pwc-archive/papers-with-abstracts huggingface dataset:
A model for cholera with infectiousness of deceased individuals and vaccination
Authors: Abdramane Annour Saad, Julien Arino, Patrick M Tchepmo Djomegni, Mahamat S Daoussa Haggar
Date: 2025-04-10 00:00:00

A cholera transmission model incorporating water-borne and horizontal transmissions as well as infectivity of deceased individuals is formulated and studied. The model also describes an imperfect and waning vaccination. Global stability of the disease-free equilibrium is proved when the basic reproduction number is less than one. It is also proved that there are bistable situations, where when the vaccination reproduction number is less than one, there are two endemic equilibria, although it is shown numerically that the region where this occurs is small. The computational analysis also considers the local asymptotic stability of endemic equilibria and the interplay between vaccination strategy, vaccine efficacy and waning.
Using theory-driven Integrated Population Models to evaluate competitive outcomes in stage-structured systems
Authors: Matthieu Paquet, Frédéric Barraquand
Date: 2025-04-09 00:00:00

Predicting competitive outcomes typically requires fitting dynamical models to data, from which interaction strengths and coexistence indicators such as invasion criteria can be produced. Methods that allow to propagate parameter uncertainty are particularly indicated. These should ideally allow for competition between and within species at various life-stages, and make the best out of multiple data sources, each of which can be relatively scarce by statistical standards. Here, we embed a mathematical model of stage-structured competition between two species, producing analytical invasion criteria, into a two-species Integrated Population Model. The community-level IPM allows to combine counts, capture-recapture, and fecundity data into a single statistical framework, and the Bayesian formulation of the IPM fully propagates parameter uncertainty into invasion criteria. Model fitting demonstrates that we can correctly predict coexistence through reciprocal invasion when present, but that interaction strengths are not always estimable, depending on the prior chosen. Our competitive exclusion scenario is shown to be harder to identify, although our model allows to at least flag this scenario as uncertain rather than mistakenly present it as coexistence. Our results confirm the importance of accounting for uncertainty in the prediction of competitive outcomes.
Choices in the transformative Anthropocene
Authors: Miguel Pinheiro, Pablo Pena Rodrigues
Date: 2025-04-07 00:00:00
Tasks: Decision Making

Unprecedented imbalances and growing human impacts characterize the Anthropocene. It highlights the urgency of better choices, and the perspectives outlined here can inform our decision-making process. The Biocentric-Technological way stresses the need to change the human niche to recalibrate human-ecological interactions in order to halt the process of biosphere transformation. The Bio-Anthropogenic way is a middle path between the extremes, based on strategies that could lead to novel ecosystems and a symbiotic relationship between humans and new organisms. This will require ethical, cultural and technological changes towards a less harmful transformation. The final is the Anthropocentric way, which is the outcome of current human behavior and population growth. Failure to develop new environmentally friendly technologies and cultures will lead to the collapse of current life-support systems, leading to hazardous scenarios. These perspectives can help us to choose a safer and more sustainable future.
Cooperation guides evolution in a minimal model of biological evolution
Authors: Conor Houghton
Date: 2025-04-07 00:00:00
Tasks: Diversity

A challenging simulation of evolutionary dynamics based on a three-state cellular automaton is used as a test of how cooperation can drive the evolution of complex traits. Building on the approach of Wolfram (2025), the fitness of a cellular automaton is determined by the number of updates it endures before returning to an empty configuration. In our population-based model, groups of automata are assessed either by summing individual fitness or by the maximum fitness in the group; this second scheme mimics cooperation by allowing fit rules to support their less fit group mates. We show that cooperation substantially improves the population's ability to evolve longer-lived rules, illustrating a Baldwin Effect for Cooperation: higher diversity across individuals unlock otherwise inaccessible evolutionary paths.
Representation Meets Optimization: Training PINNs and PIKANs for Gray-Box Discovery in Systems Pharmacology
Authors: Nazanin Ahmadi Daryakenari, Khemraj Shukla, George Em Karniadakis
Date: 2025-04-10 00:00:00
Tasks: Computational Efficiency, Kolmogorov-Arnold Networks, Second-order methods

