Amygdala reactivity in ethnic minorities and its relationship to the social environment: an fMRI study

Background: Ethnic minority individuals have an increased risk of developing a psychotic disorder, particularly if they live in areas of ethnic segregation, or low own group ethnic density. The neurobiological mechanisms underlying this ethnic minority associated risk are unknown. We used functional MRI to investigate neural responses to faces of different ethnicity, in individuals of black ethnicity, and a control group of white British ethnicity individuals. Methods: In total 20 individuals of black ethnicity, and 22 individuals of white British ethnicity underwent a 3T MRI scan while viewing faces of black and white ethnicity. Own group ethnic density was calculated from the 2011 census. Neighbourhood segregation was quantified using the Index of Dissimilarity method. Results: At the within-group level, both groups showed greater right amygdala activation to outgroup faces. Between groups, the black ethnicity group showed greater right amygdala activation to white faces, compared to the white ethnicity group. Within the black ethnicity group, individuals living in areas of lower own group ethnic density showed greater right amygdala reactivity to white faces (r = −0.61, p = 0.01). Conclusions: This is the first time an increased amygdala response to white faces has been demonstrated in individuals of black ethnicity. In the black ethnicity group, correlations were observed between amygdala response and neighbourhood variables associated with increased psychosis risk. These results may have relevance for our understanding of the increased rates of paranoia and psychotic disorders in ethnic minority individuals

Social Interactions vs Revisions, What is important for Promotion in Wikipedia?

In epistemic community, people are said to be selected on their knowledge contribution to the project (articles, codes, etc.) However, the socialization process is an important factor for inclusion, sustainability as a contributor, and promotion. Finally, what does matter to be promoted? being a good contributor? being a good animator? knowing the boss? We explore this question looking at the process of election for administrator in the English Wikipedia community. We modeled the candidates according to their revisions and/or social attributes. These attributes are used to construct a predictive model of promotion success, based on the candidates's past behavior, computed thanks to a random forest algorithm. Our model combining knowledge contribution variables and social networking variables successfully explain 78% of the results which is better than the former models. It also helps to refine the criterion for election. If the number of knowledge contributions is the most important element, social interactions come close second to explain the election. But being connected with the future peers (the admins) can make the difference between success and failure, making this epistemic community a very social community too

Mining geriatric assessment data for in-patient fall prediction models and high-risk subgroups

<p>Abstract</p> <p>Background</p> <p>Hospital in-patient falls constitute a prominent problem in terms of costs and consequences. Geriatric institutions are most often affected, and common screening tools cannot predict in-patient falls consistently. Our objectives are to derive comprehensible fall risk classification models from a large data set of geriatric in-patients' assessment data and to evaluate their predictive performance (aim#1), and to identify high-risk subgroups from the data (aim#2).</p> <p>Methods</p> <p>A data set of n = 5,176 single in-patient episodes covering 1.5 years of admissions to a geriatric hospital were extracted from the hospital's data base and matched with fall incident reports (n = 493). A classification tree model was induced using the C4.5 algorithm as well as a logistic regression model, and their predictive performance was evaluated. Furthermore, high-risk subgroups were identified from extracted classification rules with a support of more than 100 instances.</p> <p>Results</p> <p>The classification tree model showed an overall classification accuracy of 66%, with a sensitivity of 55.4%, a specificity of 67.1%, positive and negative predictive values of 15% resp. 93.5%. Five high-risk groups were identified, defined by high age, low Barthel index, cognitive impairment, multi-medication and co-morbidity.</p> <p>Conclusions</p> <p>Our results show that a little more than half of the fallers may be identified correctly by our model, but the positive predictive value is too low to be applicable. Non-fallers, on the other hand, may be sorted out with the model quite well. The high-risk subgroups and the risk factors identified (age, low ADL score, cognitive impairment, institutionalization, polypharmacy and co-morbidity) reflect domain knowledge and may be used to screen certain subgroups of patients with a high risk of falling. Classification models derived from a large data set using data mining methods can compete with current dedicated fall risk screening tools, yet lack diagnostic precision. High-risk subgroups may be identified automatically from existing geriatric assessment data, especially when combined with domain knowledge in a hybrid classification model. Further work is necessary to validate our approach in a controlled prospective setting.</p

Resequencing and comparative genomics of stagonospora nodorum: Sectional gene absence and effector discovery

Stagonospora nodorum is an important wheat (Triticum aestivum) pathogen in many parts of the world, causing major yield losses. It was the first species in the large fungal Dothideomycete class to be genome sequenced. The reference genome sequence (SN15) has been instrumental in the discovery of genes encoding necrotrophic effectors that induce disease symptoms in specific host genotypes. Here we present the genome sequence of two further S. nodorum strains (Sn4 and Sn79) that differ in their effector repertoire from the reference. Sn79 is avirulent on wheat and produces no apparent effectors when infiltrated onto many cultivars and mapping population parents. Sn4 is pathogenic on wheat and has virulences not found in SN15. The new strains, sequenced with short-read Illumina chemistry, are compared with SN15 by a combination of mapping and de novo assembly approaches.Each of the genomes contains a large number of strain-specific genes, many of which have no meaningful similarity to any known gene. Large contiguous sections of the reference genome are absent in the two newly sequenced strains. We refer to these differences as “sectional gene absences.” The presence of genes in pathogenic strains and absence in Sn79 is added to computationally predicted properties of known proteins to produce a list of likely effector candidates. Transposon insertion was observed in the mitochondrial genomes of virulent strains where the avirulent strain retained the likely ancestral sequence. The study suggests that short-read enabled comparative genomics is an effective way to both identify new S. nodorum effector candidates and to illuminate evolutionary processes in this species

