pwc-archive/papers-with-abstracts huggingface dataset:
Invariants for level-1 phylogenetic networks under the Cavendar-Farris-Neyman Model
Authors: Joseph Cummings, Benjamin Hollering, Christopher Manon
Date: 2021-02-05 00:00:00

Phylogenetic networks can model more complicated evolutionary phenomena that trees fail to capture such as horizontal gene transfer and hybridization. The same Markov models that are used to model evolution on trees can also be extended to networks and similar questions, such as the identifiability of the network parameter or the invariants of the model, can be asked. In this paper we focus on finding the invariants of the Cavendar-Farris-Neyman (CFN) model on level-1 phylogenetic networks. We do this by reducing the problem to finding invariants of sunlet networks, which are level-1 networks consisting of a single cycle with leaves at each vertex. We then determine all quadratic invariants in the sunlet network ideal which we conjecture generate the full ideal.
Direction and Constraint in Phenotypic Evolution: Dimension Reduction and Global Proportionality in Phenotype Fluctuation and Responses
Authors: Kunihiko Kaneko, Chikara Furusawa
Date: 2021-02-05 00:00:00
Tasks: Dimensionality Reduction

A macroscopic theory for describing cellular states during steady-growth is presented, which is based on the consistency between cellular growth and molecular replication, as well as the robustness of phenotypes against perturbations. Adaptive changes in high-dimensional phenotypes were shown to be restricted within a low-dimensional slow manifold, from which a macroscopic law for cellular states was derived, which was confirmed by adaptation experiments on bacteria under stress. Next, the theory was extended to phenotypic evolution, leading to proportionality between phenotypic responses against genetic evolution and environmental adaptation. The link between robustness to noise and mutation, as a result of robustness in developmental dynamics to perturbations, showed proportionality between phenotypic plasticity by genetic changes and by environmental noise. Accordingly, directionality and constraint in phenotypic evolution was quantitatively formulated in terms of phenotypic fluctuation and the response against environmental change. The evolutionary relevance of slow modes in controlling high-dimensional phenotypes is discussed.
Modelling COVID-19 Transmission Dynamics in Ghana
Authors: Edward Acheampong, Eric Okyere, Samuel Iddi, Joseph H. K. Bonney, Jonathan A. D. Wattis, Rachel L. Gomes
Date: 2021-02-05 00:00:00

In late 2019, a novel coronavirus, the SARS-CoV-2 outbreak was identified in Wuhan, China and later spread to every corner of the globe. Whilst the number of infection-induced deaths in Ghana, West Africa are minimal when compared with the rest of the world, the impact on the local health service is still significant. Compartmental models are a useful framework for investigating transmission of diseases in societies. To understand how the infection will spread and how to limit the outbreak. We have developed a modified SEIR compartmental model with nine compartments (CoVCom9) to describe the dynamics of SARS-CoV-2 transmission in Ghana. We have carried out a detailed mathematical analysis of the CoVCom9, including the derivation of the basic reproduction number, $\mathcal{R}_{0}$. In particular, we have shown that the disease-free equilibrium is globally asymptotically stable when $\mathcal{R}_{0}<1$ via a candidate Lyapunov function. Using the SARS-CoV-2 reported data for confirmed-positive cases and deaths from March 13 to August 10, 2020, we have parametrised the CoVCom9 model. The results of this fit show good agreement with data. We used Latin hypercube sampling-rank correlation coefficient (LHS-PRCC) to investigate the uncertainty and sensitivity of $\mathcal{R}_{0}$ since the results derived are significant in controlling the spread of SARS-CoV-2. We estimate that over this five month period, the basic reproduction number is given by $\mathcal{R}_{0} = 3.110$, with the 95\% confidence interval being $2.042 \leq \mathcal{R}_0 \leq 3.240$, and the mean value being $\mathcal{R}_{0}=2.623$. Of the 32 parameters in the model, we find that just six have a significant influence on $\mathcal{R}_{0}$, these include the rate of testing, where an increasing testing rate contributes to the reduction of $\mathcal{R}_{0}$.
Modeling and Forecasting of COVID-19 Spreading by Delayed Stochastic Differential Equations
Authors: Marouane Mahrouf, Adnane Boukhouima, Houssine Zine, El Mehdi Lotfi, Delfim F. M. Torres, Noura Yousfi
Date: 2021-02-04 00:00:00

