pwc-archive/papers-with-abstracts huggingface dataset:
Method for Evaluating the Number of Signal Sources and Application to Non-invasive Brain-computer Interface
Authors: Alexandra Bernadotte, Victor Buchstaber
Date: 2024-09-26 00:00:00
Tasks: Brain Computer Interface, Time Series

This paper provides a brief introduction of the mathematical theory behind the time series unfolding method. The algorithms presented serve as a valuable mathematical and analytical tool for analyzing data collected from brain-computer interfaces. In our study, we implement a mathematical model based on polyharmonic signals to interpret the data from brain-computer interface sensors. The analysis of data coming to the brain-computer interface sensors is based on a mathematical model of the signal in the form of a polyharmonic signal. Our main focus is on addressing the problem of evaluating the number of sources, or active brain oscillators. The efficiency of our approach is demonstrated through analysis of data recorded from a non-invasive brain-computer interface developed by the author.
Inner ear morphology in wild versus laboratory house mice
Authors: Sabrina Renaud, Léa Amar, Pascale Chevret, Caroline Romestaing, Jean-Pierre Quéré, Corinne Régis, Renaud Lebrun
Date: 2024-10-17 00:00:00

The semicircular canals of the inner ear are involved in balance and velocity control. Being crucial to ensure efficient mobility, their morphology exhibits an evolutionary conservatism attributed to stabilizing selection. Release of selection in slow-moving animals has been argued to lead to morphological divergence and increased inter-individual variation. In its natural habitat, the house mouse Mus musculus moves in a tridimensional space where efficient balance is required. In contrast, laboratory mice in standard cages are severely restricted in their ability to move, which possibly reduces selection on the inner ear morphology. This effect was tested by comparing four groups of mice: several populations of wild mice trapped in commensal habitats in France; their second-generation laboratory offspring, to assess plastic effects related to breeding conditions; a standard laboratory strain (Swiss) that evolved for many generations in a regime of mobility reduction; and hybrids between wild offspring and Swiss mice. The morphology of the semicircular canals was quantified using a set of 3D landmarks and semi-landmarks analyzed using geometric morphometric protocols. Levels of inter-population, inter-individual (disparity) and intra-individual (asymmetry) variation were compared. All wild mice shared a similar inner ear morphology, in contrast to the important divergence of the Swiss strain. The release of selection in the laboratory strain obviously allowed for an important and rapid drift in the otherwise conserved structure. Shared traits between the inner ear of the lab strain and domestic pigs suggested a common response to mobility reduction in captivity. The lab-bred offspring of wild mice also differed from their wild relatives, suggesting plastic response related to maternal locomotory behavior, since inner ear morphology matures before birth in mammals. The signature observed in lab-bred wild mice and the lab strain was however not congruent, suggesting that plasticity did not participate to the divergence of the laboratory strain. However, contrary to the expectation, wild mice displayed slightly higher levels of inter-individual variation than laboratory mice, possibly due to the higher levels of genetic variance within and among wild populations compared to the lab strain. Differences in fluctuating asymmetry levels were detected, with the laboratory strain occasionally displaying higher asymmetry scores than its wild relatives. This suggests that there may indeed be a release of selection and/or a decrease in developmental stability in the laboratory strain.
Reading Yule in light of the history and present of macroevolution
Authors: Matt Pennell, Ailene MacPherson
Date: 2024-10-15 00:00:00
Tasks: Diversity

Yule's 1925 paper introducing the branching model that bears his name was a landmark contribution to the biodiversity sciences. In his paper, Yule developed stochastic models to explain the observed distribution of species across genera and to test hypotheses about the relationship between clade age, diversity, and geographic range. Here we discuss the intellectual context in which Yule produced this work, highlight Yule's key mathematical and conceptual contributions using both his and more modern derivations, and critically examine some of the assumptions of his work through a modern lens. We then document the strange trajectory of his work through the history of macroevolutionary thought and discuss how the fundamental challenges he grappled with -- such as defining higher taxa, linking microevolutionary population dynamics to macroevolutionary rates, and accounting for inconsistent taxonomic practices -- remain with us a century later.
Bayesian inference of mixed Gaussian phylogenetic models
Authors: Bayu Brahmantio, Krzysztof Bartoszek, Etka Yapar
Date: 2024-10-15 00:00:00
Tasks: Bayesian Inference

