pwc-archive/papers-with-abstracts huggingface dataset:
Reproductive system and interaction with fauna in a Mediterranean Pyrophite shrub
Authors: Josué-Antonio Nescolarde-Selva, José-Luis Usó-Doménech, Kristian Alonso-Stenberg
Date: 2025-03-06 00:00:00

The ULEX model, in its present state, involves the study of the biomass and the population of the shrub Ulex parviflorus Pourret, but while being a dynamic model, it is static in the sense that it does not imply the appearance of new specimens of this plant. As a complement to the ULEX model in its two dynamic and spatial aspects, and with the idea of extending the model, the authors have introduced from a biological and statistical point of view four characteristics of this species, flowering, pollination, fructification, taking special interest in the role played by the pollinators (bees) and dispersion of seeds.
Sunlight-heated refugia protect frogs from chytridiomycosis: a mathematical modelling study
Authors: Claire M. Miller, Michael J. Lydeamore, Lee Berger, Lee F. Skerratt, Jennifer A. Flegg, Anthony W. Waddle, Patricia Therese Campbell
Date: 2025-03-10 00:00:00
Tasks: Management

The fungal disease Chytridiomycosis poses a threat to frog populations worldwide. It has driven over 90 amphibian species to extinction and severely affected hundreds more. Difficulties in disease management have shown a need for novel conservation approaches. We present a novel mathematical model for chytridiomycosis transmission in frogs that includes the natural history of infection, to test the hypothesis that sunlight-heated refugia reduce transmission. The model is fit using approximate Bayesian computation to experimental data where a cohort of frogs, a fixed subset of which had cleared a prior infection, were provided access to either sunlight-heated or shaded refugia. Using our model, we can estimate the extent to which prior chytridiomycosis infection protects against subsequent infection, and quantify the effect of sunlight-heating of refugia. Results estimate a 40% reduction in chytridiomycosis transmission when frogs have access to sunlight-heated refugia, compared to shaded refugia. This strongly supports the hypothesis that the sunlight-heated refugia reduce disease transmission. Frogs that were infected and recovered were estimated to have a reduction in susceptibility of approximately 97% compared to frogs with no prior infection. This research provides quantitative evidence supporting sunlight-heated refugia as an effective disease management tool for chytridiomycosis in frog populations. By estimating both the impact of refugia and the protective effects of prior infection, the model provides an evidence base for implementing sunlight-heated refugia as part of amphibian conservation strategies. This work represents an important first step in using mathematical modelling to inform policy on the design and implementation of habitat-based interventions to support amphibian population recovery and long-term sustainability.
Stochastic Time to Extinction of an SIQS Epidemic Model with Quiescence
Authors: Usman Sanusi, Sona John, Johannes Mueller, Aurélien Tellier
Date: 2025-03-08 00:00:00

Parasite quiescence is the ability for the pathogen to be inactive, with respect to metabolism and infectiousness, for some amount of time and then become active (infectious) again. The population is thus composed of an inactive proportion, and an active part in which evolution and reproduction takes place. In this paper, we investigate the effect of parasite quiescence on the time to extinction of infectious disease epidemics. We build a Susceptible-Infected-Quiescent-Susceptible (SIQS) epidemiological model. Hereby, host individuals infected by a quiescent parasite strain cannot recover, but are not infectious. We particularly focus on stochastic effects. We show that the quiescent state does not affect the reproduction number, but for a wide range of parameters the model behaves as an SIS model at a slower time scale, given by the fraction of time infected individuals are within the I state (and not in the Q state). This finding, proven using a time scale argument and singular perturbation theory for Markov processes, is illustrated and validated by numerical experiments based on the quasi-steady state distribution. We find here that the result even holds without a distinct time scale separation. Our results highlight the influence of quiescence as a bet-hedging strategy against disease stochastic extinction, and are relevant for predicting infectious disease dynamics in small populations.
Assessing the performance of compartmental and renewal models for learning $R_{t}$ using spatially heterogeneous epidemic simulations on real geographies
Authors: Matthew Ghosh, Yunli Qi, Abbie Evans, Tom Reed, Lara Herriott, Ioana Bouros, Ben Lambert, David J. Gavaghan, Katherine M. Shepherd, Richard Creswell, Kit Gallagher
Date: 2025-03-06 00:00:00

