Self-Concept Transformation on Senior Secondary School Students' Academic Achievement in Central Zone, Plateau State, Nigeria

Economics is one of the popular subjects in the Senior Secondary School (SSS) curriculum, and it covers the fundamental aspects of human lives concerning scarcity of resources. Proper understanding of the basic concept will enable students to realize the benefit of the subject. This study examined the effects of the self-concept transformation package on senior secondary school student's academic achievement in the quantitative aspect of Economics in Central Zone, Plateau State, Nigeria. A quasi-experimental research design, the non-equivalent control- group design, was used for this study. The sample size consisted of 105 economics SS2 students from intact classes of the four sampled schools. There were 50 and 55 students in the experimental and control group, respectively. Multi-Stage Cluster sampling techniques were used for this study. The instruments used for data collection were the Multi-dimensional Self-Concept Scales (MSS) and Quantitative Economics Achievement Test (QEAT). Research questions were analyzed using descriptive statistics, while the t-test of independent sample and Analysis of Covariance (ANCOVA) was used to test the hypotheses. The statistical package for social sciences (SPSS) version 23 was used for the analysis. The results show that the self-concept transformation package positively and significantly affected students' self-concept and academic achievement in quantitative economics. The study recommended that the use of abusive words on students by Parents and guardians should be discouraged to strengthening their student's self-concept

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management