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Coronary artery disease (CAD), the most common manifestation of cardiovascular disease, remains the most common cause of mortality in the United States. Risk assessment is key for primary prevention of coronary events and coronary artery calcium (CAC) scoring using computed tomography (CT) is one such non-invasive tool. Despite the proven clinical value of CAC, the current clinical practice implementation for CAC has limitations such as the lack of insurance coverage for the test, need for capital-intensive CT machines, specialized imaging protocols, and accredited 3D imaging labs for analysis (including personnel and software). Perhaps the greatest gap is the millions of patients who undergo routine chest CT exams and demonstrate coronary artery calcification, but their presence is not often reported or quantitation is not feasible. We present two deep learning models that automate CAC scoring demonstrating advantages in automated scoring for both dedicated gated coronary CT exams and routine non-gated chest CTs performed for other reasons to allow opportunistic screening. First, we trained a gated coronary CT model for CAC scoring that showed near perfect agreement (mean difference in scores = -2.86; Cohen's Kappa = 0.89, P

Hybrid electric vehicles (HEVs) are set to play a critical role in the future of the automotive industry. To operate efficiently, HEVs require a robust energy management strategy (EMS) that decides whether the vehicle is powered by the engine or electric motors while managing the battery's state of charge. The EMS must rapidly adapt to driver demands and optimize energy usage, ideally predicting battery charge rates and fuel consumption to adjust the powertrain in real time, even under unpredictable driving conditions. As HEVs become more prevalent, EMS technologies will advance to improve predictive capabilities. This analysis provides an overview of current EMS systems, including both rule-based and optimization-based approaches. It explores the evolution of EMS development through a technology roadmap, highlighting the integration of advanced algorithms such as reinforcement learning and deep learning. The analysis addresses the technologies that underly this evolution, including machine learning, cloud computing, computer vision, and swarm technology. Key advances and challenges in these technologies are discussed, along with their implications for the next generation of EMS systems for HEVs. The analysis of these technologies indicates that they will play a key role in the evolution of EMS technology, allowing it to better optimize driver needs and fuel economy. [ABSTRACT FROM AUTHOR]

Recent advances in all-solid-state battery (ASSB) research have significantly addressed key obstacles hindering their widespread adoption in electric vehicles (EVs). This review highlights major innovations, including ultrathin electrolyte membranes, nanomaterials for enhanced conductivity, and novel manufacturing techniques, all contributing to improved ASSB performance, safety, and scalability. These developments effectively tackle the limitations of traditional lithium-ion batteries, such as safety issues, limited energy density, and a reduced cycle life. Noteworthy achievements include freestanding ceramic electrolyte films like the 25 μm thick Li0.34La0.56TiO3 film, which enhance energy density and power output, and solid polymer electrolytes like the polyvinyl nitrile boroxane electrolyte, which offer improved mechanical robustness and electrochemical performance. Hybrid solid electrolytes combine the best properties of inorganic and polymer materials, providing superior ionic conductivity and mechanical flexibility. The scalable production of ultrathin composite polymer electrolytes shows promise for high-performance, cost-effective ASSBs. However, challenges remain in optimizing manufacturing processes, enhancing electrode-electrolyte interfaces, exploring sustainable materials, and standardizing testing protocols. Continued collaboration among academia, industry, and government is essential for driving innovation, accelerating commercialization, and achieving a sustainable energy future, fully realizing the transformative potential of ASSB technology for EVs and beyond. [ABSTRACT FROM AUTHOR]

Executive function plays an important role in promoting learning and social-emotional development in children. Neonatal hypoglycemia associates with executive function difficulties at 4.5 years, but little is known about the development of executive function over time in children born at risk of neonatal hypoglycemia. We aimed to describe the stability of executive function from early to mid-childhood in children born at risk of neonatal hypoglycemia and its association with neonatal hypoglycemia. Participants in a prospective cohort study of infants born at risk for neonatal hypoglycemia were assessed at ages 2, 4.5, and 9–10 years. We assessed executive function with batteries of performance-based and questionnaire-based measures, and classified children into one of four stability groups (persistent typical, intermittent typical, intermittent difficulty, and persistent difficulty) based on dichotomized scores (typical versus low at each age). Multinomial logistic regression was used to determine the associations between neonatal hypoglycemia and executive function stability groups. Three hundred and nine children, of whom 197 (64%) experienced neonatal hypoglycemia were assessed. The majority of children had stable and typical performance-based (63%) and questionnaire-based (68%) executive function across all three ages. Around one-third (30–36%) of children had transient difficulties, and only a few (0.3–1.9%) showed persistent difficulties in executive function at all ages. There was no consistent evidence of an association between neonatal hypoglycemia and the stability of executive function. Neonatal hypoglycemia does not appear to predict a specific pattern of development of executive function in children born at risk. [ABSTRACT FROM AUTHOR]

Byline: Rajesh. Shah, Maharra. Hussain Hyperechoic breast lesions are relatively uncommon, constituting only 0.6-6% of all breast masses and 0.6% of all biopsied lesions. The majority of these lesions are [...]