The time-varying reproduction number ($R_t$) gives an indication of the trajectory of an infectious disease outbreak. Commonly used frameworks for inferring $R_t$ from epidemiological time series include those based on compartmental models (such as the SEIR model) and renewal equation models. These inference methods are usually validated using synthetic data generated from a simple model, often from the same class of model as the inference framework. However, in a real outbreak the transmission processes, and thus the infection data collected, are much more complex. The performance of common $R_t$ inference methods on data with similar complexity to real world scenarios has been subject to less comprehensive validation. We therefore propose evaluating these inference methods on outbreak data generated from a sophisticated, geographically accurate agent-based model. We illustrate this proposed method by generating synthetic data for two outbreaks in Northern Ireland: one with minimal spatial heterogeneity, and one with additional heterogeneity. We find that the simple SEIR model struggles with the greater heterogeneity, while the renewal equation model demonstrates greater robustness to spatial heterogeneity, though is sensitive to the accuracy of the generation time distribution used in inference. Our approach represents a principled way to benchmark epidemiological inference tools and is built upon an open-source software platform for reproducible epidemic simulation and inference.
The Nature of Organization in Living Systems
Authors: Pedro Márquez-Zacarías, Andrés Ortiz-Muñoz, Emma P. Bingham
Date: 2025-03-05 00:00:00
Tasks: Philosophy

Living systems are thermodynamically open but closed in their organization. In other words, even though their material components turn over constantly, a material-independent property persists, which we call organization. Moreover, organization comes from within organisms themselves, which requires us to explain how this self-organization is established and maintained. In this paper we propose a mathematical and conceptual framework to understand the kinds of organized systems that living systems are, aiming to explain how self-organization emerges from more basic elemental processes. Additionally, we map our own notions to existing traditions in theoretical biology and philosophy, aiming to bring the main formal ideas into conceptual congruence.
Petri Net Modeling of Root Hair Response to Phosphate Starvation in Arabidopsis Thaliana
Authors: Amber H. B. Fijn, Casper H. Stiekema, Stijn Boere, Marijan Višić, Lu Cao
Date: 2025-03-10 00:00:00

Limited availability of inorganic phosphate (Pi) in soil is an important constraint to plant growth. In order to understand better the underlying mechanism of plant response to Pi, the response to phosphate starvation in Arabidopsis thaliana was investigated through use of Petri Nets, a formal language suitable for bio-modeling. A. thaliana displays a range of responses to deal with Pi starvation, but special attention was paid to root hair elongation in this study. A central player in the root hair pathway is the transcription factor ROOT HAIR DEFECTIVE 6-LIKE 4 (RSL4), which has been found to be upregulated during the Pi stress. A Petri Net was created which could simulate the gene regulatory networks responsible for the increase in root hair length, as well as the resulting increase in root hair length. Notably, discrepancies between the model and the literature suggested an important role for RSL2 in regulating RSL4. In the future, the net designed in the current study could be used as a platform to develop hypotheses about the interaction between RSL2 and RSL4.
Modeling HIF-ILK Interaction Using Continuous Petri Nets
Authors: Viktor Gilin, Sanne Laauwen, Yuying Xia, Noria Yousufi, Lu Cao
Date: 2025-03-10 00:00:00

Oxygen concentration in tumor micro-environment is a well-established signal that can induce aggressive cancer behaviour. In particular, low oxygen levels (hypoxia) activate the Hypoxia-Inducible Factor(HIF) pathway which has an array of target systems. One of these systems is Integrin-Linked Kinase (ILK) pathway, which influences key signaling pathways for cell survival, proliferation, and migration. Hence, this paper aimed to explore the interconnection between these two pathways. Using the Petri net modeling tool Snoopy, an established HIF network model was transformed to be a continuous Petri net. Subsequently, the network was expanded to incorporate a feedback element from the ILK pathway to HIF, based on gene expression data. The resulting model conserved the oxygen switch response of the original HIF model and positively amplified HIF's output. Therefore, this model provides a starting point for establishing a system reflecting crucial effect on hypoxia-induced cancer behavior, and could potentially serve as a basis for future drug development.
Divide and Conquer Self-Supervised Learning for High-Content Imaging
Authors: Lucas Farndale, Paul Henderson, Edward W Roberts, Ke Yuan
Date: 2025-03-10 00:00:00
Tasks: Representation Learning, Self-Supervised Learning

