CCPLS reveals cell-type-specific spatial dependence of transcriptomes in single cells

Motivation: Cell-cell communications regulate internal cellular states of the cell, e.g., gene expression and cell functions, and play pivotal roles in normal development and disease states. Furthermore, single-cell RNA sequencing methods have revealed cell-to-cell expression variability of highly variable genes (HVGs), which is also crucial. Nevertheless, the regulation on cell-to-cell expression variability of HVGs via cell-cell communications is still unexplored. The recent advent of spatial transcriptome measurement methods has linked gene expression profiles to the spatial context of single cells, which has provided opportunities to reveal those regulations. The existing computational methods extract genes with expression levels that are influenced by neighboring cell types based on the spatial transcriptome data. However, limitations remain in the quantitativeness and interpretability: it neither focuses on HVGs, considers cooperation of neighboring cell types, nor quantifies the degree of regulation with each neighboring cell type. Results: Here, we propose CCPLS (Cell-Cell communications analysis by Partial Least Square regression modeling), which is a statistical framework for identifying cell-cell communications as the effects of multiple neighboring cell types on cell-to-cell expression variability of HVGs, based on the spatial transcriptome data. For each cell type, CCPLS performs PLS regression modeling and reports coefficients as the quantitative index of the cell-cell communications. Evaluation using simulated data showed our method accurately estimated effects of multiple neighboring cell types on HVGs. Furthermore, by applying CCPLS to the two real datasets, we demonstrate CCPLS can be used to extract biologically interpretable insights from the inferred cell-cell communications.

The Influence of Technical and Contextual Variables of the Last Stroke on Point Outcome in Men’s and Women’s Singles Badminton

The purpose of this study was to identify the effects of the technical and context-related variables of last strokes in rallies on the point outcomes of both men’s and women’s players in elite singles badminton matches. A total of 100 matches during the 2018 and 2019 seasons were analyzed, and the data of 4,080 men’s rallies and 4,339 women’s rallies were collected. The technical variables including strokes per rally, forehand strokes, overhead strokes, and defensive action, and the context-related variables including game status, result against serve, importance of rally, and importance of set, were modeled with Probit regression modeling as the predictor variables. The binary variables of “winner or not” and “error or not” were considered the response variables. The results showed that defensive actions had the greatest impacts on the winners and errors of both the men’s and women’s singles players, and the forehand and overhead strokes were negatively associated with the winners and errors of the women’s singles players and the winners of the men’s singles players. No significant effects were found for the strokes per rally on the winners and errors of the men’s singles players, while significant effects were found for the women’s singles players. The context-related variables appeared to have positive effects on the winners and negative effects on the errors of both sexes. These findings can provide important insights for coaches and players to evaluate their performances of last strokes in rallies and to improve training interventions and match tactics and strategies.

Global Rise of Depression Prevalence Amid the COVID-19 Pandemic

The immense burden of depressive disorders is on the rise, with global prevalence estimates in 2017 ranging from 4% to 13%. The novel coronavirus SARS-CoV-2 is likely to impact the established risk factors for depressive disorders. Thus, a rapid increase in depression prevalence can be expected amid the COVID-19 pandemic. Using epidemiologic data (N=111,225) derived from an extant online survey “Measuring Worldwide COVID-19 Attitudes and Beliefs” (launched by Fetzer and colleagues, March-April 2020) in 178 countries, we examined age-dependent global prevalence of depression and assessed the impact of social factors caused by the COVID-19 pandemic on depressive symptomatology. Point prevalence of depression was measured using the PHQ8 standard cut-off score (i.e., ≥10). Correlates of depressive symptoms were analyzed with hierarchical regression modeling separately in three age groups, i.e., 18-34, 35-54 and 55+ years. We found that nearly 20% of individuals globally revealed significant symptoms of depression, including 27% of young, 15% middle-aged, 9% adults aged 55+. These data suggest that the prevalence of depression is 2-5 times higher than global estimates preceding the COVID-19 pandemic. Regression modeling explained approx. 50% variability in depressive symptoms across the three age groups. Increased risk of depression was found in females, single or divorced individuals, and those who presented poorer health and higher anxiety. Social restrictions amid the COVID-19 pandemic were marginal risks for depression. Together, this study highlights the impact of the COVID-19 pandemic on the mental health of people of different ages and urges the development of increased access to psychological interventions.

The Predictive Model of Oral Squamous Cell Survival Carcinoma: A Methodology of Validation

Background. Cancer is primarily caused by smoking, alcohol, betel quit, a series of genetic alterations, and epigenetic abnormalities in signaling pathways, which result in a variety of phenotypes that favor the development of OSCC. Oral squamous cell carcinoma (OSCC) is the most common type of oral cancer, accounting for 80–90% of all oral malignant neoplasms. Oral cancer is relatively common, and it is frequently curable when detected and treated early enough. The tumor-node-metastasis (TNM) staging system is used to determine patient prognosis; however, geographical inaccuracies frequently occur, affecting management. Objective. To determine the additional relationship between factors discovered by searching for sociodemographic and metastasis factors, as well as treatment outcomes, which could help improve the prediction of the survival rate in cancer patients. Material and Methods. A total of 56 patients were recruited from the ambulatory clinic at the Hospital Universiti Sains Malaysia (USM). In this retrospective study, advanced computational statistical modeling techniques were used to evaluate data descriptions of several variables such as treatment, age, and distant metastasis. The R-Studio software and syntax were used to implement and test the hazard ratio. The statistics for each sample were calculated using a combination model that included methods such as bootstrap and multiple linear regression (MLR). Results. The statistical strategy showed R demonstrates that regression modeling outperforms an R-squared. It demonstrated that when data is partitioned into a training and testing dataset, the hybrid model technique performs better at predicting the outcome. The variable validation was determined using the well-established bootstrap-integrated MLR technique. In this case, three variables are considered: age, treatment, and distant metastases. It is important to note that three things affect the hazard ratio: age ( β 1 : -0.006423; p < 2 e − 16 ), treatment ( β 2 : -0.355389; p < 2 e − 16 ), and distant metastasis ( β 3 : -0.355389; p < 2 e − 16 ). There is a 0.003469102 MSE for the linear model in this scenario. Conclusion. In this study, a hybrid approach combining bootstrapping and multiple linear regression will be developed and extensively tested. The R syntax for this methodology was designed to ensure that the researcher completely understood the illustration. In this case, a hybrid model demonstrates how this critical conclusion enables us to better understand the utility and relative contribution of the hybrid method to the outcome. The statistical technique used in this study, R, demonstrates that regression modeling outperforms R-squared values of 0.9014 and 0.00882 for the predicted mean squared error, respectively. The conclusion of the study establishes the superiority of the hybrid model technique used in the study.