pwc-archive/papers-with-abstracts huggingface dataset:
The expensive son hypothesis
Authors: Lucas Invernizzi, Jean-françois Lemaître, Mathieu Douhard
Date: 2024-12-12 00:00:00

In its initial form, the expensive son hypothesis postulates that sons from male-biased sexually dimorphic species require more food during growth than daughters, which ultimately incur fitness costs for mothers predominantly producing and rearing sons. We first dissect the evolutionary framework in which the expensive son hypothesis is rooted, and we provide a critical reappraisal of its differences from other evolutionary theories proposed in the field of sex allocation. Then, we synthesize the current (and absence of) support for the costs of producing and rearing sons on maternal fitness components (future reproduction and survival). Regarding the consequences in terms of future reproduction, we highlight that species with pronounced sexual size dimorphism display a higher cost of sons than of daughters on subsequent reproductive performance, at least in mammals. However, in most studies, the relative fitness costs of producing and rearing sons and daughters can be due to sex-biased maternal allocation strategies rather than differences in energetic demands of offspring, which constitutes an alternative mechanism to the expensive son hypothesis stricto sensu. We observe that empirical studies investigating the differential costs of sons and daughters on maternal survival in non-human animals remain rare, especially for long-term survival. Indeed, most studies have investigated the influence of offspring sex (or litter sex ratio) at year $T$ on survival at year $T+1$, and they rarely provide a support to the expensive son hypothesis. On the contrary, in humans, most studies have focused on the relationship between proportion of sons and maternal lifespan, but these results are inconsistent. Our study highlights new avenues for future research that should provide a comprehensive view of the expensive son hypothesis, by notably disentangling the effects of offspring behaviour from the effect of sex-specific maternal allocation. Moreover, we emphasize that future studies should also embrace the mechanistic side of the expensive son hypothesis, largely neglected so far, by deciphering the physiological pathways linking son's production to maternal health and fitness.
An assessment of Alberta's strategy for controlling mountain pine beetle outbreaks
Authors: Evan C. Johnson, Mark A. Lewis
Date: 2024-12-11 00:00:00

The Canadian province of Alberta spent over 500 million dollars on controlling mountain pine beetle populations, but did it work? Using a statistical modeling framework coupled with long-term field data, we examined how direct control measures, severe winters, and host-tree depletion shaped the trajectory of Alberta's mountain pine beetle outbreak between 2009 and 2020. Simulations suggest that control efforts reduced total tree mortality by 79% (95% predictive interval: 58--89%) and prevented 1.8 (0.91--4.1) trees per hectare from being killed from 2010--2020. Although cold winters had little effect on overall damage, they acted synergistically with control to end the outbreak, causing population collapse circa 2020. This synergy supports a "wait it out" strategy of mountain pine beetle management, where moderate control effort is applied until an extreme weather event delivers the final blow. Any effects of host-tree depletion via beetle attack were negligible. From an economic perspective, removing one infestation tree -- at an approximate cost of 320 CAD -- prevented the loss of roughly six (2.6--15) trees, demonstrating the potential for long-term cost-effectiveness. Our results further indicate that future outbreaks may vary widely in severity due to environmental stochasticity, with potential damage in a no-control scenario ranging from 0.41 to 9.7 trees per hectare killed (over a hypothetical 11-year period). An alternative model predicts an even wider range of outcomes: 1--40 trees per hectare. These findings highlight not only the potential of sustained control efforts in mitigating forest pest outbreaks, but also the inherent uncertainty in long-term ecological forecasting.
Biological barriers to forest pest invasions: A novel host tree slows mountain pine beetle range expansion
Authors: Evan C. Johnson, Antonia Musso, Catherine Cullingham, Mark A. Lewis
Date: 2024-12-11 00:00:00