Physics-Informed Kolmogorov-Arnold Networks (PIKANs) are gaining attention as an effective counterpart to the original multilayer perceptron-based Physics-Informed Neural Networks (PINNs). Both representation models can address inverse problems and facilitate gray-box system identification. However, a comprehensive understanding of their performance in terms of accuracy and speed remains underexplored. In particular, we introduce a modified PIKAN architecture, tanh-cPIKAN, which is based on Chebyshev polynomials for parametrization of the univariate functions with an extra nonlinearity for enhanced performance. We then present a systematic investigation of how choices of the optimizer, representation, and training configuration influence the performance of PINNs and PIKANs in the context of systems pharmacology modeling. We benchmark a wide range of first-order, second-order, and hybrid optimizers, including various learning rate schedulers. We use the new Optax library to identify the most effective combinations for learning gray-boxes under ill-posed, non-unique, and data-sparse conditions. We examine the influence of model architecture (MLP vs. KAN), numerical precision (single vs. double), the need for warm-up phases for second-order methods, and sensitivity to the initial learning rate. We also assess the optimizer scalability for larger models and analyze the trade-offs introduced by JAX in terms of computational efficiency and numerical accuracy. Using two representative systems pharmacology case studies - a pharmacokinetics model and a chemotherapy drug-response model - we offer practical guidance on selecting optimizers and representation models/architectures for robust and efficient gray-box discovery. Our findings provide actionable insights for improving the training of physics-informed networks in biomedical applications and beyond.
Artificial Intelligence for Pediatric Height Prediction Using Large-Scale Longitudinal Body Composition Data
Authors: Dohyun Chun, Hae Woon Jung, Jongho Kang, Woo Young Jang, Jihun Kim
Date: 2025-04-09 00:00:00

This study developed an accurate artificial intelligence model for predicting future height in children and adolescents using anthropometric and body composition data from the GP Cohort Study (588,546 measurements from 96,485 children aged 7-18). The model incorporated anthropometric measures, body composition, standard deviation scores, and growth velocity parameters, with performance evaluated using RMSE, MAE, and MAPE. Results showed high accuracy with males achieving average RMSE, MAE, and MAPE of 2.51 cm, 1.74 cm, and 1.14%, and females showing 2.28 cm, 1.68 cm, and 1.13%, respectively. Explainable AI approaches identified height SDS, height velocity, and soft lean mass velocity as crucial predictors. The model generated personalized growth curves by estimating individual-specific height trajectories, offering a robust tool for clinical decision support, early identification of growth disorders, and optimization of growth outcomes.
Evaluation of optimal cut-offs and dichotomous combinations for two biomarkers to improve patient selection
Authors: Gina DAngelo, Di Ran, Binbing Yu
Date: 2025-04-09 00:00:00

Background Identifying the right cut-off for continuous biomarkers in clinical trials is important to identify subgroups of patients who are at greater risk of disease or more likely to benefit from a drug. The literature in this area tends to focus on finding cut-offs for a single biomarker, whereas clinical trials more often focus on multiple biomarkers. Methods Our first objective was to compare three methods,Youden index, point closest to the (0,1) corner on the receiving operator characteristic curve (ER), and concordance probability, to find the optimal cut-offs for two biomarkers, using empirical and non-empirical approaches. Our second and main objective was to use our proposed logic indicator approach to extend the Youden index and evaluate whether a combination of biomarkers is an improvement over a single biomarker. Results The logic indicator approach created a condition in which either both biomarkers were positive or only one of the biomarkers was positive. A prostate cancer study and a simulated phase 2 lung cancer study were used to illustrate approaches to finding optimal cut-offs and comparing combined biomarkers with single biomarkers. Conclusion Our results can aid in determining whether a single biomarker or a combination of biomarkers is superior in identifying patients who are more likely to respond to treatment. This work can be of great importance in the era of personalized medicine, where many treatments do not provide clinical benefit to average patients.
DeepGDel: Deep Learning-based Gene Deletion Prediction Framework for Growth-Coupled Production in Genome-Scale Metabolic Models
Authors: Ziwei Yang, Takeyuki Tamura
Date: 2025-04-08 00:00:00