Background: Continuous traits evolution of a group of taxa that are correlated through a phylogenetic tree is commonly modelled using parametric stochastic differential equations to represent deterministic change of trait through time, while incorporating noises that represent different unobservable evolutionary pressures. Often times, a heterogeneous Gaussian process that consists of multiple parametric sub-processes is often used when the observed data come from a very diverse set of taxa. In the maximum likelihood setting, challenges can be found when exploring the involved likelihood surface and when interpreting the uncertainty around the parameters. Results: We extend the methods to tackle inference problems for mixed Gaussian phylogenetic models (MGPMs) by implementing a Bayesian scheme that can take into account biologically relevant priors. The posterior inference method is based on the Population Monte Carlo (PMC) algorithm that are easily parallelized, and using an efficient algorithm to calculate the likelihood of phylogenetically correlated observations. A model evaluation method that is based on the proximity of the posterior predictive distribution to the observed data is also implemented. Simulation study is done to test the inference and evaluation capability of the method. Finally, we test our method on a real-world dataset. Conclusion: We implement the method in the R package bgphy, available at github.com/bayubeta/bgphy. Simulation study demonstrates that the method is able to infer parameters and evaluate models properly, while its implementation on the real-world dataset indicates that a carefully selected model of evolution based on naturally occurring classifications results in a better fit to the observed data.
The exact travelling wave solutions of a KPP equation
Authors: Eugene Kogan
Date: 2024-10-14 00:00:00

We obtain the exact analytical traveling wave solutions of the Kolmogorov-Petrovskii-Piskunov equation with the reaction term belonging to the class of functions, which includes that of the (generalized) Fisher equation, for the particular values of the waves speed. Additionally we obtain the the exact analytical traveling wave solutions of the generalized Burgers-Huxley equation.
A reaction-diffusion model for Mycobacterium tuberculosis infection
Authors: C. Accarino, R. Accarino, F. Capone, R. De Luca, L. Fiorentino, G. Massa
Date: 2024-10-14 00:00:00
Tasks: model

This paper aims to investigate a reaction-diffusion model which describes in-host infection for Mycobacterium tuberculosis (Mtb) allowing random motion (i.e. linear diffusion) and chemotaxis (i.e. non-linear diffusion) of macrophages and bacteria populations. In particular, chemotaxis-driven aggregation of macrophages plays a fundamental role in the development of the Mtb infection and the production of chemokine - located in the infection site - represents an attractant for the uninfected macrophages, therefore we consider chemotaxis between infected macrophages and uninfected macrophages. The linear stability of the endemic equilibria is investigated. In particular, by looking for conditions guaranteeing that an equilibrium, stable in the absence of diffusion, becomes unstable when diffusion is allowed, the formation of Turing patterns - that biologically represent the formation of granuloma initiated by the immune cells - is investigated. The weakly nonlinear analysis is performed to deeply explore the patterns amplitude. Furthermore, via numerical simulations, the formation of Turing patterns is analysed.
Negative density-dependent dispersal of the mountain pine beetle in Alberta
Authors: Evan C. Johnson, Mark Lewis
Date: 2024-10-13 00:00:00

