Background: Surgical mortality data are collected routinely in high-income countries, yet virtually no low-or middle-income countries have outcome surveillance in place. The aim was prospectively to collect worldwide mortality data following emergency abdominal surgery, comparing findings across countries with a low, middle or high Human Development Index (HDI).Methods: This was a prospective, multicentre, cohort study. Self-selected hospitals performing emergency surgery submitted prespecified data for consecutive patients from at least one 2-week interval during July to December 2014. Postoperative mortality was analysed by hierarchical multivariable logistic regression.
This work was designed to evaluate the role of intestinal parasites on nutritional status in three rural areas of Brazil. A total of 520 children aged 1-12 years were studied through a questionnaire concerning housing, socio-economic conditions and a 24-h food intake recall. Measurements of weight and height were also performed, and three stool samples were collected on consecutive days for parasitological analysis. Scores of the standard deviation (z-scores) for the weight-for-height and height-for-age were used to characterise the growth profile. A high prevalence of intestinal parasites was detected, with Giardia lamblia (44%), Endolimax nana (43%), Ascaris lumbricoides (41%) and Trichuris trichiura (40%) being the most prevalent. Eleven per cent of the children were classified as showing stunting. Inadequate daily caloric intake was observed in 78% of the population and the proportion of those with inadequate protein intake was 34%. Logistic regression analysis was employed for the multivariate study. Stunting was significantly associated with estimators of low economic income, inadequate protein intake and polyparasitism, especially the association between Ascaris lumbricoides and Trichuris trichiura.
No abstract
Concentric tube robots have the potential to enable new minimally invasive surgical procedures by curving around anatomical obstacles to reach difficult-to-reach sites in body cavities. Planning motions for these devices is challenging in part due to their complex kinematics; concentric tube robots are composed of thin, pre-curved, telescoping tubes that can achieve a variety of shapes via extension and rotation of each of their constituent tubes. We introduce a new motion planner to maneuver these devices to clinical targets while minimizing the probability of colliding with anatomical obstacles. Unlike prior planners for these devices, we more accurately model device shape using mechanics-based models that consider torsional interaction between the tubes. We also account for the effects of uncertainty in actuation and predicted device shape. We integrate these models with a sampling-based approach based on the Rapidly-Exploring Roadmap to guarantee finding optimal plans as computation time is allowed to increase. We demonstrate our motion planner in simulation using a variety of evaluation scenarios including an anatomy-based neurosurgery case that requires maneuvering to a difficult-to-reach brain tumor at the skull base.
Under real and continuously improving manufacturing conditions, lithography hotspot detection faces several key challenges. First, real hotspots become less but harder to fix at post-layout stages; second, false alarm rate must be kept low to avoid excessive and expensive post-processing hotspot removal; third, full chip physical verification and optimization require fast turn-around time. To address these issues, we propose a high performance lithographic hotspot detection flow with ultra-fast speed and high fidelity. It consists of a novel set of hotspot signature definitions and a hierarchically refined detection flow with powerful machine learning kernels, ANN (artificial neural network) and SVM (support vector machine). We have implemented our algorithm with industry-strength engine under real manufacturing conditions in 45nm process, and showed that it significantly outperforms previous state-of-the-art algorithms in hotspot detection false alarm rate (2.4X to 2300X reduction) and simulation run-time (5X to 237X reduction), meanwhile archiving similar or slightly better hotspot detection accuracies. Such high performance lithographic hotspot detection under real manufacturing conditions is especially suitable for guiding lithography friendly physical design.
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