pwc-archive/papers-with-abstracts huggingface dataset:
Optimal virulence strategies in epidemiological models with asymptomatic transmission
Authors: C. Brandon Ogbunugafor, Sudam Surasinghe
Date: 2025-03-05 00:00:00
Tasks: Epidemiology

Asymptomatic infection has gained notoriety as an important feature of infectious disease dynamics. Despite increasing attention, there have been few rigorous examinations of how asymptomatic transmission influences pathogen evolution. In this study, we apply evolutionary invasion analysis to compute optimal strategies for viruses evolving in a system with a distinct asymptomatic transmission stage. We ask how pathogens would evolve under three conditions: with an increase in the mean infectious period in the symptomatic state, with an increase in the mean infectious period in the asymptomatic stage, and an increase in proportion proceeding through the ``mild recovery route" (where the symptomatic state was bypassed entirely). We find that an increased proportion of cases moving through a ``mild recovery route" -- which can occur with different host susceptibility or increased public health intervention -- leads to a model structure in which mutant pathogens are transmitted largely through the asymptomatic route, with slightly increased evolved virulence levels. In addition, we find that an increase in the mean infectious period of the symptomatic state has a small overall influence on the fitness of the pathogen, when effective transmission can occur via the asymptomatic route. Further, we find that virulence levels change very slightly for both the asymptomatic and symptomatic populations. In sum, our results highlight the evolutionary implications of variation in host susceptibility and public health interventions in the context of asymptomatic transmission. More generally, the findings speak to the need for more nuanced interrogations of subtle routes of transmission, as they can have profound implications in disease evolution, ecology, and epidemiology.
Social hierarchy shapes foraging decisions
Authors: Lisa Blum Moyse, Ahmed El Hady
Date: 2025-03-04 00:00:00
Tasks: Misinformation

Social foraging is a widespread form of animal foraging in which groups of individuals coordinate their decisions to exploit resources in the environment. Animals show a variety of social structures from egalitarian to hierarchical. In this study, we examine how different forms of social hierarchy shape foraging decisions. We developed a mechanistic analytically tractable model to study the underlying processes of social foraging, tying the microscopic individual to the macroscopic group levels. Based on a stochastic evidence accumulation framework, we developed a model of patch-leaving decisions in a large hierarchical group with leading and following individuals. Across a variety of information sharing mechanisms, we were able to analytically quantify emergent collective dynamics. We found that follower-leader dynamics through observations of leader movements or through counting the number of individuals in a patch confers, for most conditions, a benefit for the following individuals by increasing their accuracy in inferring patch richness. On the other hand, misinformation, through the communication of false beliefs about food rewards or patch quality, shows to be detrimental to following individuals, but paradoxically may lead to increased group cohesion. In an era where there is a huge amount of animal foraging data collected, our model provides a systematic way to conceptualize and understand those data by uncovering hidden mechanisms underlying social foraging decisions.
Unlocking tropical forest complexity: How tree assemblages in secondary forests boost biodiversity conservation
Authors: Maïri Souza Oliveira, Maxime Lenormand, Sandra Luque, Nelson A. Zamora, Samuel Alleaume, Adriana C. Aguilar Porras, Marvin U. Castillo, Eduardo Chacón-Madrigal, Diego Delgado, Luis Gustavo Hernández Sánchez, Marie-Ange Ngo Bieng, Ruperto M. Quesada, Gilberth S. Solano, Pedro M. Zúñiga
Date: 2025-03-04 00:00:00

