Privileged Mexican migrants in Europe: Distinctions and cosmopolitanism on social networking sites

This article examines the ways in which classed distinctions are related to the construction of increasingly cosmopolitan identities on Social Networking Sites (SNSs) amongst Mexican migrants from relatively privileged backgrounds living in Europe. It centres on how user demographics shape many of the concerns and outcomes pertaining to the use of SNSs. It considers the implications of the fact that SNSs are predominantly used by a demographic considered as non-marginalized, mobile and as possessing relatively privileged economic, cultural and social backgrounds. It analyses the ways in which online identities are constructed on SNS profiles using multimedia content to represent specific lifestyles and cultural practices that are used to make distinctions amongst participants, and are related to social, cultural and economic capital. A critical analysis is presented as to how users represent cosmopolitan identities online through the display of tastes and lifestyles in SNS content and into how these representations relate to users’ privileged positions in Mexican society. Bourdieu’s concept of distinction is used to emphasize the utility of considering different forms of capital in analysing the use of SNSs and profile content generated by a specific demographic. This article demonstrates how the analysis of SNS use may contribute towards an understanding of how classed distinctions are made based on this use and of how users negotiate the posting of profile content according to these distinctions and manage (select, edit and share) their representations

The Mexican European diaspora: class, race and distinctions on social networking sites

This article presents original user-based research concerning a Mexican European Diaspora's use of Social Networking Sites (SNSs) for making racial and classed distinctions through the course of sharing and viewing content on their networked profiles. Bourdieu’s theoretical framework is used to address the ways in which SNSs usage fosters social capital, exclusion and displays of prejudice and discrimination. We also found useful the analysis of racial distinctions based on participants' references to physical attributes. To illustrate the use of racial and classed distinctions, we specifically consider the use of Facebook and also of A Small World (ASW), an exclusive SNS, regarded as one of few designed for use by 'the wealthy' [Ruiz, 2008. "Five Social Networking Sites for the Wealthy." Forbes. Accessed June 11, 2010. http://www.forbes.com/2008/05/02/social-networks-vip-tech-personal-cx_nr_0502style.html]. We analyse the term 'naco', a pejorative Mexican term commonly used to refer to vulgarity or inferiority amongst Mexican European and privileged diaspora with strong racial, classed and gendered connotations. The empirical material for this study is based on ethnographic research. Through the analysis of displayed images and text as well as well questionnaires and interviews we found tacit or explicit exclusion, discrimination and segregation through close-knit SNS networks engaged in by our participants, a Mexican privileged European diaspora

Discriminative motif discovery in DNA and protein sequences using the DEME algorithm

<p>Abstract</p> <p>Background</p> <p>Motif discovery aims to detect short, highly conserved patterns in a collection of unaligned DNA or protein sequences. Discriminative motif finding algorithms aim to increase the sensitivity and selectivity of motif discovery by utilizing a second set of sequences, and searching only for patterns that can differentiate the two sets of sequences. Potential applications of discriminative motif discovery include discovering transcription factor binding site motifs in ChIP-chip data and finding protein motifs involved in thermal stability using sets of orthologous proteins from thermophilic and mesophilic organisms.</p> <p>Results</p> <p>We describe DEME, a discriminative motif discovery algorithm for use with protein and DNA sequences. Input to DEME is two sets of sequences; a "positive" set and a "negative" set. DEME represents motifs using a probabilistic model, and uses a novel combination of global and local search to find the motif that optimally discriminates between the two sets of sequences. DEME is unique among discriminative motif finders in that it uses an informative Bayesian prior on protein motif columns, allowing it to incorporate prior knowledge of residue characteristics. We also introduce four, synthetic, discriminative motif discovery problems that are designed for evaluating discriminative motif finders in various biologically motivated contexts. We test DEME using these synthetic problems and on two biological problems: finding yeast transcription factor binding motifs in ChIP-chip data, and finding motifs that discriminate between groups of thermophilic and mesophilic orthologous proteins.</p> <p>Conclusion</p> <p>Using artificial data, we show that DEME is more effective than a non-discriminative approach when there are "decoy" motifs or when a variant of the motif is present in the "negative" sequences. With real data, we show that DEME is as good, but not better than non-discriminative algorithms at discovering yeast transcription factor binding motifs. We also show that DEME can find highly informative thermal-stability protein motifs. Binaries for the stand-alone program DEME is free for academic use and is available at <url>http://bioinformatics.org.au/deme/</url></p

What lies beneath: exploring links between asylum policy and hate crime in the UK

This paper explores the link between increasing incidents of hate crime and the asylum policy of successive British governments with its central emphasis on deterrence. The constant problematisation of asylum seekers in the media and political discourse ensures that 'anti-immigrant' prejudice becomes mainstr earned as a common-sense response. The victims are not only the asylum seekers hoping for a better life but democratic society itself with its inherent values of pluralism and tolerance debased and destabilised

