pwc-archive/papers-with-abstracts huggingface dataset:
Active filtering: a predictive function of recurrent circuits of sensory cortex
Authors: Mark H. Histed
Date: 2025-01-17 00:00:00

Our brains encode many features of the sensory world into memories: we can sing along with songs we have heard before, interpret spoken and written language composed of words we have learned, and recognize faces and objects. Where are these memories stored? Each area of the cerebral cortex has a huge number of local, recurrent, excitatory-excitatory synapses, as many as 500 million per cubic millimeter. Here I review evidence that cortical recurrent connectivity in sensory cortex is a substrate for sensory memories. Evidence suggests that the local recurrent network encodes the structure of natural sensory input, and that it does so via active filtering, transforming network inputs to boost or select those associated with natural sensation. This is a form of predictive processing, in which the cortical recurrent network selectively amplifies some input patterns and attenuates others, and a form of memory.
Toxicological Evaluation of Phytochemicals and Heavy Metals in Ficus exasperata Vahl (Sandpaper) Leaves obtained in Birnin Kebbi, Nigeria
Authors: Tajudeen O. Yahaya, Titilola F. Salisu, Israel O. Obaroh, Muyiwa A. Adelabu, Abdulrazaq Izuafa, Jamilu B. Danjuma, Hamdalat Sheu, Ibrahim B. Abdulgafar
Date: 2025-01-19 00:00:00

Objective: Ficus exasperata Vahl (Sandpaper tree) is extensively used in Nigeria to treat diseases, but a dearth of documentation about its toxicity exists. This information is crucial because pollutants can contaminate medicinal plants. This study determined the heavy metal and phytochemical content of methanolic leaf extract of F exasperata obtained in Birnin Kebbi, Nigeria. Material and Methods: The lethality of the plant was also assessed using 70 wild shrimps divided equally into seven groups. Group 1 (negative control), groups 2 and 3 (positive controls) were exposed to 500 and 1000ppm of formaldehyde, respectively; and groups 4-7 were exposed to 1000, 2000, 4000, and 8000ppm of extracts, respectively, for 96 hours. Results: The phytochemistry revealed high levels of flavonoids and saponins and moderate levels of tannins and phenols. The heavy metal analysis revealed non-tolerable levels of cadmium, copper, and lead, while zinc was within the tolerable limit. The negative control recorded 10% mortality, 1000 and 2000 ppm (20% each), 4000ppm (70%), and 8000 ppm (100%). Conclusion: These results inferred safe doses of the plant's extract in low and medium concentrations but toxic and fatal at high doses over a period of time. Consumers are advised to seek an expert's guidance before using it.
Evolvable Soma Theory of Ageing: Insights from Computer Simulations
Authors: Alessandro Fontana, Marios Kyriazis
Date: 2025-01-23 00:00:00

Biological evolution continuously refines the design of species, resulting in highly optimised organisms over hundreds of millennia. Intuitively, we expect that random changes-evolution's primary mechanism-are more likely to be harmful than beneficial, leading to widespread detrimental effects in evolving species. The Evolvable Soma Theory of Ageing (ESTA) suggests that ageing is the cumulative result of these harmful effects, which predominantly cause bodily damage, while a few may lead to beneficial adaptations that evolution can exploit. While the disposable soma theory views ageing as a consequence of limited evolutionary pressure, ESTA posits that ageing is essentially evolution in action. In this study, we gather evidence supporting this theory through computer simulations. We conduct experiments using a platform where genes are linked to onset values that determine when they are expressed. Three scenarios are tested: one with single-point fitness evaluation, constant mutation rate and fixed gene onsets; one with single-point fitness evaluation, onset-dependent mutation rate and fixed gene onsets; and one with spread fitness evaluation, onset-dependent mutation rate and evolvable gene onsets. The last scenario, which embodies the evolvable soma hypothesis, demonstrates superior performance in both algorithmic efficiency and biological plausibility compared to the others.
Tikhonov-Fenichel Reductions and their Application to a Novel Modelling Approach for Mutualism
Authors: Johannes Apelt, Volkmar Liebscher
Date: 2025-01-21 00:00:00