Secondary forests now dominate tropical landscapes and play a crucial role in achieving COP15 conservation objectives. This study develops a replicable national approach to identifying and characterising forest ecosystems, with a focus on the role of secondary forests. We hypothesised that dominant tree species in the forest canopy serve as reliable indicators for delineating forest ecosystems and untangling biodiversity complexity. Using national inventories, we identified in situ clusters through hierarchical clustering based on dominant species abundance dissimilarity, determined using the Importance Variable Index. These clusters were characterised by analysing species assemblages and their interactions. We then applied object-oriented Random Forest modelling, segmenting the national forest cover using NDVI to identify the forest ecosystems derived from in situ clusters. Freely available spectral (Sentinel-2) and environmental data were used in the model to delineate and characterise key forest ecosystems. We finished with an assessment of distribution of secondary and old-growth forests within ecosystems. In Costa Rica, 495 dominant tree species defined 10 in situ clusters, with 7 main clusters successfully modelled. The modelling (F1-score: 0.73, macro F1-score: 0.58) and species-based characterisation highlighted the main ecological trends of these ecosystems, which are distinguished by specific species dominance, topography, climate, and vegetation dynamics, aligning with local forest classifications. The analysis of secondary forest distribution provided an initial assessment of ecosystem vulnerability by evaluating their role in forest maintenance and dynamics. This approach also underscored the major challenge of in situ data acquisition
Dynamical analysis of an HIV infection model including quiescent cells and immune response
Authors: Ibrahim Nali, Attila Dénes, Abdessamad Tridane, Xueyong Zhou
Date: 2025-02-26 00:00:00
Tasks: Sensitivity

This research gives a thorough examination of an HIV infection model that includes quiescent cells and immune response dynamics in the host. The model, represented by a system of ordinary differential equations, captures the complex interaction between the host's immune response and viral infection. The study focuses on the model's fundamental aspects, such as equilibrium analysis, computing the basic reproduction number $\mathcal{R}_0$, stability analysis, bifurcation phenomena, numerical simulations, and sensitivity analysis. The analysis reveals both an infection equilibrium, which indicates the persistence of the illness, and an infection-free equilibrium, which represents disease control possibilities. Applying matrix-theoretical approaches, stability analysis proved that the infection-free equilibrium is both locally and globally stable for $\mathcal{R}_0 < 1$. For the situation of $\mathcal{R}_0 > 1$, the infection equilibrium is locally asymptotically stable via the Routh--Hurwitz criterion. We also studied the uniform persistence of the infection, demonstrating that the infection remains present above a positive threshold under certain conditions. The study also found a transcritical forward-type bifurcation at $\mathcal{R}_0 = 1$, indicating a critical threshold that affects the system's behavior. The model's temporal dynamics are studied using numerical simulations, and sensitivity analysis identifies the most significant variables by assessing the effects of parameter changes on system behavior.
TEDDY: A Family Of Foundation Models For Understanding Single Cell Biology
Authors: Alexis Chevalier, Soumya Ghosh, Urvi Awasthi, James Watkins, Julia Bieniewska, Nichita Mitrea, Olga Kotova, Kirill Shkura, Andrew Noble, Michael Steinbaugh, Julien Delile, Christoph Meier, Leonid Zhukov, Iya Khalil, Srayanta Mukherjee, Judith Mueller
Date: 2025-03-05 00:00:00
Tasks: Drug Discovery

Understanding the biological mechanism of disease is critical for medicine, and in particular drug discovery. AI-powered analysis of genome-scale biological data hold great potential in this regard. The increasing availability of single-cell RNA sequencing data has enabled the development of large foundation models for disease biology. However, existing foundation models either do not improve or only modestly improve over task-specific models in downstream applications. Here, we explored two avenues for improving the state-of-the-art. First, we scaled the pre-training dataset to 116 million cells, which is larger than those used by previous models. Second, we leveraged the availability of large-scale biological annotations as a form of supervision during pre-training. We trained the TEDDY family of models comprising six transformer-based state-of-the-art single-cell foundation models with 70 million, 160 million, and 400 million parameters. We vetted our models on two downstream evaluation tasks -- identifying the underlying disease state of held-out donors not seen during training and distinguishing healthy cells from diseased ones for disease conditions and donors not seen during training. Scaling experiments showed that performance improved predictably with both data volume and parameter count. Our models showed substantial improvement over existing work on the first task and more muted improvements on the second.
Spatially-Structured Models of Viral Dynamics: A Scoping Review
Authors: Thomas Williams, James M. McCaw, James M. Osborne
Date: 2025-03-05 00:00:00

