HIV/AIDS-related stigma and HIV test uptake in Ghana: evidence from the 2008 Demographic and Health Survey

The study examined the association between HIV test uptake and socioeconomic characteristics of individuals, including HIV-related stigma behaviours. The study also investigated the socioeconomic determinants of HIV-related stigma in Ghana. Cross tabulations and logistic regression techniques were applied to data from the 2008 Ghana Demographic and Health Survey. The results showed significantly low HIV test uptake and some level of HIV-related stigma prevalence in Ghana. Higher wealth status, educational attainment and HIV-related stigma were significant determinants of HIV test uptake. Aside wealth status and education, rural place of residence and religious affiliation were positive and significant determinants of HIV-related stigma. The findings call for comprehensive HIV education including treatment, prevention and care. Legislations to discourage stigma and improve HIV-testing will be critical policy steps in the right direction.

Learning Interpretable Dynamics from Images of a Freely Rotating 3D Rigid Body

In many real-world settings, image observations of freely rotating 3D rigid bodies, such as satellites, may be available when low-dimensional measurements are not. However, the high-dimensionality of image data precludes the use of classical estimation techniques to learn the dynamics and a lack of interpretability reduces the usefulness of standard deep learning methods. In this work, we present a physics-informed neural network model to estimate and predict 3D rotational dynamics from image sequences. We achieve this using a multi-stage prediction pipeline that maps individual images to a latent representation homeomorphic to $\mathbf{SO}(3)$, computes angular velocities from latent pairs, and predicts future latent states using the Hamiltonian equations of motion with a learned representation of the Hamiltonian. We demonstrate the efficacy of our approach on a new rotating rigid-body dataset with sequences of rotating cubes and rectangular prisms with uniform and non-uniform density.Comment: 8 pages, 7 figure

Comparative genomics in acid mine drainage biofilm communities reveals metabolic and structural differentiation of co-occurring archaea

Background Metal sulfide mineral dissolution during bioleaching and acid mine drainage (AMD) formation creates an environment that is inhospitable to most life. Despite dominance by a small number of bacteria, AMD microbial biofilm communities contain a notable variety of coexisting and closely related Euryarchaea, most of which have defied cultivation efforts. For this reason, we used metagenomics to analyze variation in gene content that may contribute to niche differentiation among co-occurring AMD archaea. Our analyses targeted members of the Thermoplasmatales and related archaea. These results greatly expand genomic information available for this archaeal order. Results We reconstructed near-complete genomes for uncultivated, relatively low abundance organisms A-, E-, and Gplasma, members of Thermoplasmatales order, and for a novel organism, Iplasma. Genomic analyses of these organisms, as well as Ferroplasma type I and II, reveal that all are facultative aerobic heterotrophs with the ability to use many of the same carbon substrates, including methanol. Most of the genomes share genes for toxic metal resistance and surface-layer production. Only Aplasma and Eplasma have a full suite of flagellar genes whereas all but the Ferroplasma spp. have genes for pili production. Cryogenic-electron microscopy (cryo-EM) and tomography (cryo-ET) strengthen these metagenomics-based ultrastructural predictions. Notably, only Aplasma, Gplasma and the Ferroplasma spp. have predicted iron oxidation genes and Eplasma and Iplasma lack most genes for cobalamin, valine, (iso)leucine and histidine synthesis. Conclusion The Thermoplasmatales AMD archaea share a large number of metabolic capabilities. All of the uncultivated organisms studied here (A-, E-, G-, and Iplasma) are metabolically very similar to characterized Ferroplasma spp., differentiating themselves mainly in their genetic capabilities for biosynthesis, motility, and possibly iron oxidation. These results indicate that subtle, but important genomic differences, coupled with unknown differences in gene expression, distinguish these organisms enough to allow for co-existence. Overall this study reveals shared features of organisms from the Thermoplasmatales lineage and provides new insights into the functioning of AMD communities.United States. Dept. of Energy. Genomics:GTL (Grant DE-FG02-05ER64134)National Science Foundation (U.S.). Graduate Research Fellowshi