Pan-parastagonospora comparative genome analysis-effector prediction and genome evolution

We report a fungal pan-genome study involving Parastagonospora spp., including 21 isolates of the wheat (Triticum aestivum) pathogen Parastagonospora nodorum, 10 of the grass-infecting Parastagonospora avenae, and 2 of a closely related undefined sister species. We observed substantial variation in the distribution of polymorphisms across the pan-genome, including repeat-induced point mutations, diversifying selection and gene gains and losses.We also discovered chromosome-scale inter and intraspecific presence/absence variation of some sequences, suggesting the occurrence of one or more accessory chromosomes or regions that may play a role in host-pathogen interactions. The presence of known pathogenicity effector loci SnToxA, SnTox1, and SnTox3 varied substantially among isolates. Three P. nodorum isolates lacked functional versions for all three loci, whereas three P. avenae isolates carried one or both of the SnTox1 and SnTox3 genes, indicating previously unrecognized potential for discovering additional effectors in the P. nodorum-wheat pathosystem. We utilized the pangenomic comparative analysis to improve the prediction of pathogenicity effector candidates, recovering the three confirmed effectors among our top-ranked candidates. We propose applying this pan-genomic approach to identify the effector repertoire involved in other host-microbe interactions involving necrotrophic pathogens in the Pezizomycotina

WordCluster: detecting clusters of DNA words and genomic elements

<p>Abstract</p> <p>Background</p> <p>Many <it>k-</it>mers (or DNA words) and genomic elements are known to be spatially clustered in the genome. Well established examples are the genes, TFBSs, CpG dinucleotides, microRNA genes and ultra-conserved non-coding regions. Currently, no algorithm exists to find these clusters in a statistically comprehensible way. The detection of clustering often relies on densities and sliding-window approaches or arbitrarily chosen distance thresholds.</p> <p>Results</p> <p>We introduce here an algorithm to detect clusters of DNA words (<it>k-</it>mers), or any other genomic element, based on the distance between consecutive copies and an assigned statistical significance. We implemented the method into a web server connected to a MySQL backend, which also determines the co-localization with gene annotations. We demonstrate the usefulness of this approach by detecting the clusters of CAG/CTG (cytosine contexts that can be methylated in undifferentiated cells), showing that the degree of methylation vary drastically between inside and outside of the clusters. As another example, we used <it>WordCluster </it>to search for statistically significant clusters of olfactory receptor (OR) genes in the human genome.</p> <p>Conclusions</p> <p><it>WordCluster </it>seems to predict biological meaningful clusters of DNA words (<it>k-</it>mers) and genomic entities. The implementation of the method into a web server is available at <url>http://bioinfo2.ugr.es/wordCluster/wordCluster.php</url> including additional features like the detection of co-localization with gene regions or the annotation enrichment tool for functional analysis of overlapped genes.</p

MetaFIND: A feature analysis tool for metabolomics data

<p>Abstract</p> <p>Background</p> <p>Metabolomics, or metabonomics, refers to the quantitative analysis of all metabolites present within a biological sample and is generally carried out using NMR spectroscopy or Mass Spectrometry. Such analysis produces a set of peaks, or <it>features</it>, indicative of the metabolic composition of the sample and may be used as a basis for sample classification. Feature selection may be employed to improve classification accuracy or aid model explanation by establishing a subset of class discriminating features. Factors such as experimental noise, choice of technique and threshold selection may adversely affect the set of selected features retrieved. Furthermore, the high dimensionality and multi-collinearity inherent within metabolomics data may exacerbate discrepancies between the set of features retrieved and those required to provide a complete explanation of metabolite signatures. Given these issues, the latter in particular, we present the MetaFIND application for 'post-feature selection' correlation analysis of metabolomics data.</p> <p>Results</p> <p>In our evaluation we show how MetaFIND may be used to elucidate metabolite signatures from the set of features selected by diverse techniques over two metabolomics datasets. Importantly, we also show how MetaFIND may augment standard feature selection and aid the discovery of additional significant features, including those which represent novel class discriminating metabolites. MetaFIND also supports the discovery of higher level metabolite correlations.</p> <p>Conclusion</p> <p>Standard feature selection techniques may fail to capture the full set of relevant features in the case of high dimensional, multi-collinear metabolomics data. We show that the MetaFIND 'post-feature selection' analysis tool may aid metabolite signature elucidation, feature discovery and inference of metabolic correlations.</p

Abundant toxin-related genes in the genomes of beneficial symbionts from deep-sea hydrothermal vent mussels

Bathymodiolus mussels live in symbiosis with intracellular sulfur-oxidizing (SOX) bacteria that provide them with nutrition. We sequenced the SOX symbiont genomes from two Bathymodiolus species. Comparison of these symbiont genomes with those of their closest relatives revealed that the symbionts have undergone genome rearrangements, and up to 35% of their genes may have been acquired by horizontal gene transfer. Many of the genes specific to the symbionts were homologs of virulence genes. We discovered an abundant and diverse array of genes similar to insecticidal toxins of nematode and aphid symbionts, and toxins of pathogens such as Yersinia and Vibrio. Transcriptomics and proteomics revealed that the SOX symbionts express the toxin-related genes (TRGs) in their hosts. We hypothesize that the symbionts use these TRGs in beneficial interactions with their host, including protection against parasites. This would explain why a mutualistic symbiont would contain such a remarkable 'arsenal' of TRG