The Role of PPARgamma in the Cyclooxygenase Pathway in Lung Cancer.

Decreased expression of peroxisome proliferator activated receptor-gamma (PPARgamma) and high levels of the proinflammatory enzyme cyclooxygenase-2 (COX-2) have been observed in many tumor types. Both reduced (PPARgamma) expression and elevated COX-2 within the tumor are associated with poor prognosis in lung cancer patients, and recent work has indicated that these signaling pathways may be interrelated. Synthetic (PPARgamma) agonists such as the thiazolidinedione (TZD) class of anti-diabetic drugs can decrease COX-2 levels, inhibit growth of non-small-cell lung cancer (NSCLC) cells in vitro, and block tumor progression in xenograft models. TZDs alter the expression of COX-2 and consequent production of the protumorigenic inflammatory molecule prostaglandin E2 (PGE2) through both (PPARgamma) dependent and independent mechanisms. Certain TZDs also reduce expression of PGE2 receptors or upregulate the PGE2 catabolic enzyme 15-prostaglandin dehydrogenase. As several COX-2 enzymatic products have antitumor properties and specific COX-2 inhibition has been associated with increased risk of adverse cardiac events, directly reducing the effects or concentration of PGE2 may provide a more safe and effective strategy for lung cancer treatment. Understanding the mechanisms underlying these effects may be helpful for designing anticancer therapies. This article summarizes recent research on the relationship between (PPARgamma), TZDs, and the COX-2/PGE2 pathways in lung cancer

The SNARE protein FolVam7 mediates intracellular trafficking to regulate conidiogenesis and pathogenicity in Fusarium oxysporum f. sp. lycopersici.

Soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNAREs) are conserved in fungi, plants and animals. The Vam7 gene encodes a v-SNARE protein that involved in vesicle trafficking in fungi. Here, we identified and characterized the function of FolVam7, a homologue of the yeast SNARE protein Vam7p in Fusarium oxysporum f. sp. lycopersici (Fol), a fungal pathogen of tomato. FolVam7 contains SNARE and PX (Phox homology) domains that are indispensable for normal localization and function of FolVam7. Targeted gene deletion showed that FolVam7-mediated vesicle trafficking is important for vegetative growth, asexual development, conidial morphology and plant infection. Further cytological examinations revealed that FolVam7 is localized to vesicles and vacuole membranes in the hyphae stage. Moreover, the ΔFolvam7 mutant is insensitive to salt and osmotic stresses and hypersensitive to cell wall stressors. Taken together, our results suggested that FolVam7-mediated vesicle trafficking promotes vegetative growth, conidiogenesis and pathogenicity of Fol

W4IPS: A Web-based Interactive Power System Simulation Environment For Power System Security Analysis

Modern power systems are increasingly evolving Cyber-Physical Systems (CPS) that feature close interaction between Information and Communication Technology (ICT), physical and electrical devices, and human factors. The interactivity and security of CPS are the essential building blocks for the reliability, stability and economic operation of power systems. This paper presents a web-based interactive multi-user power system simulation environment and open source toolset (W4IPS) whose main features are a publish/subscribe structure, a real-time data sharing capability, role-based multi-user visualizations, distributed multi-user interactive controls, an easy to use and deploy web interface, and flexible and extensible support for communication protocols. The paper demonstrates the use of W4IPS features as an ideal platform for contingency response training and cyber security analysis, with an emphasis on interactivity and expandability. In particular, we present the use cases and the results of W4IPS in power system operation education and security analysis

"Just a little bit on the outside for the whole time": Social belonging confidence and the persistence of Machine Learning and Artificial Intelligence students

The growing field of machine learning (ML) and artificial intelligence (AI) presents a unique and unexplored case within persistence research, meaning it is unclear how past findings from engineering will apply to this developing field. We conduct an exploratory study to gain an initial understanding of persistence in this field and identify fruitful directions for future work. One factor that has been shown to predict persistence in engineering is belonging; we study belonging through the lens of confidence, and discuss how attention to social belonging confidence may help to increase diversity in the profession. In this research paper, we conduct a small set of interviews with students in ML/AI courses. Thematic analysis of these interviews revealed initial differences in how students see a career in ML/AI, which diverge based on interest and programming confidence. We identified how exposure and initiation, the interpretation of ML and AI field boundaries, and beliefs of the skills required to succeed might influence students' intentions to persist. We discuss differences in how students describe being motivated by social belonging and the importance of close mentorship. We motivate further persistence research in ML/AI with particular focus on social belonging and close mentorship, the role of intersectional identity, and introductory ML/AI courses.Comment: Published in the 2023 Annual Conference of the American Society for Engineering Educatio

Advancing a Model of Students' Intentional Persistence in Machine Learning and Artificial Intelligence

Machine Learning (ML) and Artificial Intelligence (AI) are powering the applications we use, the decisions we make, and the decisions made about us. We have seen numerous examples of non-equitable outcomes, from facial recognition algorithms to recidivism algorithms, when they are designed without diversity in mind. Thus, we must take action to promote diversity among those in this field. A critical step in this work is understanding why some students who choose to study ML/AI later leave the field. While the persistence of diverse populations has been studied in engineering, there is a lack of research investigating factors that influence persistence in ML/AI. In this work, we present the advancement of a model of intentional persistence in ML/AI by surveying students in ML/AI courses. We examine persistence across demographic groups, such as gender, international student status, student loan status, and visible minority status. We investigate independent variables that distinguish ML/AI from other STEM fields, such as the varying emphasis on non-technical skills, the ambiguous ethical implications of the work, and the highly competitive and lucrative nature of the field. Our findings suggest that short-term intentional persistence is associated with academic enrollment factors such as major and level of study. Long-term intentional persistence is correlated with measures of professional role confidence. Unique to our study, we show that wanting your work to have a positive social benefit is a negative predictor of long-term intentional persistence, and women generally care more about this. We provide recommendations to educators to meaningfully discuss ML/AI ethics in classes and encourage the development of interpersonal skills to help increase diversity in the field.Comment: Presented at the 2022 Annual Conference of the American Society for Engineering Educatio

The Role of PPARγ in the Cyclooxygenase Pathway in Lung Cancer

Decreased expression of peroxisome proliferator activated receptor-γ (PPARγ) and high levels of the proinflammatory enzyme cyclooxygenase-2 (COX-2) have been observed in many tumor types. Both reduced (PPARγ) expression and elevated COX-2 within the tumor are associated with poor prognosis in lung cancer patients, and recent work has indicated that these signaling pathways may be interrelated. Synthetic (PPARγ) agonists such as the thiazolidinedione (TZD) class of anti-diabetic drugs can decrease COX-2 levels, inhibit growth of non-small-cell lung cancer (NSCLC) cells in vitro, and block tumor progression in xenograft models. TZDs alter the expression of COX-2 and consequent production of the protumorigenic inflammatory molecule prostaglandin E2 (PGE2) through both (PPARγ) dependent and independent mechanisms. Certain TZDs also reduce expression of PGE2 receptors or upregulate the PGE2 catabolic enzyme 15-prostaglandin dehydrogenase. As several COX-2 enzymatic products have antitumor properties and specific COX-2 inhibition has been associated with increased risk of adverse cardiac events, directly reducing the effects or concentration of PGE2 may provide a more safe and effective strategy for lung cancer treatment. Understanding the mechanisms underlying these effects may be helpful for designing anticancer therapies. This article summarizes recent research on the relationship between (PPARγ), TZDs, and the COX-2/PGE2 pathways in lung cancer