When formulating a model there is a trade-off between model complexity and (biological) realism. In the present paper we demonstrate how model reduction from a precise mechanistic "super model" to simpler conceptual models using Tikhonov-Fenichel reductions, an algebraic approach to singular perturbation theory, can mitigate this problem. Compared to traditional methods for time scale separations (Tikhonov's theorem, quasi-steady state assumption), Tikhonov-Fenichel reductions have the advantage that we can compute a reduction directly for a separation of rates into slow and fast ones instead of a separation of components of the system. Moreover, we can find all such reductions algorithmically. In the present paper we use Tikhonov-Fenichel reductions to analyse a mutualism model tailored towards lichens with an explicit description of the interaction. We find that (1) the implicit description of the interaction given in the reductions by interaction terms (functional responses) varies depending on the scenario, (2) there is a tendency for the mycobiont, an obligate mutualist, to always benefit from the interaction while it can be detrimental for the photobiont, a facultative mutualist, depending on the parameters, (3) our model is capable of describing the shift from mutualism to parasitism, (4) our model can produce bistability with multiple stable fixed points in the interior of the first orthant. To analyse the reductions we formalize and discuss a mathematical criterion that categorizes two-species interactions. Throughout the paper we focus on the relation between the mathematics behind Tikhonov-Fenichel reductions and their biological interpretation.
WinPCA: A package for windowed principal component analysis
Authors: L. Moritz Blumer, Jeffrey M. Good, Richard Durbin
Date: 2025-01-21 00:00:00

Principal component analysis (PCA) is routinely used in population genetics to assess genetic structure. With chromosomal reference genomes and population-scale whole genome-sequencing becoming increasingly accessible, contemporary studies often include characterizations of the genomic landscape as it varies along chromosomes, commonly termed genome scans. While traditional summary statistics like FST and dXY remain integral to characterizing the genomic divergence profile, PCA fundamentally differs by providing single-sample resolution, thereby making results intuitively interpretable to help identify polymorphic inversions, introgression and other types of divergent sequence. Here, we introduce WinPCA, a user-friendly package to compute, polarize and visualize genetic principal components in windows along the genome. To accommodate low-coverage whole genome-sequencing datasets, WinPCA can optionally make use of PCAngsd methods to compute principal components in a genotype likelihood framework. WinPCA accepts variant data in either VCF or BEAGLE format and can generate rich plots for interactive data exploration and downstream presentation.
Predicting high dengue incidence in municipalities of Brazil using path signatures
Authors: Daniel A. M. Villela
Date: 2024-12-27 00:00:00
Tasks: Data Augmentation, Epidemiology, Specificity

Predicting whether to expect a high incidence of infectious diseases is critical for health surveillance. In the epidemiology of dengue, environmental conditions can significantly impact the transmission of the virus. Utilizing epidemiological indicators alongside environmental variables can enhance predictions of dengue incidence risk. This study analyzed a dataset of weekly case numbers, temperature, and humidity across Brazilian municipalities to forecast the risk of high dengue incidence using data from 2014 to 2023. The framework involved constructing path signatures and applying lasso regression for binary outcomes. Sensitivity reached 75%, while specificity was extremely high, ranging from 75% to 100%. The best performance was observed with information gathered after 35 weeks of observations using data augmentation via embedding techniques. The use of path signatures effectively captures the stream of information given by epidemiological and climate variables that influence dengue transmission. This framework could be instrumental in optimizing resources to predict high dengue risk in municipalities in Brazil and other countries after learning these country patterns.
Redefining Influenza Transmission Seasonality Using the Novel Seasonality Index
Authors: Branislava Lalic, Vladimir Koci, Ana Firanj Sremac, Zorana Jovanovic Andersen
Date: 2025-01-23 00:00:00
Tasks: Epidemiology

