Health Sciences

The course presents the field of sustainable health and environment from an East Asian perspective in a globalized world. It covers facts and developments in issues related to sustainable health and environment through cross-country lectures, multimedia viewing, panel discussion, and group projects and presentations. The sciences of sustainable health and environment cover broad and intersected disciplines from health sciences, physical sciences to social sciences locally, regionally, and globally. Views of sustainable health and environment are cultivated from current and historical perspectives as well as local and regional living experience. Global perspectives are further cultivated through in-class discussion among students, group projects by cross-country teams, and essay writing. Guest lectures by distinguished experts in the fields of sustainable health and environmental sciences provide global perspectives on sustainable issues.

This course provides insight into the essentials of genetic and evolutionary models and their applications in biology, medicine, and psychology. It starts with the mechanisms that cause evolutionary change: natural selection, inheritance, and gene expression. In order to make these mechanisms understandable for students, the essentials of molecular, Mendel, and population genetics are discussed, followed by the evolution of life cycles, sex, and sexual selection. After discussing kin selection, this course uses genomic imprinting to explain genetic conflicts. Game theory is also used to explain the models that treat conflicts. The course finishes with the evolution of the human brain and the impact of evolutionary concepts in medicine. Introduction to Biology is a required prerequisite.

The course covers the system of mutual aid in China. Students acquire basic knowledge and skills of how deal with emergencies in real life, in order to stay safe and protect themselves.

This course introduces a general statistical approach to the design of laboratory and similar experiments. It covers how to analyze the resulting data, including statistical quality control, in order to assure accuracy and precision so that reliable and reproducible conclusions can be drawn concerning the relations being studied. Along with the statistical and methodological content of the course, a number of concrete and frequently used pharmaceutical applications (designed experiments) are presented. Examples include clinical trials (including crossover and repeated measures designs), toxicity testing, bio-equivalence analyses, assay validation, and design and analysis of epidemiological surveys.