197 research outputs found

    ํƒˆ๊ทผ๋Œ€ ์‹œ๋Œ€์˜ ์ „ํ›„ ๊ธฐ๋Šฅ์ฃผ์˜ ๋ฏผ์กฑ์ง€์˜ ์˜์˜ : ์ค‘๊ตญ ๋ฏผ์กฑ์ง€์˜ ํ™ฉ๊ธˆ๊ธฐ๋กœ๋ถ€ํ„ฐ์˜ ๊ตํ›ˆ

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    ์ด ๊ธ€์€ ํ•„์ž๊ฐ€ ๋น„๊ต๋ฌธํ™”์—ฐ๊ตฌ์†Œ ์ฝœ๋กœํ‚ค์—„์—์„œ ๋ฐœํ‘œํ•œ The Worth of Postwar Functionalist Ethnography in the Postmodern Age: Lessons from the Golden Age of China Ethnography๋ฅผ ๋ฒˆ์—ญํ•œ ๊ฒƒ์ด๋‹ค.1957๋…„ ๋ฒ ๋ฅด๋‚˜๋ฅด ๊ฐˆ๋ฆฐ(Bemard Gallin)๊ณผ ๋ฆฌํƒ€ ๊ฐˆ๋ฆฐ(Rita Gallin)์€ ์‹ ์‹ฑ(ๆ–ฐ่ˆˆ) ๋งˆ์„์—์„œ ํ˜„์ง€์กฐ์‚ฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜๊ธฐ ์œ„ํ•ด ์ฐฝํ™”(ๅฝฐๅŒ–)ํ˜„์— ๋„์ฐฉํ–ˆ๊ณ (Gallin 1966: 4-5), ์•„๋” ์šธํ”„(Arthur Wolf)์™€ ๋งˆ์ €๋ฆฌ ์šธํ”„(Margery Wolf)๋Š” ์‹œ์•„์‹œ์กฐ์šฐ(ไธ‹่ฅฟๅ‘จ) ๋งˆ์„์„ ์—ฐ๊ตฌํ•˜๊ธฐ ์œ„ํ•ด ํƒ€์ดํŽ˜์ด(ๅฐๅŒ—)ํ˜„์— ๋„์ฐฉํ–ˆ๋‹ค(Wolf 1968: vii). ์ค‘๊ตญ ๋ฏผ์กฑ์ง€์˜ ํ™ฉ๊ธˆ๊ธฐ๊ฐ€ ์‹œ์ž‘๋œ ์‹œ์ ์€ ์ด์™€ ๊ฐ™์ด ๊ธฐ๋ก์— ๋ช…์‹œ๋˜์–ด ์žˆ๋Š” ๋ฐ˜๋ฉด, ๊ทธ๊ฒƒ์ด ๋๋‚œ ์‹œ์ ์€ ๋ถ„๋ช…์น˜๊ฐ€ ์•Š๋‹ค. ์ค‘๊ตญ ๊ณผํ•™์›(์•„์ง ์ค‘๊ตญ ์‚ฌํšŒ๊ณผํ•™์›์œผ๋กœ ๋‚˜๋‰˜์ง€ ์•Š์•˜๋˜)์ด ์ผ๋‹จ์˜ ์„œ๊ตฌ ์‚ฌํšŒ๊ณผํ•™์ž๋“ค์—๊ฒŒ ์ค‘๊ตญ ๋†์ดŒ์— ๋Œ€ํ•œ ์‹คํ—˜์ ์ธ ํ˜„์ง€ ์กฐ์‚ฌ๋ฅผ ํ—ˆ๊ฐ€ํ•ด ์ฃผ์—ˆ๋‹ค๋Š” ๊ณต์‹ ๋ฐœํ‘œ๋ฅผ ํ† ๋Œ€๋กœ ๊ทธ ์‹œ๊ธฐ ๋ฅผ ๋Œ€๋žต 1978๋…„ ๋ง ๊ฒฝ์œผ๋กœ ์ถ”์ • ํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ทธ ๋‹น์‹œ๋Š” ๋Œ€๋งŒ์ด ์ •์น˜์ ์œผ๋กœ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ธ๋ฅ˜ํ•™์ ์œผ๋กœ๋„ ์ค‘๊ตญ์„ ๋Œ€์‹ ํ•˜๋˜ ์‹œ๊ธฐ์˜€๋‹ค. ์ค‘๊ตญ ๋ณธํ† ๋กœ๋ถ€ํ„ฐ ์ถ•์ถœ๋‹นํ•œ ์™ธ๊ตญ ์ธ๋ฅ˜ํ•™๊ณผ๋“ค(๋Œ€๋‹ค์ˆ˜๊ฐ€ ๋ฏธ๊ตญ์ธ์ด์—ˆ๋˜)์€ ์ฐจ์„ ์ฑ…์œผ๋กœ ๋Œ€๋งŒ์œผ๋กœ ์˜ฎ๊ฒจ๊ฐ”๋‹ค. ๋‹น์‹œ ๋Œ€๋งŒ์—๋Š” ์ค‘๊ตญ ๋ฌธํ™”๊ฐ€ ๊ณ„์†ํ•ด์„œ ๋ฒˆ์ฐฝํ•˜๊ณ  ์žˆ์—ˆ์„ ๋ฟ ์•„๋‹ˆ๋ผ ๊ณต์‚ฐ์ฃผ์˜์ž๋“ค์˜ ์นจํƒˆ๋„ ์กด์žฌํ•˜์ง€ ์•Š์•˜๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ํ™ฉ๊ธˆ๊ธฐ์˜ ๋Œ€๋งŒ์˜ ๋ฏผ์กฑ์ง€๋“ค์€ ๋Œ€๋งŒ์ด ์ค‘๊ตญ์œผ๋กœ ๊ฐ„์ฃผ๋˜์—ˆ๋˜ ๋‹น์‹œ์˜ ์ •์น˜์  ์ƒํ™ฉ๊ณผ ์ง์ ‘์ ์ธ ๊ด€๋ จ์ด ์—†๋Š” ์ผ๋ จ์˜ ํŽธ๊ฒฌ์„ ๋‹ด๊ณ  ์žˆ๋Š”๋ฐ, ์ด๋Ÿฌํ•œ ํŽธ๊ฒฌ์€ 80๋…„๋Œ€์™€ 90๋…„๋Œ€์˜ ๋ถˆ์™„์ „ํ•œ ํ˜๋ช…์„ ๊ฑฐ์น˜๊ธฐ ์ด์ „์˜ ์ธ๋ฅ˜ํ•™์˜ ์ด๋ก ์  ๋ถ„์œ„๊ธฐ์™€ ๋ฐ€์ ‘ํ•˜๊ฒŒ ๊ด€๋ จ๋˜์–ด ์žˆ๋Š” ๊ฒƒ์ด ๋ถ„๋ช…ํ•˜๋‹ค. ์ด๋Ÿฌํ•œ ํŽธ๊ฒฌ์—๋Š” ๋ฌธํ™”์  ์ด์ฒด์„ฑ์ด๋ผ๋Š” ์ด๋…๊ณผ ์ ์‘์ฃผ์˜(adaptationism), ๊ณต์‹œ์ฃผ์˜(synchronicism) ๋“ฑ์ด ํฌํ•จ๋œ๋‹ค. ๋‚˜๋Š” ์ด๋“ค ํŽธ๊ฒฌ๋“ค๊ณผ ์ด๋Ÿฌํ•œ ํŽธ๊ฒฌ๋“ค์„ ๋น„ํŒ์  ๊ด€์ ์œผ๋กœ ๋ฐœ์ „์‹œํ‚จ ๋ฏผ์กฑ์ง€๋“ค์„ ์ฃผ์ œ๋กœ ๋‹ค๋ฃจ์–ด ๋ณด๋ ค ๊ณ  ํ•œ๋‹ค. ํ™ฉ๊ธˆ๊ธฐ์˜ ๋ฏผ์กฑ์ง€๋“ค์„ ๋น„๋‚œํ•˜๊ธฐ ์œ„ํ•ด์„œ ์ด ๊ธ€์„ ์“ฐ๋Š” ๊ฒƒ์€ ์•„๋‹ˆ๋‹ค. ํ•˜๋ฌผ๋ฉฐ ์ด๋“ค ๋ฏผ์กฑ์ง€๋ฅผ ์ค‘๊ตญ ๊ณต์‚ฐ๋‹น์ด ์†Œ์œ„ ์—ญ์‚ฌ์  ๊ณผ์‹ค์ด๋ผ๊ณ  ๋ถˆ๋ €๋˜ ๊ฒƒ์œผ๋กœ ์น˜๋ถ€ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๋”๋”์šฑ ์•„๋‹ˆ๋‹ค. ๋‚˜๋Š” ์˜คํžˆ๋ ค ๊ฑด์„ค์ ์ธ ์„ธ ๊ฐ€์ง€ ์งˆ๋ฌธ์„ ๋˜์ง€๊ณ ์ž ํ•œ๋‹ค. ์ฒซ์งธ, ํ™ฉ๊ธˆ๊ธฐ์˜ ์ธ๋ฅ˜ํ•™์ž๋“ค์ด ๋Œ€๋งŒ์— ๊ด€ํ•œ ๊ธ€์„ ์“ฐ๋Š” ๊ณผ์ •์—์„œ ๋ฌด์—‡์ด ํฌํ•จ๋˜๊ณ  ๋ฌด์—‡์ด ๋ฐฐ์ œ๋˜์—ˆ๋Š”๊ฐ€? ์ด๋Ÿฌํ•œ ๋ฐฐ์ œ์™€ ํฌํ•จ์˜ ์œ ํ˜•์ด ์—ญ์‚ฌ์  ๋ณ€ํ™”์™€ ์—ฐ์†์— ๊ด€ํ•œ ๊ธฐ์ˆ ๊ณผ ํ•ด์„์˜ ๊ฐ€๋Šฅ์„ฑ์— ์–ด๋–ค ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”๊ฐ€? ๋‘˜์งธ, ํŠน์ •ํ•œ ์„ ํ—˜์  ๊ฐ€์ •์ด ๋‚˜ ํŽธ๊ฒฌ๊ณผ ๊ด€๋ จ์‹œ์ผœ ๋ณผ ๋•Œ ํ™ฉ๊ธˆ๊ธฐ ๋ฏผ์กฑ์ง€๋“ค์˜ ์ด๋ก ์  ๋ฌธ์ œ๋“ค์€ ๊ทธ๊ฒƒ๋“ค์„ ์ดํ•ดํ•˜๊ณ  ๋‹ค๋ฃจ๋Š” ์šฐ๋ฆฌ๋“ค ์ž์‹ ์˜ ๊ฐ€๋Šฅ์„ฑ์„ ์–ด๋–ค ์‹์œผ๋กœ ์ œ์•ฝํ•˜๋Š”๊ฐ€? ๋งˆ์ง€๋ง‰์œผ๋กœ, ์ด๋Ÿฌํ•œ ๋ฏผ์กฑ์ง€๋“ค์€ ํ”ํžˆ ์ค‘๊ตญ์ ์ธ ๊ฒƒ์œผ๋กœ ์–ธ๊ธ‰๋˜๋Š” ์ƒํ˜ธ ์—ฐ๊ด€๋œ ๋ฌธํ™”์  ๋ณ€์ดํ˜•๋“ค์˜ ๋งฅ๋ฝ ์•ˆ์—์„œ ๋Œ€๋งŒ ๋ฌธํ™”์— ๋Œ€ํ•ด ์–ด๋–ค ์‹œ์‚ฌ์ ์„ ์ œ๊ณตํ•˜๋Š”๊ฐ€

