Altitudinal variation in soil organic carbon stock in coniferous subtropical and broadleaf temperate forests in Garhwal Himalaya

<p>Abstract</p> <p>Background</p> <p>The Himalayan zones, with dense forest vegetation, cover a fifth part of India and store a third part of the country reserves of soil organic carbon (SOC). However, the details of altitudinal distribution of these carbon stocks, which are vulnerable to forest management and climate change impacts, are not well known.</p> <p>Results</p> <p>This article reports the results of measuring the stocks of SOC along altitudinal gradients. The study was carried out in the coniferous subtropical and broadleaf temperate forests of Garhwal Himalaya. The stocks of SOC were found to be decreasing with altitude: from 185.6 to 160.8 t C ha^-1and from 141.6 to 124.8 t C ha^-1in temperature (<it>Quercus leucotrichophora</it>) and subtropical (<it>Pinus roxburghii</it>) forests, respectively.</p> <p>Conclusion</p> <p>The results of this study lead to conclusion that the ability of soil to stabilize soil organic matter depends negatively on altitude and call for comprehensive theoretical explanation</p

Data mining of high density genomic variant data for prediction of Alzheimer's disease risk

<p>Abstract</p> <p>Background</p> <p>The discovery of genetic associations is an important factor in the understanding of human illness to derive disease pathways. Identifying multiple interacting genetic mutations associated with disease remains challenging in studying the etiology of complex diseases. And although recently new single nucleotide polymorphisms (SNPs) at genes implicated in immune response, cholesterol/lipid metabolism, and cell membrane processes have been confirmed by genome-wide association studies (GWAS) to be associated with late-onset Alzheimer's disease (LOAD), a percentage of AD heritability continues to be unexplained. We try to find other genetic variants that may influence LOAD risk utilizing data mining methods.</p> <p>Methods</p> <p>Two different approaches were devised to select SNPs associated with LOAD in a publicly available GWAS data set consisting of three cohorts. In both approaches, single-locus analysis (logistic regression) was conducted to filter the data with a less conservative p-value than the Bonferroni threshold; this resulted in a subset of SNPs used next in multi-locus analysis (random forest (RF)). In the second approach, we took into account prior biological knowledge, and performed sample stratification and linkage disequilibrium (LD) in addition to logistic regression analysis to preselect loci to input into the RF classifier construction step.</p> <p>Results</p> <p>The first approach gave 199 SNPs mostly associated with genes in calcium signaling, cell adhesion, endocytosis, immune response, and synaptic function. These SNPs together with <it>APOE and GAB2 </it>SNPs formed a predictive subset for LOAD status with an average error of 9.8% using 10-fold cross validation (CV) in RF modeling. Nineteen variants in LD with <it>ST5, TRPC1, ATG10, ANO3, NDUFA12, and NISCH </it>respectively, genes linked directly or indirectly with neurobiology, were identified with the second approach. These variants were part of a model that included <it>APOE </it>and <it>GAB2 </it>SNPs to predict LOAD risk which produced a 10-fold CV average error of 17.5% in the classification modeling.</p> <p>Conclusions</p> <p>With the two proposed approaches, we identified a large subset of SNPs in genes mostly clustered around specific pathways/functions and a smaller set of SNPs, within or in proximity to five genes not previously reported, that may be relevant for the prediction/understanding of AD.</p

MEASUREMENT OF FRACTURE-TOUGHNESS OF BIMATERIAL INTERFACES AND A STRESS-BASED APPROACH TO THEIR FRACTURE

The possibility of using a compact tension type of specimen geometry for the measurement of interface fracture toughness for sandwich construction has been examined. Three material pairs have been considered. The fracture toughness data and its variation with the load phase angle are presented. The possibility of specifying the condition for the onset of fracture in terms of the tangential stress at a finite distance ahead of the crack tip is examined. A very encouraging trend is found

Dependence of ``critical cloud fraction' on aerosol composition

Recent studies, over regions influenced by biomass burning aerosol, have shown that it is possible to define a critical cloud fraction' (CCF) at which the aerosol direct radiative forcing switch from a cooling to a warming effect. Using 4 years of multi-satellite data analysis, we show that CCF varies with aerosol composition and changed from 0.28 to 0.13 from postmonsoon to winter as a result of shift from less absorbing to moderately absorbing aerosol. Our results indicate that we can estimate aerosol absorption from space using independently measured top of the atmosphere (TOA) fluxes Cloud Aerosol Lidar with Orthogonal Polarization-Moderate resolution Imaging Spectroradiometer-Clouds and the Earth's Radiant Energy System (CALIPSO-MODIS-CERES)] combined algorithms for example