Soil legacy data rescue via GlobalSoilMap and other international and national initiatives The International Center for Tropical Agriculture (CIAT) believes that open access contributes to its mission of reducing hunger and poverty, and improving human nutrition in the tropics through research aimed at increasing the eco-efficiency of agriculture. CIAT is committed to creating and sharing knowledge and information openly and globally. We do this through collaborative research as well as through the open sharing of our data, tools, and publications.
The characteristics of Japanese soil were developed by wide paddy field farming, the influence of volcanic ash on soil, and a perudic moisture regime under temperate climatic conditions. Major soil classifications in Japan have developed in line with public work projects and soils in Japan have tended to be classified independently depending on the land use, such as cultivated area, forest, and so forth.
Although several reports suggest that Alzheimer's disease (AD) is associated with shortened telomere length, the clinical relevance of this has not yet been fully elucidated. This study was conducted to clarify the correlation of telomere length with clinical characteristics and ApoE phenotypes in 74 AD patients. Telomere length was determined from genomic DNA extracted from whole blood by quantitative real-time polymerase chain reaction. We found no significant difference in telomere length between the AD and non-dementia elderly control (n = 35) groups. Furthermore, no significant correlation was found among telomere length and the severity of cognitive decline and disease duration, age, or gender difference. However, telomere length was significantly shorter in AD patients with the ApoE4 homozygote than in those with the ApoE4 heterozygote (p < .001) and noncarriers (p < .001). These findings suggest that shortened telomere length may be associated with the ApoE4 homozygote in AD patients.
This study aimed to improve the accuracy of spatial prediction for soil organic matter, potential mineralizable carbon (PMC) and soil organic carbon (SOC), using secondary information, namely topographic and vegetation information, in northern Kazakhstan. Secondary information included elevation (ELEV), mean curvature (MEANC), compound topographic index (CTI) and slope (SLOPE) obtained from a digital elevation model, and enhanced vegetation index (VI) values obtained from a moderate resolution imaging spectroradiometer (MODIS). The prediction methods were statistical (multiple linear regression between soil organic matter and secondary information) and geostatistical algorithms (regression-kriging Model-C and simple kriging with varying local means [SKlm]). The VI, ELEV and MEANC were selected as the independent variables for predicting PMC and SOC. However, MEANC showed an opposite effect on PMC and SOC accumulation patterns. Model validity revealed that SKlm was the most appropriate method for predicting PMC and SOC spatial patterns because model validity revealed the smallest errors for this method. Maps from the kriged estimates showed that a combination of secondary information and geostatistical techniques can improve the accuracy of spatial prediction in study areas.
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