In genome-scale constraint-based metabolic models, gene deletion strategies are crucial for achieving growth-coupled production, where cell growth and target metabolite production are simultaneously achieved. While computational methods for calculating gene deletions have been widely explored and contribute to developing gene deletion strategy databases, current approaches are limited in leveraging new data-driven paradigms, such as machine learning, for more efficient strain design. Therefore, it is necessary to propose a fundamental framework for this objective. In this study, we first formulate the problem of gene deletion strategy prediction and then propose a framework for predicting gene deletion strategies for growth-coupled production in genome-scale metabolic models. The proposed framework leverages deep learning algorithms to learn and integrate sequential gene and metabolite data representation, enabling the automatic gene deletion strategy prediction. Computational experiment results demonstrate the feasibility of the proposed framework, showing substantial improvements over baseline methods. Specifically, the proposed framework achieves a 14.69%, 22.52%, and 13.03% increase in overall accuracy across three metabolic models of different scales under study, while maintaining balanced precision and recall in predicting gene deletion statuses. The source code and examples for the framework are publicly available at https://github.com/MetNetComp/DeepGDel.
Predicting Survivability of Cancer Patients with Metastatic Patterns Using Explainable AI
Authors: Polycarp Nalela, Deepthi Rao, Praveen Rao
Date: 2025-04-07 00:00:00
Tasks: Prognosis, Survival Analysis

Cancer remains a leading global health challenge and a major cause of mortality. This study leverages machine learning (ML) to predict the survivability of cancer patients with metastatic patterns using the comprehensive MSK-MET dataset, which includes genomic and clinical data from 25,775 patients across 27 cancer types. We evaluated five ML models-XGBoost, Na\"ive Bayes, Decision Tree, Logistic Regression, and Random Fores using hyperparameter tuning and grid search. XGBoost emerged as the best performer with an area under the curve (AUC) of 0.82. To enhance model interpretability, SHapley Additive exPlanations (SHAP) were applied, revealing key predictors such as metastatic site count, tumor mutation burden, fraction of genome altered, and organ-specific metastases. Further survival analysis using Kaplan-Meier curves, Cox Proportional Hazards models, and XGBoost Survival Analysis identified significant predictors of patient outcomes, offering actionable insights for clinicians. These findings could aid in personalized prognosis and treatment planning, ultimately improving patient care.
EquiCPI: SE(3)-Equivariant Geometric Deep Learning for Structure-Aware Prediction of Compound-Protein Interactions
Authors: Ngoc-Quang Nguyen
Date: 2025-04-07 00:00:00
Tasks: Drug Discovery, Re-Ranking

Accurate prediction of compound-protein interactions (CPI) remains a cornerstone challenge in computational drug discovery. While existing sequence-based approaches leverage molecular fingerprints or graph representations, they critically overlook three-dimensional (3D) structural determinants of binding affinity. To bridge this gap, we present EquiCPI, an end-to-end geometric deep learning framework that synergizes first-principles structural modeling with SE(3)-equivariant neural networks. Our pipeline transforms raw sequences into 3D atomic coordinates via ESMFold for proteins and DiffDock-L for ligands, followed by physics-guided conformer re-ranking and equivariant feature learning. At its core, EquiCPI employs SE(3)-equivariant message passing over atomic point clouds, preserving symmetry under rotations, translations, and reflections, while hierarchically encoding local interaction patterns through tensor products of spherical harmonics. The proposed model is evaluated on BindingDB (affinity prediction) and DUD-E (virtual screening), EquiCPI achieves performance on par with or exceeding the state-of-the-art deep learning competitors.
Sub-Clustering for Class Distance Recalculation in Long-Tailed Drug Classification
Authors: Yujia Su, Xinjie Li, Lionel Z. Wang
Date: 2025-04-07 00:00:00
Tasks: Contrastive Learning

