Alzheimer’s disease-associated peptide Aβ₄₂ mobilizes ER Ca²⁺ via InsP₃R-dependent and -independent mechanisms

Dysregulation of Ca2+ homeostasis is considered to contribute to the toxic action of the Alzheimer’s Disease (AD) associated Amyloid β-peptide (Aβ). Ca2+ fluxes across the plasma membrane and release from intracellular stores have both been reported to underlie the Ca2+ fluxes induced by Aβ42. Here, we investigated the contribution of Ca2+ release from the endoplasmic reticulum (ER) to the effects of Aβ42 upon Ca2+ homeostasis and the mechanism by which Aβ42 elicited these effects. Consistent with previous reports, application of soluble oligomeric forms of Aβ42 exhibited Ca2+ mobilizing properties. The Aβ42-stimulated Ca2+ signals persisted in the absence of extracellular Ca2+ indicating a significant contribution of Ca2+ release from the ER Ca2+ store to the generation of these signals. Moreover, inositol 1,4,5-trisphosphate (InsP3) signaling contributed to Aβ42-stimulated Ca2+ release. The Ca2+ mobilizing effect of Aβ42 was also observed when applied to permeabilized cells deficient in InsP3 receptors revealing an additional direct effect of internalized Aβ42 upon the ER, and a mechanism for induction of toxicity by intracellular Aβ42

Variational Sparse Coding

Unsupervised discovery of interpretable features and controllable generation with highdimensional data are currently major challenges in machine learning, with applications in data visualisation, clustering and artificial data synthesis. We propose a model based on variational auto-encoders (VAEs) in which interpretation is induced through latent space sparsity with a mixture of Spike and Slab distributions as prior. We derive an evidence lower bound for this model and propose a specific training method for recovering disentangled features as sparse elements in latent vectors. In our experiments, we demonstrate superior disentanglement performance to standard VAE approaches when an estimate of the number of true sources of variation is not available and objects display different combinations of attributes. Furthermore, the new model provides unique capabilities, such as recovering feature exploitation, synthesising samples that share attributes with a given input object and controlling both discrete and continuous features upon generation

Variational Sparse Coding

Unsupervised discovery of interpretable features and controllable generation with highdimensional data are currently major challenges in machine learning, with applications in data visualisation, clustering and artificial data synthesis. We propose a model based on variational auto-encoders (VAEs) in which interpretation is induced through latent space sparsity with a mixture of Spike and Slab distributions as prior. We derive an evidence lower bound for this model and propose a specific training method for recovering disentangled features as sparse elements in latent vectors. In our experiments, we demonstrate superior disentanglement performance to standard VAE approaches when an estimate of the number of true sources of variation is not available and objects display different combinations of attributes. Furthermore, the new model provides unique capabilities, such as recovering feature exploitation, synthesising samples that share attributes with a given input object and controlling both discrete and continuous features upon generation

Identification of new genes in Sinorhizobium meliloti using the Genome Sequencer FLX system

<p>Abstract</p> <p>Background</p> <p><it>Sinorhizobium meliloti </it>is an agriculturally important model symbiont. There is an ongoing need to update and improve its genome annotation. In this study, we used a high-throughput pyrosequencing approach to sequence the transcriptome of <it>S. meliloti</it>, and search for new bacterial genes missed in the previous genome annotation. This is the first report of sequencing a bacterial transcriptome using the pyrosequencing technology.</p> <p>Results</p> <p>Our pilot sequencing run generated 19,005 reads with an average length of 136 nucleotides per read. From these data, we identified 20 new genes. These new gene transcripts were confirmed by RT-PCR and their possible functions were analyzed.</p> <p>Conclusion</p> <p>Our results indicate that high-throughput sequence analysis of bacterial transcriptomes is feasible and next-generation sequencing technologies will greatly facilitate the discovery of new genes and improve genome annotation.</p

Mapping the Gap of Water and Erosion Control Measures in the Rapidly Urbanizing Mbezi River Catchment of Dar es Salaam

In rapidly urbanizing catchments, increase in stormwater runoff may cause serious erosion and frequent floods if stormwater management systems are improper and dysfunctional. Through GIS-based modelling, field investigations, resident’s questionnaire survey, and interviews with officials, the study set out to assesses the coverage and efficiency of drainage infrastructure in Mbezi River catchment basin in Dar es Salaam, Tanzania. Between 2003 and 2016, the catchment imperviousness increased by 41%, causing flood incidents, massive erosion, and numerous pollution sources. Residents strive to address stormwater hazards using terraces, hedges, and physical barriers; however, the problems persist, indicating lack of coordination and poor causality understanding between land-use changes and catchment impacts. Small-scale stormwater harvesting was exercised by 75% of the households, pointing to water supply challenges. Municipal stormwater management efforts was limited to roadside drains covering 17% of road lengths in the catchment, and 65% of those did not meet their design standards. Interviews with officials revealed a need for improved co-understanding and collaborative initiatives to bolster integrated water management. The study suggests a need to adopt a new urban stormwater management paradigm, appropriate for both residents and authorities. Without this new discourse, the urbanization led stormwater increase might jeopardize the liveability of the entire catchment

A Systematic Review of the Effectiveness of Children’s Behavioral Health Interventions in Psychiatric Residential Treatment Facilities

Objective The purpose of this systematic review was to synthesize quantitative or mixed-method studies that evaluate the efficacy of interventions with youth in the context of psychiatric residential treatment facilities (PRTFs) in the United States. Methods Systematic review procedures were conducted to identify relevant studies, both published and from the gray literature in the United States. Search terms were informed via consultation with a university social science reference librarian, and four electronic databases were searched. Using a priori inclusion and exclusion criteria, team-based search procedures yielded a final sample of 47 relevant studies. Results Studies varied with respect to publication status; sample size; research design; youth gender identity; youth racial/ethnic identity; youth behavioral, psychological, and developmental or intellectual concerns at intake; outcomes measures; and interventions evaluated. Evaluated interventions could be clustered into one of five categories: (a) modifications to system of treatment, (b) therapeutic modalities, (c) educational/alternative programs, (d) practice behaviors, and (e) post-discharge engagement. The majority of studies noted youth outcome improvements; however, some studies also yielded mixed, inconclusive, or null results. Conclusions We would characterize the breadth and depth of research in this area to be insufficient in providing PRTF stakeholders a clear and firm understanding of “what works” for youth. Thus, one major implication of our review is the need for more research and efforts to incentivize the evaluation of ongoing practices in youth PRTFs. Still, this systematic review can serve as a convenient reference that can inform tentatively PRTF stakeholders’ decisions about the selection of interventions or practice behaviors

Gene Expression and Isoform Variation Analysis using Affymetrix Exon Arrays

Correction to Bemmo A, Benovoy D, Kwan T, Gaffney DJ, Jensen RV, Majewski J: Gene expression and isoform variation analysis using Affymetrix Exon Arrays. BMC Genomics 2008, 9: 529