Dams, Roads, and Bridges: (Re)defining Work and Masculinity in American Indian Literature of the Great Plains, 1968-Present

This master\u27s thesis explores the intersections of labor, socioeconomic class, and constructed American Indian masculinities in the literature of indigenous writers of the Great Plains published after the Native American Renaissance of the late 1960s. By engaging scholars and theorists from multiple disciplines--including Native labor historians such as Colleen O\u27Neill and Alexandra Harmon, (trans)indigenous studies scholars such as Chadwick Allen and Philip Deloria, and Native literary and cultural critics such as Gerald Vizenor and Louis Owens--this thesis offers an American Studies approach to definitions and expressions of work, wealth, and masculinity in American Indian literature of the Great Plains. With chapters on D\u27Arcy McNickle\u27s posthumous Wind From an Enemy Sky (1978), Carter Revard\u27s poetry and mixed-genre memoirs, and Thomas King\u27s Truth and Bright Water (1999), this thesis emphasizes the roles of cross-cultural apprenticeships for young Native protagonists whose socioeconomic opportunities are often obstructed, threatened, or complicated by dams, roads, and bridges, both literal and metaphorical, as they seek ways to engage (or circumvent) the capitalist marketplace on their own terms. In highlighting each protagonist\u27s relationship to blood (family and community), land, and memory, the chapters reveal how the respective Native authors challenge and reimagine stereotypes regarding Native workers and offer more complicated and nuanced discussions of Native traditions in modernity. (173 pages

The Mystery of the Cosmic Diffuse Ultraviolet Background Radiation

The diffuse cosmic background radiation in the GALEX far ultraviolet (FUV, 1300 \AA\ - 1700 \AA) is deduced to originate only partially in the dust-scattered radiation of FUV-emitting stars: the source of a substantial fraction of the FUV background radiation remains a mystery. The radiation is remarkably uniform at both far northern and far southern Galactic latitudes, and it increases toward lower Galactic latitudes at all Galactic longitudes. We examine speculation that it might be due to interaction of the dark matter with the nuclei of the interstellar medium but we are unable to point to a plausible mechanism for an effective interaction. We also explore the possibility that we are seeing radiation from bright FUV-emitting stars scattering from a "second population" of interstellar grains---grains that are small compared with FUV wavelengths. Such grains are known to exist (Draine 2011) and they scatter with very high albedo, with an isotropic scattering pattern. However, comparison with the observed distribution (deduced from their $100\ \mu$m emission) of grains at high Galactic latitudes shows no correlation between the grains' location and the observed FUV emission. Our modeling of the FUV scattering by small grains also shows that there must be remarkably few such "smaller" grains at high Galactic latitudes, both North and South; this likely means simply that there is very little interstellar dust of any kind at the Galactic poles, in agreement with Perry & Johnston (1982). We also review our limited knowledge of the cosmic diffuse background at ultraviolet wavelengths shortward of Lyman $\alpha$---it could be that our "second component" of the diffuse far-ultraviolet background persists shortward of the Lyman limit, and is the cause of the re-ionization of the Universe (Kollmeier et al. 2014).Comment: 73 pages, 31 figures, ApJ accepte

Can Individual Investors Beat the Market?

We document strong persistence in the performance of trades of individual investors. Investors classified in the top 10 percent place other trades that on average earn excess returns of 15 basis points per day. A rolling-forward strategy of going long firms purchased by previously successful investors and shorting firms purchased by previously unsuccessful investors results in excess returns of 5 basis points per day. These returns are not confined to small stocks nor to stocks in which the investors are likely to have inside information. Our results suggest that skillful individual investors exploit market inefficiencies to earn abnormal profits, above and beyond any profits available from well-known strategies based upon size, value, or momentum.Individual Investors, Market Efficiency, Performance Persistence

The Needle is Moving in CA K-8 Science: Integration with ELA, Integration of the Sciences, and Returning Science as a K-8 Core Subject

