The identification of informative genes from multiple datasets with increasing complexity

Background In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes. Results In this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes. Conclusions We show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events

Decentralizing the Social Web

International audienceFor over a decade, standards bodies like the IETF and W3C have attempted to prevent the centralization of the Web via the use of open standards for 'permission-less innovation.' Yet today, these standards , from OAuth to RSS, seem to have failed to prevent the massive centralization of the Web at the hands of a few major corporations like Google and Facebook. We'll delve deep into the lessons of failed attempts to replace DNS like XRIs, identity systems like OpenID, and metadata formats like the Semantic Web, all of which were recuperated by centralized platforms like Facebook as Facebook Connect and the "Like" Button. Learning from the past, a new generation of blockchain standards and governance mechanisms may be our last, best chance to save the Web

Unfitting, uncomfortable, unacademic: a sociological reading of an interactive mobile phone app in university lectures

Abstract Scholarly literature on education technology uptake has been dominated by technological determinist readings of students’ technology use. However, in recent years there has been a move by sociologists of education to highlight how the contexts in which educational technologies are introduced are not tabula rasa but socially and culturally complex. This study approaches technology as a social construct, arguing that students construct discursive meaning of, rather than simply respond to, technologies for learning. The study explores students’ constructions of a mobile learning app that was introduced into lectures during a year-long university course. Students largely rejected the app, constructing it as unfitting for the context, a socially uncomfortable experience and an unacademic way of learning. The paper highlights the limitations of technological determinism and closes by arguing for readings of educational technologies that pay close attention to students’ voices

Disciplinary Power and Impression Management in the Trials of the Stansted 15

We bring Foucauldian and Goffmanian frameworks into dialogue to show how repressive and disciplinary power operate in the criminal trials of social movement activists. We do so through an ethnographic account of the trials on terrorism-related charges of a group of anti-deportation direct action protesters known as the Stansted 15, complemented by interviews with defendants. We argue that the prosecution of these activists on terrorism-related charges creates conditions of constraint which effectively serve to collapse the space for political and normative challenge, and obliges them to develop impression management strategies internalising and reproducing the court’s expressive regime. We see these trials therefore as a normalising procedure whose goal is not the repressive application of custodial sentences, but rather a disciplinary disarming of radical critique so that leniency can be applied. At stake here, therefore, is the production through trial of the ideal disciplined liberal political subject

A genetic locus and gene expression patterns associated with the priming effect on lettuce seed germination at elevated temperatures

Seeds of most cultivated varieties of lettuce (Lactuca sativa L.) fail to germinate at warm temperatures (i.e., above 25–30°C). Seed priming (controlled hydration followed by drying) alleviates this thermoinhibition by increasing the maximum germination temperature. We conducted a quantitative trait locus (QTL) analysis of seed germination responses to priming using a recombinant inbred line (RIL) population derived from a cross between L. sativa cv. Salinas and L. serriola accession UC96US23. Priming significantly increased the maximum germination temperature of the RIL population, and a single major QTL was responsible for 47% of the phenotypic variation due to priming. This QTL collocated with Htg6.1, a major QTL from UC96US23 associated with high temperature germination capacity. Seeds of three near-isogenic lines (NILs) carrying an Htg6.1 introgression from UC96US23 in a Salinas genetic background exhibited synergistic increases in maximum germination temperature in response to priming. LsNCED4, a gene encoding a key enzyme (9-cis-epoxycarotinoid dioxygenase) in the abscisic acid biosynthetic pathway, maps precisely with Htg6.1. Expression of LsNCED4 after imbibition for 24 h at high temperature was greater in non-primed seeds of Salinas, of a second cultivar (Titan) and of NILs containing Htg6.1 compared to primed seeds of the same genotypes. In contrast, expression of genes encoding regulated enzymes in the gibberellin and ethylene biosynthetic pathways (LsGA3ox1 and LsACS1, respectively) was enhanced by priming and suppressed by imbibition at elevated temperatures. Developmental and temperature regulation of hormonal biosynthetic pathways is associated with seed priming effects on germination temperature sensitivity

Oncolytic Measles Virotherapy and Opposition to Measles Vaccination

Recent measles epidemics in US and European cities where vaccination coverage has declined are providing a harsh reminder for the need to maintain protective levels of immunity across the entire population. Vaccine uptake rates have been declining in large part because of public misinformation regarding a possible association between measles vaccination and autism for which there is no scientific basis. The purpose of this article is to address a new misinformed antivaccination argument-that measles immunity is undesirable because measles virus is protective against cancer. Having worked for many years to develop engineered measles viruses as anticancer therapies, we have concluded (1) that measles is not protective against cancer and (2) that its potential utility as a cancer therapy will be enhanced, not diminished, by prior vaccination

Genome-Wide Analysis of Protein-Protein Interactions and Involvement of Viral Proteins in SARS-CoV Replication

Analyses of viral protein-protein interactions are an important step to understand viral protein functions and their underlying molecular mechanisms. In this study, we adopted a mammalian two-hybrid system to screen the genome-wide intraviral protein-protein interactions of SARS coronavirus (SARS-CoV) and therefrom revealed a number of novel interactions which could be partly confirmed by in vitro biochemical assays. Three pairs of the interactions identified were detected in both directions: non-structural protein (nsp) 10 and nsp14, nsp10 and nsp16, and nsp7 and nsp8. The interactions between the multifunctional nsp10 and nsp14 or nsp16, which are the unique proteins found in the members of Nidovirales with large RNA genomes including coronaviruses and toroviruses, may have important implication for the mechanisms of replication/transcription complex assembly and functions of these viruses. Using a SARS-CoV replicon expressing a luciferase reporter under the control of a transcription regulating sequence, it has been shown that several viral proteins (N, X and SUD domains of nsp3, and nsp12) provided in trans stimulated the replicon reporter activity, indicating that these proteins may regulate coronavirus replication and transcription. Collectively, our findings provide a basis and platform for further characterization of the functions and mechanisms of coronavirus proteins

Adaptations to Climate-Mediated Selective Pressures in Humans

Humans inhabit a remarkably diverse range of environments, and adaptation through natural selection has likely played a central role in the capacity to survive and thrive in extreme climates. Unlike numerous studies that used only population genetic data to search for evidence of selection, here we scan the human genome for selection signals by identifying the SNPs with the strongest correlations between allele frequencies and climate across 61 worldwide populations. We find a striking enrichment of genic and nonsynonymous SNPs relative to non-genic SNPs among those that are strongly correlated with these climate variables. Among the most extreme signals, several overlap with those from GWAS, including SNPs associated with pigmentation and autoimmune diseases. Further, we find an enrichment of strong signals in gene sets related to UV radiation, infection and immunity, and cancer. Our results imply that adaptations to climate shaped the spatial distribution of variation in humans