Study of social tension based on electronic social networks big data

The article presents a methodology for assessing the level of social tension according to the data of electronic social networks. The calculation of the level of social tension according to the specified method is carried out automatically using software tools and requires the participation of the researcher only at the level of an analytical conclusion. An approach to identifying the dissatisfaction of the population at the level of the simplest actions of social network accounts has been described in detail. The necessity of identifying certain spheres of public life for the identification of population discontent has been substantiated, the indicated spheres have been highlighted and described. An indicator has been developed that makes it possible to calculate the level of social tension within each of the identified spheres of public life, taking into account the level of negative messages (posts and comments) and the discussion of each topic. A generalising indicator of the level of social tension has been developed, combining individual indicators of tension by topic. The calculation of the generalising indicator makes it possible to identify the level of social tension in a region or other territorial entity and track its dynamics in any perspective, including retrospectively.The proposed methodology for assessing social tension based on data from electronic social networks differs significantly from existing sociological and statistical approaches. Its main advantage lies in the minimal time lag between the dynamics of social tension, reflected in the social network, and its identification, which expands the possibilities of a prompt response to the growth of negative moods in society. Another difference of the proposed approach is the possibility of multiple repetitions of calculations with minimal, in contrast to the sociological method, the cost of additional resources for each subsequent iteration of the calculation of the indicator value

Re-evaluating the phylogeny of Sipuncula through transcriptomics

Sipunculans (also known as peanut worms) are an ancient group of exclusively marine worms with a global distribution and a fossil record that dates back to the Early Cambrian. The systematics of sipunculans, now considered a distinct subclade of Annelida, has been studied for decades using morphological and molecular characters, and has reached the limits of Sanger-based approaches. Here, we reevaluate their family-level phylogeny by comparative transcriptomic analysis of eight species representing all known families within Sipuncula. Two data matrices with alternative gene occupancy levels (large matrix with 675 genes and 62% missing data; reduced matrix with 141 genes and 23% missing data) were analysed using concatenation and gene-tree methods, yielding congruent results and resolving each internal node with maximum support. We thus corroborate prior phylogenetic work based on molecular data, resolve outstanding issues with respect to the familial relationships of Aspidosiphonidae, Antillesomatidae and Phascolosomatidae, and highlight the next area of focus for sipunculan systematics

CYGD: the Comprehensive Yeast Genome Database

The Comprehensive Yeast Genome Database (CYGD) compiles a comprehensive data resource for information on the cellular functions of the yeast Saccharomyces cerevisiae and related species, chosen as the best understood model organism for eukaryotes. The database serves as a common resource generated by a European consortium, going beyond the provision of sequence information and functional annotations on individual genes and proteins. In addition, it provides information on the physical and functional interactions among proteins as well as other genetic elements. These cellular networks include metabolic and regulatory pathways, signal transduction and transport processes as well as co-regulated gene clusters. As more yeast genomes are published, their annotation becomes greatly facilitated using S.cerevisiae as a reference. CYGD provides a way of exploring related genomes with the aid of the S.cerevisiae genome as a backbone and SIMAP, the Similarity Matrix of Proteins. The comprehensive resource is available under http://mips.gsf.de/genre/proj/yeast/

Knowledge-based energy functions for computational studies of proteins

This chapter discusses theoretical framework and methods for developing knowledge-based potential functions essential for protein structure prediction, protein-protein interaction, and protein sequence design. We discuss in some details about the Miyazawa-Jernigan contact statistical potential, distance-dependent statistical potentials, as well as geometric statistical potentials. We also describe a geometric model for developing both linear and non-linear potential functions by optimization. Applications of knowledge-based potential functions in protein-decoy discrimination, in protein-protein interactions, and in protein design are then described. Several issues of knowledge-based potential functions are finally discussed.Comment: 57 pages, 6 figures. To be published in a book by Springe

Efficient Reconstruction of Metabolic Pathways by Bidirectional Chemical Search

One of the main challenges in systems biology is the establishment of the metabolome: a catalogue of the metabolites and biochemical reactions present in a specific organism. Current knowledge of biochemical pathways as stored in public databases such as KEGG, is based on carefully curated genomic evidence for the presence of specific metabolites and enzymes that activate particular biochemical reactions. In this paper, we present an efficient method to build a substantial portion of the artificial chemistry defined by the metabolites and biochemical reactions in a given metabolic pathway, which is based on bidirectional chemical search. Computational results on the pathways stored in KEGG reveal novel biochemical pathways

Human Stressors Are Driving Coastal Benthic Long-Lived Sessile Fan Mussel Pinna nobilis Population Structure More than Environmental Stressors.