There is growing recognition in both the experimental and modelling literature of the importance of spatial structure to the dynamics of viral infections in tissues. Aided by the evolution of computing power and motivated by recent biological insights, there has been an explosion of new, spatially-explicit models for within-host viral dynamics in recent years. This development has only been accelerated in the wake of the COVID-19 pandemic. Spatially-structured models offer improved biological realism and can account for dynamics which cannot be well-described by conventional, mean-field approaches. However, despite their growing popularity, spatially-structured models of viral dynamics are underused in biological applications. One major obstacle to the wider application of such models is the huge variety in approaches taken, with little consensus as to which features should be included and how they should be implemented for a given biological context. Previous reviews of the field have focused on specific modelling frameworks or on models for particular viral species. Here, we instead apply a scoping review approach to the literature of spatially-structured viral dynamics models as a whole to provide an exhaustive update of the state of the field. Our analysis is structured along two axes, methodology and viral species, in order to examine the breadth of techniques used and the requirements of different biological applications. We then discuss the contributions of mathematical and computational modelling to our understanding of key spatially-structured aspects of viral dynamics, and suggest key themes for future model development to improve robustness and biological utility.
Multimodal AI predicts clinical outcomes of drug combinations from preclinical data
Authors: Yepeng Huang, Xiaorui Su, Varun Ullanat, Ivy Liang, Lindsay Clegg, Damilola Olabode, Nicholas Ho, Bino John, Megan Gibbs, Marinka Zitnik
Date: 2025-03-04 00:00:00
Tasks: Large Language Model

Predicting clinical outcomes from preclinical data is essential for identifying safe and effective drug combinations. Current models rely on structural or target-based features to identify high-efficacy, low-toxicity drug combinations. However, these approaches fail to incorporate the multimodal data necessary for accurate, clinically-relevant predictions. Here, we introduce MADRIGAL, a multimodal AI model that learns from structural, pathway, cell viability, and transcriptomic data to predict drug combination effects across 953 clinical outcomes and 21842 compounds, including combinations of approved drugs and novel compounds in development. MADRIGAL uses a transformer bottleneck module to unify preclinical drug data modalities while handling missing data during training and inference--a major challenge in multimodal learning. It outperforms single-modality methods and state-of-the-art models in predicting adverse drug interactions. MADRIGAL performs virtual screening of anticancer drug combinations and supports polypharmacy management for type II diabetes and metabolic dysfunction-associated steatohepatitis (MASH). It identifies transporter-mediated drug interactions. MADRIGAL predicts resmetirom, the first and only FDA-approved drug for MASH, among therapies with the most favorable safety profile. It supports personalized cancer therapy by integrating genomic profiles from cancer patients. Using primary acute myeloid leukemia samples and patient-derived xenograft models, it predicts the efficacy of personalized drug combinations. Integrating MADRIGAL with a large language model allows users to describe clinical outcomes in natural language, improving safety assessment by identifying potential adverse interactions and toxicity risks. MADRIGAL provides a multimodal approach for designing combination therapies with improved predictive accuracy and clinical relevance.
Prediction of amino acid content in live black soldier fly larvae using near infrared spectroscopy
Authors: R. M. Zaalberg, L. B. Andersen, S. J. Noel, A. J. Buitenhuis, K. Jensen, G. Gebreyesus
Date: 2025-03-04 00:00:00
Tasks: Nutrition

