Segmentally Variable Genes: A New Perspective on Adaptation

Genomic sequence variation is the hallmark of life and is key to understanding diversity and adaptation among the numerous microorganisms on earth. Analysis of the sequenced microbial genomes suggests that genes are evolving at many different rates. We have attempted to derive a new classification of genes into three broad categories: lineage-specific genes that evolve rapidly and appear unique to individual species or strains; highly conserved genes that frequently perform housekeeping functions; and partially variable genes that contain highly variable regions, at least 70 amino acids long, interspersed among well-conserved regions. The latter we term segmentally variable genes (SVGs), and we suggest that they are especially interesting targets for biochemical studies. Among these genes are ones necessary to deal with the environment, including genes involved in host–pathogen interactions, defense mechanisms, and intracellular responses to internal and environmental changes. For the most part, the detailed function of these variable regions remains unknown. We propose that they are likely to perform important binding functions responsible for protein–protein, protein–nucleic acid, or protein–small molecule interactions. Discerning their function and identifying their binding partners may offer biologists new insights into the basic mechanisms of adaptation, context-dependent evolution, and the interaction between microbes and their environment. Segmentally variable genes show a mosaic pattern of one or more rapidly evolving, variable regions. Discerning their function may provide new insights into the forces that shape genome diversity and adaptationNational Science Foundation (998088, 0239435

Using triangulation to identify word senses

Word sense disambiguation is the task of determining which sense of a word is intended from its context. Previous methods have found the lack of training data and the restrictiveness of dictionaries' choices of senses to be major stumbling blocks. A robust novel algorithm is presented that uses multiple dictionaries, the Internet, clustering and triangulation to attempt to discern the most useful senses of a given word and learn how they can be disambiguated. The algorithm is explained, and some promising sample results are given

Enterprise profiles in deprived areas: Are they distinctive?

This paper examines the extent to which segmenting business activity on the basis of the relative deprivation of a given area provides additional understanding (in terms of analysis and policy) that is not obtained by alternative divisions, e.g., by sector, size, etc. The paper is primarily motivated by the explicit inclusion of a deprived area dimension to many UK small business/enterprise policies introduced since 1997. We use two datasets drawn from the customer records of Barclays Bank PLC to obtain an initial analysis of the business stocks and dynamics in deprived and non-deprived areas of England. The data indicate that the deprived areas of England vary systematically from the wider economy in terms of several business stock characteristics and associated dynamics. These differences include a lower proportion of business service firms, lower female involvement in the owner-manager base and a poorer risk profile. The analysis supports the view that there are likely to be benefits from the tailoring of small business/ enterprise policies to sub-national levels

Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus

In systems of multiple agents, identifying the cause of observed agent dynamics is challenging. Often, these agents operate in diverse, non-stationary environments, where models rely on hand-crafted environment-specific features to infer influential regions in the system's surroundings. To overcome the limitations of these inflexible models, we present GP-LAPLACE, a technique for locating sources and sinks from trajectories in time-varying fields. Using Gaussian processes, we jointly infer a spatio-temporal vector field, as well as canonical vector calculus operations on that field. Notably, we do this from only agent trajectories without requiring knowledge of the environment, and also obtain a metric for denoting the significance of inferred causal features in the environment by exploiting our probabilistic method. To evaluate our approach, we apply it to both synthetic and real-world GPS data, demonstrating the applicability of our technique in the presence of multiple agents, as well as its superiority over existing methods.Comment: KDD '18 Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Pages 1254-1262, 9 pages, 5 figures, conference submission, University of Oxford. arXiv admin note: text overlap with arXiv:1709.0235

Statistical Models of Reconstructed Phase Spaces for Signal Classification

This paper introduces a novel approach to the analysis and classification of time series signals using statistical models of reconstructed phase spaces. With sufficient dimension, such reconstructed phase spaces are, with probability one, guaranteed to be topologically equivalent to the state dynamics of the generating system, and, therefore, may contain information that is absent in analysis and classification methods rooted in linear assumptions. Parametric and nonparametric distributions are introduced as statistical representations over the multidimensional reconstructed phase space, with classification accomplished through methods such as Bayes maximum likelihood and artificial neural networks (ANNs). The technique is demonstrated on heart arrhythmia classification and speech recognition. This new approach is shown to be a viable and effective alternative to traditional signal classification approaches, particularly for signals with strong nonlinear characteristics

A Peptide Core Motif for Binding to Heterotrimeric G Protein α Subunits

Recently, in vitro selection using mRNA display was used to identify a novel peptide sequence that binds with high affinity to G{alpha}i1. The peptide was minimized to a 9-residue sequence (R6A-1) that retains high affinity and specificity for the GDP-bound state of G{alpha}i1 and acts as a guanine nucleotide dissociation inhibitor (GDI). Here we demonstrate that the R6A-1 peptide interacts with G{alpha} subunits representing all four G protein classes, acting as a core motif for G{alpha} interaction. This contrasts with the consensus G protein regulatory(GPR) sequence, a 28-mer peptide GDI derived from the GoLoco (G{alpha}i/0-Loco interaction)/GPR motif that shares no homology with R6A-1 and binds only to G{alpha}i1-3 in this assay. Binding of R6A-1 is generally specific to the GDP-bound state of the G{alpha} subunits and excludes association with G{beta}{gamma}. R6A-G{alpha}i1 complexes are resistant to trypsin digestion and exhibit distinct stability in the presence of Mg2+, suggesting that the R6A and GPR peptides exert their activities using different mechanisms. Studies using G{alpha}i1/G{alpha}s chimeras identify two regions of G{alpha}i1 (residues 1–35 and 57–88) as determinants for strong R6A-Gi{alpha}1 interaction. Residues flanking the R6A-1 peptide confer unique binding properties, indicating that the core motif could be used as a starting point for the development of peptides exhibiting novel activities and/or specificity for particular G protein subclasses or nucleotide-bound states

Open Access to Research Is in the Public Interest

Bevin Engelward andPLoS Biology editorial board member Richard Roberts give their perspective on the importance of public access to science