ObjectiveThe aim of the present study was to assess the prevalence of hypertension and cardiovascular risk factors among the native indigenous of Jaguapiru village in Dourados, Mato Grosso do Sul, Brazil.MethodA cross-sectional, population-based study was conducted with adult indigenous aged 18 years or more. The subjects' blood pressure was measured twice, and the mean of the two measurements was calculated. Body weight, height, capillary blood glucose and waist circumference were measured. Pregnant women, individuals using glucocorticoids, and non-indigenous villagers and their offspring were excluded. Multivariate regression analyses were conducted on the socio-demographic and clinical independent variables. Interactions between independent variables were also tested.ResultsWe included 1,608 native indigenous eligible to the research. The prevalence of hypertension was 29.5% (95% CI: 27–31.5), with no significant difference between the genders. For both men and women, diastolic hypertension was more common than systolic hypertension. The prevalence of hypertension was higher among obese, diabetic, and older participants, as well as those who consumed alcohol, had a lower educational level, or had a family history of hypertension. There was no association between hypertension and tobacco smoking or family income.ConclusionHypertension among the indigenous from Jaguapiru village was similar to the prevalence in the Brazilians, but may have a more negative effect in such disadvantaged population. The associated factors we found can help drawing prevention policies.

The prevalence of DM and impaired glucose tolerance was lower in this sample compared to the Brazilian population. However, the prevalence of obesity was higher, and that of hypertension was similar. Nutritional guidance and encouragement of physical activity are recommended in Jaguapiru as preventive measures for DM.

Objective: To estimate the prevalence of obesity and overweight and associated factors in indigenous people of the Jaguapiru village in Central Brazil. Methods: We conducted a population-based cross-sectional study between January 2009 and July 2011 in the adult native population of the Jaguapiru village, Central Brazil. Sociodemographic and lifestyle data were obtained; anthropometric measures, arterial blood pressure, and blood glucose were measured. The independent variables were tested by Poisson regression, and the interactions between them were analyzed. Results: 1,608 indigenous people (982 females, mean age 37.7 ± 15.1 years) were included. The prevalence of obesity was 23.2% (95% CI 20.9-25.1%). Obesity was more prevalent among 40- to 49-year-old and overweight among 50- to 59-year-old persons. Obesity was positively associated with female sex, higher income, and hypertension. Among indigenous people, interactions were found with hypertension and sedentary lifestyle - hypertension in males and sedentary lifestyle in females. Conclusions: The prevalence of obesity and overweight in indigenous people of the Jaguapiru village is high. Males as well as hypertensive and higher family income individuals have higher rates. Sedentary lifestyle and hypertension leverage the rates of obesity. Prevention and adequate public health policies can be critical for the control of excess weight and its comorbidities among Brazilian indigenous people.

Data movement between the CPU and main memory is a first-order obstacle against improving performance, scalability, and energy efficiency in modern systems. Computer systems employ a range of techniques to reduce overheads tied to data movement, spanning from traditional mechanisms (e.g., deep multi-level cache hierarchies, aggressive hardware prefetchers) to emerging techniques such as Near-Data Processing (NDP), where some computation is moved close to memory. Prior NDP works investigate the root causes of data movement bottlenecks using different profiling methodologies and tools. However, there is still a lack of understanding about the key metrics that can identify different data movement bottlenecks and their relation to traditional and emerging data movement mitigation mechanisms. Our goal is to methodically identify potential sources of data movement over a broad set of applications and to comprehensively compare traditional compute-centric data movement mitigation techniques (e.g., caching and prefetching) to more memory-centric techniques (e.g., NDP), thereby developing a rigorous understanding of the best techniques to mitigate each source of data movement.With this goal in mind, we perform the first large-scale characterization of a wide variety of applications, across a wide range of application domains, to identify fundamental program properties that lead to data movement to/from main memory. We develop the first systematic methodology to classify applications based on the sources contributing to data movement bottlenecks. From our large-scale characterization of 77K functions across 345 applications, we select 144 functions to form the first open-source benchmark suite (DAMOV) for main memory data movement studies. We select a diverse range of functions that (1) represent different types of data movement bottlenecks, and (2) come from a wide range of application domains. Using NDP as a case study, we identify new insights about the different data movement bottlenecks and use these insights to determine the most suitable data movement mitigation mechanism for a particular application. We open-source DAMOV and the complete source code for our new characterization methodology at https://github.com/CMU-SAFARI/DAMOV. CCS Concepts: • Hardware → Dynamic memory; • Computing methodologies → Model development and analysis; • Computer systems organization → Architectures.

Ischemic preconditioning or pentoxifylline alone protect the intestinal mucosa from ischemia/reperfusion injury. However, they do not have a synergistic effect when applied together.

Stencil computation is one of the most used kernels in a wide variety of scientific applications, ranging from large-scale weather prediction to solving partial differential equations. Stencil computations are characterized by three unique properties: (1) low arithmetic intensity, (2) limited temporal data reuse, and (3) regular and predictable data access pattern. As a result, stencil computations are typically bandwidth-bound workloads, which only experience limited benefits from the deep cache hierarchy of modern CPUs.In this work, we propose Casper, a near-cache accelerator consisting of specialized stencil compute units connected to the lastlevel cache (LLC) of a traditional CPU. Casper is based on two key ideas: (1) avoiding the cost of moving rarely reused data through the cache hierarchy, and (2) exploiting the regularity of the data accesses and the inherent parallelism of the stencil computation to increase the overall performance. With minimal changes in LLC address decoding logic and data placement, Casper performs stencil computations at the peak bandwidth of the LLC. We show that, by tightly coupling lightweight stencil compute units near to LLC, Casper improves performance of stencil kernels by 1.65× on average, while reducing the energy consumption by 35% compared to a commercial high-performance multi-core processor. Moreover, Casper provides a 37× improvement in performance-per-area compared to a state-of-the-art GPU.

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