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This study examines the temporal variations and spatial distributions of annual precipitation over Central Asia during the periods of 1901–2013, 1951–2013, and 1979–2013 using the latest version of Global Precipitation Climatology Centre (GPCC) full data reanalysis version 7 (GPCC V7) data set. The linear trend and multiperiods of the precipitation over the entire region and plain and mountainous area separately are analysed by linear least square method and ensemble empirical mode decomposition method. An overall increasing trend [0.66 mm (10 years)−1] is found for the entire region during 1901–2013, which is smaller than that of 1951–2013. The regional annual precipitation exhibits multi‐decadal variations, with a sharp decline during 1901–1944, followed by an increase until 1980s, and a fluctuation thereafter. During 1979–2013, the mountainous area shows a greater increasing trend than the entire region. Furthermore, the regional annual precipitation has exhibited high‐frequency variations with 3‐year and 6‐year quasiperiods and a low‐frequency variation with 28‐year quasiperiods. In terms of the spatial distribution, increasing trend in the annual precipitation is found in Xinjiang and decreasing trends appear over the five countries of Central Asia during 1951–2013. Empirical orthogonal function results show that the mountainous area is the large variability centre of the annual precipitation. The dominant mode of interannual variability in Central Asia annual precipitation is related to El Niño‐Southern Oscillation, which explains about 17% of the interannual variance during 1951–2013. The results of this study describe the long‐term variation in the annual precipitation over Central Asia as well as its relationship with some key climate indices in great detail, which will benefit the understanding and the prediction of the climate variations in this region.

BackgroundLichen is a classic mutualistic organism and the lichenization is one of the fungal symbioses. The lichen-forming fungus Endocarpon pusillum is living in symbiosis with the green alga Diplosphaera chodatii Bialsuknia as a lichen in the arid regions.Results454 and Illumina technologies were used to sequence the genome of E. pusillum. A total of 9,285 genes were annotated in the 37.5 Mb genome of E. pusillum. Analyses of the genes provided direct molecular evidence for certain natural characteristics, such as homothallic reproduction and drought-tolerance. Comparative genomics analysis indicated that the expansion and contraction of some protein families in the E. pusillum genome reflect the specific relationship with its photosynthetic partner (D. chodatii). Co-culture experiments using the lichen-forming fungus E. pusillum and its algal partner allowed the functional identification of genes involved in the nitrogen and carbon transfer between both symbionts, and three lectins without signal peptide domains were found to be essential for the symbiotic recognition in the lichen; interestingly, the ratio of the biomass of both lichen-forming fungus and its photosynthetic partner and their contact time were found to be important for the interaction between these two symbionts.ConclusionsThe present study lays a genomic analysis of the lichen-forming fungus E. pusillum for demonstrating its general biological features and the traits of the interaction between this fungus and its photosynthetic partner D. chodatii, and will provide research basis for investigating the nature of its drought resistance and symbiosis.

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Spatial metabolomics can reveal intercellular heterogeneity and tissue organization. To achieve highest spatial resolution, we reported a novel Spatial single nuclEar metAboloMics (SEAM) method, a scalable platform combining high resolution imaging mass spectrometry (IMS) and a series of computational algorithms, that can display multiscale/multicolor tissue tomography together with identification and clustering of single nuclei by their in situ metabolic fingerprints. We firstly applied SEAM to a range of wild type mouse tissues, then delineate a consistent pattern of metabolic zonation in mouse liver. We further studied spatial metabolome in human fibrotic liver. Intriguingly, we discovered novel subpopulations of hepatocytes with special metabolic features associated with their proximity to fibrotic niche, which was further validated by spatial transcriptomics with Geo-seq. These demonstrations highlight how SEAM may be used to explore the spatial metabolome and tissue anatomy at single cell level, hence leading to a deeper understanding of the tissue metabolic organization.