Utilization, Utility, and Variability in Usage of Adjunctive Hyperbaric Oxygen Therapy in Spinal Management: A Review of the Literature

The objective of this review was to understand the clinical utilization, utility, and variability in the usage of adjunctive hyperbaric oxygen therapy (HBOT). Surgical site infection is associated with high morbidity and mortality, increased health care expenditure, and decreased quality of life. With the increasing prevalence of adult spinal deformity and spinal fusion surgery, it is imperative to understand the potential benefits of adjunctive treatments. HBOT is a safe and common procedure indicated to treat various medical conditions. We conducted a literature search across 3 databases for English articles published between December 1, 2019 and December 1, 2000. Thirteen studies were included. HBOT may lessen the duration of antimicrobial therapy and mitigate instrument removal and revision surgery. The current usage indications for HBOT are supported by level III evidence for chronic osteomyelitis and level IV evidence for osteoradionecrosis. However, the same level of evidence exists to support the beneficial use of adjunctive HBOT for non complicated spinal infections within 2 months after surgery. When cultured, the most common organisms were Staphylococcus aureus and other low-virulence organisms. The most common treatment protocol consists of 90-minute sessions of 100% Fio2 at 2-3 atmosphere absolute with a mean of 35.3 ± 11.6 sessions for 5.2 ± 1.4 weeks. Adjunctive HBOT should be considered in select high-risk patients. Further improvements in diagnosis and categorization of spinal infections are necessary and will indelibly aid the decision making for the initiation of HBOT

A Semi-Quantitative, Synteny-Based Method to Improve Functional Predictions for Hypothetical and Poorly Annotated Bacterial and Archaeal Genes

During microbial evolution, genome rearrangement increases with increasing sequence divergence. If the relationship between synteny and sequence divergence can be modeled, gene clusters in genomes of distantly related organisms exhibiting anomalous synteny can be identified and used to infer functional conservation. We applied the phylogenetic pairwise comparison method to establish and model a strong correlation between synteny and sequence divergence in all 634 available Archaeal and Bacterial genomes from the NCBI database and four newly assembled genomes of uncultivated Archaea from an acid mine drainage (AMD) community. In parallel, we established and modeled the trend between synteny and functional relatedness in the 118 genomes available in the STRING database. By combining these models, we developed a gene functional annotation method that weights evolutionary distance to estimate the probability of functional associations of syntenous proteins between genome pairs. The method was applied to the hypothetical proteins and poorly annotated genes in newly assembled acid mine drainage Archaeal genomes to add or improve gene annotations. This is the first method to assign possible functions to poorly annotated genes through quantification of the probability of gene functional relationships based on synteny at a significant evolutionary distance, and has the potential for broad application

Npas1+ Pallidal Neurons Target Striatal Projection Neurons

Compelling evidence demonstrates the external globus pallidus (GPe) plays a key role in processing sensorimotor information. An anatomical projection from the GPe to the dorsal striatum (dStr) has been described for decades. However, the cellular target and functional impact of this projection remain unknown. Using cell-specific transgenic mice, modern monosynaptic tracing techniques, and optogenetics-based mapping, we discovered that GPe neurons provide inhibitory inputs to direct- and indirect-pathway striatal projection neurons (SPNs). Our results indicate that the GPe input to SPNs arises primarily from Npas1- expressing neurons and is strengthened in a chronic Parkinson’s disease (PD) model. Alterations of the GPe-SPN input in a PD model argue for the critical position of this connection in regulating basal ganglia motor output, arguing that strengthening of GPe-SPN connection is maladaptive and may underlie the hypokinetic symptoms in PD

