975 resultados para Simon, Jack Aaron.


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Contains the memoirs of Lt. Jack A. Simon who served as a B-17 navigator in the U.S. Army Air Force during WWII. Simon flew twenty seven missions over Germany before his plane was shot down near Derben. His internment ended when American troops liberated Simon's prisoner of war camp in Spring 1945.

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Skeletal muscle mitochondrial content and oxidative capacity are important determinants of muscle function and whole-body health. Mitochondrial content and function are enhanced by endurance exercise and impaired in states or diseases where muscle function is compromised, such as myopathies, muscular dystrophies, neuromuscular diseases, and age-related muscle atrophy. Hence, elucidating the mechanisms that control muscle mitochondrial content and oxidative function can provide new insights into states and diseases that affect muscle health. In past studies, we identified Perm1 (PPARGC1- and ESRR-induced regulator, muscle 1) as a gene induced by endurance exercise in skeletal muscle, and regulating mitochondrial oxidative function in cultured myotubes. The capacity of Perm1 to regulate muscle mitochondrial content and function in vivo is not yet known. In this study, we use adeno-associated viral (AAV) vectors to increase Perm1 expression in skeletal muscles of 4-wk-old mice. Compared to control vector, AAV1-Perm1 leads to significant increases in mitochondrial content and oxidative capacity (by 40-80%). Moreover, AAV1-Perm1-transduced muscles show increased capillary density and resistance to fatigue (by 33 and 31%, respectively), without prominent changes in fiber-type composition. These findings suggest that Perm1 selectively regulates mitochondrial biogenesis and oxidative function, and implicate Perm1 in muscle adaptations that also occur in response to endurance exercise.-Cho, Y., Hazen, B. C., Gandra, P. G., Ward, S. R., Schenk, S., Russell, A. P., Kralli, A. Perm1 enhances mitochondrial biogenesis, oxidative capacity, and fatigue resistance in adult skeletal muscle.

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Background The Global Burden of Disease, Injuries, and Risk Factor study 2013 (GBD 2013) is the first of a series of annual updates of the GBD. Risk factor quantification, particularly of modifiable risk factors, can help to identify emerging threats to population health and opportunities for prevention. The GBD 2013 provides a timely opportunity to update the comparative risk assessment with new data for exposure, relative risks, and evidence on the appropriate counterfactual risk distribution. Methods Attributable deaths, years of life lost, years lived with disability, and disability-adjusted life-years (DALYs) have been estimated for 79 risks or clusters of risks using the GBD 2010 methods. Risk–outcome pairs meeting explicit evidence criteria were assessed for 188 countries for the period 1990–2013 by age and sex using three inputs: risk exposure, relative risks, and the theoretical minimum risk exposure level (TMREL). Risks are organised into a hierarchy with blocks of behavioural, environmental and occupational, and metabolic risks at the first level of the hierarchy. The next level in the hierarchy includes nine clusters of related risks and two individual risks, with more detail provided at levels 3 and 4 of the hierarchy. Compared with GBD 2010, six new risk factors have been added: handwashing practices, occupational exposure to trichloroethylene, childhood wasting, childhood stunting, unsafe sex, and low glomerular filtration rate. For most risks, data for exposure were synthesised with a Bayesian meta-regression method, DisMod-MR 2.0, or spatial-temporal Gaussian process regression. Relative risks were based on meta-regressions of published cohort and intervention studies. Attributable burden for clusters of risks and all risks combined took into account evidence on the mediation of some risks such as high body-mass index (BMI) through other risks such as high systolic blood pressure and high cholesterol. Findings All risks combined account for 57·2% (95% uncertainty interval [UI] 55·8–58·5) of deaths and 41·6% (40·1–43·0) of DALYs. Risks quantified account for 87·9% (86·5–89·3) of cardiovascular disease DALYs, ranging to a low of 0% for neonatal disorders and neglected tropical diseases and malaria. In terms of global DALYs in 2013, six risks or clusters of risks each caused more than 5% of DALYs: dietary risks accounting for 11·3 million deaths and 241·4 million DALYs, high systolic blood pressure for 10·4 million deaths and 208·1 million DALYs, child and maternal malnutrition for 1·7 million deaths and 176·9 million DALYs, tobacco smoke for 6·1 million deaths and 143·5 million DALYs, air pollution for 5·5 million deaths and 141·5 million DALYs, and high BMI for 4·4 million deaths and 134·0 million DALYs. Risk factor patterns vary across regions and countries and with time. In sub-Saharan Africa, the leading risk factors are child and maternal malnutrition, unsafe sex, and unsafe water, sanitation, and handwashing. In women, in nearly all countries in the Americas, north Africa, and the Middle East, and in many other high-income countries, high BMI is the leading risk factor, with high systolic blood pressure as the leading risk in most of Central and Eastern Europe and south and east Asia. For men, high systolic blood pressure or tobacco use are the leading risks in nearly all high-income countries, in north Africa and the Middle East, Europe, and Asia. For men and women, unsafe sex is the leading risk in a corridor from Kenya to South Africa. Interpretation Behavioural, environmental and occupational, and metabolic risks can explain half of global mortality and more than one-third of global DALYs providing many opportunities for prevention. Of the larger risks, the attributable burden of high BMI has increased in the past 23 years. In view of the prominence of behavioural risk factors, behavioural and social science research on interventions for these risks should be strengthened. Many prevention and primary care policy options are available now to act on key risks.

