We report improved whole-genome shotgun sequences for the genomes of indica and japonica rice, both with multimegabase contiguity, or almost 1,000-fold improvement over the drafts of 2002. Tested against a nonredundant collection of 19,079 full-length cDNAs, 97.7% of the genes are aligned, without fragmentation, to the mapped super-scaffolds of one or the other genome. We introduce a gene identification procedure for plants that does not rely on similarity to known genes to remove erroneous predictions resulting from transposable elements. Using the available EST data to adjust for residual errors in the predictions, the estimated gene count is at least 38,000–40,000. Only 2%–3% of the genes are unique to any one subspecies, comparable to the amount of sequence that might still be missing. Despite this lack of variation in gene content, there is enormous variation in the intergenic regions. At least a quarter of the two sequences could not be aligned, and where they could be aligned, single nucleotide polymorphism (SNP) rates varied from as little as 3.0 SNP/kb in the coding regions to 27.6 SNP/kb in the transposable elements. A more inclusive new approach for analyzing duplication history is introduced here. It reveals an ancient whole-genome duplication, a recent segmental duplication on Chromosomes 11 and 12, and massive ongoing individual gene duplications. We find 18 distinct pairs of duplicated segments that cover 65.7% of the genome; 17 of these pairs date back to a common time before the divergence of the grasses. More important, ongoing individual gene duplications provide a never-ending source of raw material for gene genesis and are major contributors to the differences between members of the grass family.

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Key message Candidate genes were identified and the role of phytohormones such as JA-Me and ABA in the synthesis of S-RNase was emphasized in pear selfincompatibility. Abstract Self-incompatibility (SI) occurs widely in flowering plants as an intraspecific reproductive barrier. This phenomenon promotes variation within species, but for some species such as Pyrus, SI is a nuisance rather than a benefit in agricultural production. Although many studies have been conducted on SI in pears, its mechanism remains unclear. In this study, high-throughput Illumina RNA sequencing (RNA-seq) was used to identify SI-related genes in pear styles. Using transcriptome comparisons, differentially expressed genes of unpollinated (UP), crosspollinated (CP), and self-pollinated (SP) styles were identified after 48 h. A total of 1796 and 1890 genes were identified in DSC (UP vs. CP) and DSI (UP vs. SP), respectively. KEGG analysis revealed that genes involved in the ''plant hormone signal transduction pathway'' and ''plant-pathogen interaction pathway'' were significantly enriched in DSI (UP vs. SP) compared to those in DSC (UP vs. CP). The expression level of S-glycoprotein ribonuclease (S-RNase) was dramatically reduced in cross-pollinated (CP) styles. To better understand the relationship between the expression patterns of S-RNase and two major KEGG pathways, the concentrations of phytohormones were measured, and the expression pattern of S-RNase was analysed using qRT-PCR. Our results demonstrate that methyl jasmonate and abscisic acid may enhance the expression level of S-RNase, and pollination can affect the synthesis of methyl jasmonate and abscisic acid in pear styles. Overall, this study is a global transcriptome analysis of SI in pear. A relationship between self-rejection, plant hormones, and pathogen defence was shown in pear.Keywords Pear (Pyrus bretschneideri Rehd.) Á Selfincompatibility Á Transcriptome Á S-RNase Á Methyl jasmonate

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In order to improve the performance of Least Mean Square (LMS) based system identification of sparse systems, a new adaptive algorithm is proposed which utilizes the sparsity property of such systems. A general approximating approach on l 0 norm -a typical metric of system sparsity, is proposed and integrated into the cost function of the LMS algorithm. This integration is equivalent to add a zero attractor in the iterations, by which the convergence rate of small coefficients, that dominate the sparse system, can be effectively improved. Moreover, using partial updating method, the computational complexity is reduced. The simulations demonstrate that the proposed algorithm can effectively improve the performance of LMS-based identification algorithms on sparse system.

