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tive randomized control trials and the absence of data from the large COVATA study from the published literature. However, results from this systematic analysis of published research provide positive evidence for the potential efficacy of TCZ to treat severe COVID-19, validating the ethical basis and merit of ongoing randomized controlled clinical trials.When people are confronted with health proposals during the coronavirus disease 2019 (COVID-19) pandemic, it has been suggested that fear of COVID-19 can serve protective functions and ensure public health compliance. However, health proposal repetition and its perceived efficacy also influence the behavior intention toward the proposal, which has not yet been confirmed in the COVID-19 context. The present study aims to examine whether the extended parallel process model (EPPM) can be generalized to a naturalistic context like the COVID-19 pandemic. Additionally, we will explore how repetition of a health proposal is involved with the EPPM. In this study, two groups of participants are exposed to the same health proposal related to COVID-19, where one group is exposed once and another group twice. They then fill out a questionnaire consisting of items concerning behavior intention and adapted from the Risk Behavior Diagnosis Scale. Structural equation modeling will be used to determine the multivariate associations between the variables. We predict that repetition of the health proposal will associate with response efficacy (i.e., a belief about the effectiveness of the health proposal in deterring the threat) and perceived susceptibility (i.e., a belief about the risk of experiencing the threat). It is also predicted that following the EPPM, behavior intention will associate with both perceived efficacy of the health proposal, which will underlie response efficacy, and perceived threat of COVID-19, which will underlie perceived susceptibility. We will discuss the process, based on the model, where health message repetition affects behavior intention during the COVID-19 pandemic.Recent discussions in the sport and exercise science community have focused on the appropriate use and reporting of effect sizes. Sport and exercise scientists often analyze repeated-measures data, from which mean differences are reported. To aid the interpretation of these data, standardized mean differences (SMD) are commonly reported as a description of effect size. In this manuscript, we hope to alleviate some confusion. First, we provide a philosophical framework for conceptualizing SMDs; that is, by dichotomizing them into two groups magnitude-based and signal-to-noise SMDs. Second, we describe the statistical properties of SMDs and their implications. Finally, we provide high-level recommendations for how sport and exercise scientists can thoughtfully report raw effect sizes, SMDs, or other effect sizes for their own studies. This conceptual framework provides sport and exercise scientists with the background necessary to make and justify their choice of an SMD.Portable chest X-ray (pCXR) has become an indispensable tool in the management of Coronavirus Disease 2019 (COVID-19) lung infection. This study employed deep-learning convolutional neural networks to classify COVID-19 lung infections on pCXR from normal and related lung infections to potentially enable more timely and accurate diagnosis. This retrospect study employed deep-learning convolutional neural network (CNN) with transfer learning to classify based on pCXRs COVID-19 pneumonia (N = 455) on pCXR from normal (N = 532), bacterial pneumonia (N = 492), and non-COVID viral pneumonia (N = 552). The data was randomly split into 75% training and 25% testing, randomly. A five-fold cross-validation was used for the testing set separately. Performance was evaluated using receiver-operating curve analysis. Comparison was made with CNN operated on the whole pCXR and segmented lungs. CNN accurately classified COVID-19 pCXR from those of normal, bacterial pneumonia, and non-COVID-19 viral pneumonia patients in a multiclass model. The overall sensitivity, specificity, accuracy, and AUC were 0.79, 0.93, and 0.79, 0.85 respectively (whole pCXR), and were 0.91, 0.93, 0.88, and 0.89 (CXR of segmented lung). The performance was generally better using segmented lungs. Heatmaps showed that CNN accurately localized areas of hazy appearance, ground glass opacity and/or consolidation on the pCXR. Deep-learning convolutional neural network with transfer learning accurately classifies COVID-19 on portable chest X-ray against normal, bacterial pneumonia or non-COVID viral pneumonia. This approach has the potential to help radiologists and frontline physicians by providing more timely and accurate diagnosis.Cadmium pollution is becoming a serious problem due to its nondegradability and substantial negative influence on the normal growth of crops, thereby harming human health through the food chain. selleck products Rhizospheric bacteria play important roles in crop tolerance. However, there is little experimental evidence which demonstrates how various cadmium concentrations affect the bacterial community in wheat fields including rhizosphere microorganisms and nonrhizosphere (bulk) microorganisms. In this study, 16S rRNA amplicon sequencing technology was used to investigate bacterial communities in rhizosphere and bulk soils under different levels of pollution in terms of cadmium concentration. Both the richness and diversity of the rhizosphere microorganism community were higher under nonpolluted soil and very mild and mild cadmium-contaminated soils than compared with bulk soil, with a shift in community profile observed under severe cadmium pollution. Moreover, cadmium at various concentrations had greater influence on bacterial composition than for the nonpolluted site. In addition, redundancy analysis (RDA) and Spearman’s analysis elucidated the impact of exchangeable Cd and total Cd on bacterial community abundance and composition. This study suggests that cadmium imposes a distinct effect on bacterial community, both in bulk and rhizosphere soils of wheat fields. This study increases our understanding of how bacterial communities in wheat fields shaped under different concentrations of cadmium.