pwc-archive/papers-with-abstracts huggingface dataset:
Viral Hitchhikers and Macroevolution: A Novel Hypothesis on Explosive Speciation
Authors: Mario E. Lozano, Marcela G. Pilloff
Date: 2025-06-04 00:00:00

Mobile genetic elements (e.g., endogenous viruses, LINEs, SINEs) can transfer between genomes, even between species, triggering dramatic genetic changes. Endogenous viral elements (EVEs) arise when infectious viruses integrate into the host germline. EVEs integrate at specific sites; their genes or regulatory regions can be exapted and could induce chromosomal rearrangement. We propose that EVEs participate in adaptive radiations and that their parent viruses, through interspecific transfer, could initiate new species formation. By synchronously affecting multiple individuals, viral outbreaks generate shared genomic changes that both facilitate reproductive isolation and provide the simultaneous modifications needed for groups to emerge as founders of new species. We suggest horizontal viral transfer during the K-Pg accelerated mammalian radiation linking viral epidemics to macroevolutionary diversification. This theoretical work proposes endogenous viruses as catalysts for explosive speciation.
Generalized Lotka-Volterra systems with quenched random interactions and saturating functional response
Authors: Marco Zenari, Francesco Ferraro, Sandro Azaele, Amos Maritan, Samir Suweis
Date: 2025-06-04 00:00:00
Tasks: Diversity

The generalized Lotka-Volterra (GLV) equations with quenched random interactions have been extensively used to investigate the stability and dynamics of complex ecosystems. However, the standard linear interaction model suffers from pathological unbounded growth, especially under strong cooperation or heterogeneity. This work addresses that limitation by introducing a Monod-type saturating functional response into the GLV framework. Using Dynamical Mean Field Theory, we derive analytical expressions for the species abundance distribution in the Unique Fixed Point phase and show the suppression of unbounded dynamics. Numerical simulations reveal a rich dynamical structure in the Multiple Attractor phase, including a transition between high-dimensional chaotic and low-volatility regimes, governed by interaction symmetry. These findings offer a more ecologically realistic foundation for disordered ecosystem models and highlight the role of nonlinearity and symmetry in shaping the diversity and resilience of large ecological communities.
A note on metapopulation models
Authors: Diepreye Ayabina, Hasan Sevil, Adam Kleczkowski, M. Gabriela M. Gomes
Date: 2025-06-04 00:00:00
Tasks: Epidemiology

Metapopulation models are commonly used in ecology, evolution, and epidemiology. These models usually entail homogeneity assumptions within patches and study networks of migration between patches to generate insights into conservation of species, differentiation of populations, and persistence of infectious diseases. Here, focusing on infectious disease epidemiology, we take a complementary approach and study the effects of individual variation within patches while neglecting any form of disease transmission between patches. Consistently with previous work on single populations, we show how metapopulation models that neglect in-patch heterogeneity also underestimate basic reproduction numbers ($\mathcal{R}_{0}$) and the effort required to control or eliminate infectious diseases by uniform interventions. We then go beyond this confirmatory result and introduce a scheme to infer distributions of individual susceptibility or exposure to infection based on suitable stratifications of a population into patches. We apply the resulting metapopulation models to a simple case study of the COVID-19 pandemic.
Pattern formation within phenotype-structured chemotactic populations
Authors: Tommaso Lorenzi, Kevin J. Painter
Date: 2025-06-03 00:00:00
Tasks: Sensitivity

Populations can become spatially organised through chemotaxis autoattraction, wherein population members release their own chemoattractant. Standard models of this process usually assume phenotypic homogeneity, but recent studies have shed illumination on the inherent heterogeneity within populations: in terms of chemotactic behaviour, trait heterogeneity can range from the sensitivity to attractant gradients to the rate at which attractants are produced. We propose a framework that accounts for this heterogeneity, extending the standard Keller-Segel model to a non-local formulation in which the population is continuously structured across some phenotype state space. Focussing on autoattraction, we allow both the chemotactic sensitivity and the rate of attractant secretion to vary across the population and suppose members can switch between different phenotype states. We extend classical Turing-type linear stability analyses to determine the impact of phenotypic structuring on pattern formation, showing that the rate of switching influences both the critical condition for self-organisation and subsequent pattern dynamics. Scenarios in which the chemotactic sensitivity and attractant secretion are positively or negatively correlated are used to highlight the significance of these results.
Negligible effects of environmental fluctuations on the maintenance of coral biodiversity: A test of five storage effects
Authors: Evan C. Johnson, Sean R. Connolly
Date: 2025-06-03 00:00:00