The impact of climate conditions on influenza epidemiology has mostly been studied by addressing a singular aspect of transmission and a climate variable correlating to it. As climate change unfolds at an unprecedented rate, we urgently need new multidisciplinary approaches that can embrace complexity of disease transmission in the fast-changing environment and help us better understand the implications for health. In this study, we have implemented a novel seasonality index to capture a vast network of climate, infectious, and socio-behavioural mechanisms influencing a seasonal influenza epidemic. We hypothesize that intricate, region-specific behavioural patterns are cross regulating the influenza spreading and dynamics of epidemics with changes in meteorological conditions within a specific season. To better understand the phenomena, we analysed weekly surveillance data from temperate European countries and redefined seasonal transitions using the seasonality index. This approach allowed us to characterize influenza seasonality more accurately in relation to specific atmospheric conditions. Key findings include: i) a strong correlation between influenza infection rates and the seasonality index across different climate zones and social groups, and ii) a high linear correlation between winter duration, determined by the seasonality index, and the time scale of low-frequency peaks in the infection rates power spectral density.
Computational modelling of biological systems now and then: revisiting tools and visions from the beginning of the century
Authors: Axel Loewe, Peter J. Hunter, Peter Kohl
Date: 2025-01-22 00:00:00
Tasks: Diagnostic, Uncertainty Quantification

Since the turn of the millennium, computational modelling of biological systems has evolved remarkably and sees matured use spanning basic and clinical research. While the topic of the peri-millennial debate about the virtues and limitations of 'reductionism and integrationism' seems less controversial today, a new apparent dichotomy dominates discussions: mechanistic vs. data-driven modelling. In light of this distinction, we provide an overview of recent achievements and new challenges with a focus on the cardiovascular system. Attention has shifted from generating a universal model of the human to either models of individual humans (digital twins) or entire cohorts of models representative of clinical populations to enable in silico clinical trials. Disease-specific parameterisation, inter-individual and intra-individual variability, uncertainty quantification as well as interoperable, standardised, and quality-controlled data are important issues today, which call for open tools, data and metadata standards, as well as strong community interactions. The quantitative, biophysical, and highly controlled approach provided by in silico methods has become an integral part of physiological and medical research. In silico methods have the potential to accelerate future progress also in the fields of integrated multi-physics modelling, multi-scale models, virtual cohort studies, and machine learning beyond what is feasible today. In fact, mechanistic and data-driven modelling can complement each other synergistically and fuel tomorrow's artificial intelligence applications to further our understanding of physiology and disease mechanisms, to generate new hypotheses and assess their plausibility, and thus to contribute to the evolution of preventive, diagnostic, and therapeutic approaches.
Intelligent Exercise and Feedback System for Social Healthcare using LLMOps
Authors: Yeongrak Choi, TaeYoung Kim, Hyung Soo Han
Date: 2025-01-22 00:00:00
Tasks: Management

This study addresses the growing demand for personalized feedback in healthcare platforms and social communities by introducing an LLMOps-based system for automated exercise analysis and personalized recommendations. Current healthcare platforms rely heavily on manual analysis and generic health advice, limiting user engagement and health promotion effectiveness. We developed a system that leverages Large Language Models (LLM) to automatically analyze user activity data from the "Ounwan" exercise recording community. The system integrates LLMOps with LLM APIs, containerized infrastructure, and CI/CD practices to efficiently process large-scale user activity data, identify patterns, and generate personalized recommendations. The architecture ensures scalability, reliability, and security for large-scale healthcare communities. Evaluation results demonstrate the system's effectiveness in three key metrics: exercise classification, duration prediction, and caloric expenditure estimation. This approach improves the efficiency of community management while providing more accurate and personalized feedback to users, addressing the limitations of traditional manual analysis methods.
Derivation from kinetic theory and 2-D pattern analysis of chemotaxis models for Multiple Sclerosis
Authors: Marzia Bisi, Maria Groppi, Giorgio Martalò, Romina Travaglini
Date: 2025-01-20 00:00:00