Self-supervised representation learning methods often fail to learn subtle or complex features, which can be dominated by simpler patterns which are much easier to learn. This limitation is particularly problematic in applications to science and engineering, as complex features can be critical for discovery and analysis. To address this, we introduce Split Component Embedding Registration (SpliCER), a novel architecture which splits the image into sections and distils information from each section to guide the model to learn more subtle and complex features without compromising on simpler features. SpliCER is compatible with any self-supervised loss function and can be integrated into existing methods without modification. The primary contributions of this work are as follows: i) we demonstrate that existing self-supervised methods can learn shortcut solutions when simple and complex features are both present; ii) we introduce a novel self-supervised training method, SpliCER, to overcome the limitations of existing methods, and achieve significant downstream performance improvements; iii) we demonstrate the effectiveness of SpliCER in cutting-edge medical and geospatial imaging settings. SpliCER offers a powerful new tool for representation learning, enabling models to uncover complex features which could be overlooked by other methods.
Subdomains of Post-COVID-Syndrome (PCS) -- A Population-Based Study
Authors: Sabrina Ballhausen, Anne-Kathrin Ruß, Wolfgang Lieb, Anna Horn, Lilian Krist, Julia Fricke, Carmen Scheibenbogen, Klaus F. Rabe, Walter Maetzler, Corina Maetzler, Martin Laudien, Derk Frank, Jan Heyckendorf, Olga Miljukov, Karl Georg Haeusler, Nour Eddine El Mokthari, Martin Witzenrath, Jörg Janne Vehreschild, Katharina S. Appel, Irina Chaplinskaya-Sobol, Thalea Tamminga, Carolin Nürnberger, Lena Schmidbauer, Caroline Morbach, Stefan Störk, Jens-Peter Reese, Thomas Keil, Peter Heuschmann, Stefan Schreiber, Michael Krawczak, Thomas Bahmer
Date: 2025-03-10 00:00:00

Post-COVID Syndrome (PCS), encompassing the multifaceted sequelae of COVID-19, can be severity-graded using a score comprising 12 different long-term symptom complexes. Acute COVID-19 severity and individual resilience were previously identified as key predictors of this score. This study validated these predictors and examined their relationship to PCS symptom complexes, using an expanded dataset (n=3,372) from the COVIDOM cohort study. Classification and Regression Tree (CART) analysis resolved the detailed relationship between the predictors and the constituting symptom complexes of the PCS score. Among newly recruited COVIDOM participants (n=1,930), the PCS score was again found to be associated with both its putative predictors. Of the score-constituting symptom complexes, neurological symptoms, sleep disturbance, and fatigue were predicted by individual resilience, whereas acute disease severity predicted exercise intolerance, chemosensory deficits, joint or muscle pain, signs of infection, and fatigue. These associations inspired the definition of two novel PCS scores that included the above-mentioned subsets of symptom complexes only. Both novel scores were inversely correlated with quality of life, measured by the EQ-5D-5L index. The newly defined scores may enhance the assessment of PCS severity, both in a research context and to delineate distinct PCS subdomains with different therapeutic and interventional needs in clinical practise.
VenusMutHub: A systematic evaluation of protein mutation effect predictors on small-scale experimental data
Authors: Liang Zhang, Hua Pang, Chenghao Zhang, Song Li, Yang Tan, Fan Jiang, Mingchen Li, Yuanxi Yu, Ziyi Zhou, Banghao Wu, Bingxin Zhou, Hao liu, Pan Tan, Liang Hong
Date: 2025-03-06 00:00:00

