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Abstract Background This field evaluation was designed to evaluate the efficacy of a new porcine reproductive and respiratory syndrome virus‐2 (PRRSV‐2) modified live virus vaccine at three independent pig farms. Methods Three farms were selected for this study based on their respiratory disease status caused by PRRSV‐2 infection in post‐weaning and growing pigs. Each farm housed a total of 40, 18‐day‐old pigs that were randomly allocated to one of two treatment groups. Pigs were administered a 1.0 mL dose of the bivalent vaccine intramuscularly at 21 days of age in accordance with the manufacturer's recommendations, whereas unvaccinated pigs were administered a single dose of phosphate buffered saline at the same age. Results Vaccinated groups were measured and calculated significantly (p

Abstract Bipolar disorder is a leading contributor to disability, premature mortality, and suicide. Early identification of risk for bipolar disorder using generalizable predictive models trained on diverse cohorts around the United States could improve targeted assessment of high risk individuals, reduce misdiagnosis, and improve the allocation of limited mental health resources. This observational case-control study intended to develop and validate generalizable predictive models of bipolar disorder as part of the multisite, multinational PsycheMERGE Network across diverse and large biobanks with linked electronic health records (EHRs) from three academic medical centers: in the Northeast (Massachusetts General Brigham), the Mid-Atlantic (Geisinger) and the Mid-South (Vanderbilt University Medical Center). Predictive models were developed and valid with multiple algorithms at each study site: random forests, gradient boosting machines, penalized regression, including stacked ensemble learning algorithms combining them. Predictors were limited to widely available EHR-based features agnostic to a common data model including demographics, diagnostic codes, and medications. The main study outcome was bipolar disorder diagnosis as defined by the International Cohort Collection for Bipolar Disorder, 2015. In total, the study included records for 3,529,569 patients including 12,533 cases (0.3%) of bipolar disorder. After internal and external validation, algorithms demonstrated optimal performance in their respective development sites. The stacked ensemble achieved the best combination of overall discrimination (AUC = 0.82–0.87) and calibration performance with positive predictive values above 5% in the highest risk quantiles at all three study sites. In conclusion, generalizable predictive models of risk for bipolar disorder can be feasibly developed across diverse sites to enable precision medicine. Comparison of a range of machine learning methods indicated that an ensemble approach provides the best performance overall but required local retraining. These models will be disseminated via the PsycheMERGE Network website.

Abstract A fundamental understanding of the electrochemical behavior of hybrid perovskite and nitrogen-doped (N-doped) carbon is essential for the development of perovskite-based electrocatalysts in various sustainable energy device applications. In particular, the selection and modification of suitable carbon support are important for enhancing the oxygen reduction reaction (ORR) of non-platinum group metal electrocatalysts in fuel cells. Herein, we address hybrid materials composed of three representative N-doped carbon supports (BP-2000, Vulcan XC-72 and P-CNF) with valid surface areas and different series of single, double and triple perovskites: Ba0.5Sr0.5Co0.8Fe0.2O3−δ, (Pr0.5Ba0.5)CoO3−δ, and Nd1.5Ba1.5CoFeMnO9−δ (NBCFM), respectively. The combination of NBCFM and N-doped BP-2000 produces a half-wave potential of 0.74 V and a current density of 5.42 mA cm−2 at 0.5 V versus reversible hydrogen electrode, comparable to those of the commercial Pt/C electrocatalyst (0.76 V, 5.21 mA cm−2). Based on physicochemical and electrochemical analyses, we have confirmed a significant improvement in the catalytic performance of low-conductivity perovskite catalyst in the ORR when nitrogen-doped carbon with enhanced electrical conductivity is introduced. Furthermore, it has been observed that nitrogen dopants play active sites, contributing to additional performance enhancement when hybridized with perovskite.

