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Until recently, receiving a European or North American-style medical education in Southeast Asia was a profoundly transformative experience, as western conceptions of the body differed significantly from indigenous knowledge and explanations. Further, conceptions of the human body had to be translated into local languages and related to vernacular views of health, disease, and healing. Translating the Body is the first book to present the history of biomedical education across Southeast Asia. The contributors chart and analyze the organization of western medical education in Southeast Asia, public health education campaigns in the region, and the ways in which practitioners of what came to be conceived of as “traditional medicine” in many Southeast Asian countries organized themselves in response.
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Various reasons are attributed to poor student performance in physical science courses such as lack of motivation, lack of ability, and/or the overall difficulty of these courses. One overlooked reason is a lack of self-awareness as to preparation level. Through a study over a two-year period, students at all levels (freshman through M.S.) of a chemistry program were surveyed and asked to self-report predictions of their score on examinations. At all levels, strong evidence of the Kruger–Dunning effect was seen where higher performing students tended to underpredict their examination scores while the lowest performing students tended to grossly overpredict their scores.
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The article examines the gender roles imposed on women in Spain under the Franco regime. It revisits Mercè Rodoreda’s 1962 novel The Time of the Doves and deciphers the female protagonist’s silence in the context of the post-Civil War period. Drawing on Julia Kristeva’s conception of the abject, it explores how silence becomes informative of the protagonist’s mental resistance to the patriarchal imposition, the helplessness and powerlessness a common woman feels under the Franco regime, and the gradual growth of female subjectivity.
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A clear distinction between oncocytoma and chromophobe renal cell carcinoma (chRCC) is critically important for clinical management of patients. But it may often be difficult to distinguish the two entities based on hematoxylin and eosin (H and E) stained sections alone. In this study, second harmonic generation (SHG) signals which are very specific to collagen were used to image collagen fibril structure. We conduct a pilot study to develop a new diagnostic method based on the analysis of collagen associated with kidney tumors using convolutional neural networks (CNNs). CNNs comprise a type of machine learning process well-suited for drawing information out of images. This study examines a CNN model's ability to differentiate between oncocytoma (benign), and chRCC (malignant) kidney tumor images acquired with second harmonic generation (SHG), which is very specific for collagen matrix. To the best of our knowledge, this is the first study that attempts to distinguish the two entities based on their collagen structure. The model developed from this study demonstrated an overall classification accuracy of 68.7% with a specificity of 66.3% and sensitivity of 74.6%. While these results reflect an ability to classify the kidney tumors better than chance, further studies will be carried out to (a) better realize the tumor classification potential of this method with a larger sample size and (b) combining SHG with two-photon excited intrinsic fluorescence signal to achieve better classification. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.
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This paper investigates a possibility of sustainable growth in a multi-output endogenous growth framework where the capital accumulation takes place mainly through the production of the dirty manufactured goods. It is shown that in a closed economy, economic growth is not environmentally sustainable, even under an optimal pollution tax unless the consumption elasticity of substitution between clean and dirty goods approaches infinity as in a small open economy which exports dirty goods. There exists a minimal threshold level of the ratio of clean to dirty capital that ensures sustainable growth in a closed economy.
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Resonance Raman spectroscopy using 532nm excitation was used to distinguish normal brain tissue from different grades of glioma tissues. Principal component analysis was used to analyze the spectral data and achieved high accuracy. © 2018 The Author(s).
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