In this paper, a class of reaction-diffusion equations for Multiple Sclerosis is presented. These models are derived by means of a diffusive limit starting from a proper kinetic description, taking account of the underlying microscopic interactions among cells. At the macroscopic level, we discuss the necessary conditions for Turing instability phenomena and the formation of two-dimensional patterns, whose shape and stability are investigated by means of a weakly nonlinear analysis. Some numerical simulations, confirming and extending theoretical results, are proposed for a specific scenario.
Multilineage-differentiating stress-enduring cells alleviate neuropathic pain in mice by secreting TGF-b and IL-10
Authors: Yayu Zhao, Ying Fei, Yunyun Cai, Zhongya Wei, Ying Chen, Yuhua Ji, Xue Chen, Dongmei Zhang, Gang Chen
Date: 2025-01-20 00:00:00

Neuropathic pain is a chronic condition characterized by damage to and dysfunction of the peripheral or central nervous system. There are currently no effective treatment options available for neuropathic pain, and existing drugs often provide only temporary relief with potential side effects. Multilineage-differentiating stress-enduring (Muse) cells are characterized by high expansion potential, a stable phenotype and strong immunosuppression. These properties make them attractive candidates for therapeutics for neuropathic pain management. In this study, we conducted a series of experiments to evaluate the effect of Muse cells on neuropathic pain. Muse cells from different species demonstrated analgesic potential by reversing CCI-induced neuropathic pain. Protein profiling revealed a high degree of similarity between Muse cells and BMSCs. The intrathecal injection of Muse cells effectively reduced neuropathic pain in various mouse models, resulting in better analgesic effects than the administration of equivalent low doses of BMSCs. Immunohistochemical analysis and qPCR revealed the ability of Muse cells to inhibit spinal cord neuroinflammation caused by SNI. In addition, Transwell and ELISA revealed that Muse cells migrated through the injured dorsal root ganglion (DRG) via the CCR7-CCL21 chemotactic axis. In addition, the secretion of TGF-b and IL-10 by Muse cells was identified as the mechanism underlying the analgesic effect of Muse cells. The capacity of Muse cells to mitigate neuroinflammation and produce analgesic effects via the modulation of TGF-b and IL-10 underscores their potential as promising therapeutic approaches for the treatment of neuropathic pain.
Fixed-budget simulation method for growing cell populations
Authors: Shaoqing Chen, Zhou Fang, Zheng Hu, Da Zhou
Date: 2025-01-19 00:00:00

Investigating the dynamics of growing cell populations is crucial for unraveling key biological mechanisms in living organisms, with many important applications in therapeutics and biochemical engineering. Classical agent-based simulation algorithms are often inefficient for these systems because they track each individual cell, making them impractical for fast (or even exponentially) growing cell populations. To address this challenge, we introduce a novel stochastic simulation approach based on a Feynman-Kac-like representation of the population dynamics. This method, named the Feynman-Kac-inspired Gillespie's Stochastic Simulation Algorithm (FKG-SSA), always employs a fixed number of independently simulated cells for Monte Carlo computation of the system, resulting in a constant computational complexity regardless of the population size. Furthermore, we theoretically show the statistical consistency of the proposed method, indicating its accuracy and reliability. Finally, a couple of biologically relevant numerical examples are presented to illustrate the approach. Overall, the proposed FKG-SSA effectively addresses the challenge of simulating growing cell populations, providing a solid foundation for better analysis of these systems.
EMICSS: Added-value annotations for EMDB entries
Authors: Amudha K. Duraisamy, Neli Fonseca, Gerard J. Kleywegt, Ardan Patwardhan, Kyle L. Morris
Date: 2025-01-18 00:00:00

