Biomass production on low-grade land is needed to meet future energy demands and minimize resource conflicts. This, however, requires improvements in plant water-use efficiency (WUE) that are beyond conventional C3 and C4 dedicated bioenergy crops. Here we present the first global-scale geographic information system (GIS)-based productivity model of two highly water-efficient crassulacean acid metabolism (CAM) candidates: Agave tequilana and Opuntia ficus-indica. Features of these plants that translate to WUE advantages over C3 and C4 bioenergy crops include nocturnal stomatal opening, rapid rectifier-like root hydraulic conductivity responses to fluctuating soil water potential and the capacity to buffer against periods of drought. Yield simulations for the year 2070 were performed under the four representative concentration pathway (RCPs) scenarios presented in the IPCC's 5th Assessment Report. Simulations on low-grade land suggest that O. ficus-indica alone has the capacity to meet ‘extreme’ bioenergy demand scenarios (>600 EJ yr−1) and is highly resilient to climate change (−1%). Agave tequilana is moderately impacted (−11%). These results are significant because bioenergy demand scenarios >600 EJ yr−1 could be met without significantly increasing conflicts with food production and contributing to deforestation. Both CAM candidates outperformed the C4 bioenergy crop, Panicum virgatum L. (switchgrass) in arid zones in the latitudinal range 30°S–30°N.
摘要： Multiparent Advanced Generation Intercross (MAGIC) mapping populations offer unique opportunities and challenges for marker and QTL mapping in crop species. We have constructed the first eight-parent MAGIC genetic map for wheat, comprising 18 601 SNP markers. We validated the accuracy of our map against the wheat genome sequence and found an improvement in accuracy compared to published genetic maps. Our map shows a notable increase in precision resulting from the three generations of intercrossing required to create the population. This is most pronounced in the pericentromeric regions of the chromosomes. Sixteen percent of mapped markers exhibited segregation distortion (SD) with many occurring in long (>20 cM) blocks. Some of the longest and most distorted blocks were collinear with noncentromeric high-marker-density regions of the genome, suggesting they were candidates for introgression fragments introduced into the bread wheat gene pool from other grass species. We investigated two of these linkage blocks in detail and found strong evidence that one on chromosome 4AL, showing SD against the founder Robigus, is an interspecific introgression fragment. The completed map is available from http://www.niab.com/pages/id/326/Resources.
摘要： In this write-up we review and update our recent lattice QCD calculation of B→K∗, Bs→ϕ, and Bs→K∗form factors [arXiv:1310.3722]. These unquenched calculations, performed in the low-recoil kinematic regime, provide a significant improvement over the use of extrapolated light cone sum rule results. The fits presented here include further kinematic constraints and estimates of additional correlations between the different form factor shape parameters. We use these form factors along with Standard Model determinations of Wilson coefficients to give Standard Model predictions for several observables [arXiv:1310.3887]. The modest improvements to the form factor fits lead to improved determinations of FL, the fraction of longitudinally polarized vector mesons, but have little effect on most other observables.
摘要： In this paper, we report upon our recent work aimed at improving and adapting machine learning algorithms to automatically classify nanoscience images acquired by the Scanning Electron Microscope (SEM). This is done by coupling supervised and unsupervised learning approaches. We first investigate supervised learning on a ten-category data set of images and compare the performance of the different models in terms of training accuracy. Then, we reduce the dimensionality of the features through autoencoders to perform unsupervised learning on a subset of images in a selected range of scales (from 1 μm to 2 μm). Finally, we compare different clustering methods to uncover intrinsic structures in the images.