Association between high cystatin C levels and carotid atherosclerosis

AIM To investigate the association between carotid atherosclerosis and cystatin C(CysC) and to determine the optimal CysC cut-off value.METHODS One hundred twenty-eight subjects were included in this study. Atherosclerosis was defined as a maximum carotid plaque thickness(MCPT) of greater than 2 mm. A receiver operating characteristic curve analysis was used to determine the diagnostic value of serum CysC for atherosclerosis. The subjects were divided into two groups according to the CysC cut-off value. We screenedfor diabetes, hypertension, dyslipidemia, smoking status, alcohol consumption, and exercise behavior. The association between atherosclerosis and CysC levels was assessed using multivariate analysis.RESULTS The subjects were then divided into two groups according to the CysC cut-off value(0.73 mg/L). The median age of the high CysC group was 72 years(85% males), whereas that of the low CysC group was 61 years(63% males). The CysC levels were significantly correlated with Cr and estimated glomerular filtration rate(eGFR) values. Bodymass index, visceral fat area, hypertension, diabetes mellitus, and MCPT were significantly higher in the high CysC group than in the low CysC group. Furthermore, the eG FR was significantly lower in the high CysC group. Regarding lifestyle habits, only the exercise level was lower in the high CysC group than in the low CysC group. Multivariate analysis, adjusted for age and sex, revealed that high CysC levels were significantly associated with an MCPT of ≥ 2 mm(odds ratio: 2.92; 95%CI: 1.13-7.99).CONCLUSION Higher CysC levels were associated with an MCPT of ≥ 2 mm. The CysC cut-off value of 0.73 mg/L appears to aid in the diagnosis of atherosclerosis.

A Study on Tropical Land Cover Classification Using ALOS PALSAR 50 m Ortho-Rectified Mosaic Data

The main objective of this study is to find better classifier of mapping tropical land covers using Synthetic Aperture Radar (SAR) imagery. The data used are Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) 50 m ortho-rectified mosaic data. Training data for forest, herbaceous, agriculture, urban and water body in the test area located in Kalimantan were collected. To achieve more accurate classification, a modified slope correction formula was created to calibrate the intensity distortions of SAR data. The accuracy of two classifiers called Sequential Minimal Optimization (SMO) and Random Forest (RF) were applied and compared in this study. We focused on object-based approach due to its capability of providing both spatial and spectral information. Optimal combination of features was selected from 32 sets of features based on layer value, texture and geometry. The overall accuracy of land cover classification using RF classifier and SMO classifier was 46.8% and 55.6% respectively, and that of forest and non-forest classification was 74.4% and 79.4% respectively. This indicates that RF classifier has better performance than SMO classifier.

Mangrove Forests Mapping in the Southern Part of Japan Using Landsat ETM+ with DEM

A regional map of mangrove forests was produced for six islands located in the southern part of Japan by integrating the spectral analyses of Landsat Enhanced Thematic Mapper plus (ETM+) images with a digital elevation model (DEM). Several attempts were applied to propose a reliable method, which can be used to map the distribution of mangrove forests at a regional scale. The methodology used in this study comprised of obtaining the difference between Normalized Difference Water Index (NDWI) and Normalized Difference Vegetation Index (NDVI), band ratio 5/4, and band 5, from Landsat ETM+, and integrating them with the topographic information. The integration of spectral analyses with topographic data has clearly separated the mangrove forests from other vegetation. An accuracy assessment was carried out in order to check the accuracy of the results. High overall accuracy ranging from 89.3% to 93.6% was achieved, which increased the opportunity to use this methodology in other countries rich in mangrove forests.

Comparison of a New Percent Tree Cover Dataset with Existing One and Categorical Land Cover Datasets in Eurasia

The global tree cover percentage is an important parameter used to understand the global environment. However, the available percent tree cover products on global or continental-scale are few, and efforts to quantitatively validate these maps have been limited. We produced a new percent tree cover dataset at 500 m resolution in 2008 for Eurasia using reference data interpreted from Google Earth. It is a part of percent tree cover (PTC) data in Global Mapping project. In this study, the dataset was compared with existing global percent tree cover dataset, MODIS Vegetation Continuous Fields, MOD44B. We assessed the agreement of these datasets with two existing global categorical land cover datasets and statistic data in Eurasia. The result showed that estimates of tree cover in our new map and MOD44B were relatively similar at randomly sampled sites. Our map and MOD44B agreed with either or both of land cover maps at 93% of sites and 91% of sites, respectively, for pixel blocks. However, we found that MOD44B disagreed with our map and categorical land cover datasets at about half of the sampled sites where the difference of tree cover percentage between our map and MOD44B was large, especially in the areas with significant differences (more than 50%). Disagreed areas were concentrated in forests of Russia and Indonesia, and in herbaceous dominated vegetation of UK and Ireland. We also found that both our map and MOD44B were somewhat different from the data reported by FRA 2010.

Production of global land cover data-GLCNMO

Global land cover is one of the fundamental contents of Digital Earth.The Global Mapping project coordinated by the International Steering Committee for Global Mapping has produced a 1-km global land cover dataset
Global Land Cover by National Mapping Organizations.It has 20 land cover classes defined using the Land Cover Classification System.Of them,14 classes were derived using supervised classification.The remaining six were classified independently:urban,tree open,mangrove,wetland,snow/ice,andwater.Primary source data of this land cover mapping were eight periods of 16-day composite 7-band 1-km MODIS data of 2003.Training data for supervised classification were collected using Landsat images,MODIS NDVI seasonal change patterns,Google Earth,Virtual Earth,existing regional maps,and expert’s comments.The overall accuracy is 76.5%and the overall accuracy with the weight of the mapped area coverage is 81.2%.The data are available from the Global Mapping project website(http://www.iscgm.org/).TheMODISdata used,land cover training data,and a list of existing regional maps are also available from the CEReS website.This mapping attempt demonstrates that training/validation data accumulation from different mapping projects must be promoted to support future global land cover mapping.