In protein engineering, while computational models are increasingly used to predict mutation effects, their evaluations primarily rely on high-throughput deep mutational scanning (DMS) experiments that use surrogate readouts, which may not adequately capture the complex biochemical properties of interest. Many proteins and their functions cannot be assessed through high-throughput methods due to technical limitations or the nature of the desired properties, and this is particularly true for the real industrial application scenario. Therefore, the desired testing datasets, will be small-size (~10-100) experimental data for each protein, and involve as many proteins as possible and as many properties as possible, which is, however, lacking. Here, we present VenusMutHub, a comprehensive benchmark study using 905 small-scale experimental datasets curated from published literature and public databases, spanning 527 proteins across diverse functional properties including stability, activity, binding affinity, and selectivity. These datasets feature direct biochemical measurements rather than surrogate readouts, providing a more rigorous assessment of model performance in predicting mutations that affect specific molecular functions. We evaluate 23 computational models across various methodological paradigms, such as sequence-based, structure-informed and evolutionary approaches. This benchmark provides practical guidance for selecting appropriate prediction methods in protein engineering applications where accurate prediction of specific functional properties is crucial.
Interpretable High-order Knowledge Graph Neural Network for Predicting Synthetic Lethality in Human Cancers
Authors: Xuexin Chen, Ruichu Cai, Zhengting Huang, Zijian Li, Jie Zheng, Min Wu
Date: 2025-03-08 00:00:00
Tasks: Graph Neural Network, Knowledge Graphs

Synthetic lethality (SL) is a promising gene interaction for cancer therapy. Recent SL prediction methods integrate knowledge graphs (KGs) into graph neural networks (GNNs) and employ attention mechanisms to extract local subgraphs as explanations for target gene pairs. However, attention mechanisms often lack fidelity, typically generate a single explanation per gene pair, and fail to ensure trustworthy high-order structures in their explanations. To overcome these limitations, we propose Diverse Graph Information Bottleneck for Synthetic Lethality (DGIB4SL), a KG-based GNN that generates multiple faithful explanations for the same gene pair and effectively encodes high-order structures. Specifically, we introduce a novel DGIB objective, integrating a Determinant Point Process (DPP) constraint into the standard IB objective, and employ 13 motif-based adjacency matrices to capture high-order structures in gene representations. Experimental results show that DGIB4SL outperforms state-of-the-art baselines and provides multiple explanations for SL prediction, revealing diverse biological mechanisms underlying SL inference.
Hierarchical Functional Group Ranking via IUPAC Name Analysis for Drug Discovery: A Case Study on TDP1 Inhibitors
Authors: Mariya L. Ivanova, Nicola Russo, Konstantin Nikolic
Date: 2025-03-07 00:00:00
Tasks: Drug Discovery

The article proposes a computational approach that can generate a descending order of the IUPAC-notated functional groups based on their importance for a given case study. Thus, a reduced list of functional groups could be obtained from which drug discovery can be successfully initiated. The approach, applicable to any study case with sufficient data, was demonstrated using a PubChem bioassay focused on TDP1 inhibitors. The Scikit Learn interpretation of the Random Forest Classifier (RFC) algorithm was employed. The machine learning (ML) model RFC obtained 70.9% accuracy, 73.1% precision, 66.1% recall, 69.4% F1 and 70.8% receiver-operating characteristic (ROC). In addition to the main study, the CID_SID ML model was developed, which, using only the PubChem compound and substance identifiers (CIDs and SIDs) data, can predict with 85.2% accuracy, 94.2% precision, 75% precision, F1 of 83.5% F1 and 85.2% ROC whether a compound is a TDP1 inhibitor.
A Morse Transform for Drug Discovery
Authors: Alexander M. Tanaka, Aras T. Asaad, Richard Cooper, Vidit Nanda
Date: 2025-03-06 00:00:00
Tasks: Drug Discovery

We introduce a new ligand-based virtual screening (LBVS) framework that uses piecewise linear (PL) Morse theory to predict ligand binding potential. We model ligands as simplicial complexes via a pruned Delaunay triangulation, and catalogue the critical points across multiple directional height functions. This produces a rich feature vector, consisting of crucial topological features -- peaks, troughs, and saddles -- that characterise ligand surfaces relevant to binding interactions. Unlike contemporary LBVS methods that rely on computationally-intensive deep neural networks, we require only a lightweight classifier. The Morse theoretic approach achieves state-of-the-art performance on standard datasets while offering an interpretable feature vector and scalable method for ligand prioritization in early-stage drug discovery.
InfoSEM: A Deep Generative Model with Informative Priors for Gene Regulatory Network Inference
Authors: Tianyu Cui, Song-Jun Xu, Artem Moskalev, Shuwei Li, Tommaso Mansi, Mangal Prakash, Rui Liao
Date: 2025-03-06 00:00:00
Tasks: Benchmarking