Many waterbird populations have become increasingly dependent on agricultural habitats for feeding. While habitat destruction has been proposed as a key reason forcing waterbirds to move from natural habitats to agricultural habitats, few have used long‐term data to test this hypothesis. The Siberian crane ( Leucogeranus leucogeranus ) is an IUCN Critically Endangered species. About 98% of its global population winters at Poyang Lake, China. Recently, many cranes shifted from feeding in natural wetlands to agricultural habitats. Here, we integrate bird surveys, Vallisneria tuber (the traditional food of cranes in natural wetlands) surveys, water level data, and remotely sensed images from 1999 to 2016 to explore the drivers of this habitat shift. Changes in Siberian crane numbers in natural wetlands and agricultural fields indicated that the habitat shift occurred in the winters of 2015–2016. Analyses using generalized linear mixed models suggested that crane numbers in natural wetlands were positively related to tuber density and the interaction between dry season (October–March) water level and tuber density. The changes in tuber density and dry season water level in 2015–2016 indicated that tuber disappearance may have been the primary driver of the habitat shift, with a smaller effect of high water level. Submerged plants at Poyang Lake have degraded seriously in the past two decades. The plant degradation at Shahu Lake, a sublake of Poyang Lake, may have been caused by high spring water, high winter temperature, and low summer temperature. However, the drivers of tuber disappearance at Poyang Lake may not be restricted to these variables. Because Poyang Lake is an important refuge for many waterbirds in the Yangtze River floodplain, it is urgent to take effective measures to restore its submerged plants and ecosystem health. Agricultural fields can be important refuges for Siberian cranes, mitigating the negative impacts of wetland deterioration.

The Yangtze River and its watershed have undergone vast changes resulting from centuries of human impacts, yet ecological knowledge of the system is limited. The seasonal variation and spatial variation of three sub‐lakes of Poyang Lake, a huge wetland in the middle Yangtze Basin, were investigated to examine how fish assemblages respond to seasonal hydrology and associated environmental conditions. In all three sub‐lakes, fish assemblage structure revealed strong variations associated with seasonal water level fluctuation. Fish species richness in all sub‐lakes was highest during the middle of the monsoon season and lowest during the dry season. Fish numerical abundance and biomass varied significantly, with several of the most common species having inconsistent patterns of seasonal variation among sub‐lakes. Fish assemblage structure was significantly associated with environmental gradients defined by water level, aquatic macrophyte coverage, conductivity and dissolved oxygen concentration. Assemblage composition in all three sub‐lakes underwent strongest shifts between December and April, the period when water levels were lowest and fishing has the greatest impact on fish stocks. Future impacts that change the hydrology of the middle Yangtze would alter the dynamics of habitat connectivity and affect environmental conditions and fish assemblages of the Poyang Lake wetland system.

Human influenza infections display a strongly seasonal pattern. However, whether H7N9 and H5N1 infections correlate with climate factors has not been examined. Here, we analyzed 350 cases of H7N9 infection and 47 cases of H5N1 infection. The spatial characteristics of these cases revealed that H5N1 infections mainly occurred in the South, Middle, and Northwest of China, while the occurrence of H7N9 was concentrated in coastal areas of East and South of China. Aside from spatial-temporal characteristics, the most adaptive meteorological conditions for the occurrence of human infections by these two viral subtypes were different. We found that H7N9 infections correlate with climate factors, especially temperature (TEM) and relative humidity (RHU), while H5N1 infections correlate with TEM and atmospheric pressure (PRS). Hence, we propose a risky window (TEM 4–14 °C and RHU 65–95%) for H7N9 infection and (TEM 2–22 °C and PRS 980-1025 kPa) for H5N1 infection. Our results represent the first step in determining the effects of climate factors on two different virus infections in China and provide warning guidelines for the future when provinces fall into the risky windows. These findings revealed integrated predictive meteorological factors rooted in statistic data that enable the establishment of preventive actions and precautionary measures against future outbreaks.