Oxidation of Thiols to Disulfides using an Environmentally “Green” Organocatalyst and New Mechanistic Insights

The selective oxidation of thiols to disulfides is an area of great importance in the areas of materials and medicinal chemistry research. The production of polymers, rubber, pharmaceuticals, and the folding of proteins in biological systems all rely on the formation of disulfide bonds. Herein, we introduce a stoichiometric and electrocatalytic method for the oxidation of various pharmaceutically and biologically relevant thiols into their respective disulfides in more environmentally benign solvents such as water and alcohol solvents. The scope of the transformation was evaluated and a detailed mechanistic study involving control experiments, experimental kinetic studies, and computational investigations led to new insights into how the oxidation takes place via an unusual anionic process

Effects of forcing differences and initial conditions on inter-model agreement in the VolMIP volc-pinatubo-full experiment

This paper provides initial results from a multi-model ensemble analysis based on the volc-pinatubo-full experiment performed within the Model Intercomparison Project on the climatic response to Volcanic forcing (VolMIP) as part of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). The volc-pinatubo-full experiment is based on an ensemble of volcanic forcing-only climate simulations with the same volcanic aerosol dataset across the participating models (the 1991-1993 Pinatubo period from the CMIP6-GloSSAC dataset). The simulations are conducted within an idealized experimental design where initial states are sampled consistently across models from the CMIP6-piControl simulation providing unperturbed preindustrial background conditions. The multi-model ensemble includes output from an initial set of six participating Earth system models (CanESM5, GISS-E2.1-G, IPSL-CM6A-LR, MIROC-E2SL, MPI-ESM1.2-LR and UKESM1). The results show overall good agreement between the different models on the global and hemispheric scales concerning the surface climate responses, thus demonstrating the overall effectiveness of VolMIP's experimental design. However, small yet significant inter-model discrepancies are found in radiative fluxes, especially in the tropics, that preliminary analyses link with minor differences in forcing implementation; model physics, notably aerosol-radiation interactions; the simulation and sampling of El Niño-Southern Oscillation (ENSO); and, possibly, the simulation of climate feedbacks operating in the tropics. We discuss the volc-pinatubo-full protocol and highlight the advantages of volcanic forcing experiments defined within a carefully designed protocol with respect to emerging modelling approaches based on large ensemble transient simulations. We identify how the VolMIP strategy could be improved in future phases of the initiative to ensure a cleaner sampling protocol with greater focus on the evolving state of ENSO in the pre-eruption period

Tobacco Use and Attachment Style in Appalachia

Tobacco has been recognized as the number one cause of preventable death in America and results in almost 5.2 million years of potential life lost each year. The use of tobacco products is highly correlated with pulmonary disease, cardiovascular disease, and other forms of chronic illness in America. New tobacco products are trending in the tobacco market such as the water pipe/hookah and e-cigarettes. With e-cigarettes and other newer forms of tobacco on the rise, it is important to look at the underlying factors for using all kinds of tobacco products as a means of prevention. Certain adult attachment styles (secure, preoccupied, dismissing-avoidant, and fearful-avoidant) in emotionally meaningful relationships could be indicators for physical illness, mental illness, and even addiction. This study investigated whether or not there is a relationship between tobacco use and attachment style. Based on a university-wide survey that was sent out at a university in Appalachia with 522 participants, demographic data revealed 68.5% (n = 358) did not currently use tobacco products. Of those who did currently use tobacco products 54.5% (n = 90) were male, 84.8% (n = 140) were undergraduate students, and 66.7% (n = 110) were between the ages of 18-25. For individuals who used tobacco 23.5% (n = 38) were in the secure attachment group, 27.8% (n = 45) were in the dismissing-avoidant attachment group, 30.2% (n = 49) were in the fearful-avoidant attachment group, and 18.5% (n = 30) were in the preoccupied attachment group. Chi Square analysis demonstrated that attachment style was significantly (p \u3c 0.001) different between tobacco users and non-users revealing that there is a possibility for prevention of smoking initiation through the development of a secure attachment style

Family and Friends to the Rescue: Experiences of Rural Elders With Heart Failure

The purpose of this study was to describe the experiences of rural community-dwelling older adults with heart failure who required assistance with activities of daily living (ADLs) and instrumental ADLs (IADLs). The context of the study was a rural area in a southern U.S. state. Twenty older adults with ADL/IADL needs living in the rural area were recruited during hospitalization and interviewed in their homes after discharge. The semi-structured interview focused on ADLs/IADLs and community resources. This qualitative descriptive study used hermeneutic methods for analysis. Four themes were identified: Accepting Limitations, Disappointments and Unmet Expectations, Figure It Out, and Complex Connections. The findings indicate that despite the older adults’ medical conditions, they were able to set up complex arrangements, which allowed them to remain in their homes. Understanding the help older adults require after discharge will assist nurses in developing programs that are available, accessible, and acceptable to older adults who live in rural areas

Stream Temperature Predictions for River Basin Management in the Pacific Northwest and Mid-Atlantic Regions Using Machine Learning