Understanding the mountain pine beetle's dispersal patterns is critical for evaluating its threat to Canada's boreal forests. It is generally believed that higher beetle densities lead to increased long-distance dispersal due to aggregation pheromones becoming repellent at high densities, causing beetles to seek areas with less competition. However, using helicopter surveys of infested trees, along with statistical models, we find no evidence supporting a positive relationship. Instead, we observe a weak negative association between population density and dispersal at all spatial scales. A possible explanation is that at low population densities, beetles cannot successfully attack healthy trees and must travel farther to find weakened hosts. Even so, the influence of beetle density on dispersal is minor compared to the spatiotemporal variation in the overall (density-independent) scale of dispersal, as revealed by our models. This variation accounts for the MPB's erratic range expansion across western Alberta, which varied from 20 km to 220 km annually.
Using multi-delay discrete delay differential equations to accurately simulate models with distributed delays
Authors: Tyler Cassidy
Date: 2024-10-12 00:00:00

Delayed processes are ubiquitous throughout biology. These delays may arise through maturation processes or as the result of complex multi-step networks, and mathematical models with distributed delays are increasingly used to capture the heterogeneity present in these delayed processes. Typically, these distributed delay differential equations are simulated by discretizing the distributed delay and using existing tools for the resulting multi-delay delay differential equations or by using an equivalent representation under additional assumptions on the delayed process. Here, we use the existing framework of functional continuous Runge-Kutta methods to confirm the convergence of this common approach. Our analysis formalizes the intuition that the least accurate numerical method dominates the error. We give a number of examples to illustrate the predicted convergence, derive a new class of equivalences between distributed delay and discrete delay differential equations, and give conditions for the existence of breaking points in the distributed delay differential equation. Finally, our work shows how recently reported multi-delay complexity collapse arises naturally from the convergence of equations with multiple discrete delays to equations with distributed delays, offering insight into the dynamics of the Mackey-Glass equation.
Recovering complex ecological dynamics from time series using state-space universal dynamic equations
Authors: Jack H. Buckner, Zechariah D. Meunier, Jorge Arroyo-Esquivel, Nathan Fitzpatrick, Ariel Greiner, Lisa C. McManus, James R. Watson
Date: 2024-10-11 00:00:00
Tasks: Time Series

Ecological systems often exhibit complex nonlinear dynamics like oscillations, chaos, and regime shifts. Universal dynamic equations have shown promise in modeling complex dynamics by combining known functional forms with neural networks that represent unknown relationships. However, these methods do not yet accommodate the forms of uncertainty common to ecological datasets. To address this limitation, we developed state-space universal dynamic equations by combining universal differential equations with a state-space modeling framework, accounting for uncertainty. We tested this framework on two simulated and two empirical case studies and found that this method can recover nonlinear biological interactions that produce complex behaviors, including chaos and regime shifts. Their forecasting performance is context-dependent, with the best performance being achieved on chaotic and oscillating time series. This new approach leveraging both ecological theory and data-driven machine learning offers a promising new way to make accurate and useful predictions of ecosystem change.
Nonlinear dynamics of CAR-T cell therapy
Authors: Artur Cesar Fassoni, Denis Carvalho Braga
Date: 2024-10-10 00:00:00

Chimeric antigen receptor T-cell (CAR-T) therapy is considered a promising cancer treatment. The dynamic response to this therapy can be broadly divided into a short-term phase, ranging from weeks to months, and a long-term phase, ranging from months to years. While the short-term response, encompassing the multiphasic kinetics of CAR-T cells, is better understood, the mechanisms underlying the outcomes of the long-term response, characterized by sustained remission, relapse, or disease progression, remain less understood due to limited clinical data. Here, we analyze the long-term dynamics of a previously validated mathematical model of CAR-T cell therapy. We perform a comprehensive stability and bifurcation analysis, examining model equilibria and their dynamics over the entire parameter space. Our results show that therapy failure results from a combination of insufficient CAR-T cell proliferation and increased tumor immunosuppression. By combining different techniques of nonlinear dynamics, we identify Hopf and Bogdanov-Takens bifurcations, which allow to elucidate the mechanisms behind oscillatory remissions and transitions to tumor escape. In particular, rapid expansion of CAR-T cells leads to oscillatory tumor control, while increased tumor immunosuppression destabilizes these oscillations, resulting in transient remissions followed by relapse. Our study highlights different mathematical tools to study nonlinear models and provides critical insights into the nonlinear dynamics of CAR-T therapy arising from the complex interplay between CAR-T cells and tumor cells.
Near-ideal selection for the Standard Genetic Code
Authors: Michael Yarus
Date: 2024-10-10 00:00:00

