27 research outputs found

    Algorithm to estimate daily PAR at the ocean surface from GOCI data: description and evaluation

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    Photosynthetically available radiation (PAR) reaching the ocean surface controls phytoplankton growth, primary productivity, and evolution within marine ecosystems. Therefore, accurate daily PAR estimates are important for a broad range of marine biology and biogeochemistry applications. In this study, hourly data from Geostationary Ocean Color Imager (GOCI), the world’s first geostationary ocean color sensor, was employed to estimate daily mean PAR at the ocean surface around the Korean Peninsula using a budget model based on plane-parallel theory. In situ PAR data collected from two ocean research stations (Socheong-cho and Ieodo) were used to evaluate the accuracy of the GOCI PAR estimates. First, the instantaneous in situ measurements were checked for calibration and exposure errors against Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer calculations under the clearest sky conditions and adjusted to eliminate biases. After adjustment, the root-means-square error (RMSE) between 6S and in situ PAR data was reduced from 6.08 (4.81%) and 3.82 (3.93%) mol/m2/day to 2.85 (2.26%) and 1.74 (1.21%) mol/m2/day at the Socheong-cho and Ieodo stations, respectively, and the coefficient of determination R2 was 0.99. Then, the GOCI daily mean PAR estimated by the initial algorithm were corrected using the 2015 adjusted in situ daily PAR measurements collected under clear-sky conditions. The daily mean PAR values derived from GOCI data in all conditions were improved after the correction, with RMSE reduced from 4.58 (8.30%) to 2.57 (4.65%) mol/m2/day and R2 = 0.97. The comparison statistics were similar for 2015 and 2016 combined, with RMSE of 2.52 (4.38%) and mean bias error (MBE) of –0.40 (–0.70%), indicating that the correction was also effective in cloudy conditions. On the other hand, daily PAR estimates from Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Himawari Imager (AHI) yielded larger RMSE of 6.24 (10.40%) mol/m2/day and MBE of –2.49 (–4.15%) mol/m2/day (MODIS) and RMSE of 3.71 (6.51%) mol/m2/day and MBE of –2.65 (–4.65%) mol/m2/day (AHI) against in situ measurements. The GOCI-based daily PAR model developed in this study is reliable and suitable for investigating the marine environment around the Korean Peninsula

    Chlorophyll and Total Suspended Materials Concentrations and Remote Sensing Reflectance Data measured at the Red Tide Area of Jinhae, Geoje, and East Sea during August from 1998 to 2003 and August 2013

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    The chlorophyll and total suspended materials concentrations and remote sensing reflectance data were observed for red tides occurring every summer in waters around the Korean Peninsula. In observation area and date, the field survey were performed (1) in the Jinhae and Geoje coasts during August 1998, August 1999, August 2001, and August 2003, (2) in East Sea coast during August 2013. The remote sensing reflectance data were obtained from portable spectroradiometer. The chlorophyll concentration data were obtained from spectrophotometric method and the total suspended materials concentration data were obtained from filter-weight difference method. The remote sensing reflectance data were validated using Moon et al.(2012). The chlorophyll concentration data were validated using baseline correction and subtraction of 750 nm value, and the total suspended materials concentration data were validated using variation of humidity

    Nomogram to Predict Insignificant Prostate Cancer at Radical Prostatectomy in Korean Men: A Multi-Center Study

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    PURPOSE: Due to the availability of serum prostate specific antigen (PSA) testing, the detection rate of insignificant prostate cancer (IPC) is increasing. To ensure better treatment decisions, we developed a nomogram to predict the probability of IPC. MATERIALS AND METHODS: The study population consisted of 1,471 patients who were treated at multiple institutions by radical prostatectomy without neoadjuvant therapy from 1995 to 2008. We obtained nonrandom samples of n = 1,031 for nomogram development, leaving n = 440 for nomogram validation. IPC was defined as pathologic organ-confined disease and a tumor volume of 0.5 cc or less without Gleason grade 4 or 5. Multivariate logistic regression model (MLRM) coefficients were used to construct a nomogram to predict IPC from five variables, including serum prostate specific antigen, clinical stage, biopsy Gleason score, positive cores ratio and maximum % of tumor in any core. The performance characteristics were internally validated from 200 bootstrap resamples to reduce overfit bias. External validation was also performed in another cohort. RESULTS: Overall, 67 (6.5%) patients had a so-called "insignificant" tumor in nomogram development cohort. PSA, clinical stage, biopsy Gleason score, positive core ratio and maximum % of biopsy tumor represented significant predictors of the presence of IPC. The resulting nomogram had excellent discrimination accuracy, with a bootstrapped concordance index of 0.827. CONCLUSION: Our current nomogram provides sufficiently accurate information in clinical practice that may be useful to patients and clinicians when various treatment options for screen-detected prostate cancer are consideredope

    Clinical Significance of a Large Difference (≥ 2 points) between Biopsy and Post-prostatectomy Pathological Gleason Scores in Patients with Prostate Cancer