Coastal degradation and habitat disruption are severely compromising sessile marine species. The fan shell Pinna nobilis is an endemic, vulnerable species and the largest bivalve in the Mediterranean basin. In spite of species legal protection, fan shell populations are declining. Models analyzed the contributions of environmental (mean depth, wave height, maximum wave height, period of waves with high energy and mean direction of wave source) versus human-derived stressors (anchoring, protection status, sewage effluents, fishing activity and diving) as explanatory variables depicting Pinna nobilis populations at a mesoscale level. Human stressors were explaining most of the variability in density spatial distribution of fan shell, significantly disturbing benthic communities. Habitat protection affected P. nobilis structure and physical aggression by anchoring reveals a high impact on densities. Environmental variables instead played a secondary role, indicating that global change processes are not so relevant in coastal benthic communities as human-derived impacts.Versión del editor4,411

Genetic and oceanographic tools reveal high population connectivity and diversity in the endangered pen shell Pinna nobilis

For marine meta-populations with source-sink dynamics knowledge about genetic connectivity is important to conserve biodiversity and design marine protected areas (MPAs). We evaluate connectivity of a Mediterranean sessile species, Pinna nobilis. To address a large geographical scale, partial sequences of cytochrome oxidase I (COI, 590 bp) were used to evaluate phylogeographical patterns in the Western Mediterranean, and in the whole basin using overlapping sequences from the literature (243 bp). Additionally, we combined (1) larval trajectories based on oceanographic currents and early life-history traits and (2) 10 highly polymorphic microsatellite loci collected in the Western Mediterranean. COI results provided evidence for high diversity and low inter-population differentiation. Microsatellite genotypes showed increasing genetic differentiation with oceanographic transport time (isolation by oceanographic distance (IBD) set by marine currents). Genetic differentiation was detected between Banyuls and Murcia and between Murcia and Mallorca. However, no genetic break was detected between the Balearic populations and the mainland. Migration rates together with numerical Lagrangian simulations showed that (i) the Ebro Delta is a larval source for the Balearic populations (ii) Alicante is a sink population, accumulating allelic diversity from nearby populations. The inferred connectivity can be applied in the development of MPA networks in the Western Mediterranean.Spanish Ministry of Economy and Competitiveness [CTM2009-07013]; Ramon y Cajal Fellowship [RYC2014-14970]; Conselleria d'Innovacio, Recerca i Turisme of the Balearic Government; Spanish Ministry of Economy, Industry and Competitiveness IFCT [IF/00998/2014]; FCT [SFRH/BPD/63703/2009, SFRH/BPD/107878/2015, EXCL/AAG-GLO/0661/2012]; National Science Foundation [OCE-1419450]; Albert II of Monaco Foundationinfo:eu-repo/semantics/publishedVersio

Influence of Different Plant Species on Methane Emissions from Soil in a Restored Swiss Wetland

Plants are a major factor influencing methane emissions from wetlands, along with environmental parameters such as water table, temperature, pH, nutrients and soil carbon substrate. We conducted a field experiment to study how different plant species influence methane emissions from a wetland in Switzerland. The top 0.5 m of soil at this site had been removed five years earlier, leaving a substrate with very low methanogenic activity. We found a sixfold difference among plant species in their effect on methane emission rates: Molinia caerulea and Lysimachia vulgaris caused low emission rates, whereas Senecio paludosus, Carex flava, Juncus effusus and Typha latifolia caused relatively high rates. Centaurea jacea, Iris sibirica, and Carex davalliana caused intermediate rates. However, we found no effect of either plant biomass or plant functional groups – based on life form or productivity of the habitat – upon methane emission. Emissions were much lower than those usually reported in temperate wetlands, which we attribute to reduced concentrations of labile carbon following topsoil removal. Thus, unlike most wetland sites, methane production in this site was probably fuelled chiefly by root exudation from living plants and from root decay. We conclude that in most wetlands, where concentrations of labile carbon are much higher, these sources account for only a small proportion of the methane emitted. Our study confirms that plant species composition does influence methane emission from wetlands, and should be considered when developing measures to mitigate the greenhouse gas emissions

Symbolic arithmetic knowledge without instruction

This article was published in the journal, Nature [© The Nature Publishing Group]. The definitive version is available at: http://dx.doi.org/10.1038/nature05850Symbolic arithmetic is fundamental to science, technology and economics, but its acquisition by children typically requires years of effort, instruction and drill. When adults perform mental arithmetic, they activate nonsymbolic, approximate number representations and their performance suffers if this nonsymbolic system is impaired. Nonsymbolic number representations also allow adults, children, and even infants to add or subtract pairs of dot arrays and to compare the resulting sum or difference to a third array, provided that only approximate accuracy is required. Here we report that young children, who have mastered verbal counting and are on the threshold of arithmetic instruction, can build on their nonsymbolic number system to perform symbolic addition and subtraction. Children across a broad socio-economic spectrum solved symbolic problems involving approximate addition or subtraction of large numbers, both in a laboratory test and in a school setting. Aspects of symbolic arithmetic therefore lie within the reach of children who have learned no algorithms for manipulating numerical symbols. Our findings help to delimit the sources of children’s difficulties learning symbolic arithmetic, and they suggest ways to enhance children’s engagement with formal mathematics