Since battery systems typically account for over 40% of the cost of an electric vehicle, their mid-life replacements are exceptional. Therefore, the battery’s lifespan must exceed that of the vehicle. To ensure long-term and safe use, accurate state-of-charge (SOC) estimation must be maintained throughout the battery’s lifespan. This requires appropriate updates to parameters, such as capacity, in the battery model. In this context, dual extended Kalman filters, which simultaneously estimate both states and parameters, have gained interest. While existing reports on simultaneous estimators seemed promising, our study found that they performed well under low levels of battery aging but encountered issues at higher levels. Accurately reflecting the actual physicochemical changes of the parameters in aging cells is challenging for two reasons: the limited number of measurements of terminal voltage available for numerous parameters, and the weak observability of the capacity. Therefore, we combined the simultaneous estimator with a capacity estimator operated separately during charging and a sequential estimator specialized for an enhanced self-correcting model, achieving SOC accuracy within 5% even when the SOH decreased by 30%. However, there is still much work to be carried out to implement sequential estimators in battery management systems operating in real time with limited computational resources.

BackgroundPrior suicide attempts are a relatively strong risk factor for future suicide attempts. There is growing interest in using longitudinal electronic health record (EHR) data to derive statistical risk prediction models for future suicide attempts and other suicidal behavior outcomes. However, model performance may be inflated by a largely unrecognized form of “data leakage” during model training: diagnostic codes for suicide attempt outcomes may refer to prior attempts that are also included in the model as predictors. ObjectiveWe aimed to develop an automated rule for determining when documented suicide attempt diagnostic codes identify distinct suicide attempt events. MethodsFrom a large health care system’s EHR, we randomly sampled suicide attempt codes for 300 patients with at least one pair of suicide attempt codes documented at least one but no more than 90 days apart. Supervised chart reviewers assigned the clinical settings (ie, emergency department [ED] versus non-ED), methods of suicide attempt, and intercode interval (number of days). The probability (or positive predictive value) that the second suicide attempt code in a given pair of codes referred to a distinct suicide attempt event from its preceding suicide attempt code was calculated by clinical setting, method, and intercode interval. ResultsOf 1015 code pairs reviewed, 835 (82.3%) were nonindependent (ie, the 2 codes referred to the same suicide attempt event). When the second code in a pair was documented in a clinical setting other than the ED, it represented a distinct suicide attempt 3.3% of the time. The more time elapsed between codes, the more likely the second code in a pair referred to a distinct suicide attempt event from its preceding code. Code pairs in which the second suicide attempt code was assigned in an ED at least 5 days after its preceding suicide attempt code had a positive predictive value of 0.90. ConclusionsEHR-based suicide risk prediction models that include International Classification of Diseases codes for prior suicide attempts as a predictor may be highly susceptible to bias due to data leakage in model training. We derived a simple rule to distinguish codes that reflect new, independent suicide attempts: suicide attempt codes documented in an ED setting at least 5 days after a preceding suicide attempt code can be confidently treated as new events in EHR-based suicide risk prediction models. This rule has the potential to minimize upward bias in model performance when prior suicide attempts are included as predictors in EHR-based suicide risk prediction models.