Stream temperature (Ts) is an important water quality parameter that affects ecosystem health and human water use for beneficial purposes. Accurate Ts predictions at different spatial and temporal scales can inform water management decisions that account for the effects of changing climate and extreme events. In particular, widespread predictions of Ts in unmonitored stream reaches can enable decision makers to be responsive to changes caused by unforeseen disturbances. In this study, we demonstrate the use of classical machine learning (ML) models, support vector regression and gradient boosted trees (XGBoost), for monthly Ts predictions in 78 pristine and human-impacted catchments of the Mid-Atlantic and Pacific Northwest hydrologic regions spanning different geologies, climate, and land use. The ML models were trained using long-term monitoring data from 1980–2020 for three scenarios: (1) temporal predictions at a single site, (2) temporal predictions for multiple sites within a region, and (3) spatiotemporal predictions in unmonitored basins (PUB). In the first two scenarios, the ML models predicted Ts with median root mean squared errors (RMSE) of 0.69–0.84 °C and 0.92–1.02 °C across different model types for the temporal predictions at single and multiple sites respectively. For the PUB scenario, we used a bootstrap aggregation approach using models trained with different subsets of data, for which an ensemble XGBoost implementation outperformed all other modeling configurations (median RMSE 0.62 °C).The ML models improved median monthly Ts estimates compared to baseline statistical multi-linear regression models by 15–48% depending on the site and scenario. Air temperature was found to be the primary driver of monthly Ts for all sites, with secondary influence of month of the year (seasonality) and solar radiation, while discharge was a significant predictor at only 10 sites. The predictive performance of the ML models was robust to configuration changes in model setup and inputs, but was influenced by the distance to the nearest dam with RMSE <1 °C at sites situated greater than 16 and 44 km from a dam for the temporal single site and regional scenarios, and over 1.4 km from a dam for the PUB scenario. Our results show that classical ML models with solely meteorological inputs can be used for spatial and temporal predictions of monthly Ts in pristine and managed basins with reasonable (<1 °C) accuracy for most locations

Can machine learning accelerate process understanding and decision-relevant predictions of river water quality?

The global decline of water quality in rivers and streams has resulted in a pressing need to design new watershed management strategies. Water quality can be affected by multiple stressors including population growth, land use change, global warming, and extreme events, with repercussions on human and ecosystem health. A scientific understanding of factors affecting riverine water quality and predictions at local to regional scales, and at sub-daily to decadal timescales are needed for optimal management of watersheds and river basins. Here, we discuss how machine learning (ML) can enable development of more accurate, computationally tractable, and scalable models for analysis and predictions of river water quality. We review relevant state-of-the art applications of ML for water quality models and discuss opportunities to improve the use of ML with emerging computational and mathematical methods for model selection, hyperparameter optimization, incorporating process knowledge into ML models, improving explainablity, uncertainty quantification, and model-data integration. We then present considerations for using ML to address water quality problems given their scale and complexity, available data and computational resources, and stakeholder needs. When combined with decades of process understanding, interdisciplinary advances in knowledge-guided ML, information theory, data integration, and analytics can help address fundamental science questions and enable decision-relevant predictions of riverine water quality

Comorbidities Predict Length of Stay Among Patients Admitted with Peripheral Artery Disease– An Analysis of The National Inpatient Sample.

The global prevalence of peripheral artery disease (PAD) is estimated to be about 120 million, making up about 25.6% of the worldwide burden of cardiovascular diseases (CVD). In the United States (U.S.), the prevalence of PAD is about 7%, representing nearly 8 million adults. There is a higher prevalence of disease in Blacks and non-Hispanic Whites, with approximately 30% of Blacks and 20% of non-Hispanic Whites developing PAD in their lifetime. The strong risk factors associated with PAD include smoking, diabetes, hypertension, age, and male sex. Our study aimed to estimate the effects of obesity, alcohol abuse, renal failure, and hypertension on patients’ length of stay (LOS) among patients admitted with a diagnosis of PAD. Using the 2012 U.S. National Inpatient Sample database, we included 336,790 patients with PAD as a separate comorbidity during their index admission. Our main outcome variable was patients’ total length of stay (LOS) during the index admission. We categorized LOS \u3c 1 into next day discharge (NDD) and LOS \u3e 1 into non-NDD. Our predictor variables were hypertension, obesity, alcohol abuse and renal failure. We ran descriptive statistics to delineate the baseline characteristics of our sample population, and bivariate analysis with t-test and chi-square analysis. Multivariable logistic regression was used to estimate odds of non-NDD given our comorbidities; obesity, hypertension, alcohol abuse, renal failure while adjusting for age, race, and sex. We reported frequencies, p-values, and odd ratios (ORs) at a 95% significance level with alpha at 0.05. Of our final sample, 54.8% were males while 45.2% were females and the mean age of patients was 71.7 + 12.8. Hypertension, obesity, alcohol abuse and renal failure were present in 75%, 12%, 3.4%, and 30.9% of patients, respectively. Majority (75%) of the patients were white, while Black and Hispanic patients made up 13.3% and 7.1%, respectively. In our adjusted model, we found that patients with hypertension had 12% lower odds of non-NDD (OR = 0.88, CI= 0.86-0.90, P\u3c0.0001) compared to those without hypertension, females had 20% increase in the odds of non-NDD compared to males (OR = 1.20, CI= 1.18-1.23, P\u3c0.0001), patients with obesity, alcohol abuse and renal failure had 39%, 43% and 45% increase in odds of non-NDD compared to those without these comorbidities. (OR = 1.39, CI= 1.34-1.44, P\u3c0.0001), (OR = 1.43, CI= 1.35-1.52, P\u3c0.0001), (OR = 1.45, CI= 1.42-1.49, P\u3c0.0001). Given the significant association between obesity, alcohol abuse, and renal failure with prolonged hospital stay in patients admitted to hospital with PAD, our study highlights the importance of adequate management of pre-existing patients\u27 comorbidities. This is expected to improve overall length of stay and total healthcare utilization and costs, among patients with PAD