삼출성 중이염 소아의 중이액에서 폐구균의 분자적 진단 (Molecular Diagnosis of Streptococcus pneumoniae in Middle Ear Fluids from Children with Otitis Media with Effusion)
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- Pediatric Infection and Vaccine
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- 제22권2호
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- pp.106-112
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- 2015
목적: 장기간의 항생제 치료는 중이염 어린이 환자의 중이액으로부터 원인균이 배양되는 것을 방해한다. 본 연구는 배양 음성 중이액으로부터 분자적 진단에 의한 신속한 균 검출 가능성 여부를 확인하고자 하였다. 방법: 폐구균 lytA 유전자를 표적으로 하는 PCR과 LAMP로 민감도와 특이도를 비교 결정하고, 임상중이액에서의 폐구균 검출에 적용하였다. 결과: PCR 기법에 의한 폐구균 검출 최소한계는 약
간 전이 암은 이전에는 수술을 통한 외과적 절제가 주요 치료기법이었지만 방사선 치료 기법의 발전으로 인해 점차 방사선치료의 시행이 늘어나고 있다. 18F-FDG PET 영상은 간 전이 암 진단 시 더욱 우세한 민감도와 특이도를 보이며, 치료계획용 CT 영상과 더불어 종양조직의 위치를 정의하는 중요한 영상장비로 자리매김하고 있다. 본 연구에서는 간 전이 암의 18F-FDG PET 영상에 나타난 종양영역을 영상분할기법 적용하였으며 PET영상의 여러 인자들이 영상분할기법들에 미치는 영향을 알아보았다. 2009년부터 2012년까지 방사선 치료를 받은 간전이 환자들 중 18F-FDG PET/CT 촬영을 시행한 13명의 환자들의 치료계획용 CT와 PET/CT 영상을 얻었다. 그 뒤 PET 영상의 관심영역을 설정하기 위하여 3가지 영상 분할 기법인 상대적문턱기법, 기울기기법, 영역성장기법을 적용하였다. 이 결과들을 바탕으로 GTV와 각 영상 기법으로 구현된 종양 영역과 부피 비교를 시행하였으며 영상 분할 기법에 영향을 미치는 영상인자들과의 관계를 회귀 분석하였다. GTV (Gross Tumor Volume)의 평균 부피는
삼각주는 하천이 운반하던 퇴적물이 호수나 바다, 하천의 본류와 같은 상대적으로 저 에너지의 환경을 만나 쌓인 퇴적 지형이다. 그 중 하천의 합류 지역에 생긴 삼각주는 하천 기하와 수리학적 특성에 큰 영향을 줄 수 있기 때문에 하천 관리 및 연구에서 중요하게 다루어진다. 최근 낙동강 내 대규모 준설과 보 건설로 하천 합류 지역의 평형상태가 깨지고 있다. 하지만 하천의 자연 회복성으로 인한 지속적인 퇴적으로, 인위적인 준설 이전의 자연상태로 되돌아가고 있다. 시계열 관측 결과 합류 지역의 삼각주는 준설 이후 지속적으로 성장하다가, 일정 크기에 도달하면 전반적인 크기의 변화없이 소규모의 성장과 후퇴를 반복하는 동적 평형상태에 이른다. 본 연구에서는 합류 지역 삼각주가 지류의 유사량과 본류의 유량에 따라 체결된 동적 평형 상태에 도달한다는 가정을 바탕으로 합류 지역의 퇴적-침식 작용을 설명하는 모델을 개발하였다. 모델은 지류 공급 퇴적물의 퇴적과 본류로 인한 침식 작용, 두 가지 기작을 토대로 한다. 모델에 사용된 낙동강을 대표하는 침식 계수는 낙동강 내 주된 합류 지역을 이용하여 추정했다. 개발된 모델을 이용하여 지류 유사량과 본류 유량에 따른 합류 지역 삼각주 평형 위치의 민감도 분석을 수행했고, 이후 주된 합류 지역의 연평균 유량, 유사량 데이터를 활용하여 낙동강 합류부 삼각주들의 동적 평형 위치를 예측하였다. 마지막으로 감천-낙동강에 기록된 일별 유량과 유사량 데이터를 활용하여 감천-낙동강 삼각주의 발달에 대한 모의 실험을 진행하였다. 모델을 통해 각 합류부 삼각주의 형성 여부를 예측하였고, 감천-낙동강 삼각주의 거동의 경향 또한 잘 예측하였지만, 단순화 과정에서 발생한 오차와 한계로 인해 감천-낙동강 삼각주에서 실제 발생하는 후퇴를 정확히 예측하지는 못하였다. 본 연구 결과는 합류 지역을 통한 낙동강 본류의 유사 공급량에 대한 기초 정보를 제공하여 하천 정비 및 유지에 기초 모델로 사용할 수 있다.