The novel coronavirus disease (COVID-19) pneumonia has posed a great threat to the world recent months by causing many deaths and enormous economic damage worldwide. The first case of COVID-19 in Morocco was reported on 2 March 2020, and the number of reported cases has increased day by day. In this work, we extend the well-known SIR compartmental model to deterministic and stochastic time-delayed models in order to predict the epidemiological trend of COVID-19 in Morocco and to assess the potential role of multiple preventive measures and strategies imposed by Moroccan authorities. The main features of the work include the well-posedness of the models and conditions under which the COVID-19 may become extinct or persist in the population. Parameter values have been estimated from real data and numerical simulations are presented for forecasting the COVID-19 spreading as well as verification of theoretical results.
A new generalization of Parrondo's games to three players and its application in genetic switches
Authors: Atiyeh Fotoohinasab
Date: 2021-01-23 00:00:00

Parrondo's paradox indicates a paradoxical situation in which a winning expectation may occur in sequences of losing games. There are many versions of the original Parrondo's games in the literature, but the games are played by two players in all of them. We introduce a new extended version of games played by three players and a three-sided biased dice instead of two players and a biased coin in this work. In the first step, we find the part of the parameters space where the games are played fairly. After adding noise to fair probabilities, we combine two games randomly, periodically, and nonlinearly and obtain the conditions under which the paradox can occur. This generalized model can be applied in all science and engineering fields. It can also be used for genetic switches. Genetic switches are often made by two reactive elements, but the existence of more elements can lead to more existing decisions for cells. Each genetic switch can be considered a game in which the reactive elements compete to increase their molecular concentrations. We present three genetic networks based on a new generalized Parrondo's games model, consisting of two noisy genetic switches. The combination of them can increase network robustness to noise. Each switch can also be used as an initial pattern to construct a synthetic switch to change undesirable cells' fate.
Exclusion and multiplicity for stable communities in Lotka-Volterra systems
Authors: Won Eui Hong, Robert L. Pego
Date: 2021-02-01 00:00:00
Tasks: LEMMA

For classic Lotka-Volterra systems governing many interacting species, we establish an exclusion principle that rules out the existence of linearly asymptotically stable steady states in subcommunitites of communities that admit a stable state which is internally D-stable. This type of stability is known to be ensured, e.g., by diagonal dominance or Volterra-Lyapunov stability conditions. By consequence, the number of stable steady states of this type is bounded by Sperner's lemma on anti-chains in a poset. The number of stable steady states can nevertheless be very large if there are many groups of species that strongly inhibit outsiders but have weak interactions among themselves. By examples we also show that in general it is possible for a stable community to contain a stable subcommunity consisting of a single species. Thus a recent empirical finding to the contrary, in a study of random competitive systems by Lischke and L\"offler (Theo. Pop. Biol. 115 (2017) 24--34), does not hold without qualification.
Introduction to population dynamics and resource exploitation
Authors: Enrico Canuto, Daniele Mazza
Date: 2021-01-29 00:00:00
Tasks: parameter estimation

