Assessment of Progressive Muscle Relaxation (PMR) as a Stress-Reducing Technique for First-Year Veterinary Students.

The veterinary profession continually strives to address wellness issues such as compassion fatigue, burnout, stress, anxiety, and depression. Wellness issues may begin during the professional curriculum when students experience intense academic, clinical, social, and personal demands on their time. The purpose of this article was to assess the use of progressive muscle relaxation (PMR) as a simple, non-invasive stress reduction technique for first-year veterinary students (n = 101) at a US veterinary college. Students completed a 38-item questionnaire, the Smith Relaxation States Inventory 3 (SRSI3), both before and after performing PMR. Scores for the categories of basic relaxation, mindfulness, positive energy, transcendence, and stress were assessed. Female students (n = 92) had significant (p < .05) improvement in basic relaxation, mindfulness, and stress after completing PMR. Male students (n = 9) had significant (p < .05) improvement in basic relaxation and stress after completing PMR. When grouped according to age, all students had significant (p < .05) improvement in the categories of basic relaxation and stress. Students in the 22-year-old (n = 31), 23-year-old (n = 29), 24-year-old (n = 15), and 25-year-old or greater (n = 17) groups also had significant improvement (p < .05) in mindfulness. Additionally, students in the 23-year-old group had significant (p < .05) improvement in positive energy. These results support the use of PMR as a potential self-care strategy for students to implement during their academic and professional careers.

Differentially expressed heterogeneous overdispersion genes testing for count data.

The mRNA-seq data analysis is a powerful technology for inferring information from biological systems of interest. Specifically, the sequenced RNA fragments are aligned with genomic reference sequences, and we count the number of sequence fragments corresponding to each gene for each condition. A gene is identified as differentially expressed (DE) if the difference in its count numbers between conditions is statistically significant. Several statistical analysis methods have been developed to detect DE genes based on RNA-seq data. However, the existing methods could suffer decreasing power to identify DE genes arising from overdispersion and limited sample size, where overdispersion refers to the empirical phenomenon that the variance of read counts is larger than the mean of read counts. We propose a new differential expression analysis procedure: heterogeneous overdispersion genes testing (DEHOGT) based on heterogeneous overdispersion modeling and a post-hoc inference procedure. DEHOGT integrates sample information from all conditions and provides a more flexible and adaptive overdispersion modeling for the RNA-seq read count. DEHOGT adopts a gene-wise estimation scheme to enhance the detection power of differentially expressed genes when the number of replicates is limited as long as the number of conditions is large. DEHOGT is tested on the synthetic RNA-seq read count data and outperforms two popular existing methods, DESeq2 and EdgeR, in detecting DE genes. We apply the proposed method to a test dataset using RNAseq data from microglial cells. DEHOGT tends to detect more differently expressed genes potentially related to microglial cells under different stress hormones treatments.

Differentially Expressed Heterogeneous Overdispersion Genes Testing for Count Data.

The mRNA-seq data analysis is a powerful technology for inferring information from biological systems of interest. Specifically, the sequenced RNA fragments are aligned with genomic reference sequences, and we count the number of sequence fragments corresponding to each gene for each condition. A gene is identified as differentially expressed (DE) if the difference in its count numbers between conditions is statistically significant. Several statistical analysis methods have been developed to detect DE genes based on RNA-seq data. However, the existing methods could suffer decreasing power to identify DE genes arising from overdispersion and limited sample size. We propose a new differential expression analysis procedure: heterogeneous overdispersion genes testing (DEHOGT) based on heterogeneous overdispersion modeling and a post-hoc inference procedure. DEHOGT integrates sample information from all conditions and provides a more flexible and adaptive overdispersion modeling for the RNA-seq read count. DEHOGT adopts a gene-wise estimation scheme to enhance the detection power of differentially expressed genes. DEHOGT is tested on the synthetic RNA-seq read count data and outperforms two popular existing methods, DESeq and EdgeR, in detecting DE genes. We apply the proposed method to a test dataset using RNAseq data from microglial cells. DEHOGT tends to detect more differently expressed genes potentially related to microglial cells under different stress hormones treatments.