The storage effect is a general explanation for ecological coexistence, wherein different species specialize on different states of a fluctuating environment, e.g., hot vs. cold years. Despite the storage effect's prominence in theoretical ecology, we lack evidence on whether it maintains biodiversity in nature. Here, we examine five storage effects in a community of 11 coral species from the Great Barrier Reef, using detailed size-structured demographic data collected over five years. We parameterize integral projection models, simulate coral communities, and quantify coexistence mechanisms through Modern Coexistence Theory. Results show that storage effects promote coexistence but are weak compared to fitness differences. Despite coral communities exhibiting theoretical prerequisites for strong temporal niche partitioning, the storage effect plays only a minor role in maintaining coral biodiversity. This aligns with growing evidence that storage effects are weak across ecosystems. Coral coexistence likely depends more on spatial processes, including microhabitat partitioning and asymmetric dispersal.
Climate benefits of afforestation and reforestation with varying species mixtures and densities in the north-western boreal lands
Authors: Enoch Ofosua, Kevin Bradley Dsouzaa, Daniel Chukwuemeka Amaogud, Jerome Pigeond, Richard Boudreaultc, Juan Moreno-Crubb, Pooneh Maghoule, Yuri Leonenko
Date: 2025-06-03 00:00:00

The boreal forest plays a crucial role as a global carbon sink. This study uses two 250-year simulations of Canada's Taiga Plains, an area targeted by the 2 Billion Trees Program to evaluate afforestation and reforestation strategies that vary by species mix, planting density, and surface albedo. Medium density stands, 600 to 1400 trees per hectare, composed of mixed species with approximately 25 to 40 percent deciduous trees sequestered 15 to 30 percent more net ecosystem carbon than conifer monocultures. These benefits stem from a combination of rapid early growth, long-term carbon retention, and enhanced resilience to disturbance. Replanting understocked stands with such mixtures increased long-term carbon storage by 18 to 30 percent relative to prevailing scenarios. When surface albedo was considered, pure evergreen or deciduous stands showed a reduction in climate benefit by 6 to 20 percent, while mixed stands maintained net cooling and achieved the highest sequestration rates, approximately 4.6 to 4.7 tons of carbon dioxide equivalent per hectare per year. Scenarios involving partial harvesting followed by replanting sustained or improved ecosystem carbon stocks, about 300 to 340 tons of carbon per hectare, and productivity, roughly 1.6 to 2.0 tons of carbon per hectare per year, without increasing ecological risk. Overall, integrating fast-growing deciduous species with long-lived conifers at moderate planting densities enhances the climate mitigation potential of boreal afforestation and reforestation efforts and offers guidance for reforestation policy in similar high latitude ecosystems.
Inheritance of intracellular viral RNA in a multiscale model of hepatitis C infection
Authors: Tyler Cassidy, Giulia Belluccini, Sarafa A. Iyaniwura, Ruy M. Ribeiro, Alan S. Perelson
Date: 2025-06-01 00:00:00

Multiscale mathematical models of hepatitis C infection have been instrumental in our understanding of direct acting antivirals. These models include the mechanisms driving intracellular viral production and explicitly model the intracellular concentration of viral RNA. Incorporating proliferation of infected hepatocytes in these models can be subtle, as infected daughter cells inherit viral RNA from the proliferating mother cell. In this note, we show how to incorporate this inheritance within a multiscale model of HCV infection. As in typical multiscale models of HCV infection, we show that this model is mathematically equivalent to a system of ordinary differential equations and perform bifurcation analysis of the resulting ODE that demonstrates that proliferation of infected hepatocytes can lead to infection persistence even if the basic repoductive number is less than one.
Not Just $N_e$ $N_e$-more: New Applications for SMC from Ecology to Phylogenies
Authors: David Peede, Trevor Cousins, Arun Durvasula, Anastasia Ignatieva, Toby G. L. Kovacs, Alba Nieto, Emily E. Puckett, Elizabeth T. Chevy
Date: 2025-05-31 00:00:00

