Participant Recruitment of African American College Students at an Historically Black College and University (HBCU): Challenges and Strategies for Health-Related Research

Lack of research participation among African Americans is problematic for population relevant health disparity research. The purpose of this paper is to identify and describe challenges and strategies in recruitment of African American college students for health related research being conducted at a small Historically Black College or University (HBCU). Upon completion of a recruitment and retention literature review, study investigators constructed and tested a culturally-specific, direct-appeal protocol to recruit participants. Major barriers to recruitment of African American college students included discrete sources of distrust, lack of understanding of the research process, and logistical concerns. Implementation of a culturally-specific, direct appeal protocol led to a significant improvement in recruitment and retention of student participants. It is imperative that researchers demystify scientific investigation as a first step towards building trust between themselves and target populations, particularly those from traditionally underrepresented groups. Reasons for distrust, a need for trust and trust building strategies are offered here

False Recall in the Deese–Roediger–Mcdermott Paradigm: The Roles of Gist and Associative Strength

Theories of false memories, particularly in the Deese–Roediger–McDermott (DRM) paradigm, focus on word association strength and gist. Backward associative strength (BAS) is a strong predictor of false recall in this paradigm. However, other than being defined as a measure of association between studied list words and falsely recalled nonpresented critical words, there is little understanding of this variable. In Experiment 1, we used a knowledge-type taxonomy to classify the semantic relations in DRM stimuli. These knowledge types predicted false-recall probability, as well as BAS itself, with the most important being situation features, synonyms, and taxonomic relations. In three subsequent experiments, we demonstrated that lists composed solely of situation features can elicit a gist and produce false memories, particularly when monitoring processes are made more difficult. Our results identify the semantic factors that underlie BAS and suggest how considering semantic relations leads to a better understanding of gist formation

Antibiotics Alter Pocillopora Coral-Symbiodiniaceae-Bacteria Interactions and Cause Microbial Dysbiosis During Heat Stress

Symbioses between eukaryotes and their associated microbial communities are fundamental processes that affect organisms’ ecology and evolution. A unique example of this is reef-building corals that maintain symbiotic associations with dinoflagellate algae (Symbiodiniaceae) and bacteria that affect coral health through various mechanisms. However, little is understood about how coral-associated bacteria communities affect holobiont heat tolerance. In this study, we investigated these interactions in four Pocillopora coral colonies belonging to three cryptic species by subjecting fragments to treatments with antibiotics intended to suppress the normal bacteria community, followed by acute heat stress. Separate treatments with only antibiotics or heat stress were conducted to compare the effects of individual stressors on holobiont transcriptome responses and microbiome shifts. Across all Pocillopora species examined, combined antibiotics and heat stress treatment significantly altered coral-associated bacteria communities and caused major changes in both coral and Cladocopium algal symbiont gene expression. Individually, heat stress impaired Pocillopora protein translation and activated DNA repair processes, while antibiotics treatments caused downregulation of Pocillopora amino acid and inorganic ion transport and metabolism genes and Cladocopium photosynthesis genes. Combined antibiotics-heat stress treatments caused synergistic effects on Pocillopora and Cladocopium gene expression including enhanced expression of oxidative stress response genes, programed cell death pathways and proteolytic enzymes that indicate an exacerbated response to heat stress following bacteria community suppression. Collectively, these results provide further evidence that corals and their Symbiodiniaceae and bacteria communities engage in highly coordinated metabolic interactions that are crucial for coral holobiont health, homeostasis, and heat tolerance

Application of the Evolution-Variable Manifold Approach to Cavity-Stabilized Ethylene Combustion

For combustion in high-speed flows, radical-formation time scales and ignition delay times may be similar to, or dominate, relevant flow time scales. Reliable modeling of induction and autoignition processes is critical to the prediction of combustor performance. The evolution-variable manifold (EVM) approach of Cymbalist and Dimotakis uses a transported scalar to track the evolution of the reaction processes, from induction leading to autoignition and subsequent robust combustion. In the present work, the EVM method is implemented in a computational fluid dynamics code in which wall-modeled large-eddy simulations are performed for two ethylene-air high-speed combustion cases. The detailed thermochemical state of the reacting fluid is tabulated as a function of a reduced number of state variables that include density, energy, mixture fraction, and the reaction-evolution variable. A thermodynamically consistent numerical flux function is developed and the approach for coupling the large-eddy simulation to the EVM framework is discussed. It is found that particular attention must be given to the solution of the energy equation to obtain accurate and computationally stable results. The results show that the LES-EVM approach shows promise for the simulation of turbulent combustion of hydrocarbons in high-speed flows, including those dominated by ignition delay, and encompass regions of thin reaction fronts as well as distributed reaction zones