Black soldier fly (Hermetia illucens) larvae are emerging as a sustainable protein source for animal feed and human nutrition. Ensuring consistent amino acid composition is crucial for quality control, necessitating rapid, non-destructive assessment methods, particularly for selective breeding. This study validates near-infrared (NIR) spectroscopy with partial least squares (PLS) regression as a reliable tool for predicting free amino acid (FAA) profiles in live larvae. Using a FOSS DS2500 spectrometer (400-2500 nm), 204 live larval samples reared on 17 diets with varying protein (2.3-89.8 percent) and sugar (8.5-96.0 percent) levels were scanned, followed by FAA extraction. Alanine (6.50 +/- 3.52 mg/g) and proline (5.61 +/- 4.45 mg/g) were the most abundant FAAs, with key NIR absorption bands at 1670-1786 nm and 2300-2366 nm enabling accurate predictions. PLS models showed strong predictive power for individual FAAs (e.g., glutamate: R^2 = 0.88; asparagine: R^2 = 0.78; alanine: R^2 = 0.65) and total FAAs (R^2 = 0.64). These results highlight NIR's potential for real-time quality control and phenotyping in BSF production and breeding.
Algebraic approaches for the decomposition of reaction networks and the determination of existence and number of steady states
Authors: Joseph M. Sauder, Bruce P. Ayati, Ryan Kinser
Date: 2025-03-03 00:00:00

Chemical reaction network theory provides powerful tools for rigorously understanding chemical reactions and the dynamical systems and differential equations that represent them. A frequent issue with mathematical analyses of these networks is the reliance on explicit parameter values which in many cases cannot be determined experimentally. This can make analyzing a dynamical system infeasible, particularly when the size of the system is large. One approach is to analyze subnetworks of the full network and use the results for a full analysis. Our focus is on the equilibria of reaction networks. Gr\"obner basis computation is a useful approach for solving the polynomial equations which correspond to equilibria of a dynamical system. We identify a class of networks for which Gr\"obner basis computations of subnetworks can be used to reconstruct the more expensive Gr\"obner basis computation of the whole network. We compliment this result with tools to determine if a steady state can exist, and if so, how many.
dyAb: Flow Matching for Flexible Antibody Design with AlphaFold-driven Pre-binding Antigen
Authors: Cheng Tan, Yijie Zhang, Zhangyang Gao, Yufei Huang, Haitao Lin, Lirong Wu, Fandi Wu, Mathieu Blanchette, Stan. Z. Li
Date: 2025-03-01 00:00:00

The development of therapeutic antibodies heavily relies on accurate predictions of how antigens will interact with antibodies. Existing computational methods in antibody design often overlook crucial conformational changes that antigens undergo during the binding process, significantly impacting the reliability of the resulting antibodies. To bridge this gap, we introduce dyAb, a flexible framework that incorporates AlphaFold2-driven predictions to model pre-binding antigen structures and specifically addresses the dynamic nature of antigen conformation changes. Our dyAb model leverages a unique combination of coarse-grained interface alignment and fine-grained flow matching techniques to simulate the interaction dynamics and structural evolution of the antigen-antibody complex, providing a realistic representation of the binding process. Extensive experiments show that dyAb significantly outperforms existing models in antibody design involving changing antigen conformations. These results highlight dyAb's potential to streamline the design process for therapeutic antibodies, promising more efficient development cycles and improved outcomes in clinical applications.
Unveiling sex dimorphism in the healthy cardiac anatomy: fundamental differences between male and female heart shapes
Authors: Beatrice Moscoloni, Cameron Beeche, Julio A. Chirinos, Patrick Segers, Mathias Peirlinck
Date: 2025-02-28 00:00:00
Tasks: Anatomy

Sex-based differences in cardiovascular disease are well documented, yet the precise nature and extent of these discrepancies in cardiac anatomy remain incompletely understood. Traditional scaling models often fail to capture the interplay of age, blood pressure, and body size, prompting a more nuanced investigation. Here, we employ statistical shape modeling in a healthy subset (n=456) of the UK Biobank to explore sex-specific variations in biventricular anatomy. We reconstruct 3D meshes and perform multivariate analyses of shape coefficients, controlling for age, blood pressure, and various body size metrics. Our findings reveal that sex alone explains at least 25 percent of morphological variability, with strong discrimination between men and women (AUC=0.96-0.71) persisting even after correction for confounders. Notably, the most discriminative modes highlight pronounced differences in cardiac chamber volumes, the anterior-posterior width of the right ventricle, and the relative positioning of the cardiac chambers. These results underscore that sex has a fundamental influence on cardiac morphology, which may have important clinical implications for differing cardiac structural assessments in men and women. Future work should investigate how these anatomical differences manifest in various cardiovascular conditions, ultimately paving the way for more precise risk stratification and personalized therapeutic strategies for both men and women.
Bayesian Estimation of Corporate Default Spreads
Authors: Maksim Papenkov, Beau Robinette
Date: 2025-03-04 00:00:00

