. In the mammalian embryo, blastocyst formation exemplifies a self-organized system, involving the correct spatio-temporal segregation of three distinct cell fates: trophectoderm (TE), epiblast (EPI), and primitive endoderm (PRE). Despite the significance of this class of processes, quantifying the information content of self-organizing patterning systems remains challenging due to the complexity and the qualitative diversity of developmental mechanisms. In this study, we applied a recently proposed information-theoretical framework which quantifies the self-organization potential of cell-fate patterning systems, employing a utility function that integrates (local) positional information and (global) correlational information extracted from developmental pattern ensembles. Specifically, we examined a stochastic and spatially resolved simulation model of EPI-PRE lineage proportioning, evaluating its information content across various simulation scenarios with different numbers of system cells. To overcome the computational challenges hindering the application of this novel framework, we developed a mathematical strategy that indirectly maps the low-dimensional cell-fate counting probability space to the high-dimensional cell-fate patterning probability space, enabling the estimation of self-organization potential for general cell-fate proportioning processes. Overall, this novel information-theoretical framework provides a promising, universal approach for quantifying self-organization in developmental biology. By formalizing measures of self-organization, the employed quantification framework offers a valuable tool for uncovering insights into the underlying principles of cell-fate specification and the emergence of complexity in early developmental systems.
Initial Luminally Deposited FGF4 Critically Influences Blastocyst Patterning
Authors: Michael A. Ramirez-Sierra, Thomas R. Sokolowski
Date: 2025-05-29 00:00:00

Luminogenesis, the formation of a fluid-filled cavity (lumen), is an essential process in early mammalian embryonic development, coinciding with the second cell-fate decision that differentiates the inner-cell-mass (ICM) into epiblast (EPI) and primitive endoderm (PRE) tissues. Based on experiments, the blastocyst lumen is hypothesized to influence EPI-PRE tissue specification, but its particular functional role remains theoretically underexplored. In this study, we extended our stochastic ICM differentiation model to incorporate both the blastocyst lumen (blastocoel) and the trophectoderm (TE) as adjacent compartments where the primary signaling protein (FGF4) for EPI-PRE differentiation can diffuse, degrade, or accumulate. This extended ICM model allows for a spatially resolved analysis of EPI-PRE lineage proportioning under the influence of luminally deposited FGF4 molecules. Our results reveal that the blastocoel acts as a localized signaling source, while the TE functions as an embryo-wide signaling sink, guiding cell-fate decisions within the ICM. A critical determinant of the ideal target system behavior is the initial amount of luminally deposited FGF4, which is required to recapitulate the correct spatio-temporal patterning of EPI and PRE (blastocyst) cell lineages. Notably, this requirement is independent of ICM population size and shape, highlighting the robustness of the FGF4 signaling process. Our study also underscores the potential of integrating single-cell gene expression and cell-cell communication dynamics simulations with tissue-level morphogenesis representations. By combining spatial-stochastic modeling with agent-based frameworks, we could enhance the exploration of the intricate interplay between gene regulation, signaling, and morphogenetic processes that govern early embryonic development.
Topological Machine Learning for Protein-Nucleic Acid Binding Affinity Changes Upon Mutation
Authors: Xiang Liu, JunJie Wee, Guo-Wei Wei
Date: 2025-05-28 00:00:00
Tasks: Topological Data Analysis

Understanding how protein mutations affect protein-nucleic acid binding is critical for unraveling disease mechanisms and advancing therapies. Current experimental approaches are laborious, and computational methods remain limited in accuracy. To address this challenge, we propose a novel topological machine learning model (TopoML) combining persistent Laplacian (from topological data analysis) with multi-perspective features: physicochemical properties, topological structures, and protein Transformer-derived sequence embeddings. This integrative framework captures robust representations of protein-nucleic acid binding interactions. To validate the proposed method, we employ two datasets, a protein-DNA dataset with 596 single-point amino acid mutations, and a protein-RNA dataset with 710 single-point amino acid mutations. We show that the proposed TopoML model outperforms state-of-the-art methods in predicting mutation-induced binding affinity changes for protein-DNA and protein-RNA complexes.