757 research outputs found

    Role Of The Nuclear Receptor PparĪ³ In Clear Cell Renal And Bladder Urotheial Carcinoma

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    The nuclear receptor peroxisome proliferator-activated receptor gamma (PPARĪ³) has a well-characterized role in the developmental process of adipogenesis and transcriptional regulation of lipid metabolism. However, its expression patterns and functions in various cancer subtypes are less understood. My studies investigate the role of PPARĪ³ in two distinct cancers of the urinary tract: clear cell renal cell carcinoma (ccRCC) and bladder urothelial carcinoma (UC). In ccRCC, I hypothesized that PPARĪ³ activity contributes to the aberrant lipid accumulation phenotype characteristic of this disease, thereby promoting tumor progression. Through ChIP-seq, I demonstrated that PPARĪ³ and its heterodimeric DNA binding partner retinoid X receptor (RXR) occupy both adipose-shared and ccRCC-specific sites throughout the genome. However, based on a number of in vitro and in vivo assays evaluating ccRCC viability, proliferation, migration, and effects on lipid metabolism, I concluded that PPARĪ³ was dispensable for these processes and ccRCC progression. I also studied the role of PPARĪ³ in UC, a cancer which displays copy number amplification and mRNA overexpression of PPARG or RXRA in ~30% of tumors. In contrast to the results obtained in ccRCC, I demonstrated that genetic and pharmacological inhibition of PPARĪ³ reduces tumor growth via cell cycle arrest. Furthermore, I identified a candidate list of PPARĪ³-regulated genes in UC based on ChIP- and RNA-seq analyses of cell culture models, as well as gene expression data from primary patient samples. Together, my studies illuminate the remarkable cell type-specific functions of PPARĪ³ in urinary tract cancers, and provide rationale for the pharmacological targeting of its transcriptional effectors in a subset of tumors

    Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making

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    It is widely believed that one's peers influence product adoption behaviors. This relationship has been linked to the number of signals a decision-maker receives in a social network. But it is unclear if these same principles hold when the pattern by which it receives these signals vary and when peer influence is directed towards choices which are not optimal. To investigate that, we manipulate social signal exposure in an online controlled experiment using a game with human participants. Each participant in the game makes a decision among choices with differing utilities. We observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the choices, decision-makers tend to deviate from the obvious optimal decision when their peers make similar decision which we call the influence decision, (2) when the quantity of social signals vary over time, the forwarding probability of the influence decision and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals. To better understand how these rules of peer influence could be used in modeling applications of real world diffusion and in networked environments, we use our behavioral findings to simulate spreading dynamics in real world case studies. We specifically try to see how cumulative influence plays out in the presence of user uncertainty and measure its outcome on rumor diffusion, which we model as an example of sub-optimal choice diffusion. Together, our simulation results indicate that sequential peer effects from the influence decision overcomes individual uncertainty to guide faster rumor diffusion over time. However, when the rate of diffusion is slow in the beginning, user uncertainty can have a substantial role compared to peer influence in deciding the adoption trajectory of a piece of questionable information

    N-Glycosylation Regulates Pannexin 2 Localization but Is Not Required for Interacting with Pannexin 1.

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    Pannexins (Panx1, 2, 3) are channel-forming glycoproteins expressed in mammalian tissues. We previously reported that N-glycosylation acts as a regulator of the localization and intermixing of Panx1 and Panx3, but its effects on Panx2 are currently unknown. Panx1 and Panx2 intermixing can regulate channel properties, and both pannexins have been implicated in neuronal cell death after ischemia. Our objectives were to validate the predicted N-glycosylation site of Panx2 and to study the effects of Panx2 glycosylation on localization and its capacity to interact with Panx1. We used site-directed mutagenesis, enzymatic de-glycosylation, cell-surface biotinylation, co-immunoprecipitation, and confocal microscopy. Our results showed that N86 is the only N-glycosylation site of Panx2. Panx2 and the N86Q mutant are predominantly localized to the endoplasmic reticulum (ER) and cis-Golgi matrix with limited cell surface localization was seen only in the presence of Panx1. The Panx2 N86Q mutant is glycosylation-deficient and tends to aggregate in the ER reducing its cell surface trafficking but it can still interact with Panx1. Our study indicates that N-glycosylation may be important for folding and trafficking of Panx2. We found that the un-glycosylated forms of Panx1 and 2 can readily interact, regulating their localization and potentially their channel function in cells where they are co-expressed

    Teaching Business: Looking at the Support Needs of Instructors

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    In 2018, Ithaka S+R began a new research program investigating scholarsā€™ undergraduate teaching practices. As a first foray in this program, we looked at the teaching practices and needs of instructors teaching in business and business related disciplines. The project was undertaken collaboratively with research teams at 14 academic libraries in the United States and we thank those institutions and their researchers for partnering with us

    Probabilistic causation

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    In this paper we will proceed in the following way: firstly we will give In this paper we will proceed in the following way: firstly we will give a brief reminder of the main tenets of the philosophical program connected to strong probabilistic causation; then we will recall some of the principal problems that the mathematization of probability-based causal theories have tried to solve in the last two decades (with a certain degree of success, which explains the two different incipit quoted above). Eventually we will discuss the connection between kinds of probabilities and kinds of probabilistic causations, an issue that has a strong impact on the problem of determinism
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