본 연구는 학생들의 생산적인 과학 참여에 영향을 미치는 과학공감의 역할을 조사하고, 해당 과정에서 나타나는 과학공감의 주요 요소를 분석하는 것을 목표로 하였다. 초등학교 5학년 학생 12명이 본 연구에 참여하였고, 일반공감 능력을 기준으로 세 집단으로 나누어 디자인적 사고 과정을 적용한 생산적 과학 참여 수업을 진행하였다. 문제 해결의 다섯 단계 중 아이디어 생성과 프로토타입에서 소집단의 발화를 분석하여 과학공감과 과학실천 측면에서 관찰하였다. 분석결과, 디자인적 사고 과정을 효과적으로 적용한 과학 공감은 학생들의 생산적 과학 참여를 촉진하는 것으로 나타났다. 이에 세 집단의 과학공감 및 과학실천의 평균 발화 빈도가 아이디어 생성 단계에서는 증가한 후 감소하는 경향이 나타났으며, 프로토타입 단계에서는 후반부에 오히려 발화 빈도가 증가했다. 그러나 일반공감 능력이 낮은 소집단은 프로토타입 단계에서 협력적 의사소통의 결여로 과학공감과 과학실천의 발화 빈도가 감소하는 것으로 관찰되었다. 뿐만 아니라, 학습자들은 생산적인 과학 참여 상황에서 5가지 주요 과학공감 구성요소가 모두 발화로 드러났다. 높은 일반공감 능력 집단에서는 타인과의 과학공감 요소인 공감적 이해와 공감적 걱정이 높은 빈도로 나타났으나, 낮은 일반공감 능력 집단에서는 연구대상과의 과학공감 요소인 민감성, 과학적 상상, 상황적 관심이 상대적으로 더 높은 빈도로 관찰되었다. 이러한 결과는 과학 수업에서 타인뿐만 아니라 연구대상과의 과학적으로 공감하는 경험이, 일반적인 공감 능력과는 별개의 중요한 특성으로 학생들의 생산적 과학 활동을 다양한 참여를 유발하며 이를 지속하게 할 수 있음을 시사한다. 본 연구는 과학공감이 학생들의 생산적 과학 참여에 다양한 방향으로 영향을 미칠 수 있다는 가능성을 제시하여, 과학 수업에서 과학공감의 이론적 구조와 안정성에 대한 논의에 기여할 것으로 판단된다.