Polymer electrolyte membrane fuel cells (PEMFCs) have reached the commercialization phase, representing a promising approach to curbing carbon emissions. However, greater durability of PEMFCs is of paramount importance to ensure their long-term viability and effectiveness, and catalyst development has become a focal point of research. Pt nanoparticles supported on carbon materials (Pt/C) are the primary catalysts used in PEMFCs. Accomplishing both a high dispersion of uniform metal particles on the carbon support and robust adhesion between the metal particles and the carbon support is imperative for superior stability, and will thereby, advance the practical applications of PEMFCs in sustainable energy solutions. Ultrasound-assisted polyol synthesis (UPS) has emerged as a suitable method for synthesizing catalysts with a well-defined metal-support structure, characterized by the high dispersion and uniformity of metal nanoparticles. In this study, we focused on the effect of ultrasound on the synthesis of Pt/C via UPS and the resulting enhanced stability of Pt/C catalysts. Therefore, we compared Pt/C synthesized using a conventional polyol synthesis (Pt/C_P) and Pt/C synthesized via UPS (Pt/C_U) under similar synthesis conditions. The two catalysts had a similar Pt content and the average particle size of the Pt nanoparticles was similar; however, the uniformity and dispersion of Pt nanoparticles in Pt/C_U were better than those of Pt/C_P. Moreover, ex/in-situ analyses performed in a high-temperature environment, in which nanoparticles tend to agglomerate, have revealed that Pt/C_U exhibited a notable improvement in the adhesion of Pt particles to the carbon support compared with that of Pt/C_P. The enhanced adhesion is crucial for maintaining the stability of the catalyst, ultimately contributing to a better durability in practical applications. Ultrasound was applied to the carbon support without the Pt precursor under the same UPS conditions used to synthesize Pt/C_U to identify the reason for the increased adhesion between the Pt particles and the carbon support in Pt/C_U, and we discovered that oxygen functional groups (C-O, C = O, and O-C = O) for anchoring site of Pt particles were generated in the carbon support. Pt/C_U displayed an increase in stability in an electrochemical accelerated stress test (AST) in an acidic electrolyte. The physical and chemical effects of ultrasound on the synthesis of Pt/C via UPS were identified, and we concluded that UPS is suitable for synthesizing carbon supported electrocatalysts with high stability.

Carbon dioxide (CO2) cycloaddition with epoxide can yield value-added cyclic carbonates with 100 % atom efficiency. Moreover, zeolitic imidazolate frameworks (ZIFs) have emerged as promising catalysts for this reaction as their basic and acidic sites can effectively activate CO2 and epoxide molecules. Here, novel mixed-metal ZIFs containing Pd and Co ions (PdCo-ZIFs) that are linked by benzimidazole ligands were synthesized, after which their structures were comprehensively characterized. Despite the inherent two-dimensional nanosheet structure of Co-ZIF, PdCo-ZIFs exhibited a large surface area, with a hierarchical porous structure and profoundly increased active site concentrations (10–20 times higher than those of Co-ZIFs), exhibiting significantly increased catalytic activity toward CO2 cycloaddition. Additionally, the relationship between the catalytic activity and structural characteristics of ZIFs correlated, and the representative Pd2Co8-ZIF catalyst achieved a maximum product yield of 97.3 %, with a high turnover number (5754) and turnover frequency (479 h−1) for cyclic carbonates, attributed to the well-developed porous structure and defect sites that could not be observed in single-metal ZIF materials.

Throughout the COVID-19 pandemic, graduate students have faced increased risk of mental health challenges. Research suggests that experiencing adversity may induce positive psychological changes, called post-traumatic growth (PTG). These changes can include improved relationships with others, perceptions of oneself, and enjoyment of life. Few existing studies have explored this phenomenon among graduate students. This secondary data analysis of a survey conducted in November 2020 among graduate students at a private R1 University in the northeast United States examined graduate students' levels and correlates of PTG during the COVID-19 pandemic. Students had a low level of PTG, with a mean score of 10.31 out of 50. Linear regression models showed significant positive relationships between anxiety and PTG and between a measure of self-reported impact of the pandemic and PTG. Non-White minorities also had significantly greater PTG than White participants. Experiencing more negative impact due to the pandemic and ruminating about the pandemic were correlated with greater PTG. These findings advance research on the patterns of PTG during the COVID-19 pandemic and can inform future studies of graduate students’ coping mechanisms and support efforts to promote pandemic recovery and resilience.