The paper was suggested by a brief note of the second author about the application of the Hubbert curve to predict decay of resource exploitation. A further suggestion came from the interpretation of the Hubbert curve in terms of a specific Lotka Volterra (LV) equation. The link with population dynamics was obvious as logistic function and LV equation were proposed within the demography science field. Mathematical population dynamics has a history of about two centuries. The first principle and model of population dynamics can be regarded the exponential law of Malthus. In the XIX century, the Malthusian demographic model was first refined to include mortality rate by Gompertz. In the early XIX century the model was further refined by Verhulst by introducing the standard logistic function. The previous models only concern the population of a single species. In the early XX century, the American demographer Lotka and the Italian mathematician Volterra proposed a pair of state equations which describe the population dynamics of two competing species, the predator and the prey. This paper is concerned with the single and two-species fundamental equations: the logistic and LV equation. The paper starts with the generalized logistic equation whose free response is derived together with equilibrium points and stability properties. The parameter estimation of the logistic function is applied to the raw data of the US crude oil production. The paper proceeds with the Lotka Volterra equation of the competition between two species, with the goal of applying it to resource exploitation. At the end, a limiting version of the LV equation is studied since it describes a competition model between the production rate of exploited resources and the relevant capital stock employed in the exploitation.
A two-sex branching process with oscillations: application to predator-prey systems
Authors: Cristina Gutierrez, Carmen Minuesa
Date: 2021-01-27 00:00:00

A two-type two-sex branching process is introduced with the aim of describing the interaction of predator and prey populations with sexual reproduction and promiscuous mating. In each generation and in each species the total number of individuals which mate and produce offspring is controlled by a binomial distribution with size given by this number of individuals and probability of success depending on the density of preys per predator. The resulting model enables us to depict the typical cyclic behaviour of predator-prey systems under some mild assumptions on the shape of the function that characterises the probability of survival of the previous binomial distribution. We present some basic results about fixation and extinction of both species as well as conditions for the coexistence of both of them. We also analyse the suitability of the process to model real ecosystems comparing our model with a real dataset.
Monitoring and Forecasting COVID-19: Statistical Heuristic Regression, Susceptible-Infected-Removed model and, Spatial Stochastics
Authors: Pedro L. de Andres, Lucia de Andres-Bragado, Linard D. Hoessly
Date: 2021-01-26 00:00:00
Tasks: regression

The COVID-19 pandemic has had worldwide devastating effects on human lives, highlighting the need for tools to predict its development. Dynamics of such public-health threats can often be efficiently analysed through simple models that help to make quantitative timely policy decisions. We benchmark a minimal version of a Susceptible-Infected-Removed model for infectious diseases (SIR) coupled with a simple least-squares Statistical Heuristic Regression (SHR) based on a lognormal distribution. We derived the three free parameters for both models in several cases and tested them against the amount of data needed to bring accuracy in predictions. The SHR model is approximately +/- 2% accurate about 20 days past the second inflexion point in the daily curve of cases, while the SIR model reaches a similar accuracy a fortnight before. All the analyzed cases assert the utility of SHR and SIR approximants as a useful tool to forecast the evolution of the disease. Finally, we have studied simulated stochastic individual-based SIR dynamics, which yields a detailed spatial and temporal view of the disease that cannot be given by SIR or SHR methods.
Stochastic Modeling of an Infectious Disease Part II: Simulation Experiments and Verification of the Analysis
Authors: Hisashi Kobayashi
Date: 2021-01-26 00:00:00

In Part 1, we introduced a stochastic model of an infectious disease, based on the BDI (birth and death with immigration) process. We showed that random processes defined by this model can capture the essence of the stochastic, often erratic, behavior of the infection process. The most significant finding was that it is negative binomial distributed with small r, hence it is geometrically distributed with an exceedingly long tail. This leads to a much larger disparity in the epidemic patterns than has been known to the modeling community. In Part 2 we conduct simulation experiments by implementing an event-driven simulator. Several independent runs are presented to verify the findings reported in Part 1. The enormous variations among the sample paths obtained from several consecutive and independent runs with statistically identical conditions confirm our analysis. Note that the epidemic pattern that we observe is merely one sample path taken out of infinitely many paths. Thus, one such path cannot represent the ensemble of the underlying random process. By plotting the simulation runs in the semi-log scale, we can see that the probabilistic chances in the early phase of the infection process determine the behavior of the process. Once the process has reached a considerable number, the weak law of large numbers sets in, and the process behaves less erratically than in the early phase. One important implication of our findings is that it would be a futile effort to attempt to identify all plausible causes of the epidemic patterns. Mere luck may play a more significant role than most people believe. It would be worth remarking that the BDI process might be applicable to explain the disparity in wealth among individuals of similar earning power, expenditure, and investment portfolio.
Modeling the Allee effects induced by cost of predation fear and its carry-over effects
Authors: Sourav Kumar Sasmal, Yasuhiro Takeuchi
Date: 2021-01-26 00:00:00