Risk-averse investors often wish to exclude stocks from their portfolios that bear high credit risk, which is a measure of a firm's likelihood of bankruptcy. This risk is commonly estimated by constructing signals from quarterly accounting items, such as debt and income volatility. While such information may provide a rich description of a firm's credit risk, the low-frequency with which the data is released implies that investors may be operating with outdated information. In this paper we circumvent this problem by developing a high-frequency credit risk proxy via corporate default spreads which are estimated from daily bond price data. We accomplish this by adapting classic yield curve estimation methods to a corporate bond setting, leveraging advances in Bayesian estimation to ensure higher model stability when working with small sample data which also allows us to directly model the uncertainty of our predictions.
Complex discontinuities of $\surd\overline{\text{Fredholm determinants}}$ in the Volterra Stein-Stein model
Authors: Eduardo Abi Jaber, Maxime Guellil
Date: 2025-03-04 00:00:00

We study complex discontinuities arising from the miscomputation of the Fourier-Laplace transform in the Volterra Stein-Stein model, which involves the complex square root of a Fredholm determinant. Discontinuities occur when the determinant crosses the negative real axis. We characterize these crossings for the joint Fourier-Laplace transform of the integrated variance and log-price. Additionally, we derive a corrected formula for the Fourier-Laplace transform and develop efficient numerical techniques to detect and compute these crossings. Applying our algorithms to Fourier-based pricing in the rough Stein-Stein model, we achieve a significant increase in accuracy while drastically reducing computational cost compared to existing methods.
Large language models in finance : what is financial sentiment?
Authors: Kemal Kirtac, Guido Germano
Date: 2025-03-05 00:00:00
Tasks: Algorithmic Trading, Decision Making, Sentiment Analysis, Sentiment Classification, Stock Prediction, Text Generation

Financial sentiment has become a crucial yet complex concept in finance, increasingly used in market forecasting and investment strategies. Despite its growing importance, there remains a need to define and understand what financial sentiment truly represents and how it can be effectively measured. We explore the nature of financial sentiment and investigate how large language models (LLMs) contribute to its estimation. We trace the evolution of sentiment measurement in finance, from market-based and lexicon-based methods to advanced natural language processing techniques. The emergence of LLMs has significantly enhanced sentiment analysis, providing deeper contextual understanding and greater accuracy in extracting sentiment from financial text. We examine how BERT-based models, such as RoBERTa and FinBERT, are optimized for structured sentiment classification, while GPT-based models, including GPT-4, OPT, and LLaMA, excel in financial text generation and real-time sentiment interpretation. A comparative analysis of bidirectional and autoregressive transformer architectures highlights their respective roles in investor sentiment analysis, algorithmic trading, and financial decision-making. By exploring what financial sentiment is and how it is estimated within LLMs, we provide insights into the growing role of AI-driven sentiment analysis in finance.
Constructing elicitable risk measures
Authors: Akif Ince, Marlon Moresco, Ilaria Peri, Silvana M. Pesenti
Date: 2025-03-05 00:00:00

We provide a constructive way of defining new elicitable risk measures that are characterised by a multiplicative scoring function. We show that depending on the choice of the scoring function's components, the resulting risk measure possesses properties such as monotonicity, translation invariance, convexity, and positive homogeneity. Our framework encompasses the majority of well-known elicitable risk measures including all elicitable convex and coherent risk measures. Our setting moreover allows to construct novel elicitable risk measures that are, for example, convex but not coherent. Furthermore, we discuss how higher-order elicitability, such as jointly eliciting the mean and variance or different quantile levels, fall within our setting.