Stochastic Blockmodeling for the Analysis of Big Data

The aim of this paper is to consider the stochastic blockmodel to obtain clusters of units as regards patterns of similar relations; moreover we want to analyze the relations between clusters. Blockmodeling is a technique usually applied in social network analysis focusing on the relations between \u201cactors\u201d i.e. units. In our time people and devices constantly generate data. The network is generating location and other data that keeps services running and ready to use in every moment. This rapid development in the availability and access to data has induced the need for better analysis techniques to understand the various phenomena. Blockmodeling techniques and Clustering algorithms, can be used for this aim. In this paper application regards the Web

Arsonists or firefighters? Affectiveness in agile software development

In this paper, we present an analysis of more than 500K comments from open-source repositories of software systems developed using agile methodologies. Our aim is to empirically determine how developers interact with each other under certain psychological conditions generated by politeness, sentiment and emotion expressed within developers' comments. Developers involved in an open-source projects do not usually know each other; they mainly communicate through mailing lists, chat, and tools such as issue tracking systems. The way in which they communicate a ects the development process and the productivity of the people involved in the project. We evaluated politeness, sentiment and emotions of comments posted by agile developers and studied the communication ow to understand how they interacted in the presence of impolite and negative comments (and vice versa). Our analysis shows that \ re ghters" prevail. When in presence of impolite or negative comments, the probability of the next comment being impolite or negative is 13% and 25%, respectively; ANGER however, has a probability of 40% of being followed by a further ANGER comment. The result could help managers take control the development phases of a system, since social aspects can seriously a ect a developer's productivity. In a distributed agile environment this may have a particular resonance

A Relational Event Approach to Modeling Behavioral Dynamics

This chapter provides an introduction to the analysis of relational event data (i.e., actions, interactions, or other events involving multiple actors that occur over time) within the R/statnet platform. We begin by reviewing the basics of relational event modeling, with an emphasis on models with piecewise constant hazards. We then discuss estimation for dyadic and more general relational event models using the relevent package, with an emphasis on hands-on applications of the methods and interpretation of results. Statnet is a collection of packages for the R statistical computing system that supports the representation, manipulation, visualization, modeling, simulation, and analysis of relational data. Statnet packages are contributed by a team of volunteer developers, and are made freely available under the GNU Public License. These packages are written for the R statistical computing environment, and can be used with any computing platform that supports R (including Windows, Linux, and Mac).

Distribution of sound pressure around a singing cricket: radiation pattern and asymmetry in the sound field

Male field crickets generate calls to attract distant females through tegminal stridulation: the rubbing together of the overlying right wing which bears a file of cuticular teeth against the underlying left wing which carries a sclerotised scraper. During stridulation, specialised areas of membrane on both wings are set into oscillating vibrations to produce acoustic radiation. The location of females is unknown to the calling males and thus increasing effective signal range in all directions will maximise transmission effectiveness. However, producing an omnidirectional sound field of high sound pressure levels may be problematic due to the mechanical asymmetry found in this sound generation system. Mechanical asymmetry occurs by the right wing coming to partially cover the left wing during the closing stroke phase of stridulation. As such, it is hypothesised that the sound field on the left-wing side of the animal will contain lower sound pressure components than on the right-wing side as a result of this coverage. This hypothesis was tested using a novel method to accurately record a high resolution, three dimensional mapping of sound pressure levels around restrained Gryllus bimaculatus field crickets singing under pharmacological stimulation. The results indicate that a bilateral asymmetry is present across individuals, with greater amplitude components present in the right wing side of the animal. Individual variation in sound pressure to either the right or left-wing side is also observed. However, statistically significant differences in bilateral sound field asymmetry as presented here may not affect signalling in the field

The interplay of microscopic and mesoscopic structure in complex networks

Not all nodes in a network are created equal. Differences and similarities exist at both individual node and group levels. Disentangling single node from group properties is crucial for network modeling and structural inference. Based on unbiased generative probabilistic exponential random graph models and employing distributive message passing techniques, we present an efficient algorithm that allows one to separate the contributions of individual nodes and groups of nodes to the network structure. This leads to improved detection accuracy of latent class structure in real world data sets compared to models that focus on group structure alone. Furthermore, the inclusion of hitherto neglected group specific effects in models used to assess the statistical significance of small subgraph (motif) distributions in networks may be sufficient to explain most of the observed statistics. We show the predictive power of such generative models in forecasting putative gene-disease associations in the Online Mendelian Inheritance in Man (OMIM) database. The approach is suitable for both directed and undirected uni-partite as well as for bipartite networks

Structural basis for CRISPR RNA-guided DNA recognition by Cascade

The CRISPR (clustered regularly interspaced short palindromic repeats) immune system in prokaryotes uses small guide RNAs to neutralize invading viruses and plasmids. In Escherichia coli, immunity depends on a ribonucleoprotein complex called Cascade. Here we present the composition and low-resolution structure of Cascade and show how it recognizes double-stranded DNA (dsDNA) targets in a sequence-specific manner. Cascade is a 405-kDa complex comprising five functionally essential CRISPR-associated (Cas) proteins (CasA1B2C6D1E1) and a 61-nucleotide CRISPR RNA (crRNA) with 5′-hydroxyl and 2′,3′-cyclic phosphate termini. The crRNA guides Cascade to dsDNA target sequences by forming base pairs with the complementary DNA strand while displacing the noncomplementary strand to form an R-loop. Cascade recognizes target DNA without consuming ATP, which suggests that continuous invader DNA surveillance takes place without energy investment. The structure of Cascade shows an unusual seahorse shape that undergoes conformational changes when it binds target DNA.

Adaptive Evolution of a Stress Response Protein

Some cancers are mediated by an interplay between tissue damage, pathogens and localised innate immune responses, but the mechanisms that underlie these linkages are only beginning to be unravelled.Here we identify a strong signature of adaptive evolution on the DNA sequence of the mammalian stress response gene SEP53, a member of the epidermal differentiation complex fused-gene family known for its role in suppressing cancers. The SEP53 gene appears to have been subject to adaptive evolution of a type that is commonly (though not exclusively) associated with coevolutionary arms races. A similar pattern of molecular evolution was not evident in the p53 cancer-suppressing gene.Our data thus raises the possibility that SEP53 is a component of the mucosal/epithelial innate immune response engaged in an ongoing interaction with a pathogen. Although the pathogenic stress mediating adaptive evolution of SEP53 is not known, there are a number of well-known candidates, in particular viruses with established links to carcinoma