Nonidentifiability of the Source of Intrinsic Noise in Gene Expression from Single-Burst Data

Over the last few years, experimental data on the fluctuations in gene activity between individual cells and within the same cell over time have confirmed that gene expression is a “noisy” process. This variation is in part due to the small number of molecules taking part in some of the key reactions that are involved in gene expression. One of the consequences of this is that protein production often occurs in bursts, each due to a single promoter or transcription factor binding event. Recently, the distribution of the number of proteins produced in such bursts has been experimentally measured, offering a unique opportunity to study the relative importance of different sources of noise in gene expression. Here, we provide a derivation of the theoretical probability distribution of these bursts for a wide variety of different models of gene expression. We show that there is a good fit between our theoretical distribution and that obtained from two different published experimental datasets. We then prove that, irrespective of the details of the model, the burst size distribution is always geometric and hence determined by a single parameter. Many different combinations of the biochemical rates for the constituent reactions of both transcription and translation will therefore lead to the same experimentally observed burst size distribution. It is thus impossible to identify different sources of fluctuations purely from protein burst size data or to use such data to estimate all of the model parameters. We explore methods of inferring these values when additional types of experimental data are available

“Making voices heard…”: Index on Censorship as Advocacy Journalism

The magazine Index on Censorship has sought, since its launch in 1972, to provide a space where censorship and abuses against freedom of expression have been identified, highlighted and challenged. Originally set up by a collection of writers and intellectuals who were concerned at the levels of state censorship and repression of artists in and under the influence of the Soviet Union and elsewhere, ‘Index’ has provided those championing the values of freedom of expression with a platform for highlighting human rights abuses, curtailment of civil liberties and formal and informal censorship globally. Charting its inception and development between 1971 and 1974, the paper is the first to situate the journal within the specific academic literature on activist media (Janowitz, 1975; Waisbord, 2009; Fisher, 2016). In doing so the paper advances an argument which draws on the drivers and motivations behind the publication’s launch to signal the development of a particular justification or ‘advocacy’ of a left-libertarian civic model of freedom of speech

MER41 Repeat Sequences Contain Inducible STAT1 Binding Sites

Chromatin immunoprecipitation combined with massively parallel sequencing methods (ChIP-seq) is becoming the standard approach to study interactions of transcription factors (TF) with genomic sequences. At the example of public STAT1 ChIP-seq data sets, we present novel approaches for the interpretation of ChIP-seq data

A reexamination of information theory-based methods for DNA-binding site identification

<p>Abstract</p> <p>Background</p> <p>Searching for transcription factor binding sites in genome sequences is still an open problem in bioinformatics. Despite substantial progress, search methods based on information theory remain a standard in the field, even though the full validity of their underlying assumptions has only been tested in artificial settings. Here we use newly available data on transcription factors from different bacterial genomes to make a more thorough assessment of information theory-based search methods.</p> <p>Results</p> <p>Our results reveal that conventional benchmarking against artificial sequence data leads frequently to overestimation of search efficiency. In addition, we find that sequence information by itself is often inadequate and therefore must be complemented by other cues, such as curvature, in real genomes. Furthermore, results on skewed genomes show that methods integrating skew information, such as <it>Relative Entropy</it>, are not effective because their assumptions may not hold in real genomes. The evidence suggests that binding sites tend to evolve towards genomic skew, rather than against it, and to maintain their information content through increased conservation. Based on these results, we identify several misconceptions on information theory as applied to binding sites, such as negative entropy, and we propose a revised paradigm to explain the observed results.</p> <p>Conclusion</p> <p>We conclude that, among information theory-based methods, the most unassuming search methods perform, on average, better than any other alternatives, since heuristic corrections to these methods are prone to fail when working on real data. A reexamination of information content in binding sites reveals that information content is a compound measure of search and binding affinity requirements, a fact that has important repercussions for our understanding of binding site evolution.</p

A Linear Model for Transcription Factor Binding Affinity Prediction in Protein Binding Microarrays

Protein binding microarrays (PBM) are a high throughput technology used to characterize protein-DNA binding. The arrays measure a protein's affinity toward thousands of double-stranded DNA sequences at once, producing a comprehensive binding specificity catalog. We present a linear model for predicting the binding affinity of a protein toward DNA sequences based on PBM data. Our model represents the measured intensity of an individual probe as a sum of the binding affinity contributions of the probe's subsequences. These subsequences characterize a DNA binding motif and can be used to predict the intensity of protein binding against arbitrary DNA sequences. Our method was the best performer in the Dialogue for Reverse Engineering Assessments and Methods 5 (DREAM5) transcription factor/DNA motif recognition challenge. For the DREAM5 bonus challenge, we also developed an approach for the identification of transcription factors based on their PBM binding profiles. Our approach for TF identification achieved the best performance in the bonus challenge