Array technologies have made it straightforward to monitor simultaneously the expression pattern of thousands of genes. The challenge now is to interpret such massive data sets. The first step is to extract the fundamental patterns of gene expression inherent in the data. This paper describes the application of self-organizing maps, a type of mathematical cluster analysis that is particularly well suited for recognizing and classifying features in complex, multidimensional data. The method has been implemented in a publicly available computer package, GENECLUSTER, that performs the analytical calculations and provides easy data visualization. To illustrate the value of such analysis, the approach is applied to hematopoietic differentiation in four well studied models (HL-60, U937, Jurkat, and NB4 cells).

Background: The outbreak of coronavirus disease 2019 (COVID-19) in Wuhan, Hubei province, has led to the quarantine of many residents in their homes, in order to mitigate its spread. Some of these people developed mental health problems, and many solutions have been put in place to address the mental health issues of patients and health professionals affected by the disease. However, not much attention has been given to students, particularly those from medical school. The present study aims to conduct an online survey to investigate the mental health status of students from a medical college in Hubei province. Materials and Methods: The WeChat-based survey program Questionnaire Star, which contained questions from Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder-7 (GAD-7), was utilized for the present study. Results: A total of 217 students participated in the survey. Among these students, 127 were female and 90 were male. Furthermore, 77 students (35.5%) who participated in the survey were in a state of depression, and 48 (22.1%) were in a state of anxiety. The majority of students who were in depressed (n=75) or anxiety (n=46) states had mild or moderate states. There were no significant differences in students in terms of gender, geographical location, and grade, for the prevalence of depression and anxiety. Conclusion: The present study implies that universities need to take measures to prevent, identify, and deal with mental health problems among students during large-scale stressors.