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    We investigated the clinical significance of large difference (≥ 2 points) between biopsy-derived (bGS) and post-prostatectomy Gleason scores (pGS). At 14 medical centers in Korea, 1,582 men who underwent radical prostatectomy for prostate cancer were included. According to the difference between bGS and pGS, the patients were divided into three groups: A (decreased in pGS ≥ 2, n = 30), B (changed in pGS ≤ 1, n = 1,361; control group), and C (increased in pGS ≥ 2, n = 55). We evaluated various clinicopathological factors of prostate cancer and hazards for biochemical failure. Group A showed significantly higher mean maximal percentage of cancer in the positive cores (max%) and pathological T stage than control. In group C, the number of biopsy core was significantly smaller, however, tumor volume and max% were significantly higher and more positive biopsy cores were presented than control. Worse pathological stage and more margin-positive were observed in group A and C than in control. Hazard ratio for biochemical failure was also higher in group A and C (P = 0.001). However, the groups were not independent factors in multivariate analysis. In conclusion, large difference between bGS and pGS shows poor prognosis even in the decreased group. However it is not an independent prognostic factor for biochemical failure

    Pigmented Villonodular Synovitis of the Ankle Joint

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    Dislocation after Hip Arthroplasty: Analysis of the Cause Factors

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    Risk of overhydration and low lean tissue index as measured using a body composition monitor in patients on hemodialysis: a systemic review and meta-analysis

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    Overhydration and sarcopenia, related to an individual’s nutritional status, have been associated with increased cardiovascular mortality and poor prognosis in patients on hemodialysis. The purpose of this study was to investigate the prediction of overhydration and sarcopenia on mortality in patients on hemodialysis using a body composition monitor. We conducted a systematic review and meta-analysis using a random-effects model. We searched the Cochrane Central Register, OVID MEDLINE, EMBASE and PubMed databases for all studies published prior to December 9, 2016 and reviewed the reference lists of relevant reviews, registered trials and relevant conference proceedings. The overhydration group (fluid excess, >15% vs. the normohydration group) and the low lean tissue index group ( <10%) were compared with a reference group. Six trials, consisting of 29,469 patients, were included in the pooled analysis. The pooled hazard ratio for overall survival of the overhydration group, compared with the reference normohydration group was 1.798 (95% confidence interval [CI]: 1.53–2.804, p = .001). The hazard ratio for mortality in the low lean tissue index group was 1.533 (95% CI, 1.411–1.644; p = .001) in the random-effects model. The results from the most recent study showed the greatest heterogeneity in the sensitivity analysis. Low lean tissue index and overhydration, measured using a body composition monitor, were associated with a high mortality rate in patients on hemodialysis

    Reduction of Odor Emissions from the Field Workplaces at the Pohang Steel Complex using Pyroligneous Liquid

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    In this study, the odor compounds emitted from the field workplaces at the Pohang steel complex were measured using analytical instruments and the olfactory method. In addition, the reduction of odor emissions by pyroligneous liquid was evaluated. The odor samples were obtained from slag pots and dry pits, which refrigerate hot slag, and the target compounds were hydrogen sulfide (H???S) and ammonia (NH???). The levels of odor emissions varied with meteorological and operating conditions, but emissions from dry pits were always much higher than those from slag pots. As the concentration of pyroligneous liquid increased, the concentrations of H???S and the ratios of dilution decreased. Together with this result and the guideline of odor emissions, the increase of pyroligneous liquid (more than 2.5 times) in cooling water was suggested to reduce odor emissions. Actually, the reduced levels of H???S at the slag pot and the dry pit were observed (70 and 94%, respectively) after the increased amount of pyroligneous liquid. However, in order to determine the optimal amount of pyroligneous liquid for the economical and efficient removal of odor emissions, the system of cooling water has to be improved and more samples are required for a comprehensive analysis. ??? ??????????????? ???????????????????????? ?????????????????? ???????????? ????????????????????? ?????? ??????????????? ???????????? ????????? ????????????, ????????? ????????? ????????? ?????? ???????????? ??????????????? ???????????????. ???????????? ????????? ????????? ???????????? ???????????? slag??? dry pit?????????, ?????? ??????????????? ????????????(H???S)??? ????????????(NH???)??? ???????????????. ??????????????? ?????? ??? ??????????????? ????????? ?????? ????????? ????????????, slag pot????????? dry pit?????? ???????????? ??????????????? ???????????????. ????????? ????????? ???????????? ?????? ??????????????? ????????????????????? ??????????????? ???????????? ???????????? ????????? ??????????????? ????????? ??? ?????????. ??? ????????? ???????????? ??????????????? ?????? ????????????, ????????? ???????????? 2.5??? ?????? ???????????? ?????? ???????????????. ????????? ????????? ???????????? ???????????? ????????? ???????????? ????????????(slag pot: 74%, dry pti: 94%)??? ????????? ??? ?????????. ?????? ??????????????? ??????????????? ???????????? ????????? ???????????? ???????????? ????????????, ????????? ?????????????????? ???????????? ????????? ?????? ??? ???????????? ????????? ?????? ?????? ??????????????? ?????? ???????????? ?????? ????????? ????????????.clos
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