Project-based science (PBS) curricula have project-and inquiry-based aspects that leverage the strengths of urban students from ethnic and racial groups underrepresented in science careers, potentially impacting positively these students' science achievement and attitudes and thus their college and career plans. We aimed to determine the extent to which a PBS curriculum would show this. We provided professional development to bolster urban teachers' science content knowledge (CK) and science pedagogical content knowledge (PCK) to observe the maximal impact of the PBS curriculum. We

Project-based science curricula can improve students' usable or meaningful understanding of the science content underlying a project. However, such curricula designed around "performances" wherein students design or make something do not always do this. We researched ways to design performance project-based science curricula (pPBSc) to better support the meaningful understanding of science content. Using existing curriculum design frameworks, we identified the learner's need to "create the demand" for the science content, anticipating how to use it in the performance, and to "apply" the science content, both being necessary to ensure meaningful understanding. Designing the pPBSc I, Bio we discovered how these guiding principles manifested as curriculum design challenges. We generalized from the design of I, Bio and related literature design approaches for addressing each challenge. Finally, we measured the extent to which a pPBSc incorporating these design approaches developed meaningful understanding. 652 middle grades students using I, Bio completed pre-and posttests on the science content behind the I, Bio performance. Our findings provide preliminary evidence that a pPBSc that incorporates these design approaches is consistent with gains in meaningful understanding. We discuss how the results of this work can be used to improve systematic experiments on instructional supports.

Machine-learning (ML) hardware and software system demand is burgeoning. Driven by ML applications, the number of different ML inference systems has exploded. Over 100 organizations are building ML inference chips, and the systems that incorporate existing models span at least three orders of magnitude in power consumption and four orders of magnitude in performance; they range from embedded devices to data-center solutions. Fueling the hardware are a dozen or more software frameworks and libraries. The myriad combinations of ML hardware and ML software make assessing ML-system performance in an architecture-neutral, representative, and reproducible manner challenging. There is a clear need for industry-wide standard ML benchmarking and evaluation criteria. MLPerf Inference answers that call. Driven by more than 30 organizations as well as more than 200 ML engineers and practitioners, MLPerf implements a set of rules and practices to ensure comparability across systems with wildly differing architectures. In this paper, we present the method and design principles of the initial MLPerf Inference release. The first call for submissions garnered more than 600 inference-performance measurements from 14 organizations, representing over 30 systems that show a range of capabilities.

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This review and opinion article focuses on the definitions and meanings of abnormal protein excretion in pregnancy, asking the following questions: Are our tests to determine abnormal proteinuria adequately performed? Are current guidelines for diagnosis of excessive proteinuria, especially when used to identify preeclampsia, supported by adequate data? Can the magnitude of proteinuria be used as a reliable clinical biomarker of the gravity of preeclampsia? Should timed urine collections, primarily 24-hour excretions, be supplanted by the urine protein/creatinine ratio in clinical practice? The answers to most of these questions are: We are not sure, or some guidelines are poorly supported by data and may prove erroneous. We suggest a more physiologic approach to assessment of proteinuria and believe that if clinicians and investigators looked at proteinuria in terms of how the kidney handles protein in health and disease it would lead to a more rational and evidence-based approach to proteinuria in pregnancy. Finally, we recommend that current cutoff for abnormal proteinuria be used to diagnose preeclampsia, but the level of proteinuria should not guide management. Other variables, such as status of blood pressure control, evidence of increasing organ damage in the liver and hematological systems, evidence of falling glomerular filtration rate, and signs of neurological involvement, are more reliable indicators of severity of preeclampsia.

The rate of hospital-reported medical errors in premature neonates is lower than that reported in both the adult and pediatric populations. Specific patient and organizational characteristics are associated with an increased risk of medical errors. These characteristics may help to identify opportunities to improve patient safety efforts in this vulnerable population.

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There was a significant difference in Flt1/sFlt1 immunostaining intensity when molar placentas were compared to controls. This supports a hypothesis that the phenotype of preeclampsia in molar pregnancy may result from trophoblasts overproducing at least 1 antiangiogenic protein.