From dispersed to monopolized violence: expansion and consolidation of the Primeiro Comando da Capital's hegemony in São Paulo's prisons

The present work aims to understand the process of expansion and consolidation of the organized criminal group the Primeiro Comando da Capital (PCC) in São Paulo’s prison system over the past 20 years, and the social configuration that has formed as a result of the PCCs monopolization of opportunities of power. To this end, the work of Norbert Elias is utilized to analyze empirical data collected from various sources. The article consists of two lines of analysis. First, the PCC phenomenon is approached from a macro-sociological point of view, focusing on the social, political and administrative problems that are directly or indirectly linked to the PCCs social development. Second, a figurational analysis is used to explore the social dynamics produced from this process. In comparison to the “pre-PCC” situation, it is shown that the new social configuration produced from the hegemony of the PCC consists of a complexity of interdependencies, including greater functional division and social integration. Given this intensification of mutual dependencies, the social controls on individual behavior have been expanded and centralized. Here, the structure and organization of the PCC, its political dynamics, and individual self-control are central issues. The article concludes by calling into question the view that the most significant effect of the PCCs consolidation has been social pacification of São Paulo’s prison system. Fragilities in the power of the PCC are explored, principally the precarious nature of the relationship between the PCC and state authorities, and the extent to which the PCC’s authority is imposed.

IMP: Imperial Metagenomics Pipeline for high-throughput sequence data

We have developed an in-house pipeline for the processing and analyses of sequence data generated during Illumina technology-based metagenomic studies of the human gut microbiota. Each component of the pipeline has been selected following comparative analysis of available tools; however, the modular nature of software facilitates replacement of any individual component with an alternative should a better tool become available in due course. The pipeline consists of quality analysis and trimming followed by taxonomic filtering of sequence data allowing reads associated with samples to be binned according to whether they represent human, prokaryotic (bacterial/archaeal), viral, parasite, fungal or plant DNA. Viral, parasite, fungal and plant DNA can be assigned to species level on a presence/absence basis, allowing – for example – identification of dietary intake of plant-based foodstuffs and their derivatives. Prokaryotic DNA is subject to taxonomic and functional analyses, with assignment to taxonomic hierarchies (kingdom, class, order, family, genus, species, strain/subspecies) and abundance determination. After de novo assembly of sequence reads, genes within samples are predicted and used to build a non-redundant catalogue of genes. From this catalogue, per-sample gene abundance can be determined after normalization of data based on gene length. Functional annotation of genes is achieved through mapping of gene clusters against KEGG proteins, and InterProScan. The pipeline is undergoing validation using the human faecal metagenomic data of Qin et al. (2014, Nature 513, 59–64). Outputs from the pipeline allow development of tools for the integration of metagenomic and metabolomic data, moving metagenomic studies beyond determination of gene richness and representation towards microbial-metabolite mapping. There is scope to improve the outputs from viral, parasite, fungal and plant DNA analyses, depending on the depth of sequencing associated with samples. The pipeline can easily be adapted for the analyses of environmental and non-human animal samples, and for use with data generated via non-Illumina sequencing platforms.

The Data of Things: Strategies, Patterns and Practice of Cloud-based Participatory Sensing

The broad capabilities of current mobile devices have paved the way for Mobile Crowd Sensing (MCS) applications. The success of this emerging paradigm strongly depends on the quality of received data which, in turn, is contingent to mass user participation; the broader the participation, the more useful these systems become. However, there is an ongoing trend that tries to integrate MCS applications with emerging computing paradigms such as cloud computing. The intuition is that such a transition can significantly improve the overall efficiency while at the same time it offers stronger security and privacy-preserving mechanisms for the end-user. In this position paper, we dwell on the underpinnings of incorporating cloud computing techniques to facilitate the vast amount of data collected in MCS applications. That is, we present a list of core system, security and privacy requirements that must be met if such a transition is to be successful. To this end, we first address several competing challenges not previously considered in the literature such as the scarce energy resources of battery-powered mobile devices as well as their limited computational resources that they often prevent the use of computationally heavy cryptographic operations and thus offering limited security services to the end-user. Finally, we present a use case scenario as a comprehensive example. Based on our findings, we posit open issues and challenges, and discuss possible ways to address them, so that security and privacy do not hinder the migration of MCS systems to the cloud.