Predation driven Allee effects play an important role in the dynamics of small population, however, such predation-driven Allee effects cannot occur for the model with type I functional response. It generally occurs when a generalist predator targets some specific prey. However, apart from the lethal effects of predation, there are some non-lethal effects in the presence of predator. Due to the fear of predation, positive density dependence growth may be observed at low population density, because of reduced foraging activities. Moreover, this non-lethal effect can be carried over generations. In the present manuscript, we investigate the role of predation fear and its carry-over effects in prey-predator model. First, we study the single species model in global perspective. We have shown that depending on the birth rate, our single species model describes three types of growth dynamics, namely, strong Allee dynamics, weak Allee dynamics and logistic dynamics. Then we consider the explicit dynamics of predator, with type I functional response. Basic dynamical properties, as well as global stability of each equilibria have been discussed. From our analysis, we can observe that both the fear and its carry-over effects have significant role in the stability of the coexistence equilibrium, even if for the model with type I functional response. The phenomenon paradox of enrichment can be observed in our model, which cannot be observed in the classical prey-predator model with type I functional response. However, we can see that such phenomenon can be ruled out by choosing suitable non-lethal effect parameters. Therefore, our study shows how non-lethal effects change the dynamics of a competition model, and has important biological insights, specially for the understanding of the dynamics of small populations.
Selection, recombination, and the ancestral initiation graph
Authors: Frederic Alberti, Carolin Herrmann, Ellen Baake
Date: 2021-01-25 00:00:00
Tasks: Position

Recently, the selection-recombination equation with a single selected site and an arbitrary number of neutral sites was solved by means of the ancestral selection-recombination graph. Here, we introduce a more accessible approach, namely the ancestral initiation graph. The construction is based on a discretisation of the selection-recombination equation. We apply our method to systematically explain a long-standing observation concerning the dynamics of linkage disequilibrium between two neutral loci hitchhiking along with a selected one. In particular, this clarifies the nontrivial dependence on the position of the selected site.
COVID-19: Analytic results for a modified SEIR model and comparison of different intervention strategies
Authors: Arghya Das, Abhishek Dhar, Srashti Goyal, Anupam Kundu, Saurav Pandey
Date: 2020-05-23 00:00:00

The Susceptible-Exposed-Infected-Recovered (SEIR) epidemiological model is one of the standard models of disease spreading. Here we analyse an extended SEIR model that accounts for asymptomatic carriers, believed to play an important role in COVID-19 transmission. For this model we derive a number of analytic results for important quantities such as the peak number of infections, the time taken to reach the peak and the size of the final affected population. We also propose an accurate way of specifying initial conditions for the numerics (from insufficient data) using the fact that the early time exponential growth is well-described by the dominant eigenvector of the linearized equations. Secondly we explore the effect of different intervention strategies such as social distancing (SD) and testing-quarantining (TQ). The two intervention strategies (SD and TQ) try to reduce the disease reproductive number, $R_0$, to a target value $R^{\rm target}_0 < 1$, but in distinct ways, which we implement in our model equations. We find that for the same $R^{\rm target}_0 < 1$, TQ is more efficient in controlling the pandemic than SD. However, for TQ to be effective, it has to be based on contact tracing and our study quantifies the required ratio of tests-per-day to the number of new cases-per-day. Our analysis shows that the largest eigenvalue of the linearised dynamics provides a simple understanding of the disease progression, both pre- and post- intervention, and explains observed data for many countries. We apply our results to the COVID data for India to obtain heuristic projections for the course of the pandemic, and note that the predictions strongly depend on the assumed fraction of asymptomatic carriers.
Inferring, comparing and exploring ecological networks from time-series data through R packages constructnet, disgraph and dynet
Authors: Anshuman Swain, Travis Byrum, Zhaoyi Zhuang, Luke Perry, Michael Lin, William Fagan
Date: 2021-03-29 00:00:00
Tasks: Time Series, Time Series Analysis