Following widespread outbreaks across western North America, mountain pine beetle recently expanded its range from British Columbia into Alberta. However, mountain pine beetle's eastward expansion across Canada has stalled unexpectedly, defying predictions of rapid spread through jack pine, a novel host tree. This study investigates the underlying causes of this deceleration using an integrative approach combining statistical modeling, simulations, and experimental data. We find that the slow spread is primarily due to mountain pine beetle's difficulty in finding and successfully attacking jack pine trees, rather than issues with reproduction or larval development. The underlying mechanism impeding beetle range expansion has been hypothesized to be lower pine volumes in eastern forests, which are primarily a consequence of lower stem density. However, our analysis suggests that jack pine's phenotype itself is the primary impediment. We propose that jack pine's smaller size, thinner phloem, and lower monoterpene concentrations result in weaker chemical cues during the host-finding and mass-attack stages of MPB's life cycle, ultimately leading to fewer successful attacks. These findings suggest a reduced risk of further eastward spread, but should be interpreted cautiously due to enormous policy implications and the inherent limitations of ecological forecasting.
A new oscillatory regime in two-strain epidemic models with partial cross-immunity
Authors: Nir Gavish
Date: 2024-12-10 00:00:00

Infectious diseases often involve multiple strains that interact through the immune response generated after an infection. This study investigates the conditions under which a two-strain epidemic model with partial cross-immunity can lead to self-sustained oscillations, and reveals a new oscillatory regime in these models. Contrary to previous findings, which suggested that strong cross-immunity and significant asymmetry between strains are necessary for oscillations, our results demonstrate that sustained oscillations can occur even with weak cross-immunity and weak asymmetry. Using asymptotic methods, we provide a detailed mathematical analysis showing that the steady state of coexistence becomes unstable along specific curves in the parameter space, leading to oscillatory solutions for any value of the basic reproduction number greater than one. Numerical simulations support our theoretical findings, highlighting an unexpected oscillatory region in the parameter domain. These results challenge the current understanding of oscillatory dynamics in multi-strain epidemiological models, point to an oversight in previous studies, and suggest broader conditions under which such dynamics can arise.
Argentine ants regulate traffic flow with stopped individuals
Authors: Ulrich Dobramysl, Simon Garnier, Laure-Anne Poissonnier, Audrey Dussutour, Maria Bruna
Date: 2024-12-09 00:00:00

We investigated the emerging traffic patterns of Argentine ants (Linepithema humile) as they navigated a narrow bridge between their nest and a food source. By tracking ant movements in experiments with varying bridge widths and colony sizes and analyzing the resulting trajectories, we discovered that a small subset of ants stopped for long periods of time, acting as obstacles and affecting traffic flow. Interestingly, the fraction of these stopped ants increased with wider bridges, suggesting a mechanism to reduce traffic flow to a narrower section of the bridge. To quantify transport efficiency, we measured the average speed of the ants on the bridge as a function of the pressure of ants arriving at the bridge, finding this relationship to be an increasing but saturating function of the pressure. We developed an agent-based model for ant movement and interactions to better understand these dynamics. Including stopped agents in the model was crucial to explaining the experimental observations. We further validated our hypothesis by introducing artificial obstacles on the bridges and found that our simulations accurately mirrored the experimental data when these obstacles were included. These findings provide new insights into how Argentine ants self-organize to manage traffic, highlighting a unique form of dynamic obstruction that enhances traffic flow in high-density conditions. This study advances our understanding of self-regulation in biological traffic systems and suggests potential applications for managing human traffic in congested environments.
Infinite Mixture Models for Improved Modeling of Across-Site Evolutionary Variation
Authors: Mandev S. Gill, Guy Baele, Marc A. Suchard, Philippe Lemey
Date: 2024-12-08 00:00:00

