Surveys of managed honey bee colony losses worldwide have become fundamental for engineering a sustainable and systematic approach to protect honey bees. Though China is a member of the world’s apiculture superpowers, the investigation of honey bee colony losses from Chinese government was not formally launched until recently. In this study, we investigated the colony winter losses of the western honey bee (Apis mellifera) of four consecutive years in 2013–2017 from 19 provinces in China, with a total of 2387 responding Chinese beekeepers (195 hobby beekeepers, 1789 side-line beekeepers, 403 commercial beekeepers) providing the records of overwintering mortality of honey bee colonies. The calculated colony losses were 8.7%, a relatively low mortality below the world average. There still exist considerable variations in total losses among provinces (ranging from 0.9% to 22.0%), years (ranging from 8.1% to 10.6%) and scales of apiaries (ranging from 7.5% to 10.0%). Furthermore, we deeply analyzed and estimated the effects of potential risk factors on the colonies’ winter losses, and speculated that the queen problems, the operation sizes and proportion of new queens are leading causes of the high honey bee colony mortality in China. More research and advanced technical methods are still required for correlation analysis and verification in future surveys of managed honey bee colony winter losses.
Background and Aims Chromophobe renal cell carcinoma (chRCC) is the third common pathological subtype in renal cancers. However, the underlying mechanisms of specific genetic characteristics of chRCC are currently unclear. In this study, protein expression profiles, gene ontology (GO), and survival plots were provided by integrated bioinformatics analysis to investigate key genes associated with the mechanism of tumorigenesis and prognosis of chRCC. Methods The chRCC data set of gene expression profiles and clinical data were obtained from the gdc‐client ( https://portal.gdc.cancer.gov ) deposited on The Cancer Genome Atlas (TCGA) data portal. Differentially expressed genes (DEGs) in chRCC, compared with normal samples, were analyzed by R packages “DESeq2,” “edgeR,” and “limma.” Heat maps, volcano plots, and principal component analysis (PCA) were performed for integrated analyses. GUniGO, mutant analysis, and survival plots were performed by R packages. A protein–protein interaction (PPI) network was generated and analyzed by R packages, online String software, and Cytoscape software. Survival analysis and gene expressing comparison in tumor and normal samples were used to detect the core genes of chRCC. Furthermore, the top interacting proteins were reanalyzed. Results A total of 306 upregulated genes and 678 downregulated genes were identified by a Venn diagram. Ten hub genes were extracted from PPI network. Furthermore, Alpha‐2‐Heremans‐Schmid‐glycoprotein (AHSG), one of 10 hub genes, was found to be associated with chRCC, and had a big difference in expression between survival and dead events. AHSG could predict potential prognostic and may be a diagnostic biomarker in chRCC. Conclusion This study illustrated that AHSG may be a potential therapeutic target and prognostic genetic marker for chRCC.
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