누구나 뉴스와 주가 사이에는 밀접한 관계를 있을 것이라 생각한다. 그래서 뉴스를 통해 투자기회를 찾고, 투자이익을 얻을 수 있을 것으로 기대한다. 그렇지만 너무나 많은 뉴스들이 실시간으로 생성 전파되며, 정작 어떤 뉴스가 중요한지, 뉴스가 주가에 미치는 영향은 얼마나 되는지를 알아내기는 쉽지 않다. 본 연구는 이러한 뉴스들을 수집 분석하여 주가와 어떠한 관련이 있는지 분석하였다. 뉴스는 그 속성상 특정한 양식을 갖지 않는 비정형 텍스트로 구성되어있다. 이러한 뉴스 컨텐츠를 분석하기 위해 오피니언 마이닝이라는 빅데이터 감성분석 기법을 적용하였고, 이를 통해 주가지수의 등락을 예측하는 지능형 투자의사결정 모형을 제시하였다. 그리고, 모형의 유효성을 검증하기 위하여 마이닝 결과와 주가지수 등락 간의 관계를 통계 분석하였다. 그 결과 뉴스 컨텐츠의 감성분석 결과값과 주가지수 등락과는 유의한 관계를 가지고 있었으며, 좀 더 세부적으로는 주식시장 개장 전 뉴스들과 주가지수의 등락과의 관계 또한 통계적으로 유의하여, 뉴스의 감성분석 결과를 이용해 주가지수의 변동성 예측이 가능할 것으로 판단되었다. 이렇게 도출된 투자의사결정 모형은 여러 유형의 뉴스 중에서 시황 전망 해외 뉴스가 주가지수 변동을 가장 잘 예측하는 것으로 나타났고 로지스틱 회귀분석결과 분류정확도는 주가하락 시 70.0%, 주가상승 시 78.8%이며 전체평균은 74.6%로 나타났다.
The objective of this research is to test the feasibility of developing a statewide truck traffic forecasting methodology for Wisconsin by using Origin-Destination surveys, traffic counts, classification counts, and other data that are routinely collected by the Wisconsin Department of Transportation (WisDOT). Development of a feasible model will permit estimation of future truck traffic for every major link in the network. This will provide the basis for improved estimation of future pavement deterioration. Pavement damage rises exponentially as axle weight increases, and trucks are responsible for most of the traffic-induced damage to pavement. Consequently, forecasts of truck traffic are critical to pavement management systems. The pavement Management Decision Supporting System (PMDSS) prepared by WisDOT in May 1990 combines pavement inventory and performance data with a knowledge base consisting of rules for evaluation, problem identification and rehabilitation recommendation. Without a r.easonable truck traffic forecasting methodology, PMDSS is not able to project pavement performance trends in order to make assessment and recommendations in the future years. However, none of WisDOT's existing forecasting methodologies has been designed specifically for predicting truck movements on a statewide highway network. For this research, the Origin-Destination survey data avaiiable from WisDOT, including two stateline areas, one county, and five cities, are analyzed and the zone-to'||'&'||'not;zone truck trip tables are developed. The resulting Origin-Destination Trip Length Frequency (00 TLF) distributions by trip type are applied to the Gravity Model (GM) for comparison with comparable TLFs from the GM. The gravity model is calibrated to obtain friction factor curves for the three trip types, Internal-Internal (I-I), Internal-External (I-E), and External-External (E-E). ~oth "macro-scale" calibration and "micro-scale" calibration are performed. The comparison of the statewide GM TLF with the 00 TLF for the macro-scale calibration does not provide suitable results because the available 00 survey data do not represent an unbiased sample of statewide truck trips. For the "micro-scale" calibration, "partial" GM trip tables that correspond to the 00 survey trip tables are extracted from the full statewide GM trip table. These "partial" GM trip tables are then merged and a partial GM TLF is created. The GM friction factor curves are adjusted until the partial GM TLF matches the 00 TLF. Three friction factor curves, one for each trip type, resulting from the micro-scale calibration produce a reasonable GM truck trip model. A key methodological issue for GM. calibration involves the use of multiple friction factor curves versus a single friction factor curve for each trip type in order to estimate truck trips with reasonable accuracy. A single friction factor curve for each of the three trip types was found to reproduce the 00 TLFs from the calibration data base. Given the very limited trip generation data available for this research, additional refinement of the gravity model using multiple mction factor curves for each trip type was not warranted. In the traditional urban transportation planning studies, the zonal trip productions and attractions and region-wide OD TLFs are available. However, for this research, the information available for the development .of the GM model is limited to Ground Counts (GC) and a limited set ofOD TLFs. The GM is calibrated using the limited OD data, but the OD data are not adequate to obtain good estimates of truck trip productions and attractions .. Consequently, zonal productions and attractions are estimated using zonal population as a first approximation. Then, Selected Link based (SELINK) analyses are used to adjust the productions and attractions and possibly recalibrate the GM. The SELINK adjustment process involves identifying the origins and destinations of all truck trips that are assigned to a specified "selected link" as the result of a standard traffic assignment. A link adjustment factor is computed as the ratio of the actual volume for the link (ground count) to the total assigned volume. This link adjustment factor is then applied to all of the origin and destination zones of the trips using that "selected link". Selected link based analyses are conducted by using both 16 selected links and 32 selected links. The result of SELINK analysis by u~ing 32 selected links provides the least %RMSE in the screenline volume analysis. In addition, the stability of the GM truck estimating model is preserved by using 32 selected links with three SELINK adjustments, that is, the GM remains calibrated despite substantial changes in the input productions and attractions. The coverage of zones provided by 32 selected links is satisfactory. Increasing the number of repetitions beyond four is not reasonable because the stability of GM model in reproducing the OD TLF reaches its limits. The total volume of truck traffic captured by 32 selected links is 107% of total trip productions. But more importantly, ~ELINK adjustment factors for all of the zones can be computed. Evaluation of the travel demand model resulting from the SELINK adjustments is conducted by using screenline volume analysis, functional class and route specific volume analysis, area specific volume analysis, production and attraction analysis, and Vehicle Miles of Travel (VMT) analysis. Screenline volume analysis by using four screenlines with 28 check points are used for evaluation of the adequacy of the overall model. The total trucks crossing the screenlines are compared to the ground count totals. L V/GC ratios of 0.958 by using 32 selected links and 1.001 by using 16 selected links are obtained. The %RM:SE for the four screenlines is inversely proportional to the average ground count totals by screenline .. The magnitude of %RM:SE for the four screenlines resulting from the fourth and last GM run by using 32 and 16 selected links is 22% and 31 % respectively. These results are similar to the overall %RMSE achieved for the 32 and 16 selected links themselves of 19% and 33% respectively. This implies that the SELINICanalysis results are reasonable for all sections of the state.Functional class and route specific volume analysis is possible by using the available 154 classification count check points. The truck traffic crossing the Interstate highways (ISH) with 37 check points, the US highways (USH) with 50 check points, and the State highways (STH) with 67 check points is compared to the actual ground count totals. The magnitude of the overall link volume to ground count ratio by route does not provide any specific pattern of over or underestimate. However, the %R11SE for the ISH shows the least value while that for the STH shows the largest value. This pattern is consistent with the screenline analysis and the overall relationship between %RMSE and ground count volume groups. Area specific volume analysis provides another broad statewide measure of the performance of the overall model. The truck traffic in the North area with 26 check points, the West area with 36 check points, the East area with 29 check points, and the South area with 64 check points are compared to the actual ground count totals. The four areas show similar results. No specific patterns in the L V/GC ratio by area are found. In addition, the %RMSE is computed for each of the four areas. The %RMSEs for the North, West, East, and South areas are 92%, 49%, 27%, and 35% respectively, whereas, the average ground counts are 481, 1383, 1532, and 3154 respectively. As for the screenline and volume range analyses, the %RMSE is inversely related to average link volume. 'The SELINK adjustments of productions and attractions resulted in a very substantial reduction in the total in-state zonal productions and attractions. The initial in-state zonal trip generation model can now be revised with a new trip production's trip rate (total adjusted productions/total population) and a new trip attraction's trip rate. Revised zonal production and attraction adjustment factors can then be developed that only reflect the impact of the SELINK adjustments that cause mcreases or , decreases from the revised zonal estimate of productions and attractions. Analysis of the revised production adjustment factors is conducted by plotting the factors on the state map. The east area of the state including the counties of Brown, Outagamie, Shawano, Wmnebago, Fond du Lac, Marathon shows comparatively large values of the revised adjustment factors. Overall, both small and large values of the revised adjustment factors are scattered around