    Design and development of the second generation Mars Habitat

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    The second generation of Mars Habitat is to be utilized as an advanced permanent base for 20 crew members to live on Mars for a period of 6-12 months. It is designed to be a self-contained environment accommodating five main facilities: living, working, service, medical, and a greenhouse. The objective of the design is to create a comfortable, safe, living environment. Hexamars-2 and Lavapolis-2 are two different concepts for the advanced Mars Habitat. The design team assumes there will be an initial habitat located near or on the site from earlier missions that satisfies the requirement for a short-term habitation for the crew to use while constructing Hexamars-2 or Lavapolis-2. Prefabricated structures and materials will be shipped to the site before the long-term crew members arrive. Partial construction and preparation for the long-term habitat will be done by crew members or robotics from a previous mission. The construction of the long-term base will occur in phases. Hexamars-2 consists of six sphere-shaped inflatable modules that will be partially buried below the Martian surface. The construction of each sphere will occur in ten steps. Shape charges will be used to create the crater in which the spherical structure will be placed. The interior core will be unloaded and put into place followed by the exterior structure. The foundation will be filled, the interior bladder will be inflated, floor-to-floor joists connected, and sand pockets filled. Finally, the life support system and interior partitions are put in place. Each sphere consists of three levels of which the lower level will be safe haven. Particular attention is given to structural support, the dominance of internal pressure, the process of construction, and human factors

    Dynamic contrast-enhanced magnetic resonance imaging of tumor-induced lymph flow.