Scientific studies in many areas of biology routinely employ evolutionary analyses based on the probabilistic inference of phylogenetic trees from molecular sequence data. Evolutionary processes that act at the molecular level are highly variable, and properly accounting for heterogeneity in evolutionary processes is crucial for more accurate phylogenetic inference. Nucleotide substitution rates and patterns are known to vary among sites in multiple sequence alignments, and such variation can be modeled by partitioning alignments into categories corresponding to different substitution models. Determining $\textit{a priori}$ appropriate partitions can be difficult, however, and better model fit can be achieved through flexible Bayesian infinite mixture models that simultaneously infer the number of partitions, the partition that each site belongs to, and the evolutionary parameters corresponding to each partition. Here, we consider several different types of infinite mixture models, including classic Dirichlet process mixtures, as well as novel approaches for modeling across-site evolutionary variation: hierarchical models for data with a natural group structure, and infinite hidden Markov models that account for spatial patterns in alignments. In analyses of several viral data sets, we find that different types of infinite mixture models emerge as the best choices in different scenarios. To enable these models to scale efficiently to large data sets, we adapt efficient Markov chain Monte Carlo algorithms and exploit opportunities for parallel computing. We implement this infinite mixture modeling framework in BEAST X, a widely-used software package for Bayesian phylogenetic inference.
Mating versus alternative blood sources as determinants to mosquito abundance and population resilience
Authors: Gideon A. Ngwa, Bime M. Ghakanyuy, Miranda I. Teboh-Ewungkem, Jacek Banasiak
Date: 2024-12-08 00:00:00

A deterministic nonlinear ordinary differential equation model for mosquito dynamics in which the mosquitoes can quest for blood either within a human population or within non-human/vertebrate populations is derived and studied. The model captures both the mosquito's aquatic and terrestrial forms and includes a mechanism to investigate the impact of mating on mosquito dynamics. The model uses a restricted form of homogeneous mixing based on the idea that the mosquito has a blood-feeding habit by accounting for the mosquitoes' blood-feeding preferences as well as its gonotrophic cycle. This characterization allows us to compartmentalise the total mosquito population into distinct compartments according to the spatial location of the mosquito (breeding site, resting places and questing places) as well as blood-fed status. Issues of overcrowding and intraspecific competition both within the aquatic and the terrestrial stages of the mosquito's life forms are addressed and considered in the model. Results show that the inclusion of mating induces bi-stability; a phenomenon whereby locally stable trivial and non-trivial equilibria co-exist with an unstable non-zero equilibrium. The local nature of the stable equilibria is demonstrated by numerically showing that the long-term state of the system is sensitive to initial conditions. The bi-stability state is analogous to the phenomenon of the Allee effect that has been reported in population biology. The model's results, including the derivation of the threshold parameter of the system, are comprehensively tested via numerical simulations. The output of our model has direct application to mosquito control strategies, for it clearly shows key points in the mosquito's developmental pathway that can be targeted for control purposes.
Timing consistency of T cell receptor activation in a stochastic model combining kinetic segregation and proofreading
Authors: Thorsten Prüstel, Martin Meier-Schellersheim
Date: 2024-12-09 00:00:00

T cell receptor signaling must operate reliably under tight time constraints. While assuming quite different mechanisms, two prominent models of T cell receptor activation, kinetic segregation and kinetic proofreading, both introduce a distinct time scale. However, a clear understanding of whether and how those characteristic times give rise to a consistent timing of T cell receptor activation in the presence of stochastic fluctuations has been lacking so far. Here, using a simulation approach capable of modeling molecular interactions between adjacent cell membranes, we explore a stochastic model that combines elements of kinetic segregation and proofreading. Our simulations suggest that the two mechanisms interoperate, thereby rendering the corresponding stochastic times biologically functional. Receptor activation relies on rare molecular events that are not well characterized by the mean of the underlying probability density function. Yet, a consistent timing of receptor activation can be ensured by a modest number of proofreading steps.
Cardiometabolic Risk Factors in South Asians: An Epidemiological and Anthropological Study in an Urban Populace of Eastern India
Authors: Karishma Yasmin
Date: 2024-12-08 00:00:00
Tasks: Descriptive

