MOLECULAR DETERMINANTS OF RESIDUAL DISEASE IN OVARIAN CANCER

The standard treatment for high grade serous ovarian cancer is primary cytoreductive surgery followed by adjuvant chemotherapy. Residual disease followed by surgery is associated with adverse overall and progression-free survival as well as poor response to adjuvant chemotherapy. Accurate identification of patients at high risk of residual disease will help avoid unnecessary surgeries and help in triaging these patients to neoadjuvant chemotherapy prior to interval surgical debulking. In this study, we address this clinical issue by identifying and validating molecular biomarkers that can predict the likelihood of residual disease in ovarian cancer patients. Using publically available databases and microarray datasets, we identify FABP4 and ADH1B as markers of residual disease since the high expression of these genes in tumor samples is directly associated with the incidence of residual disease. We then investigate the underlying biology of residual disease and further demonstrate that FABP4 is functionally responsible for aggressive phenotype of ovarian cancer cells that lead to residual disease in cancer patients. Using sophisticated bioinformatics techniques, several in vitro and in vivo experiments and analysis of patient samples, we explored upstream regulation of FABP4 and identified miR-409-3p as a key regulator of FABP4 expression. We further discover hypoxia as a main tumor micro-environmental factor regulating miR-409-3p and FABP4 in ovarian cancer. Using RPPA and DESI-MS imaging techniques, we explore the downstream pathways of FABP4 and discovered that FABP4 regulates several pathways associated with metastasis as well as it affects several metabolites in ovarian cancer cells. Collectively, our study provides the mechanistic understanding of residual disease biology and identifies miR-409-3p and FABP4 as potential therapeutic targets for ovarian cancer treatment

What's in a Home?: The Liminal Culture of Asian Indian Americans

Jhumpa Lahiri’s writing speaks to this world and gives voice to this generation. Her stories are raw and honest; they bring to life the dual cultural lifestyle the second generation lives. She mixes the American landscape—both cultural and physical—with the Indian. For example, while her stories are almost always located in New England, they most often take place in suburban or urban neighborhoods that could be anywhere. The overarching idea is that these cultural dilemmas happen everywhere, within these common houses and families in the U.S. I chose to use her narratives as a way to investigate my own—as a way to answer some of the questions I have about growing up in this generation. Crafting a performance enabled me to engage with Jhumpa Lahiri’s text while keeping the focus on my personal experiences. I would be the one on stage, breaking apart what I knew of Indian immigration and Lahiri’s stories and filtering it through my own experiences in order to better understand my history and my identity as an Indian American. I hoped to recognize how second generation Indian Americans reckon with the stereotypes and misrepresentations that come with growing up in a primarily white- American society as well as form a better understanding of my cultural identity, with particular regard to the history of Indian immigration in the U.S. and the cultural community of second generations that practice a dual Indian American culture.Bachelor of Art

User Intent Communication in Robot-Assisted Shopping for the Blind

The research reported in this chapter describes our work on robot-assisted shopping for the blind. In our previous research, we developed RoboCart, a robotic shopping cart for the visually impaired (Gharpure, 2008; Kulyukin et al., 2008; Kulyukin et al., 2005). RoboCart's operation includes four steps: 1) the blind shopper (henceforth the shopper) selects

Incorporating FCM and Back Propagation Neural Network for Image Segmentation

Hybrid image segmentation is proposed in this paper. The input image is firstly preprocessed in order to extract the features using Discrete Wavelet Transform (DWT) .The features are then fed to Fuzzy C-means algorithm which is unsupervised. The membership function created by Fuzzy C-means (FCM) is used as a target to be fed in neural network. Then the Back Propagation Neural network (BPN) has been trained based on targets which is obtained by (FCM) and features as input data. Combining the FCM information and neural network in unsupervised manner lead us to achieve better segmentation .The proposed algorithm is tested on various Berkeley database gray level images

Virus growth in preheated cells

The results reported in this dissertation are concerned mainly with the effect of supraoptimal temperatures on BHK-21, C13 cells and on their ability to support the growth of various viruses. The term "supraoptimal temperature" was defined as any temperature above the optimal temperature of 3

Mathematical modeling for SnO2 gas sensor based on second-order response

Sensors, work on the entrance gate for exploring dynamic environments and system response. The sensor behavior plays an important role in system modeling. Therefore, finding the mathematical model, which can describe the sensor response with maximum information over time in different environments and situations, is necessary. This paper, attempts to model the optimal transfer function of the SnO2 type gas sensor (TGS 813) for transient response and steady-state zones, based on the acquired data of the sensor. The model could be used to obtain the mathematical equation for the response, as a transfer function based on different concentrations, for the specific temperature and humidly. The data analysis has been done with Matlab software, and Genetic algorithm is further used to optimize the transfer function parameters. Moreover, the effect of variation in concentration on the transfer function coefficients has been explored. The results show that the modeling can be helpful for sensor response behavior simulation as well as physical and chemical reaction investigation

Hypoxia-upregulated microRNA-630 targets Dicer, leading to increased tumor progression

MicroRNAs (miRNAs) are small RNA molecules that affect cellular processes by controlling gene expression. Recent studies have shown that hypoxia downregulates Drosha and Dicer, key enzymes in miRNA biogenesis, causing a decreased pool of miRNAs in cancer, and resulting in increased tumor growth and metastasis. Here, we demonstrate a previously unrecognized mechanism by which hypoxia downregulates Dicer. We found that miR-630, which is upregulated under hypoxic conditions, targets and downregulates Dicer expression. In an orthotopic mouse model of ovarian cancer, delivery of miR-630 using DOPC nanoliposomes resulted in increased tumor growth and metastasis and decreased Dicer expression. Treatment with the combination of anti-miR-630 and anti-vascular endothelial growth factor antibody in mice resulted in rescue of Dicer expression and significantly decreased tumor growth and metastasis. These results indicate that targeting miR-630 is a promising approach to overcome Dicer deregulation in cancer. As demonstrated in the study, use of DOPC nanoliposomes for anti-miR delivery serves as a better alternative approach to cell line based overexpression of sense or anti-sense miRNAs, while avoiding potential in vitro selection effects. Findings from this study provide a new understanding of miRNA biogenesis downregulation observed under hypoxia and suggest therapeutic avenues to target this dysregulation in cancer

Reading Comprehension Assessment Using LLM-based Chatbot

Users who are learning to read or learning about a topic by viewing content on a device can benefit from conversational activities, such as question-answer turns for the viewed content. This disclosure describes techniques to perform natural language assessments of content that is being consumed on a user device. A chatbot is implemented using suitable technology, such as a large language model. With user permission, the model is used to generate questions that evaluate the user’s understanding of the content viewed. User provided answers are evaluated and suitable responses are provided to the user. The techniques enable automated assessment and feedback. The described features for assessment via chatbot (or virtual assistant) can be built into any application. Assessment is performed on-device and in a confidential manner