The context-dependence of mutations: a linkage of formalisms

Defining the extent of epistasis - the non-independence of the effects of mutations - is essential for understanding the relationship of genotype, phenotype, and fitness in biological systems. The applications cover many areas of biological research, including biochemistry, genomics, protein and systems engineering, medicine, and evolutionary biology. However, the quantitative definitions of epistasis vary among fields, and its analysis beyond just pairwise effects remains obscure in general. Here, we show that different definitions of epistasis are versions of a single mathematical formalism - the weighted Walsh-Hadamard transform. We discuss that one of the definitions, the backgound-averaged epistasis, is the most informative when the goal is to uncover the general epistatic structure of a biological system, a description that can be rather different from the local epistatic structure of specific model systems. Key issues are the choice of effective ensembles for averaging and to practically contend with the vast combinatorial complexity of mutations. In this regard, we discuss possible approaches for optimally learning the epistatic structure of biological systems.Comment: 6 pages, 3 figures, supplementary informatio

Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202

The impact of culture on mobile phone purchasing: A comparison between Thai and British consumers

The idea of ICT Convergence is used by many practitioners and observers - such as economists, politicians, journalists, and academics - as an important descriptor for technological change. However, a review of previous work in this field suggests that, despite more than 30 years of research on ICT Convergence, the theoretical basis of the concept of convergence is still under-researched. In particular in the IS literature, the concept has been either relegated to the sidelines or taken for granted without further reflection. Therefore, a systematic analysis of the idea of ICT Convergence from an IS perspective is needed. This paper aims to explore how the discourse of convergence is being shaped in the IS literature. In order to address this question, 317 articles published in ten leading IS journals from 1998 to 2008 have been examined. This study has been built around a Grounded Theory approach informed by Niklas Luhmann\u27s Theory of Distinction. The findings show that convergence cannot be viewed as a single concept. Five archetypes of convergence communication are identified, and a conceptualization of ICT Convergence as a double process between alignment and interoperability is suggested. The main limitation of this paper is the focus on leading IS journals

A metaproteomic approach to study human-microbial ecosystems at the mucosal luminal interface

Aberrant interactions between the host and the intestinal bacteria are thought to contribute to the pathogenesis of many digestive diseases. However, studying the complex ecosystem at the human mucosal-luminal interface (MLI) is challenging and requires an integrative systems biology approach. Therefore, we developed a novel method integrating lavage sampling of the human mucosal surface, high-throughput proteomics, and a unique suite of bioinformatic and statistical analyses. Shotgun proteomic analysis of secreted proteins recovered from the MLI confirmed the presence of both human and bacterial components. To profile the MLI metaproteome, we collected 205 mucosal lavage samples from 38 healthy subjects, and subjected them to high-throughput proteomics. The spectral data were subjected to a rigorous data processing pipeline to optimize suitability for quantitation and analysis, and then were evaluated using a set of biostatistical tools. Compared to the mucosal transcriptome, the MLI metaproteome was enriched for extracellular proteins involved in response to stimulus and immune system processes. Analysis of the metaproteome revealed significant individual-related as well as anatomic region-related (biogeographic) features. Quantitative shotgun proteomics established the identity and confirmed the biogeographic association of 49 proteins (including 3 functional protein networks) demarcating the proximal and distal colon. This robust and integrated proteomic approach is thus effective for identifying functional features of the human mucosal ecosystem, and a fresh understanding of the basic biology and disease processes at the MLI. © 2011 Li et al

Evolutionary Algorithms for Reinforcement Learning

There are two distinct approaches to solving reinforcement learning problems, namely, searching in value function space and searching in policy space. Temporal difference methods and evolutionary algorithms are well-known examples of these approaches. Kaelbling, Littman and Moore recently provided an informative survey of temporal difference methods. This article focuses on the application of evolutionary algorithms to the reinforcement learning problem, emphasizing alternative policy representations, credit assignment methods, and problem-specific genetic operators. Strengths and weaknesses of the evolutionary approach to reinforcement learning are presented, along with a survey of representative applications

An Enhanced Sampling-Based Viewpoints Cosine Visual Model for an Efficient Big Data Clustering

Bunching is registering the item's similitude includes that can be utilized to segment the information. Object similarity (or dissimilarity) features are taken into account when locating relevant data object clusters. Removing the quantity of bunch data for any information is known as the grouping inclination. Top enormous information bunching calculations, similar to single pass k-implies (spkm), k-implies ++, smaller than usual group k-implies (mbkm), are created in the groups with k worth. By and by, the k worth is alloted by one or the other client or with any outside impedance. Along these lines, it is feasible to get this worth immovable once in a while. In the wake of concentrating on related work, it is researched that visual appraisal of (bunch) propensity (Tank) and its high level visual models extraordinarily decide the obscure group propensity esteem k. Multi-perspectives based cosine measure Tank (MVCM-Tank) utilized the multi-perspectives to evaluate grouping inclination better. Be that as it may, the MVCM-Tank experiences versatility issues in regards to computational time and memory designation. This paper improves the MVCM-Tank with the inspecting methodology to defeat the versatility issue for large information grouping. Trial investigation is performed utilizing the enormous gaussian engineered datasets and large constant datasets to show the effectiveness of the proposed work

SARS-CoV-2 virus RNA sequence classification and geographical analysis with convolutional neural networks approach

Covid-19 infection, which spread to the whole world in December 2019 and is still active, caused more than 250 thousand deaths in the world today. Researches on this subject have been focused on analyzing the genetic structure of the virus, developing vaccines, the course of the disease, and its source. In this study, RNA sequences belonging to the SARS-CoV-2 virus are transformed into gene motifs with two basic image processing algorithms and classified with the convolutional neural network (CNN) models. The CNN models achieved an average of 98% Area Under Curve(AUC) value was achieved in RNA sequences classified as Asia, Europe, America, and Oceania. The resulting artificial neural network model was used for phylogenetic analysis of the variant of the virus isolated in Turkey. The classification results reached were compared with gene alignment values in the GISAID database, where SARS-CoV-2 virus records are kept all over the world. Our experimental results have revealed that now the detection of the geographic distribution of the virus with the CNN models might serve as an efficient method