Using a Student-Generated Mock Magazine Issue To Improve Students' Awareness of Diverse Scientists.

This study explores whether integrating multicultural content within a genetics laboratory course affected students' awareness of diversity and their perceptions of scientists' identities. Genetics laboratory curricula typically focus on content and experimental procedures, with cursory references to the scientists who made these discoveries. The resulting poor racial and gender representation in the curricula propagate biases about the abilities and contributions of scientists from underrepresented groups, which may adversely affect the retention and success of students in these groups. Initially, students completed a pre-test in which they were asked to recall the names of geneticists and their scientific contributions. Later students created a mock magazine issue featuring a diverse set of experts in genetics, specifically members of traditionally underrepresented gender/sexuality and/or racial/ethnic groups. To facilitate this assignment, students were randomly assigned a geneticist from a pool of active research scientists, spanning a wide range of scientific and cultural backgrounds and identities. Each student wrote a 500-word biography of their assigned geneticist and read biographies composed by peers. Then, in groups, the students categorized biographies based on student-selected unifying themes into a table of contents. On the final exam, the pre-test was repeated as a post-test. In the pre-test, scientists listed by students were 94% male and 6% female, with no members of other underrepresented groups included. In the post-test, scientists listed by students shifted to 84% male and 16% female with 18% from underrepresented groups. These data suggest that this intervention increases awareness of the multicultural nature of scientists.

Quantifying drug induced dyskinesia in Parkinson's disease patients using standardized videos.

This paper presents a video based method to quantify drug induced dyskinesias in Parkinson's disease (PD) patients. Dyskinetic movement in standard clinical videos of patients is analyzed by tracking landmark points on the video frames using non-rigid image registration. The novel application of Point Distribution Models (PDM) allows geometric variations and covariations of the landmark points to be captured from each video sequence. The PDM parameters represent quantifiable information that can be used to rate dyskinesia effectively, analogously to a neurologist's strategy of assessing the movement of multiple body parts simultaneously to effectively rate dyskinesia. A heuristic decision function is then developed using the PDM parameters to quantify the severity of the dyskinesia. The severity score using our decision function showed a high correlation to the dyskinesia rating of a neurologist on the corresponding patient videos.