The L Word, the Television Series: Analysis of Its Lesbian Subjectivity

In January 2004, Showtime, a pay cable network, launched the first-ever lesbian-themed serial drama, The L Word, which boldly showed lesbians’ sex life and brought lesbian discourse into the narrative center of queer-themed series. This essay tries to analyze how The L Word presented lesbian subjectivity through the image as well as body construction, and narrative and also discusses some arguments towards the lesbian body presentation in The L Word

Ultrasouund Modulation on TRP Channels

From the Washington University Office of Undergraduate Research Digest (WUURD), Vol. 13, 05-01-2018. Published by the Office of Undergraduate Research. Joy Zalis Kiefer, Director of Undergraduate Research and Associate Dean in the College of Arts & Sciences; Lindsey Paunovich, Editor; Helen Human, Programs Manager and Assistant Dean in the College of Arts and Sciences Mentor(s): Jianmin Cu

Labor Relations Conflict in the Workplace: Scale Development, Consequences and Solutions

Because the goals of employers and employees are often incompatible, conflicts are inevitable and an essential part of organizational life. The three studies reported in this paper addressed the issues of identifying the dimensions of workplace conflicts within organizations, exploring the consequences of conflicts, and finding appropriate methods of conflict resolution. The first study identified and developed three dimensions of labor relations conflict, including interest-based, rights-based, and emotion-based conflicts. The second study explored two sets of individual outcomes of labor relations conflicts and found labor relations conflicts had a negative effect on employee job satisfaction and affective commitment and positive effects on employee turnover intention and counterproductive work behavior. The third study tested the effectiveness of partnership practices as an alternative method of resolving labor relations conflicts. Suggestions are offered for future research on the labor relations conflict dimensions as well as its outcomes and solutions introduced in these studies

INTEGRATING HIGH THROUGHPUT DATA WITH SPATIAL SYSTEM PHARMACOLOGY MODEL TO PREDICT IMMUNOTHERAPY RESPONSE FOR TRIPLE NEGATIVE BREAST CANCER

Response to cancer immunotherapies depends on the complex and dynamic interactions between T cell recognition and killing of tumor cells that are counteracted through immunosuppressive pathways in the tumor microenvironment. Therefore, while measurements such as tumor mutational burden provide biomarkers to select patients for immunotherapy, they neither universally predict patient post-treatment outcome nor imply the mechanisms that underlie immunotherapy resistance. Recent advances in single cell RNA sequencing technology (scRNA-seq) measure cellular heterogeneity within cells of an individual tumor but have yet to realize the promise of predictive oncology. In addition to data, mechanistic multiscale computational models are being developed for predicting cellular states during tumor progression and treatment response. Incorporating single cell data from a tumor to parameterize these computational models can lead to a deeper insight into subsequent tumor progression and predictions of clinical outcomes in an individual. While the high dimensionality of single cell analysis data poses a challenge for integration, Quantitative System Pharmacology (QSP) models incorporate discrete cellular states that can be obtained directly from single cell data. Here, we integrate whole exome sequencing and scRNA-seq data from Triple Negative Breast Cancer (TNBC) patients to model neoantigen burden in tumor cells as input to a spatial Quantitative System Pharmacology (spQSP) model that comprises four compartments (tumor, tumor-draining lymph node, central and peripheral) to represent a whole patient and uses spatial agent-based model (ABM) to represent tumor volumes at the cellular scale. We use the high-throughput single-cell data to model the role of antigen burden and heterogeneity relative to the tumor microenvironment composition on predicted immunotherapy response. Using this model, we found that patients with more tumor neoantigens have better responses to immunotherapy. In addition, patients with more heterogeneous neoantigen profiles in cancer cells are predicted with worse treatment outcomes. This thesis demonstrates the feasibility of merging high throughput data to initialize cell states in multiscale computational models such as the spQSP for personalized prediction of tumor outcomes to immunotherapy