BackgroundInterest in developing machine learning models that use electronic health record data to predict patients’ risk of suicidal behavior has recently proliferated. However, whether and how such models might be implemented and useful in clinical practice remain unknown. To ultimately make automated suicide risk–prediction models useful in practice, and thus better prevent patient suicides, it is critical to partner with key stakeholders, including the frontline providers who will be using such tools, at each stage of the implementation process. ObjectiveThe aim of this focus group study is to inform ongoing and future efforts to deploy suicide risk–prediction models in clinical practice. The specific goals are to better understand hospital providers’ current practices for assessing and managing suicide risk; determine providers’ perspectives on using automated suicide risk–prediction models in practice; and identify barriers, facilitators, recommendations, and factors to consider. MethodsWe conducted 10 two-hour focus groups with a total of 40 providers from psychiatry, internal medicine and primary care, emergency medicine, and obstetrics and gynecology departments within an urban academic medical center. Audio recordings of open-ended group discussions were transcribed and coded for relevant and recurrent themes by 2 independent study staff members. All coded text was reviewed and discrepancies were resolved in consensus meetings with doctoral-level staff. ResultsAlthough most providers reported using standardized suicide risk assessment tools in their clinical practices, existing tools were commonly described as unhelpful and providers indicated dissatisfaction with current suicide risk assessment methods. Overall, providers’ general attitudes toward the practical use of automated suicide risk–prediction models and corresponding clinical decision support tools were positive. Providers were especially interested in the potential to identify high-risk patients who might be missed by traditional screening methods. Some expressed skepticism about the potential usefulness of these models in routine care; specific barriers included concerns about liability, alert fatigue, and increased demand on the health care system. Key facilitators included presenting specific patient-level features contributing to risk scores, emphasizing changes in risk over time, and developing systematic clinical workflows and provider training. Participants also recommended considering risk-prediction windows, timing of alerts, who will have access to model predictions, and variability across treatment settings. ConclusionsProviders were dissatisfied with current suicide risk assessment methods and were open to the use of a machine learning–based risk-prediction system to inform clinical decision-making. They also raised multiple concerns about potential barriers to the usefulness of this approach and suggested several possible facilitators. Future efforts in this area will benefit from incorporating systematic qualitative feedback from providers, patients, administrators, and payers on the use of these new approaches in routine care, especially given the complex, sensitive, and unfortunately still stigmatized nature of suicide risk.

Abstract The development of a low cost and highly active alternative to the commercial Pt/C catalysts used in the oxygen reduction reaction (ORR) requires a facile and environmentally-friendly synthesis process to facilitate large-scale production and provide an effective replacement. Transition metals, in conjunction with nitrogen-doped carbon, are among the most promising substitute catalysts because of their high activity, inexpensive composition, and high carbon monoxide tolerance. We prepared a polyaniline-derived Fe-N-C catalyst for oxygen reduction using a facile one-pot process with no additional reagents. This process was carried out by ultrasonicating a mixture containing an iron precursor, an aniline monomer, and carbon black. The half-wave potential of the synthesized Fe-N-C catalyst for the ORR was only 10 mV less than that of a commercial Pt/C catalyst. The optimized Fe-N-C catalyst showed outstanding performance in a practical anion exchange membrane fuel cell (AEMFC), suggesting its potential as an alternative to commercial Pt/C catalysts for the ORR.

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An adaptive design is a clinical trial design that allows for modification of a structured plan in a clinical trial based on data accumulated during pre-planned interim analyses. This flexible approach to clinical trial design improves the success rate of clinical trials while reducing time, cost, and sample size compared to conventional methods. The purpose of this study is to identify the current status of adaptive design and present key considerations for planning an appropriate adaptive design based on specific circumstances. We searched for clinical trials conducted between January 2006 to July 2021 in the Clinical Trials Registry (ClinicalTrials.gov) using keywords specified in the Food and Drug Administration Adaptive Design Clinical Trial Guidelines. In order to analyze the adaptive designs used in selected cases, we classified the results according to the phase of the clinical trial, type of indication, and the specific adaptation method employed. A total of 267 clinical trials were identified on ClinicalTrials.gov. Among them, 236 clinical trials actually applied adaptive designs and were classified according to phase, indication types, and adaptation methods. Adaptive designs were most frequently used in phase 2 clinical trials and oncology research. The most commonly used adaptation method was the adaptive treatment selection design. In the case of coronavirus disease 2019, the most frequently used designs were adaptive platform design and seamless design. Through this study, we expect to provide valuable insights and considerations for the implementation of adaptive design clinical trials in different diseases and stages. [ABSTRACT FROM AUTHOR]