Motivation: The Electron Microscopy Data Bank (EMDB) is a key repository for 3D electron microscopy (3DEM) data but lacks comprehensive annotations and connections to most of the related biological, functional, and structural data. This limitation arises from the optional nature of such information to reduce depositor burden and the complexity of maintaining up-to-date external references, often requiring depositor consent. To address these challenges, we developed EMICSS (EMDB Integration with Complexes, Structures, and Sequences), an independent system that automatically updates cross-references with over 20 external resources, including UniProt, AlphaFold DB, PubMed, Complex Portal and Gene Ontology. Results: EMICSS (https://www.ebi.ac.uk/emdb/emicss) annotations are accessible in multiple formats for every EMDB entry and its linked resources, and programmatically via the EMDB Application Programming Interface (API). EMICSS plays a crucial role supporting the EMDB website, with annotations being used on entry pages, statistics, and in the search system. Availability and implementation: EMICSS is implemented in Python and it is an open-source, distributed under the EMBL-EBI license, with core code available on GitHub (https://github.com/emdb-empiar/added_annotations).
AI-Driven Hybrid Ecological Model for Predicting Oncolytic Viral Therapy Dynamics
Authors: Abicumaran Uthamacumaran, Juri Kiyokawa, Hiroaki Wakimoto
Date: 2025-01-18 00:00:00

Oncolytic viral therapy (OVT) is an emerging precision therapy for aggressive and recurrent cancers. However, its clinical efficacy is hindered by the complexity of tumor-virus-immune interactions and the lack of predictive models for personalized treatment. This study develops a data-driven, AI-powered computational model combining time-delayed Generalized Lotka-Volterra equations with advanced optimization algorithms, including Genetic Algorithms, Differential Evolution, and Reinforcement Learning, to optimize OVT oscillations' growth and damping. We hypothesize that the model can provide accurate, real-time predictions of OVT responses while identifying key biomarkers to enhance therapeutic efficacy. The model demonstrates strong predictive accuracy, achieving mean squared error (MSE) < 0.02 and R-squared > 0.82. It also identifies experimentally validated biomarkers such as TNF, NFkB, CD81, TRAF2, IL18, and BID, among other inflammatory cytokines and extracellular matrix reconstruction factors, despite being causally agnostic and unaware of specific experimental conditions or therapeutic combinations. Gene set enrichment analysis confirmed these biosignatures as critical predictors of tumor progression and indicated that photodynamic therapy activates immune responses similar to those elicited by combined OVT and immune checkpoint inhibitors. This hybrid model represents a significant step toward precision oncology and computational medicine, enabling longitudinal, adaptive treatment regimens and developing targeted immunotherapies based on molecular signatures, potentially improving patient outcomes.
Interpolation pour l'augmentation de donnees : Application à la gestion des adventices de la canne a sucre a la Reunion
Authors: Frederick Fabre Ferber, Dominique Gay, Jean-Christophe Soulie, Jean Diatta, Odalric-Ambrym Maillard
Date: 2025-01-10 00:00:00
Tasks: Data Augmentation, Gaussian Processes, regression

Data augmentation is a crucial step in the development of robust supervised learning models, especially when dealing with limited datasets. This study explores interpolation techniques for the augmentation of geo-referenced data, with the aim of predicting the presence of Commelina benghalensis L. in sugarcane plots in La R\'eunion. Given the spatial nature of the data and the high cost of data collection, we evaluated two interpolation approaches: Gaussian processes (GPs) with different kernels and kriging with various variograms. The objectives of this work are threefold: (i) to identify which interpolation methods offer the best predictive performance for various regression algorithms, (ii) to analyze the evolution of performance as a function of the number of observations added, and (iii) to assess the spatial consistency of augmented datasets. The results show that GP-based methods, in particular with combined kernels (GP-COMB), significantly improve the performance of regression algorithms while requiring less additional data. Although kriging shows slightly lower performance, it is distinguished by a more homogeneous spatial coverage, a potential advantage in certain contexts.