Selecting social work students:lessons from research in Scotland

The issue of selection of students to social work programmes is one that remains highly contested. While it is clear that there is no single way of choosing the next generation of social work students, nevertheless, there are a number of strongly held beliefs about what ‘best practice’ means in this fraught field. These can be difficult to challenge, and even harder to shift, in spite of contrary evidence. This paper presents research conducted in Scotland in 2016 as part of the Scottish Government-sponsored Review of Social Work Education. The research set out to consider what selection processes were being used in Scotland and why; more fundamentally, it sought to explore the views of those involved in social work education alongside evidence about the outcomes of the selection processes (that is, data on student retention and success). The article concludes that while there is little evidence that one method of selection to social work programmes is intrinsically better than another, issues of fairness and transparency in selection, as well as diversity, remain pressing

Mitochondrial DNA Variant Discovery and Evaluation in Human Cardiomyopathies through Next-Generation Sequencing

Mutations in mitochondrial DNA (mtDNA) may cause maternally-inherited cardiomyopathy and heart failure. In homoplasmy all mtDNA copies contain the mutation. In heteroplasmy there is a mixture of normal and mutant copies of mtDNA. The clinical phenotype of an affected individual depends on the type of genetic defect and the ratios of mutant and normal mtDNA in affected tissues. We aimed at determining the sensitivity of next-generation sequencing compared to Sanger sequencing for mutation detection in patients with mitochondrial cardiomyopathy. We studied 18 patients with mitochondrial cardiomyopathy and two with suspected mitochondrial disease. We “shotgun” sequenced PCR-amplified mtDNA and multiplexed using a single run on Roche's 454 Genome Sequencer. By mapping to the reference sequence, we obtained 1,300× average coverage per case and identified high-confidence variants. By comparing these to >400 mtDNA substitution variants detected by Sanger, we found 98% concordance in variant detection. Simulation studies showed that >95% of the homoplasmic variants were detected at a minimum sequence coverage of 20× while heteroplasmic variants required >200× coverage. Several Sanger “misses” were detected by 454 sequencing. These included the novel heteroplasmic 7501T>C in tRNA serine 1 in a patient with sudden cardiac death. These results support a potential role of next-generation sequencing in the discovery of novel mtDNA variants with heteroplasmy below the level reliably detected with Sanger sequencing. We hope that this will assist in the identification of mtDNA mutations and key genetic determinants for cardiomyopathy and mitochondrial disease

Feature integration in natural language concepts

Two experiments measured the joint influence of three key sets of semantic features on the frequency with which artifacts (Experiment 1) or plants and creatures (Experiment 2) were categorized in familiar categories. For artifacts, current function outweighed both originally intended function and current appearance. For biological kinds, appearance and behavior, an inner biological function, and appearance and behavior of offspring all had similarly strong effects on categorization. The data were analyzed to determine whether an independent cue model or an interactive model best accounted for how the effects of the three feature sets combined. Feature integration was found to be additive for artifacts but interactive for biological kinds. In keeping with this, membership in contrasting artifact categories tended to be superadditive, indicating overlapping categories, whereas for biological kinds, it was subadditive, indicating conceptual gaps between categories. It is argued that the results underline a key domain difference between artifact and biological concepts

Deep Neural Networks for Energy and Position Reconstruction in EXO-200

We apply deep neural networks (DNN) to data from the EXO-200 experiment. In the studied cases, the DNN is able to reconstruct the relevant parameters - total energy and position - directly from raw digitized waveforms, with minimal exceptions. For the first time, the developed algorithms are evaluated on real detector calibration data. The accuracy of reconstruction either reaches or exceeds what was achieved by the conventional approaches developed by EXO-200 over the course of the experiment. Most existing DNN approaches to event reconstruction and classification in particle physics are trained on Monte Carlo simulated events. Such algorithms are inherently limited by the accuracy of the simulation. We describe a unique approach that, in an experiment such as EXO-200, allows to successfully perform certain reconstruction and analysis tasks by training the network on waveforms from experimental data, either reducing or eliminating the reliance on the Monte Carlo.Comment: Accepted version. 33 pages, 28 figure

Dynamics of mitochondrial heteroplasmy in three families investigated via a repeatable re-sequencing study

Background: Originally believed to be a rare phenomenon, heteroplasmy - the presence of more than one mitochondrial DNA (mtDNA) variant within a cell, tissue, or individual - is emerging as an important component of eukaryotic genetic diversity. Heteroplasmies can be used as genetic markers in applications ranging from forensics to cancer diagnostics. Yet the frequency of heteroplasmic alleles may vary from generation to generation due to the bottleneck occurring during oogenesis. Therefore, to understand the alterations in allele frequencies at heteroplasmic sites, it is of critical importance to investigate the dynamics of maternal mtDNA transmission. Results: Here we sequenced, at high coverage, mtDNA from blood and buccal tissues of nine individuals from three families with a total of six maternal transmission events. Using simulations and re-sequencing of clonal DNA, we devised a set of criteria for detecting polymorphic sites in heterogeneous genetic samples that is resistant to the noise originating from massively parallel sequencing technologies. Application of these criteria to nine human mtDNA samples revealed four heteroplasmic sites. Conclusions: Our results suggest that the incidence of heteroplasmy may be lower than estimated in some other recent re-sequencing studies, and that mtDNA allelic frequencies differ significantly both between tissues of the same individual and between a mother and her offspring. We designed our study in such a way that the complete analysis described here can be repeated by anyone either at our site or directly on the Amazon Cloud. Our computational pipeline can be easily modified to accommodate other applications, such as viral re-sequencing