Next-generation sequencing has yielded a vast amount of cattle genomic data for global characterization of population genetic diversity and identification of genomic regions under natural and artificial selection. However, efficient storage, querying, and visualization of such large datasets remain challenging. Here, we developed a comprehensive database, the Bovine Genome Variation Database (BGVD). It provides six main functionalities: gene search, variation search, genomic signature search, Genome Browser, alignment search tools, and the genome coordinate conversion tool. BGVD contains information on genomic variations comprising ~60.44 M SNPs, ~6.86 M indels, 76,634 CNV regions, and signatures of selective sweeps in 432 samples from modern cattle worldwide. Users can quickly retrieve distribution patterns of these variations for 54 cattle breeds through an interactive source of breed origin map, using a given gene symbol or genomic region for any of the three versions of the bovine reference genomes (ARS-UCD1.2, UMD3.1.1, and Btau 5.0.1). Signals of selection sweep are displayed as Manhattan plots and Genome Browser tracks. To further investigate and visualize the relationships between variants and signatures of selection, the Genome Browser integrates all variations, selection data, and resources, from NCBI, the UCSC Genome Browser, and Animal QTLdb. Collectively, all these features make the BGVD a useful archive for in-depth data mining and analyses of cattle biology and cattle breeding on a global scale. BGVD is publicly available at http://animal.nwsuaf.edu.cn/BosVar.

The ethyl acetate extract from the liquid fermentation of S. caelestis Aw99c exhibited high and broad antifungal activities against plant pathogenic fungi. Bioassay guide fractionation led to the discovery of two xanthones, citreamicin ε and θ. The draft genome sequence of S. caelestis Aw99c was analyzed by a similarity-based approach to elucidate the pathway for the citreamicins. A 48 kb citreamicin (cit) gene cluster with 51 open reading frames encoding type II polyketide synthases and unique polyketide tailoring enzymes was proposed based on the genome analysis and the chemical structure derivation. In vitro antifungal assay showed that citreamicin ε exhibited significant growth inhibition against the plant pathogenic fungi with MIC values ranging from 1.56 to 12.5 μM. The cellular structural change of M. grisea treated with citreamicin ε was detected by SEM and the result showed that citreamicin ε caused disruptive surface of the mycelia.

Next-generation sequencing has yielded a vast amount of cattle genomic data for the global characterization of population genetic diversity and the identification of regions of the genome under natural and artificial selection. However, efficient storage, querying and visualization of such large datasets remain challenging. Here, we developed a comprehensive Bovine Genome Variation Database (BGVD, http://animal.nwsuaf.edu.cn/BosVar ) that provides six main functionalities: Gene Search, Variation Search, Genomic Signature Search, Genome Browser, Alignment Search Tools and the Genome Coordinate Conversion Tool. The BGVD contains information on genomic variations comprising ∼60.44 M SNPs, ∼6.86 M indels, 76,634 CNV regions and signatures of selective sweeps in 432 samples from modern cattle worldwide. Users can quickly retrieve distribution patterns of these variations for 54 cattle breeds through an interactive source of breed origin map using a given gene symbol or genomic region for any of the three versions of the bovine reference genomes (ARS-UCD1.2, UMD3.1.1, and Btau 5.0.1). Signals of selection are displayed as Manhattan plots and Genome Browser tracks. To further investigate and visualize the relationships between variants and signatures of selection, the Genome Browser integrates all variations, selection data and resources from NCBI, the UCSC Genome Browser and AnimalQTLdb. Collectively, all these features make the BGVD a useful archive for in-depth data mining and analyses of cattle biology and cattle breeding on a global scale.

Considering the limited of the training samples and the effect of speckle noise in PolSAR images, which further affects the learning performance of the classifier, a recursive convolution neural network model (CNN) is proposed. Samples with high confidence of each classification result will be used as the training samples of the next training. Then, a semi-supervised model is obtained for PolSAR image classification. This model is independent of the dependence of supervised classification on manual calibration samples. Furthermore, the model is an end-to-end classification framework based on discriminative feature learning which can learn the spatial texture features of polarized SAR images automatically while performing convolution operations in CNN. In addition, this model tries to learn features that are beneficial to classification from highly confident samples. There are three advantages of the proposed model: firstly, the problem of small samples is solved by increasing the training samples from each iterative classification result. Secondly, the low confidence samples are removed in each iteration to reduce the impact of noise samples on the robustness of the model. Finally, the initialization of CNN parameters in each iteration process is based on the results of the previous learning. As a result, the parameters will be set more and more robustly, so that the entire model will not have poor performance due to random initialization.