Evolutionary theorizing resembles building an aircraft while also piloting it; new results change the scaffold for older ideas, requiring revised strategy to remain airborne. A calculated kinetic pathway exists that, under explicit quantitative assumptions, delivers the SGC (Standard Genetic Code). The pathway and evidence for it is summarized below, striving for a clearer, more complete account than was possible during its construction. Beginning with experimental amino acid-RNA interactions, code assignments are fused, codes divide and an early coding crescendo is tested for homogeneous assignments, which are then selected for independent survival in a near-empty biotic world. During escape from the site of origin and diaspora, a near-complete SGC becomes dominant by supporting proficient division. Crescendo, escape and diaspora together comprise a near-ideal least selection for a Standard Genetic Code that specifically served LUCA, likely a free-living anaerobic thermophilic microbe. Selection during diaspora conceivably made persistence across gigayears feasible.
The impact of quiescence egg on the efficacy of Wolbachia-carrying mosquito release to suppress or diminish the wild mosquito population
Authors: Luís E. S. Lopes, Cláudia P. Ferreira, Sergio M. Oliva
Date: 2024-09-25 00:00:00

An ordinary differential model is proposed to understand the role of egg quiescence on the efficacy of releasing the Wolbachia-carrying mosquito to suppress or diminish the wild mosquito population. The model has up to four steady states, which are: the persistence of the uninfected population and the extinction of the infected one, the persistence of the infected population and the extinction of the uninfected one, the extinction of both populations, and the persistence of both populations. Their stability is given by four thresholds; two are the fitness of infected and uninfected populations when isolated, and the other two are the relative fitness when they are competing. Bifurcation diagrams, phase space, and parameter space are shaped by the sex ratio that can be manipulated by the Wolbachia symbiont. A sensitive analysis indicates that the uninfected population always maximizes its fitness according to the environmental conditions throughout, changing the quiescence rate. Because Wolbachia-infected eggs do not survive quiescence or adults emerging from it are infertile, the risk of arbovirus transmission increases during or after environmental stress that triggers quiescence. This can jeopardize the use of Wolbachia-infected mosquito to control arbovirus transmissions in regions where quiescence occurs at a high rate.
A Selfish Herd with a Target
Authors: Thomas Stemler, Shannon Dee Algar, Jesse Zhou
Date: 2024-10-17 00:00:00

One of the most striking phenomena in biological systems is the tendency for biological agents to spatially aggregate, and subsequently display further collective behaviours such as rotational motion. One prominent explanation for why agents tend to aggregate is known as the selfish herd hypothesis (SHH). The SHH proposes that each agent has a "domain of danger" whose area is proportional to the risk of predation. The SHH proposes that aggregation occurs as a result of agents seeking to minimise the area of their domain. Subsequent attempts to model the SHH have had varying success in displaying aggregation, and have mostly been unable to exhibit further collective behaviours, such as aligned motion or milling. Here, we introduce a model that seeks to generalise the principles of previous SHH models, by allowing agents to aim for domains of a specific (possibly non-minimal) area or a range of areas and study the resulting collective dynamics. Moreover, the model incorporates the lack of information that biological agents have by limiting the range of movement and vision of the agents. The model shows that the possibility of further collective motion is heavily dependent on the domain area the agents aim for - with several distinct phases of collective behaviour.
End-Stage Liver Disease Comorbidities in Patients Awaiting Transplantation: Identification and Impact on Liver Transplant Survival
Authors: Julia Tacherel, Kiruthika Balakrishnan, Gyorgy Simon, Lisiane Pruinelli
Date: 2024-10-15 00:00:00
Tasks: Descriptive, Survival Analysis