This first EII evaluation publication discusses one of the major shifts above, namely the shift to integrated instruction. The integration of science and ELA is the focus of one section, and the integration of the science disciplines (i.e., earth/space, life, and physical) inherent in the MS Integrated Model is the focus of the second. Also discussed at length in this publication is a fundamental shift that is not listed above, but is equally, if not more, important: the need to teach science in the first place. In order for any of the targeted shifts to take place, teachers must devote time to teaching science on a regular basis

ERAS Protocol for Lower Extremity Orthopedic Procedures

The demand for total knee arthroplasty (TKA) and total hip arthroplasty (THA) will continue to rise as life expectancy increases. Coupling increased age with the increased prevalence of both obesity and osteoarthritis, the need for total joint arthroplasties is likely to increase (Oseka & Pecka, 2018). The frequency of arthroplasty procedures and the associated recovery period lead to heavy economic demands felt by most healthcare systems (Stowers et al., 2016). To address this burden, enhanced recovery after surgery (ERAS) protocols have been implemented at institutions and are designed to improve patient outcomes while simultaneously limiting cost and reducing readmission rates after surgery (Kaye et al., 2019a). Objectives of this quality improvement project were to research prevailing evidence-based literature on ERAS protocols, develop a facility specific protocol, and introduce the customized THA and TKA ERAS protocol to members of the perioperative team. Results of the post-implementation knowledge assessment demonstrated nurse anesthetists had a strong understanding of ERAS. Participants also indicated that the customized ERAS protocol would be implemented into practice. Obtaining surgeon and other perioperative staff buy-in to the protocol may further enhance patient safety and perioperative experience and reduce economic burden through decreased length of stay and postoperative complications

Modeling reactivity to biological macromolecules with a deep multitask network

Most small-molecule drug candidates fail before entering the market, frequently because of unexpected toxicity. Often, toxicity is detected only late in drug development, because many types of toxicities, especially idiosyncratic adverse drug reactions (IADRs), are particularly hard to predict and detect. Moreover, drug-induced liver injury (DILI) is the most frequent reason drugs are withdrawn from the market and causes 50% of acute liver failure cases in the United States. A common mechanism often underlies many types of drug toxicities, including both DILI and IADRs. Drugs are bioactivated by drug-metabolizing enzymes into reactive metabolites, which then conjugate to sites in proteins or DNA to form adducts. DNA adducts are often mutagenic and may alter the reading and copying of genes and their regulatory elements, causing gene dysregulation and even triggering cancer. Similarly, protein adducts can disrupt their normal biological functions and induce harmful immune responses. Unfortunately, reactive metabolites are not reliably detected by experiments, and it is also expensive to test drug candidates for potential to form DNA or protein adducts during the early stages of drug development. In contrast, computational methods have the potential to quickly screen for covalent binding potential, thereby flagging problematic molecules and reducing the total number of necessary experiments. Here, we train a deep convolution neural networkthe XenoSite reactivity modelusing literature data to accurately predict both sites and probability of reactivity for molecules with glutathione, cyanide, protein, and DNA. On the site level, cross-validated predictions had area under the curve (AUC) performances of 89.8% for DNA and 94.4% for protein. Furthermore, the model separated molecules electrophilically reactive with DNA and protein from nonreactive molecules with cross-validated AUC performances of 78.7% and 79.8%, respectively. On both the site- and molecule-level, the model’s performances significantly outperformed reactivity indices derived from quantum simulations that are reported in the literature. Moreover, we developed and applied a selectivity score to assess preferential reactions with the macromolecules as opposed to the common screening traps. For the entire data set of 2803 molecules, this approach yielded totals of 257 (9.2%) and 227 (8.1%) molecules predicted to be reactive only with DNA and protein, respectively, and hence those that would be missed by standard reactivity screening experiments. Site of reactivity data is an underutilized resource that can be used to not only predict if molecules are reactive, but also show where they might be modified to reduce toxicity while retaining efficacy. The XenoSite reactivity model is available at http://swami.wustl.edu/xenosite/p/reactivity