Catch Me if You Can: Adaptation from Standing Genetic Variation to a Moving Phenotypic Optimum

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Agent-based simulations have been widely applied in many disciplines, by scientists and engineers alike. Scientists use agent-based simulations to tackle global problems, including alleviating poverty, reducing violence, and predicting the impact of pandem ...

Higher-order asymptotics provide accurate approximations for use in parametric statistical modelling. In this thesis, we investigate using higher-order approximations in two-specific settings, with a particular emphasis on the tangent exponential model. ...

The resilience of the mitochondrial genome (mtDNA) to a high mutational pressure depends, in part, on negative purifying selection in the germline. A paradigm in the field has been that such selection, at least in part, takes place in primordial germ cells ...

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We introduce a reconfigurable medium for the manipulation of elastic propagation properties of Lamb waves. It is based on a shape memory polymer (SMP) with temperature-dependent Young's modulus. Waves are excited by a laser pulse and detected by a laser vi ...

In situ imageomics is a new approach to study ecological, biological and evolutionary systems wherein large image and video data sets are captured in the wild and machine learning methods are used to infer biological traits of individual organisms, animal ...

Characterizing the genetic structure of large cohorts has become increasingly important as genetic studies extend to massive, increasingly diverse biobanks. Popular methods decompose individual genomes into fractional cluster assignments with each cluster ...

With the advent of the Square Kilometre Array Observatory (SKAO), scientists will be able to directly observe the Epoch of Reionization by mapping the distribution of neutral hydrogen at different redshifts. While physically motivated results can be simula ...

To build biophysically detailed models of brain cells, circuits, and regions, a data-driven approach is increasingly being adopted. This helps to obtain a simulated activity that reproduces the experimentally recorded neural dynamics as faithfully as possi ...

The ubiquity of distributed machine learning (ML) in sensitive public domain applications calls for algorithms that protect data privacy, while being robust to faults and adversarial behaviors. Although privacy and robustness have been extensively studied ...

Catch Me if You Can: Adaptation from Standing Genetic Variation to a Moving Phenotypic Optimum

Chattez avec Graph Search

Generalizing Bulk-Synchronous Parallel Processing for Data Science: From Data to Threads and Agent-Based Simulations

Higher Order Asymptotics: Applications to Satellite Conjunction and Boundary Problems

Reanalysis of mtDNA mutations of human primordial germ cells (PGCs) reveals NUMT contamination and suggests that selection in PGCs may be positive

Epigenome-wide DNA methylation in externalizing behaviours: A review and combined analysis

The role of the NF-kappaB pathway in Amyotrophic lateral sclerosis pathogenesis

Rare predicted loss-of-function variants of type I IFN immunity genes are associated with life-threatening COVID-19

Optimization of elastic wave propagation in a reconfigurable medium by genetic algorithms with adaptive mutation probability

A Framework for Autonomic Computing for In Situ Imageomics

Neural ADMIXTURE for rapid genomic clustering

PINION: physics-informed neural network for accelerating radiative transfer simulations for cosmic reionization

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On the Privacy-Robustness-Utility Trilemma in Distributed Learning