Inferring Gene Regulatory Networks (GRNs) from gene expression data is crucial for understanding biological processes. While supervised models are reported to achieve high performance for this task, they rely on costly ground truth (GT) labels and risk learning gene-specific biases, such as class imbalances of GT interactions, rather than true regulatory mechanisms. To address these issues, we introduce InfoSEM, an unsupervised generative model that leverages textual gene embeddings as informative priors, improving GRN inference without GT labels. InfoSEM can also integrate GT labels as an additional prior when available, avoiding biases and further enhancing performance. Additionally, we propose a biologically motivated benchmarking framework that better reflects real-world applications such as biomarker discovery and reveals learned biases of existing supervised methods. InfoSEM outperforms existing models by 38.5% across four datasets using textual embeddings prior and further boosts performance by 11.1% when integrating labeled data as priors.
Proton Flows, Proton Gradients and Subcellular Architecture in Biological Energy Conversion
Authors: Luca Quaroni
Date: 2025-03-05 00:00:00
Tasks: energy management, Management

Hydrogen cations, or protons, provide the medium by which energy is stored and converted in biological systems. Such pre-eminence relies on the interplay between interfacial and bulk chemical transformations, according to mechanisms that are shared by organisms in all phyla of life. The present work provides an introduction to the fundamental aspects of biological energy management by focusing on the relationship between vectorial proton flows and the geometry of energy producing organelles in eukaryotes. The leading models of proton-mediated energy conversion, the delocalised proton (or chemiosmotic) model and the localised proton model, are presented in a complementary perspective. While the delocalised model provides a description that relies on equilibrium thermodynamics, the localised model addresses dynamic processes that are better described using out-of-equilibrium thermodynamics. The work reviews the salient aspects of such mechanisms, traces the development of our present understanding, and highlights areas that are open to future developments.
Axes that matter: PCA with a difference
Authors: Brian Huge, Antoine Savine
Date: 2025-03-09 00:00:00
Tasks: regression

We extend the scope of differential machine learning and introduce a new breed of supervised principal component analysis to reduce dimensionality of Derivatives problems. Applications include the specification and calibration of pricing models, the identification of regression features in least-square Monte-Carlo, and the pre-processing of simulated datasets for (differential) machine learning.
Risk-aware Trading Portfolio Optimization
Authors: Marco Bianchetti, Gabriele D'Acunto, Gianmarco De Francisci Morales, Yuko Kuroki, Marco Scaringi, Fabio Vitale
Date: 2025-03-06 00:00:00
Tasks: Management, Portfolio Optimization

We investigate portfolio optimization in financial markets from a trading and risk management perspective. We term this task Risk-Aware Trading Portfolio Optimization (RATPO), formulate the corresponding optimization problem, and propose an efficient Risk-Aware Trading Swarm (RATS) algorithm to solve it. The key elements of RATPO are a generic initial portfolio P, a specific set of Unique Eligible Instruments (UEIs), their combination into an Eligible Optimization Strategy (EOS), an objective function, and a set of constraints. RATS searches for an optimal EOS that, added to P, improves the objective function repecting the constraints. RATS is a specialized Particle Swarm Optimization method that leverages the parameterization of P in terms of UEIs, enables parallel computation with a large number of particles, and is fully general with respect to specific choices of the key elements, which can be customized to encode financial knowledge and needs of traders and risk managers. We showcase two RATPO applications involving a real trading portfolio made of hundreds of different financial instruments, an objective function combining both market risk (VaR) and profit&loss measures, constrains on market sensitivities and UEIs trading costs. In the case of small-sized EOS, RATS successfully identifies the optimal solution and demonstrates robustness with respect to hyper-parameters tuning. In the case of large-sized EOS, RATS markedly improves the portfolio objective value, optimizing risk and capital charge while respecting risk limits and preserving expected profits. Our work bridges the gap between the implementation of effective trading strategies and compliance with stringent regulatory and economic capital requirements, allowing a better alignment of business and risk management objectives.