Atlanta Youth Count! 2015: Homeless Youth Count and Needs Assessment

In early 2015, researchers, community advocates, service providers, and students from across metro Atlanta joined together to plan and conduct the Atlanta Youth Count and Needs Assessment (AYCNA). The goals of the project were to: 1) provide metro-Atlanta service providers, policymakers, and youth advocates practical information on the size, nature, and needs of the homeless, precariously housed, and runaway youth in our community; 2) collect information that can be used to develop and refine policies, programs, and interventions to help these youth in our community; and 3) encourage a community-wide dialogue about the needs and social determinants of youth homelessness. This document is the official public report and provides an overview of the study methodology and key findings, including the research team’s official estimates of the number of homeless youth in metro Atlanta as well as a description of key characteristics of the population derived from the survey data collected. Members of the research team are continuing to analyze and use the data to improve public and policymakers’ understanding of youth homelessness and to guide community-efforts to improve services for these young people

Generation of a CRF1-Cre transgenic rat and the role of central amygdala CRF1 cells in nociception and anxiety-like behavior

Corticotropin-releasing factor type-1 (CRF1) receptors are critical to stress responses because they allow neurons to respond to CRF released in response to stress. Our understanding of the precise role of CRF1-expressing neuronal populations in CRF-mediated behaviors has been largely limited to mouse experiments due to the lack of genetic tools available to selectively visualize and manipulate CRF1+ cells in rats. Here, we describe the generation and validation of a transgenic CRF1-Cre-tdTomato rat, which expresses a bicistronic iCre-2A-tdTomato transgene directed by 200kb of promoter and enhancer sequence surrounding the Crhr1 cDNA present within a BAC clone, that has been transgenically inserted into the rat genome. We report that Crhr1 and Cre mRNA expression are highly colocalized in CRF1-Cre-tdTomato rats within both the central amygdala (CeA), composed of mostly GABAergic neurons, and in the basolateral amygdala (BLA), composed of mostly glutamatergic neurons. In the CeA, membrane properties, inhibitory synaptic transmission, and responses to CRF bath application in tdTomato+ neurons are similar to those previously reported in GFP+ cells in CRFR1-GFP mice. We show that stimulatory DREADD receptors can be selectively targeted to CeA CRF1+ cells via virally delivered Cre-dependent transgenes, that transfected Cre/tdTomato+ cells are activated by clozapine-n-oxide in vitro and in vivo, and that activation of these cells in vivo increases anxiety-like behavior and nocifensive responses. Outside the amygdala, we show that Cre-tdTomato is expressed in several brain areas across the rostrocaudal axis of the CRF1-Cre-tdTomato rat brain, and that the expression pattern of Cre-tdTomato cells is similar to the known expression pattern of CRF1 cells. Given the accuracy of expression in the CRF1-Cre rat, modern genetic techniques used to investigate the anatomy, physiology, and behavioral function of CRF1+ neurons and circuits can now be performed in assays that require the use of rats as the model organism

Survival analysis of localized prostate cancer with deep learning.

In recent years, data-driven, deep-learning-based models have shown great promise in medical risk prediction. By utilizing the large-scale Electronic Health Record data found in the U.S. Department of Veterans Affairs, the largest integrated healthcare system in the United States, we have developed an automated, personalized risk prediction model to support the clinical decision-making process for localized prostate cancer patients. This method combines the representative power of deep learning and the analytical interpretability of parametric regression models and can implement both time-dependent and static input data. To collect a comprehensive evaluation of model performances, we calculate time-dependent C-statistics [Formula: see text] over 2-, 5-, and 10-year time horizons using either a composite outcome or prostate cancer mortality as the target event. The composite outcome combines the Prostate-Specific Antigen (PSA) test, metastasis, and prostate cancer mortality. Our longitudinal model Recurrent Deep Survival Machine (RDSM) achieved [Formula: see text] 0.85 (0.83), 0.80 (0.83), and 0.76 (0.81), while the cross-sectional model Deep Survival Machine (DSM) attained [Formula: see text] 0.85 (0.82), 0.80 (0.82), and 0.76 (0.79) for the 2-, 5-, and 10-year composite (mortality) outcomes, respectively. In addition to estimating the survival probability, our method can quantify the uncertainty associated with the prediction. The uncertainty scores show a consistent correlation with the prediction accuracy. We find PSA and prostate cancer stage information are the most important indicators in risk prediction. Our work demonstrates the utility of the data-driven machine learning model in prostate cancer risk prediction, which can play a critical role in the clinical decision system