Genomes contain the mutational footprint of an organism's evolutionary history, shaped by diverse forces including ecological factors, selective pressures, and life history traits. The sequentially Markovian coalescent (SMC) is a versatile and tractable model for the genetic genealogy of a sample of genomes, which captures this shared history. Methods that utilize the SMC, such as PSMC and MSMC, have been widely used in evolution and ecology to infer demographic histories. However, these methods ignore common biological features, such as gene flow events and structural variation. Recently, there have been several advancements that widen the applicability of SMC-based methods: inclusion of an isolation with migration model, integration with the multi-species coalescent, incorporation of ecological variables (such as selfing and dormancy), inference of dispersal rates, and many computational advances in applying these models to data. We give an overview of the SMC model and its various recent extensions, discuss examples of biological discoveries through SMC-based inference, and comment on the assumptions, benefits and drawbacks of various methods.
Why is it easier to predict the epidemic curve than to reconstruct the underlying contact network?
Authors: Dániel Keliger, Illés Horváth
Date: 2025-05-29 00:00:00
Tasks: LEMMA

We study the deterministic Susceptible-Infected-Susceptible (SIS) epidemic model on weighted graphs. In their numerical study [10] van Mieghem et al. have shown that it is possible to learn an estimated network from a finite time sample of the trajectories of the dynamics that in turn can give an accurate prediction beyond the sample time range, even though the estimated network might be qualitatively far from the ground truth. We give a mathematically rigorous derivation for this phenomenon, notably that for large networks, prediction of the epidemic curves is robust, while reconstructing the underlying network is ill-conditioned. Furthermore, we also provide an explicit formula for the underlying network when reconstruction is possible. At the heart of the explanation, we rely on Szemer\'edi's weak regularity lemma.
FAPS: A Fast Platform for Protein Structureomics Analysis
Authors: Lucas Wilken, Nihjum Paul, Troy Timmerman, Sara A. Tolba, Amara Arshad, Di wu, Wenjie Xia, Bakhtiyor Rasulev, Rick Jansen, Dali Sun
Date: 2025-06-11 00:00:00

Protein quantification and analysis are well-accepted approaches for biomarker discovery but are limited to identification without structural information. High-throughput omics data (i.e., genomics, transcriptomics, and proteomics) have become pervasive in cancer biology studies and reach well beyond more specialized areas such as metabolomics, epigenomics, pharmacogenomics, and interact-omics. However, large-scale analysis based on the structure of the biomolecules, namely structure-omics, is still underexplored due to a lack of handy tools. In response, we developed the Fast Analysis of Protein Structure (FAPS) database, a platform designed to advance quantitative proteomics to structure-omics analysis, which significantly shortens large-scale structure-omics from weeks to seconds. FAPS can serve as a new protein secondary structure database, providing a centralized and functional database for both simulated and experimentally determined bioinformatics statistics relating to secondary structure. Stored data is generated both through the structure simulation, currently SWISS-MODEL and AlphaFold, performed by high-performance computers, and the pre-existing UniProt database. FAPS provides user-friendly features that create a straightforward and effective way of accessing accurate data on the proportion of secondary structure in different protein chains, providing a fast numerical and visual reference for protein structure calculations and analysis. FAPS is accessible through http://fapsdb.org.
Image-Based Biospeckle Contrast Analysis for Rapid Antimicrobial Susceptibility Testing
Authors: M. A. Gameiro, R. F. Pinto, N. V. Lopes
Date: 2025-06-11 00:00:00

