Search

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.

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.

Copyright of Canadian Journal of Veterinary Research / Revue Canadienne de Recherche Vétérinaire is the property of Canadian Veterinary Medical Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)