In the real world, long-tailed data distributions are prevalent, making it challenging for models to effectively learn and classify tail classes. However, we discover that in the field of drug chemistry, certain tail classes exhibit higher identifiability during training due to their unique molecular structural features, a finding that significantly contrasts with the conventional understanding that tail classes are generally difficult to identify. Existing imbalance learning methods, such as resampling and cost-sensitive reweighting, overly rely on sample quantity priors, causing models to excessively focus on tail classes at the expense of head class performance. To address this issue, we propose a novel method that breaks away from the traditional static evaluation paradigm based on sample size. Instead, we establish a dynamical inter-class separability metric using feature distances between different classes. Specifically, we employ a sub-clustering contrastive learning approach to thoroughly learn the embedding features of each class, and we dynamically compute the distances between class embeddings to capture the relative positional evolution of samples from different classes in the feature space, thereby rebalancing the weights of the classification loss function. We conducted experiments on multiple existing long-tailed drug datasets and achieved competitive results by improving the accuracy of tail classes without compromising the performance of dominant classes.
Foundation Models for Environmental Science: A Survey of Emerging Frontiers
Authors: Runlong Yu, Shengyu Chen, Yiqun Xie, Huaxiu Yao, Jared Willard, Xiaowei Jia
Date: 2025-04-05 00:00:00
Tasks: Decision Making, Management, scientific discovery

Modeling environmental ecosystems is essential for effective resource management, sustainable development, and understanding complex ecological processes. However, traditional data-driven methods face challenges in capturing inherently complex and interconnected processes and are further constrained by limited observational data in many environmental applications. Foundation models, which leverages large-scale pre-training and universal representations of complex and heterogeneous data, offer transformative opportunities for capturing spatiotemporal dynamics and dependencies in environmental processes, and facilitate adaptation to a broad range of applications. This survey presents a comprehensive overview of foundation model applications in environmental science, highlighting advancements in common environmental use cases including forward prediction, data generation, data assimilation, downscaling, inverse modeling, model ensembling, and decision-making across domains. We also detail the process of developing these models, covering data collection, architecture design, training, tuning, and evaluation. Through discussions on these emerging methods as well as their future opportunities, we aim to promote interdisciplinary collaboration that accelerates advancements in machine learning for driving scientific discovery in addressing critical environmental challenges.
Discriminating BCC Subtypes Using Entropy and Mutual Information from Dermoscopic Features
Authors: Iván Matas, Begoña Acha, Francisca Silva-Clavería, Amalia Serrano, Tomás Toledo-Pastrana, Carmen Serrano
Date: 2025-04-05 00:00:00
Tasks: Diagnostic, Informativeness

Objective: To analyze the frequency and co-occurrence of dermoscopic patterns in BCC lesions and their relationship with histopathologic subtypes, using statistical analysis and Information Theory tools such as entropy, conditional entropy, mutual information, and Hamming weight. Methods: A total of 223 dermoscopic images (256x256 pixels) of histologically confirmed BCC lesions from Hospital Universitario Virgen Macarena (Seville, Spain) were analyzed. Each image was multilabel-annotated for the presence of nine dermoscopic patterns and categorized into one of four BCC subtypes: superficial, nodular, infiltrative, or micronodular. Statistical and information-theoretic methods were applied, including co-occurrence matrices, Bayesian conditional probabilities, and entropy-based metrics. Mutual information quantified the predictive value of individual and paired patterns, and decision trees were built based on diagnostic informativeness. Results: Nodular BCC was highly associated with most dermoscopic patterns, particularly arborizing telangiectasia and blue-gray ovoid nests. Superficial BCC showed stronger associations with maple leaf-like structures and shiny white-red areas. Some patterns, like spoke-wheel areas and white streaks, showed low discriminative power. Mutual information and conditional probabilities identified meaningful pattern-pair dependencies for each subtype. Decision trees revealed that subsets of patterns could enhance subtype classification by accumulating diagnostic information. Conclusions: Information Theory enables a quantitative understanding of dermoscopic patterns and their relationship with BCC subtypes. This framework highlights key diagnostic features, aids in differentiating complex cases, and supports the development of automated, pattern-based diagnostic tools. Further research with larger, more balanced datasets is encouraged.
RP-SAM2: Refining Point Prompts for Stable Surgical Instrument Segmentation
Authors: Nuren Zhaksylyk, Ibrahim Almakky, Jay Paranjape, S. Swaroop Vedula, Shameema Sikder, Vishal M. Patel, Mohammad Yaqub
Date: 2025-03-25 00:00:00
Tasks: Decoder, Segmentation