Background: This study examines cardiometabolic (CM) risk factors in an urban South Asian population, integrating medical and Anthropological perspectives to explore the effects of socio-economic, lifestyle, gender-specific factors, and cultural norms on health outcomes. Results: Analysis indicates a high prevalence of MetS and Pre-MetS, particularly among females, with significant predictors including BMI, triglycerides, total cholesterol, and waist circumference, alongside socio-genetic and lifestyle factors. Employing Elastic Net logistic regression, the researcher rigorously validated models to evaluate their predictive performance while also describing the associations and prevalence of known risk factors. The use of this method underlines the importance of combining traditional risk factors with socio-genetic, biological, economic and lifestyle variables, while Anthropological insights reveal the impact of urbanization and socio-cultural norms on health behaviors. Conclusion: The study advocates for a multidisciplinary approach in public health strategies, emphasizing the complex interplay between genetic, environmental, biological and socio-cultural influences on cardiometabolic health. This dual approach aligns with descriptive and predictive model goals. The future research should further integrate biomedical sciences with socio-cultural studies to develop culturally sensitive interventions, aiming to address the growing challenge of CM diseases in urban South Asian contexts.
Dynamical modelling of the frailty index indicates that health reaches a tipping point near age 75
Authors: Glen Pridham, Kenneth Rockwood, Andrew D. Rutenberg
Date: 2024-12-02 00:00:00

The frailty index (FI) serves as a useful quantitative summary of age-related health. We quantitatively modelled FI trajectories with age. We fit directly to longitudinal transitions in health attributes from normal to deficit and vice-versa. We used data from two large longitudinal studies: the Health and Retirement Study and the English Longitudinal Study of Ageing. The studies included 47592 individuals with 254357 total visits. Using damage (deficit emergence) and repair (deficit recovery) transitions we estimated changes to robustness and resilience, respectively. We find that both robustness and resilience decrease continuously with both increasing age and FI. Remarkably, these declines caused a tipping point in health near age 75, when damage and repair rates are equal. Beyond this tipping point, the ongoing loss of both robustness and resilience leads to a sharp increase in the FI and a commensurate increase in risk of mortality. This tipping point was observed in both sexes, noting that males showed higher initial robustness and resilience, and commensurately steeper decline, consistent with the sex-frailty paradox. We infer that robustness and resilience mitigate environmental stressors only up to an age of 75, beyond which health deficits will increasingly accumulate leading to death.
MIN: Multi-channel Interaction Network for Drug-Target Interaction with Protein Distillation
Authors: Shuqi Li, Shufang Xie, Hongda Sun, Yuhan Chen, Tao Qin, Tianjun Ke, Rui Yan
Date: 2024-11-23 00:00:00
Tasks: Contrastive Learning, Drug Discovery, Prediction, Representation Learning

Traditional drug discovery processes are both time-consuming and require extensive professional expertise. With the accumulation of drug-target interaction (DTI) data from experimental studies, leveraging modern machine-learning techniques to discern patterns between drugs and target proteins has become increasingly feasible. In this paper, we introduce the Multi-channel Interaction Network (MIN), a novel framework designed to predict DTIs through two primary components: a representation learning module and a multi-channel interaction module. The representation learning module features a C-Score Predictor-assisted screening mechanism, which selects critical residues to enhance prediction accuracy and reduce noise. The multi-channel interaction module incorporates a structure-agnostic channel, a structure-aware channel, and an extended-mixture channel, facilitating the identification of interaction patterns at various levels for optimal complementarity. Additionally, contrastive learning is utilized to harmonize the representations of diverse data types. Our experimental evaluations on public datasets demonstrate that MIN surpasses other strong DTI prediction methods. Furthermore, the case study reveals a high overlap between the residues selected by the C-Score Predictor and those in actual binding pockets, underscoring MIN's explainability capability. These findings affirm that MIN is not only a potent tool for DTI prediction but also offers fresh insights into the prediction of protein binding sites.
Poromechanical modelling of the time-dependent response of in vivo human skin during extension
Authors: Thomas Lavigne, Stéphane Urcun, Emmanuelle Jacquet, Jérôme Chambert, Aflah Elouneg, Camilo A. Suarez-Afanador, Stéphane P. A. Bordas, Giuseppe Sciumè, Pierre-Yves Rohan
Date: 2024-12-10 00:00:00