Wisconsin. This suggests that more independent variables beyond just 226; population are needed for the development of the heavy truck trip generation model. More independent variables including zonal employment data (office employees and manufacturing employees) by industry type, zonal private trucks 226; owned and zonal income data which are not available currently should be considered. A plot of frequency distribution of the in-state zones as a function of the revised production and attraction adjustment factors shows the overall " adjustment resulting from the SELINK analysis process. Overall, the revised SELINK adjustments show that the productions for many zones are reduced by, a factor of 0.5 to 0.8 while the productions for ~ relatively few zones are increased by factors from 1.1 to 4 with most of the factors in the 3.0 range. No obvious explanation for the frequency distribution could be found. The revised SELINK adjustments overall appear to be reasonable. The heavy truck VMT analysis is conducted by comparing the 1990 heavy truck VMT that is forecasted by the GM truck forecasting model, 2.975 billions, with the WisDOT computed data. This gives an estimate that is 18.3% less than the WisDOT computation of 3.642 billions of VMT. The WisDOT estimates are based on the sampling the link volumes for USH, 8TH, and CTH. This implies potential error in sampling the average link volume. The WisDOT estimate of heavy truck VMT cannot be tabulated by the three trip types, I-I, I-E ('||'&'||'pound;-I), and E-E. In contrast, the GM forecasting model shows that the proportion ofE-E VMT out of total VMT is 21.24%. In addition, tabulation of heavy truck VMT by route functional class shows that the proportion of truck traffic traversing the freeways and expressways is 76.5%. Only 14.1% of total freeway truck traffic is I-I trips, while 80% of total collector truck traffic is I-I trips. This implies that freeways are traversed mainly by I-E and E-E truck traffic while collectors are used mainly by I-I truck traffic. Other tabulations such as average heavy truck speed by trip type, average travel distance by trip type and the VMT distribution by trip type, route functional class and travel speed are useful information for highway planners to understand the characteristics of statewide heavy truck trip patternS. Heavy truck volumes for the target year 2010 are forecasted by using the GM truck forecasting model. Four scenarios are used. Fo~ better forecasting, ground count- based segment adjustment factors are developed and applied. ISH 90 '||'&'||' 94 and USH 41 are used as example routes. The forecasting results by using the ground count-based segment adjustment factors are satisfactory for long range planning purposes, but additional ground counts would be useful for USH 41. Sensitivity analysis provides estimates of the impacts of the alternative growth rates including information about changes in the trip types using key routes. The network'||'&'||'not;based GMcan easily model scenarios with different rates of growth in rural versus . . urban areas, small versus large cities, and in-state zones versus external stations. cities, and in-state zones versus external stations.
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70
The wall shear stress in the vicinity of end-to end anastomoses under steady flow conditions was measured using a flush-mounted hot-film anemometer(FMHFA) probe. The experimental measurements were in good agreement with numerical results except in flow with low Reynolds numbers. The wall shear stress increased proximal to the anastomosis in flow from the Penrose tubing (simulating an artery) to the PTFE: graft. In flow from the PTFE graft to the Penrose tubing, low wall shear stress was observed distal to the anastomosis. Abnormal distributions of wall shear stress in the vicinity of the anastomosis, resulting from the compliance mismatch between the graft and the host artery, might be an important factor of ANFH formation and the graft failure. The present study suggests a correlation between regions of the low wall shear stress and the development of anastomotic neointimal fibrous hyperplasia(ANPH) in end-to-end anastomoses. 30523 T00401030523 ^x Air pressure decay(APD) rate and ultrafiltration rate(UFR) tests were performed on new and saline rinsed dialyzers as well as those roused in patients several times. C-DAK 4000 (Cordis Dow) and CF IS-11 (Baxter Travenol) reused dialyzers obtained from the dialysis clinic were used in the present study. The new dialyzers exhibited a relatively flat APD, whereas saline rinsed and reused dialyzers showed considerable amount of decay. C-DAH dialyzers had a larger APD(11.70