Accurate surgical instrument segmentation is essential in cataract surgery for tasks such as skill assessment and workflow optimization. However, limited annotated data makes it difficult to develop fully automatic models. Prompt-based methods like SAM2 offer flexibility yet remain highly sensitive to the point prompt placement, often leading to inconsistent segmentations. We address this issue by introducing RP-SAM2, which incorporates a novel shift block and a compound loss function to stabilize point prompts. Our approach reduces annotator reliance on precise point positioning while maintaining robust segmentation capabilities. Experiments on the Cataract1k dataset demonstrate that RP-SAM2 improves segmentation accuracy, with a 2% mDSC gain, a 21.36% reduction in mHD95, and decreased variance across random single-point prompt results compared to SAM2. Additionally, on the CaDIS dataset, pseudo masks generated by RP-SAM2 for fine-tuning SAM2's mask decoder outperformed those generated by SAM2. These results highlight RP-SAM2 as a practical, stable and reliable solution for semi-automatic instrument segmentation in data-constrained medical settings. The code is available at https://github.com/BioMedIA-MBZUAI/RP-SAM2.
A Mean-Reverting Model of Exchange Rate Risk Premium Using Ornstein-Uhlenbeck Dynamics
Authors: SeungJae Hwang
Date: 2025-04-08 00:00:00

This paper examines the empirical failure of uncovered interest parity (UIP) and proposes a structural explanation based on a mean-reverting risk premium. We define a realized premium as the deviation between observed exchange rate returns and the interest rate differential, and demonstrate its strong mean-reverting behavior across multiple horizons. Motivated by this pattern, we model the risk premium using an Ornstein-Uhlenbeck (OU) process embedded within a stochastic differential equation for the exchange rate. Our model yields closed-form approximations for future exchange rate distributions, which we evaluate using coverage-based backtesting. Applied to USD/KRW data from 2010 to 2025, the model shows strong predictive performance at both short-term and long-term horizons, while underperforming at intermediate (3-month) horizons and showing conservative behavior in the tails of long-term forecasts. These results suggest that exchange rate deviations from UIP may reflect structured, forecastable dynamics rather than pure noise, and point to future modeling improvements via regime-switching or time-varying volatility.
Money as a Tensor
Authors: Mario R. Pinheiro, Mario J. Pinheiro
Date: 2025-04-01 00:00:00
Tasks: Decision Making

The proposed framework introduces a novel multidimensional representation of money using tensor analysis, enabling a more granular examination of economic interactions and capital flow. By treating money as a multidimensional entity, this approach allows for detailed tracking and modeling of sectoral, temporal, and agent-based dynamics. This enhanced perspective facilitates the design of adaptive economic policies that can effectively respond to evolving macroeconomic conditions, ensuring resilience and inclusivity in financial systems. Furthermore, the tensor-based modeling framework bridges traditional economic analyses with advanced computational techniques, offering a robust foundation for algorithmic governance and data-driven decision-making in complex economies.
Accelerating Transportation Decarbonization: The Strategic Role of Ethanol Blends and Regulatory Incentives
Authors: Eliseo Curcio
Date: 2025-03-14 00:00:00

This study evaluates ethanol blending as a practical near-term strategy for significant transportation decarbonization in the United States. Despite rapid growth in electric vehicle adoption, gasoline is projected to remain dominant, with annual demand around 135 billion gallons by 2035, necessitating immediate complementary solutions. Analysis indicates ethanol use will notably expand, driven by regulatory incentives such as RFS Renewable Identification Numbers (RINs) and IRA tax credits (45V), leading to potential market penetration of E15 at about 25% and E85 also expanding substantially. Ethanol derived from waste achieves notably lower carbon intensity at approximately 58.34 gCO2e/MJ, substantially better than conventional gasoline (~92 gCO2e/MJ), providing clear environmental advantages. Economic assessments show robust investor returns and local economic growth driven by policy incentives, including Renewable Identification Numbers (RINs) and IRA tax credits (45V). Infrastructure analysis confirms manageable costs and feasible adjustments for widespread adoption of higher ethanol blends.