Purpose: Antimicrobial resistance is a major global health concern, affecting hospital admissions and treatment success. This study aims to introduce an experimental setup for monitoring bacterial activity over time using image-based contrast as a key biomarker. Methods: The proposed method captures changes in bacterial activity by analyzing variations in image contrast. The approach is evaluated for its ability to detect antimicrobial effects over shorter time intervals compared to conventional clinical techniques. Results: The findings reveal a progressive decrease in contrast over time, suggesting its potential as an indicator of antimicrobial activity. The results highlight the method's capability for early detection of bacterial susceptibility. Conclusion: The study demonstrates that image-based contrast analysis can serve as a rapid and reliable tool for antimicrobial susceptibility testing, offering advantages over traditional methods in clinical practice.
Geometry Reduced Order Modeling (GROM) with application to modeling of glymphatic function
Authors: Andreas Solheim, Geir Ringstand, Per Kristian Eide, Kent-Andre Mardal
Date: 2025-06-11 00:00:00
Tasks: Image Registration

Computational modeling of the brain has become a key part of understanding how the brain clears metabolic waste, but patient-specific modeling on a significant scale is still out of reach with current methods. We introduce a novel approach for leveraging model order reduction techniques in computational models of brain geometries to alleviate computational costs involved in numerical simulations. Using image registration methods based on magnetic resonance imaging, we compute inter-brain mappings which allow previously computed solutions on other geometries to be mapped on to a new geometry. We investigate this approach on two example problems typical of modeling of glymphatic function, applied to a dataset of 101 MRI of human patients. We discuss the applicability of the method when applied to a patient with no known neurological disease, as well as a patient diagnosed with idiopathic Normal Pressure Hydrocephalus displaying significantly enlarged ventricles
Detecting malignant dynamics on very few blood sample using signature coefficients
Authors: Rémi Vaucher, Stéphane Chrétien
Date: 2025-06-10 00:00:00

Recent discoveries have suggested that the promising avenue of using circulating tumor DNA (ctDNA) levels in blood samples provides reasonable accuracy for cancer monitoring, with extremely low burden on the patient's side. It is known that the presence of ctDNA can result from various mechanisms leading to DNA release from cells, such as apoptosis, necrosis or active secretion. One key idea in recent cancer monitoring studies is that monitoring the dynamics of ctDNA levels might be sufficient for early multi-cancer detection. This interesting idea has been turned into commercial products, e.g. in the company named GRAIL. In the present work, we propose to explore the use of Signature theory for detecting aggressive cancer tumors based on the analysis of blood samples. Our approach combines tools from continuous time Markov modelling for the dynamics of ctDNA levels in the blood, with Signature theory for building efficient testing procedures. Signature theory is a topic of growing interest in the Machine Learning community (see Chevyrev2016 and Fermanian2021), which is now recognised as a powerful feature extraction tool for irregularly sampled signals. The method proposed in the present paper is shown to correctly address the challenging problem of overcoming the inherent data scarsity due to the extremely small number of blood samples per patient. The relevance of our approach is illustrated with extensive numerical experiments that confirm the efficiency of the proposed pipeline.
The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning
Authors: Toby Boyne, Juan S. Campos, Becky D. Langdon, Jixiang Qing, Yilin Xie, Shiqiang Zhang, Calvin Tsay, Ruth Misener, Daniel W. Davies, Kim E. Jelfs, Sarah Boyall, Thomas M. Dixon, Linden Schrecker, Jose Pablo Folch
Date: 2025-06-09 00:00:00
Tasks: Active Learning, Benchmarking, Feature Engineering, Molecular Property Prediction, Property Prediction, Time Series, Transfer Learning

Machine learning has promised to change the landscape of laboratory chemistry, with impressive results in molecular property prediction and reaction retro-synthesis. However, chemical datasets are often inaccessible to the machine learning community as they tend to require cleaning, thorough understanding of the chemistry, or are simply not available. In this paper, we introduce a novel dataset for yield prediction, providing the first-ever transient flow dataset for machine learning benchmarking, covering over 1200 process conditions. While previous datasets focus on discrete parameters, our experimental set-up allow us to sample a large number of continuous process conditions, generating new challenges for machine learning models. We focus on solvent selection, a task that is particularly difficult to model theoretically and therefore ripe for machine learning applications. We showcase benchmarking for regression algorithms, transfer-learning approaches, feature engineering, and active learning, with important applications towards solvent replacement and sustainable manufacturing.
Automating Exploratory Multiomics Research via Language Models
Authors: Shang Qu, Ning Ding, Linhai Xie, Yifei Li, Zaoqu Liu, Kaiyan Zhang, Yibai Xiong, Yuxin Zuo, Zhangren Chen, Ermo Hua, Xingtai Lv, Youbang Sun, Yang Li, Dong Li, Fuchu He, BoWen Zhou
Date: 2025-06-09 00:00:00
Tasks: Navigate