Network inference is a major field of interest for the ecological community, especially in light of the high cost and difficulty of manual observation, and easy availability of remote, long term monitoring data. In addition, comparing across similar network structures, especially with spatial, environmental, or temporal variability and, simulating processes on networks to create toy models and hypotheses - are topics of considerable interest to the researchers. A large number of methods are being developed in the network science community to achieve these objectives but either don't have their code available or an implementation in R, the language preferred by ecologists and other biologists. We provide a suite of three packages which will provide a central suite of standardized network inference methods from time-series data (constructnet), distance metrics (disgraph) and (process) simulation models (dynet) to the growing R network analysis environment and would help ecologists and biologists to perform and compare methods under one roof. These packages are implemented in a coherent, consistent framework - making comparisons across methods and metrics easier. We hope that these tools in R will help increase the accessibility of network tools to ecologists and other biologists, who the language for most of their analysis.
Putting the dynamic pathosome in practice: a novel way of analyzing longitudinal data
Authors: Peter Lenart, Daniela Kuruczova, Lubomir Kukla, Martin Scheringer, Julie Bienertova Vasku
Date: 2021-03-23 00:00:00

Previously we have developed the concept of the dynamic pathosome, which suggests that individual patterns of phenotype development, i.e., phenotypic trajectories, contain more information than is commonly appreciated and that a phenotype's past trajectory predicts its future development. In this article, we present a pathosome-inspired approach to analyzing longitudinal data by functional linear models. We demonstrate how to use this approach and compare it with classical linear models on data from the Czech section of the European Longitudinal Study of Pregnancy and Childhood (ELSPAC). Our results show that functional linear models explain more observed variance in age at menarche from height and weight data than the commonly used approaches. Furthermore, we demonstrate that functional linear models can be used to identify crucial time points that can be used to create linear models achieving almost the same performance as functional linear models. In addition, we use data from the Berkeley growth study (BGS) to demonstrate that growth trajectories from birth to 15 years can be used to explain 97% of observed variance of height at 18 years, thus supporting the notion that a phenotype's past trajectory affects its future course. Overall, this article presents experimental support for the concept of the dynamic pathosome and presents a method that can be used as a powerful tool for analyzing quantitative longitudinal data.
OnionNet-2: A Convolutional Neural Network Model for Predicting Protein-Ligand Binding Affinity based on Residue-Atom Contacting Shells
Authors: Zechen Wang, Liangzhen Zheng, Yang Liu, Yuanyuan Qu, Yong-Qiang Li, Mingwen Zhao, Yuguang Mu, Weifeng Li
Date: 2021-03-22 00:00:00

One key task in virtual screening is to accurately predict the binding affinity ($\triangle$$G$) of protein-ligand complexes. Recently, deep learning (DL) has significantly increased the predicting accuracy of scoring functions due to the extraordinary ability of DL to extract useful features from raw data. Nevertheless, more efforts still need to be paid in many aspects, for the aim of increasing prediction accuracy and decreasing computational cost. In this study, we proposed a simple scoring function (called OnionNet-2) based on convolutional neural network to predict $\triangle$$G$. The protein-ligand interactions are characterized by the number of contacts between protein residues and ligand atoms in multiple distance shells. Compared to published models, the efficacy of OnionNet-2 is demonstrated to be the best for two widely used datasets CASF-2016 and CASF-2013 benchmarks. The OnionNet-2 model was further verified by non-experimental decoy structures from docking program and the CSAR NRC-HiQ data set (a high-quality data set provided by CSAR), which showed great success. Thus, our study provides a simple but efficient scoring function for predicting protein-ligand binding free energy.