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    The growth of metastatic tumors in mice can result in markedly increased lymph flow through tumor-draining lymph nodes (LNs), which is associated with LN lymphangiogenesis. A dynamic magnetic resonance imaging (MRI) assay was developed, which uses low-molecular weight gadolinium contrast agent to label the lymphatic drainage, to visualize and quantify tumor-draining lymph flow in vivo in mice bearing metastatic melanomas. Tumor-bearing mice showed greatly increased lymph flow into and through draining LNs and into the bloodstream. Quantitative analysis established that both the amount and the rate of lymph flow through draining LNs are significantly increased in melanoma-bearing mice. In addition, the rate of appearance of contrast media in the bloodstream was significantly increased in mice bearing melanomas. These results indicate that gadolinium-based contrast-enhanced MRI provides a noninvasive assay for high-resolution spatial identification and mapping of lymphatic drainage and for dynamic measurement of changes in lymph flow associated with cancer or lymphatic dysfunction in mice. Low-molecular weight gadolinium contrast is already used for 1.5-T MRI scanning in humans, which should facilitate translation of this imaging assay

    Data-driven approach for creating synthetic electronic medical records

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    <p>Abstract</p> <p>Background</p> <p>New algorithms for disease outbreak detection are being developed to take advantage of full electronic medical records (EMRs) that contain a wealth of patient information. However, due to privacy concerns, even anonymized EMRs cannot be shared among researchers, resulting in great difficulty in comparing the effectiveness of these algorithms. To bridge the gap between novel bio-surveillance algorithms operating on full EMRs and the lack of non-identifiable EMR data, a method for generating complete and synthetic EMRs was developed.</p> <p>Methods</p> <p>This paper describes a novel methodology for generating complete synthetic EMRs both for an outbreak illness of interest (tularemia) and for background records. The method developed has three major steps: 1) synthetic patient identity and basic information generation; 2) identification of care patterns that the synthetic patients would receive based on the information present in real EMR data for similar health problems; 3) adaptation of these care patterns to the synthetic patient population.</p> <p>Results</p> <p>We generated EMRs, including visit records, clinical activity, laboratory orders/results and radiology orders/results for 203 synthetic tularemia outbreak patients. Validation of the records by a medical expert revealed problems in 19% of the records; these were subsequently corrected. We also generated background EMRs for over 3000 patients in the 4-11 yr age group. Validation of those records by a medical expert revealed problems in fewer than 3% of these background patient EMRs and the errors were subsequently rectified.</p> <p>Conclusions</p> <p>A data-driven method was developed for generating fully synthetic EMRs. The method is general and can be applied to any data set that has similar data elements (such as laboratory and radiology orders and results, clinical activity, prescription orders). The pilot synthetic outbreak records were for tularemia but our approach may be adapted to other infectious diseases. The pilot synthetic background records were in the 4-11 year old age group. The adaptations that must be made to the algorithms to produce synthetic background EMRs for other age groups are indicated.</p

    Estimating Long-Term Survival of Critically Ill Patients: The PREDICT Model

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    BACKGROUND: Long-term survival outcome of critically ill patients is important in assessing effectiveness of new treatments and making treatment decisions. We developed a prognostic model for estimation of long-term survival of critically ill patients. METHODOLOGY AND PRINCIPAL FINDINGS: This was a retrospective linked data cohort study involving 11,930 critically ill patients who survived more than 5 days in a university teaching hospital in Western Australia. Older age, male gender, co-morbidities, severe acute illness as measured by Acute Physiology and Chronic Health Evaluation II predicted mortality, and more days of vasopressor or inotropic support, mechanical ventilation, and hemofiltration within the first 5 days of intensive care unit admission were associated with a worse long-term survival up to 15 years after the onset of critical illness. Among these seven pre-selected predictors, age (explained 50% of the variability of the model, hazard ratio [HR] between 80 and 60 years old = 1.95) and co-morbidity (explained 27% of the variability, HR between Charlson co-morbidity index 5 and 0 = 2.15) were the most important determinants. A nomogram based on the pre-selected predictors is provided to allow estimation of the median survival time and also the 1-year, 3-year, 5-year, 10-year, and 15-year survival probabilities for a patient. The discrimination (adjusted c-index = 0.757, 95% confidence interval 0.745-0.769) and calibration of this prognostic model were acceptable. SIGNIFICANCE: Age, gender, co-morbidities, severity of acute illness, and the intensity and duration of intensive care therapy can be used to estimate long-term survival of critically ill patients. Age and co-morbidity are the most important determinants of long-term prognosis of critically ill patients
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