Vegetation response to climate zone dynamics and its impacts on surface soil water content and albedo in China

Extensive research has focused on the response of vegetation to climate change, including potential mechanisms and resulting impacts. Although many studies have explored the relationship between vegetation and climate change in China, research on spatiotemporal distribution changes of climate regimes using natural vegetation as an indicator is still lacking. Further, limited information is available on the response of vegetation to shifts in China's regional climatic zones. In this study, we applied Mann-Kendall, and correlation analysis to examine the variabilities in temperature, precipitation, surface soil water, normalised difference vegetation index (NDVI), and albedo in China from 1982 to 2012. Our results indicate significant shifts in the distribution of Koppen-Geiger climate classes in China from 12.08% to 18.98% between 1983 and 2012 at a significance level of 0.05 (MK). The percentage areas in the arid and continental zones expanded at a rate of 0.004%/y and 0.12%/y, respectively, while the percentage area in the temperate and alpine zones decreased by -0.05%/y and - 0.07%/y. Sensitivity fitting results between simulated and observed changes identified temperature to be a dominant control on the dynamics of temperate (r(2)= 0.98) and alpine (r(2)= 0.968) zones, while precipitation was the dominant control on the changes of arid (r(2) = 0.856) and continental (r(2) = 0.815) zones. The response of the NDVI to albedo infers a more pronounced radiative response in temperate (r = -0.82, pPeer reviewe

Pre-Clinical Drug Prioritization via Prognosis-Guided Genetic Interaction Networks

The high rates of failure in oncology drug clinical trials highlight the problems of using pre-clinical data to predict the clinical effects of drugs. Patient population heterogeneity and unpredictable physiology complicate pre-clinical cancer modeling efforts. We hypothesize that gene networks associated with cancer outcome in heterogeneous patient populations could serve as a reference for identifying drug effects. Here we propose a novel in vivo genetic interaction which we call ‘synergistic outcome determination’ (SOD), a concept similar to ‘Synthetic Lethality’. SOD is defined as the synergy of a gene pair with respect to cancer patients' outcome, whose correlation with outcome is due to cooperative, rather than independent, contributions of genes. The method combines microarray gene expression data with cancer prognostic information to identify synergistic gene-gene interactions that are then used to construct interaction networks based on gene modules (a group of genes which share similar function). In this way, we identified a cluster of important epigenetically regulated gene modules. By projecting drug sensitivity-associated genes on to the cancer-specific inter-module network, we defined a perturbation index for each drug based upon its characteristic perturbation pattern on the inter-module network. Finally, by calculating this index for compounds in the NCI Standard Agent Database, we significantly discriminated successful drugs from a broad set of test compounds, and further revealed the mechanisms of drug combinations. Thus, prognosis-guided synergistic gene-gene interaction networks could serve as an efficient in silico tool for pre-clinical drug prioritization and rational design of combinatorial therapies