End-Stage Liver Disease (ESLD), a complex condition, has high rates of co-occurring comorbidities affecting multiple organ systems. There is no clear evidence-based practice (EBP) guidelines addressing the progression of comorbidities in ESLD patients awaiting liver transplantation (LT) and their impact on survival, both pre- and post-transplant. This study aimed to identify and quantify the trajectory of the most common and deteriorating comorbidities in ESLD patients awaiting LT and to analyze their effect on patient outcomes. An initial exploratory phase to identify frequent comorbidities in ESLD patients. Relevant EBP-driven data for diagnosing and measuring the progression of these conditions were collected and organized into five research matrices. In the quantitative phase, a retrospective analysis was conducted using longitudinal de-identified data from electronic health records (EHR) for patients who underwent LT between 2011-2021. Data included demographics, labs, procedures, and medications. Descriptive statistics and survival analysis assessed the association of comorbidities with post-transplant survival. The five most frequent comorbidities identified were Diabetes Mellitus (DM), Chronic Kidney Disease (CKD), Malnutrition, Portal Hypertension (PH), and Ascites. Of the 722 patients analyzed, 68.2% were male, mean age of 54.81 (SD = 11.24), and 19.8% died post-LT. Survival analysis showed a gradual decline over time, with the most significant drop at five years post-LT. Significant predictors of post-LT survival were age at transplant (p=0.01), waitlist time (p=0.004), DM at listing (p=0.02), low albumin (p=0.03), and CKD stage 5 development after listing (p=0.04). This study highlights the variability in diagnosing and measuring comorbidities in ESLD patients and provides insights into their progression and impact on post-LT survival.
A Reanalysis of the FDA's Benefit-Risk Assessment of Moderna's mRNA-1273 COVID Vaccine: For 18-25-Year-Old Males, Risks Exceeded Benefits Relative to Hospitalizations
Authors: Paul S. Bourdon, Ram Duriseti, H. Christian Gromoll, Dyana K. Dalton, Kevin Bardosh, Allison E. Krug
Date: 2024-10-15 00:00:00

The FDA conducted a benefit-risk assessment for Moderna's COVID vaccine mRNA-1273 prior to its full approval (announced 1/31/2022). The FDA focused its assessment on males of ages 18-64 years because the agency's risk analysis was limited to vaccine-attributable myocarditis/pericarditis (VAM/P) given the excess risk among males. The FDA analysis concluded that vaccine benefits clearly outweighed risks, even for 18-25-year-old males (those at highest VAM/P risk). We reanalyze the FDA benefit-risk assessment using information available through the third week of January 2022 and focusing on 18-25-year-old males. We use the FDA's framework but extend its model by accounting for protection derived from prior COVID infection, finer age-stratification in COVID-hospitalization rates, and incidental hospitalizations (those of patients who test positive for COVID but are being treated for something else). We also use more realistic projections of Omicron-infection rates and more accurate rates of VAM/P. With hospitalizations as the principal endpoint of the analysis (those prevented by vaccination vs. those caused by VAM/P), we find a net harm: vaccine risks outweighed benefits for 18-25-year-old males, except in scenarios projecting implausibly high Omicron-infection prevalence. Comparing the projections of FDA's "most likely scenario" to ours, we find that the FDA's benefit-risk ratio, 43.33, is over 60 times more favorable than ours, 0.67. Our assessment suggests that mRNA-1273 vaccination of 18-25-year-old males generated between 16% and 63% more hospitalizations from vaccine-attributable myocarditis/pericarditis alone compared to COVID hospitalizations prevented (over a 5-month period of vaccine protection assumed by the FDA). The preceding assessment is based on assumptions drawn from data available before 1/22/22. Moreover, these assumptions are validated by subsequently available data.