This paper introduces PROTEUS, a fully automated system that produces data-driven hypotheses from raw data files. We apply PROTEUS to clinical proteogenomics, a field where effective downstream data analysis and hypothesis proposal is crucial for producing novel discoveries. PROTEUS uses separate modules to simulate different stages of the scientific process, from open-ended data exploration to specific statistical analysis and hypothesis proposal. It formulates research directions, tools, and results in terms of relationships between biological entities, using unified graph structures to manage complex research processes. We applied PROTEUS to 10 clinical multiomics datasets from published research, arriving at 360 total hypotheses. Results were evaluated through external data validation and automatic open-ended scoring. Through exploratory and iterative research, the system can navigate high-throughput and heterogeneous multiomics data to arrive at hypotheses that balance reliability and novelty. In addition to accelerating multiomic analysis, PROTEUS represents a path towards tailoring general autonomous systems to specialized scientific domains to achieve open-ended hypothesis generation from data.
Antibody Consumption-Driven Dynamic Competition: A Systems Hypothesis for the Transition from Acute Immune Response to Post-Infection Sequelae
Authors: Shi Qiru
Date: 2025-06-06 00:00:00

The mechanisms underlying the formation of post-infection sequelae are complex and remain controversial. This hypothesis integrates Bystryn's antibody feedback phenomenon and Imbiakha's immune cost theory, proposing for the first time a "Consumption-Driven Dynamic Competition of Antibody Clones" mechanism. This mechanism posits that the immune system may regulate the proliferation and differentiation of corresponding B cell clones by sensing and responding to the consumption rate of specific antibodies. This competition, driven by differences in consumption rates, might not only influence pathogen clearance efficiency and associated acute pathology during the antigen growth phase but also critically mediate the onset, development, and even resolution of post-infection sequelae during the antigen decay and homeostasis re-establishment phases. The proposed three-phase "consumption-driven dynamic competition" model provides a unified and dynamic explanatory framework for understanding the significant individual variability and dynamic evolution of post-infection immune outcomes (including the emergence and self-limitation of acute symptoms, the formation and persistence of chronic sequelae, and symptom fluctuations or resolution). It emphasizes not just specific molecules but the macroscopic dynamics of competition and selection within the immune system, offering a theoretical basis for exploring new intervention strategies for sequelae (e.g., by regulating the balance of antibody competition).
Look mom, no experimental data! Learning to score protein-ligand interactions from simulations
Authors: Michael Brocidiacono, James Wellnitz, Konstantin I. Popov, Alexander Tropsha
Date: 2025-05-31 00:00:00

Despite recent advances in protein-ligand structure prediction, deep learning methods remain limited in their ability to accurately predict binding affinities, particularly for novel protein targets dissimilar from the training set. In contrast, physics-based binding free energy calculations offer high accuracy across chemical space but are computationally prohibitive for large-scale screening. We propose a hybrid approach that approximates the accuracy of physics-based methods by training target-specific neural networks on molecular dynamics simulations of the protein in complex with random small molecules. Our method uses force matching to learn an implicit free energy landscape of ligand binding for each target. Evaluated on six proteins, our approach achieves competitive virtual screening performance using 100-500 $\mu$s of MD simulations per target. Notably, this approach achieves state-of-the-art early enrichment when using the true pose for active compounds. These results highlight the potential of physics-informed learning for virtual screening on novel targets. We publicly release the code for this paper at https://github.com/molecularmodelinglab/lfm under the MIT license.