Effect of high temperature heat treatments on the quality factor of a large-grain superconducting radio-frequency niobium cavity

Large-grain Nb has become a viable alternative to fine-grain Nb for the fabrication of superconducting radio-frequency cavities. In this contribution we report the results from a heat treatment study of a large-grain 1.5 GHz single-cell cavity made of "medium purity" Nb. The baseline surface preparation prior to heat treatment consisted of standard buffered chemical polishing. The heat treatment in the range 800 - 1400 C was done in a newly designed vacuum induction furnace. Q0 values of the order of 2x1010 at 2.0 K and peak surface magnetic field (Bp) of 90 mT were achieved reproducibly. A Q0-value of (5+-1)1010 at 2.0 K and Bp = 90 mT was obtained after heat treatment at 1400 C. This is the highest value ever reported at this temperature, frequency and field. Samples heat treated with the cavity at 1400 C were analyzed by secondary ion mass spectrometry, secondary electron microscopy, energy dispersive X-ray, point contact tunneling and X-ray diffraction and revealed a complex surface composition which includes titanium oxide, increased carbon and nitrogen content but reduced hydrogen concentration compared to a non heat-treated sample

The prevalence and impact of adolescent hospitalization to adult psychiatric units.

BACKGROUND: With increasing psychiatric hospitalizations among adolescents and constrained hospital resources, there are times when youth are hospitalized in adult inpatient psychiatry units. Evidence on the prevalence of this practice and associated impacts is lacking. AIMS: We sought to explore the prevalence, determinants, and outcomes related to the hospitalization of adolescents aged 12-17 years on adult inpatient psychiatry units in Ontario. METHODS: Using health administrative data, we constructed a cohort of adolescents with an inpatient psychiatric admission in Ontario (2007-2011). We classified adolescents as having an admission to an adult psychiatry unit or to other inpatient units. Multivariable regression models were used to estimate prevalence ratios (PR) for factors associated with adult admission, as well as risk ratios (RR) for the impact of adult admission on length of stay, discharge against medical advice, and 30-day readmission. RESULTS: Over the study period, 22.6% of adolescents with a psychiatric hospitalization (n = 16 998) had an admission to an adult psychiatry unit. Older age (16 vs. 15 years: PR = 2.27, 95% CI = 2.07-2.48; 17 vs. 15 years: PR = 2.91, 95% CI = 2.66-3.18), rural residence (PR = 1.46, 95% CI = 1.38-1.55), psychotic (PR = 1.25, 95% CI = 1.15-1.36) or personality disorder (PR = 1.59, 95% CI = 1.41-1.80) diagnoses, and involuntary status (PR = 2.18, 95% CI = 2.05-2.31) were independently associated with adult admission. Adolescents admitted to adult units were more likely to be discharged against medical advice (RR = 1.77, 95% CI = 1.45-2.17). CONCLUSIONS: Nearly one in four adolescent psychiatric admissions occurs on an adult psychiatric unit. These findings help to fill gaps in the prior literature, and highlight the need for further research to inform policy decisions and resource allocation for adolescent inpatient psychiatric care

A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts

Despite continuous improvements, precipitation forecasts are still not as accurate and reliable as those of other meteorological variables. A major contributing factor to this is that several key processes affecting precipitation distribution and intensity occur below the resolved scale of global weather models. Generative adversarial networks (GANs) have been demonstrated by the computer vision community to be successful at super-resolution problems, i.e., learning to add fine-scale structure to coarse images. Leinonen et al. (2020) previously applied a GAN to produce ensembles of reconstructed high-resolution atmospheric fields, given coarsened input data. In this paper, we demonstrate this approach can be extended to the more challenging problem of increasing the accuracy and resolution of comparatively low-resolution input from a weather forecasting model, using high-resolution radar measurements as a "ground truth". The neural network must learn to add resolution and structure whilst accounting for non-negligible forecast error. We show that GANs and VAE-GANs can match the statistical properties of state-of-the-art pointwise post-processing methods whilst creating high-resolution, spatially coherent precipitation maps. Our model compares favourably to the best existing downscaling methods in both pixel-wise and pooled CRPS scores, power spectrum information and rank histograms (used to assess calibration). We test our models and show that they perform in a range of scenarios, including heavy rainfall.Comment: Submitted to JAMES 4/4/2