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The exponential growth of studies on the biological response to ocean acidification over the last few decades has generated a large amount of data. To facilitate data comparison, a data compilation hosted at the data publisher PANGAEA was initiated in 2008 and is updated on a regular basis (doi:10.1594/PANGAEA.149999). By January 2015, a total of 581 data sets (over 4 000 000 data points) from 539 papers had been archived. Here we present the developments of this data compilation five years since its first description by Nisumaa et al. (2010). Most of study sites from which data archived are still in the Northern Hemisphere and the number of archived data from studies from the Southern Hemisphere and polar oceans are still relatively low. Data from 60 studies that investigated the response of a mix of organisms or natural communities were all added after 2010, indicating a welcomed shift from the study of individual organisms to communities and ecosystems. The initial imbalance of considerably more data archived on calcification and primary production than on other processes has improved. There is also a clear tendency towards more data archived from multifactorial studies after 2010. For easier and more effective access to ocean acidification data, the ocean acidification community is strongly encouraged to contribute to the data archiving effort, and help develop standard vocabularies describing the variables and define best practices for archiving ocean acidification data.

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Jack's Bay (the architecturalisation of memory) is a key work of the author's exhibition Lightsite, which toured Western Australian galleries from February 2006 to November 2007. It is a five-minute-long exposure photographic image captured inside a purpose-built, room-sized pinhole camera which is demountable and does not have a floor. The work depicts octogenarian Jack Morris, who for forty years held the professional salmon fishing license in the hamlet of Bremer Bay, on the SE coast of Western Australia. The pinhole camera-room is sited within sand dunes new Jack's now demolished beachside camp. Three generations of Jack's descendents stand outside the room - from his daughter to his great grand children. The light from this exterior landscape is 'projected' inside the camera-room and illuminates the interior scene which includes that part of the sand dune upon which the floorless room is erected, along with Jack who is sitting inside. The image evokes the temporality of light. Here, light itself is portrayed as the primary medium through which we both perceive and describe landscape. In this way it is through the agency of light that we construct our connectivity to landscape.

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Jack's Bay expands understandings of the role of photographic media in the representation of landscapes. It does so by combining architectural construction with B&W photographic processing techniques. A purpose-built room-sized camera obscura is first constructed over a portion of the landscape to be recorded. Photosensitive paper is applied to the interior wall surfaces and is exposed to the inverted light entering a small aperture. These photographs are subsequently developed within the camera itself and consequently 'suffer' embellishments and aberrations from the makeshift darkroom conditions. In this way the specificity of both the landscape and the event of its recording are registered in the final image. Many images were destroyed in the process. The idea of the work is to help the viewer reflect on the role media plays in our understanding of landscape and to thus question the means by which they themselves record and interpret landscape representations.

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Hot spot identification (HSID) plays a significant role in improving the safety of transportation networks. Numerous HSID methods have been proposed, developed, and evaluated in the literature. The vast majority of HSID methods reported and evaluated in the literature assume that crash data are complete, reliable, and accurate. Crash under-reporting, however, has long been recognized as a threat to the accuracy and completeness of historical traffic crash records. As a natural continuation of prior studies, the paper evaluates the influence that under-reported crashes exert on HSID methods. To conduct the evaluation, five groups of data gathered from Arizona Department of Transportation (ADOT) over the course of three years are adjusted to account for fifteen different assumed levels of under-reporting. Three identification methods are evaluated: simple ranking (SR), empirical Bayes (EB) and full Bayes (FB). Various threshold levels for establishing hotspots are explored. Finally, two evaluation criteria are compared across HSID methods. The results illustrate that the identification bias—the ability to correctly identify at risk sites--under-reporting is influenced by the degree of under-reporting. Comparatively speaking, crash under-reporting has the largest influence on the FB method and the least influence on the SR method. Additionally, the impact is positively related to the percentage of the under-reported PDO crashes and inversely related to the percentage of the under-reported injury crashes. This finding is significant because it reveals that despite PDO crashes being least severe and costly, they have the most significant influence on the accuracy of HSID.

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Recent algorithms for monocular motion capture (MoCap) estimate weak-perspective camera matrices between images using a small subset of approximately-rigid points on the human body (i.e. the torso and hip). A problem with this approach, however, is that these points are often close to coplanar, causing canonical linear factorisation algorithms for rigid structure from motion (SFM) to become extremely sensitive to noise. In this paper, we propose an alternative solution to weak-perspective SFM based on a convex relaxation of graph rigidity. We demonstrate the success of our algorithm on both synthetic and real world data, allowing for much improved solutions to marker less MoCap problems on human bodies. Finally, we propose an approach to solve the two-fold ambiguity over bone direction using a k-nearest neighbour kernel density estimator.