This paper proposes a proof of concept application of a biphasic constitutive model to identify the mechanical properties of in vivo human skin under extension. Although poromechanics theory has been extensively used to model other soft biological tissues, only a few studies have been published for skin, and most have been limited to ex vivo or in silico conditions. However, in vivo procedures are crucial to determine the subject-specific properties at different body sites. This study focuses on cyclic uni-axial extension of the upper arm skin, using unpublished data collected by Chambert et al. Our analysis shows that a two-layer finite element model allows representing all relevant features of the observed mechanical response to the imposed external loading, which was composed, in this contribution, of four loading-sustaining-unloading cycles. The Root Mean Square Error (RMSE) between the calibrated model and the measured Force-time response was 8.84e-3 N. Our biphasic model represents a preliminary step toward investigating the mechanical conditions responsible for the onset of injury. It allows for the analysis of changes in Interstitial Fluid (IF) pressure, flow, and osmotic pressure, in addition to the mechanical fields. Future work will focus on the interaction of multiple biochemical factors and the complex network of regulatory signals.
Efficient Representations of Cardiac Spatial Heterogeneity in Computational Models
Authors: Alejandro Nieto Ramos, Elizabeth M. Cherry
Date: 2024-11-24 00:00:00

It is generally assumed that all cells in models of the electrical behavior of cardiac tissue have the same properties. However, there are differences in cardiac cells that are not well characterized but cause spatial heterogeneity of the electrical properties in tissue. Optical mapping can be used to obtain experimental data from cardiac surfaces at high spatial resolution. Variations in model parameters can be defined on a coarser grid than considering each single pixel, which would allow a representation of heterogeneous tissue to be obtained more efficiently. Here, we address how coarse the parameterization grid can be while still obtaining accurate results for complicated dynamical states of spatially discordant alternans. We use the Fenton-Karma model with heterogeneity included as a smooth nonlinear gradient over space for more model parameters. To obtain the more efficient representations, we set parameter values everywhere in space based on the assumption that the exact parameter values are known at the points of the coarser grid; we assume the parameter values could be obtained from experimental data. We assign parameter values in space by fitting either a piecewise-constant or piecewise-linear function to the spatially coarse known data. We wish to identify the maximal grid spacing of such points to obtain good agreement with spatial profiles of action potential duration during complex states. We find that coarse grid spacing of about 1.0-1.6 cm generally results in spatial profiles that agree well with the true profiles for a range of different model parameters and different functions of those parameters over space. In addition, the piecewise-constant and piecewise-linear functions perform similarly. Our results to date suggest that matching the output of models of cardiac tissue to heterogeneous experimental data can be done efficiently, even during complex dynamical states.
Systematic comparison of deep generative models applied to multivariate financial time series
Authors: Howard Caulfield, James P. Gleeson
Date: 2024-12-09 00:00:00
Tasks: Management, Portfolio Optimization, Time Series

Financial time series (FTS) generation models are a core pillar to applications in finance. Risk management and portfolio optimization rely on realistic multivariate price generation models. Accordingly, there is a strong modelling literature dating back to Bachelier's Theory of Speculation in 1901. Generating FTS using deep generative models (DGMs) is still in its infancy. In this work, we systematically compare DGMs against state-of-the-art parametric alternatives for multivariate FTS generation. We initially compare both DGMs and parametric models over increasingly complex synthetic datasets. The models are evaluated through distance measures for varying distribution moments of both the full and rolling FTS. We then apply the best performing DGM models to empirical data, demonstrating the benefit of DGMs through a implied volatility trading task.