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SSR markers are desirable markers in analysis of genetic diversity, quantitative trait loci mapping and gene locating. In this study, SSR markers were developed from two genomic libraries enriched for (GA)n and (CA)n of foxtail millet [Setaria italica (L.) P. Beauv.], a crop of historical importance in China. A total of 100 SSR markers among the 193 primer pairs detected polymorphism between two mapping parents of an F(2) population, i.e. "B100" of cultivated S. italica and "A10" of wild S. viridis. Excluding 14 markers with unclear amplifications, and five markers unlinked with any linkage group, a foxtail millet SSR linkage map was constructed by integrating 81 new developed SSR markers with 20 RFLP anchored markers. The 81 SSRs covered nine chromosomes of foxtail millet. The length of the map was 1,654 cM, with an average interval distance between markers of 16.4 cM. The 81 SSR markers were not evenly distributed throughout the nine chromosomes, with Ch.8 harbouring the least (3 markers) and Ch.9 harbouring the most (18 markers). To verify the usefulness of the SSR markers developed, 37 SSR markers were randomly chosen to analyze genetic diversity of 40 foxtail millet accessions. Totally 228 alleles were detected, with an average 6.16 alleles per locus. Polymorphism information content (PIC) value for each locus ranged from 0.413 to 0.847, with an average of 0.697. A positive correlation between PIC and number of alleles and between PIC and number of repeat unit were found [0.802 and 0.429, respectively (P < 0.01)]. UPGMA analysis revealed that the 40 foxtail millet cultivars could be grouped into five clusters in which the landraces' grouping was largely consistent with ecotypes while the breeding varieties from different provinces in China tended to be grouped together.

Charging infrastructure is critical to the development of electric vehicle (EV) system. While many countries have implemented great policy efforts to promote EVs, how to build charging infrastructure to maximize overall travel electrification given how people travel has not been well studied. Mismatch of demand and infrastructure can lead to under-utilized charging stations, wasting public resources. Estimating charging demand has been challenging due to lack of realistic vehicle travel data. Public charging is different from refueling from two aspects: required time and home-charging possibility. As a result, traditional approaches for refueling demand estimation (e.g. traffic flow and vehicle ownership density) do not necessarily represent public charging demand. This research uses large-scale trajectory data of 11,880 taxis in Beijing as a case study to evaluate how travel patterns mined from big-data can inform public charging infrastructure development. Although this study assumes charging stations to be dedicated to a fleet of PHEV taxis which may not fully represent the real-world situation, the methodological framework can be used to analyze private vehicle trajectory data as well to improve our understanding of charging demand for electrified private fleet. Our results show that 1) collective vehicle parking "hotspots" are good indicators for charging demand; 2) charging stations sited using travel patterns can improve electrification rate and reduce gasoline consumption; 3) with current grid mix, emissions of CO 2 , PM, SO 2 , and NO x will increase with taxi electrification; and 4) power demand for public taxi charging has peak load around noon, overlapping with Beijing's summer peak power.

Surface molecular imprinting, especially on the surface of silica-modified magnetic nanoparticles, has been proposed as a promising strategy for protein recognition and separation. Inspired by the self-polymerization of dopamine, we synthesized a polydopamine-based molecular imprinted film coating on silica-Fe(3)O(4) nanoparticles for recognition and separation of bovine hemoglobin (BHb). Magnetic molecularly imprinted nanoparticles (about 860 nm) possess a core-shell structure. Magnetic molecularly imprinted nanoparticles (MMIP) show a relatively high adsorption capacity (4.65 ± 0.38 mg g(-1)) and excellent selectivity towards BHb with a separation factor of 2.19. MMIP with high saturation magnetization (10.33 emu g(-1)) makes it easy to separate the target protein from solution by an external magnetic field. After three continuous adsorption and elution processes, the adsorption capacity of MMIP remained at 4.30 mg g(-1). Our results suggest that MMIPs are suitable for the removal of high abundance of protein and the enrichment of low abundance of protein in proteomics.

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Local water scarcity risk (LWSR, meaning potential economic output losses in water-using sectors due to physical water scarcity) can be transmitted to downstream economies through the globalized supply chains. To understand the vulnerability of the global economy to water scarcity, we examine the impacts of local water scarcity risk on the global trade system from 1995 to 2009. We observe increasingly intensified geographical separation between physical water scarcity and production losses due to water scarcity. We identify top nation-sectors in virtual water scarcity risk (VWSR) exports (indicating local water scarcity risk in each nation transmitted to foreign nations through its exports), including agriculture and utilities in major economies such as China, India, Spain, France, and Turkey. These nation-sectors are critical to the resilience of the global economy to water scarcity. We also identify top nation-sectors in virtual water scarcity risk imports (indicating each nation's vulnerability to foreign water scarcity risk through the global trade system), highlighting their vulnerability to distant water scarcity. Our findings reveal the need for nations to collaboratively manage and conserve water resources, and lay the foundation for firms in high VWSR-importing sectors to develop strategies to mitigate such risk.

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