Student, Staff and Faculty Perspectives of Intimate Partner and Sexual Violence on Three Public University Campuses: The UC Speaks Up Protocol (Preprint)

BACKGROUND Intimate partner and sexual violence are pervasive public health issues on college and university campuses in the United States. Research is recommended for creating and maintaining effective, relevant and acceptable prevention programs and response services for student survivors. OBJECTIVE The UC Speaks Up study aims to examine factors contributing to intimate partner and sexual violence on three University of California (UC) campuses and use findings to develop and test interventions and policies to prevent violence, promote health, and lay the groundwork for subsequent, large-scale quantitative research. METHODS A mixed-methods study at UC Los Angeles, UC San Diego and UC Santa Barbara. Phase I (2017-2020) involved (1) a resource audit; (2) cultural consensus modeling of students’ perceptions of sexual consent; (3) in-depth interviews (IDIs) and focus group discussions (FGDs) with students to understand perceptions of campus environment related to experiences and prevention of, and responses to violence; and (4) IDIs with faculty, staff and community stakeholders to investigate institutional and community arrangements influencing students’ lives and experiences. Phase II (2020-ongoing) involves IDIs with student survivors to assess use and perceptions of campus/community services. Qualitative content analysis is used to generate substantive codes and sub-themes that emerge, using a thematic analysis approach. RESULTS In January 2019 we conducted 149 free-listing interviews and 214 online surveys with undergraduate and graduate/professional students for the cultural consensus modeling. Between February and June 2019: 179 IDIs were conducted with 86 undergraduate students, 21 graduate and professional students, 34 staff members, 27 faculty members, and 11 community stakeholders; and 35 FGDs (27 with undergraduate and 8 with graduate/professional students) were conducted with 201 participants. Since September 2020, 8 of 30 planned student-survivor interviews have been conducted. Recruitment is ongoing. CONCLUSIONS Data analysis and phase II data collection is ongoing. Findings will be used to develop and test interventions for preventing violence and promoting health and well-being, and ensuring survivor services are relevant, acceptable to and meet the needs of all individuals in the campus community, including those who are typically understudied. Findings will also be used to prepare for rigorous, UC system-wide public health prevention research.

Bioactivity Prediction Based on Matched Molecular Pair and Matched Molecular Series Methods

Background: Enhancing a compound’s biological activity is the central task for lead optimization in small molecules drug discovery. However, it is laborious to perform many iterative rounds of compound synthesis and bioactivity tests. To address the issue, it is highly demanding to develop high quality in silico bioactivity prediction approaches, to prioritize such more active compound derivatives and reduce the trial-and-error process. Methods: Two kinds of bioactivity prediction models based on a large-scale structure-activity relationship (SAR) database were constructed. The first one is based on the similarity of substituents and realized by matched molecular pair analysis, including SA, SA_BR, SR, and SR_BR. The second one is based on SAR transferability and realized by matched molecular series analysis, including Single MMS pair, Full MMS series, and Multi single MMS pairs. Moreover, we also defined the application domain of models by using the distance-based threshold. Results: Among seven individual models, Multi single MMS pairs bioactivity prediction model showed the best performance (R2 = 0.828, MAE = 0.406, RMSE = 0.591), and the baseline model (SA) produced the most lower prediction accuracy (R2 = 0.798, MAE = 0.446, RMSE = 0.637). The predictive accuracy could further be improved by consensus modeling (R2 = 0.842, MAE = 0.397 and RMSE = 0.563). Conclusion: An accurate prediction model for bioactivity was built with a consensus method, which was superior to all individual models. Our model should be a valuable tool for lead optimization.