A nuclear factor 1 binding site mediates the transcriptional activation of a type I collagen promoter by transforming growth factor-beta

Transforming growth factor-beta (TGF-beta) increases the steady-state RNA levels of several fibroblast extracellular matrix proteins. Using DNA transfection, we show that TGF-beta stimulates the activity of the mouse alpha 2(l) collagen promoter 5- to 10-fold in mouse NIH 3T3 and rat osteosarcoma cells. Deletion analysis indicates that a segment of this promoter between -350 and -300, overlapping a nuclear factor 1 (NF1) binding site, is needed for TGF-beta stimulation. A 3 bp substitution mutation abolishing NF1 binding to this site inhibits TGF-beta activation. Insertion of this NF1 binding site 5' to the SV40 early promoter makes the promoter TGF-beta inducible, but the 3 bp substitution does not. Similarly, when the NF1 binding site at the replication origin of adenovirus 2 and 5 is inserted 5' to the SV40 promoter, the promoter responds to TGF-beta. Therefore an NF1 binding site mediates the transcriptional activation of the mouse alpha 2(l) collagen promoter by TGF-beta

Class Attendance and Students’ Evaluations of Teaching: Do No-Shows Bias Course Ratings and Rankings?

Background: Many university departments use students’ evaluations of teaching (SET) to compare and rank courses. However, absenteeism from class is often nonrandom and, therefore, SET for different courses might not be comparable. Objective: The present study aims to answer two questions. Are SET positively biased due to absenteeism? Do procedures, which adjust for absenteeism, change course rankings? Research Design: The author discusses the problem from a missing data perspective and present empirical results from regression models to determine which factors are simultaneously associated with students’ class attendance and course ratings. In order to determine the extent of these biases, the author then corrects average ratings for students’ absenteeism and inspect changes in course rankings resulting from this adjustment. Subjects: The author analyzes SET data on the individual level. One or more course ratings are available for each student. Measures: Individual course ratings and absenteeism served as the key outcomes. Results: Absenteeism decreases with rising teaching quality. Furthermore, both factors are systematically related to student and course attributes. Weighting students’ ratings by actual absenteeism leads to mostly small changes in ranks, which follow a power law. Only a few, average courses are disproportionally influenced by the adjustment. Weighting by predicted absenteeism leads to very small changes in ranks. Again, average courses are more strongly affected than courses of very high or low in quality. Conclusions: No-shows bias course ratings and rankings. SET are more appropriate to identify high- and low-quality courses than to determine the exact ranks of average courses