DNA methylation is being increasingly recognized to play a role in regulation of hepatitis B virus (HBV) gene expression. The aim of this study was to compare the CpG island distribution among different HBV genotypes. We analyzed 176 full-length HBV genomic sequences obtained from the GenBank database, belonging to genotypes A through J, to identify the CpG islands in the HBV genomes. Our results showed that while 79 out of 176 sequences contained three conventional CpG islands (I–III) as previously described, 83 HBV sequences harbored only two of the three known islands. Novel CpG islands were identified in the remaining 14 HBV isolates and named as CpG island IV, V, and VI. Among the eight known HBV genotypes and two putative genotypes, while HBV genomes containing three CpG islands were predominant in genotypes A, B, D, E, and I; genotypes C, F, G, and H tended to contain only two CpG islands (II and III). In conclusion, the CpG islands, which are potential targets for DNA methylation mediated by the host functions, differ among HBV genotypes, and these genotype-specific differences in CpG island distribution could provide new insights into the understanding of epigenetic regulation of HBV gene expression and hepatitis B disease outcome.

Highlights d LC domain-mediated coalescence is essential for Otu deubiquitinase activity d RNAs bind LC domain and enhance Otu coalescence and its enzyme activity d Otu/Bam complex targets dTraf6 to maintain gut immune homeostasis d Dynamic regulation of Otu/Bam granules in guts controls fly lifespan

Today, obesity and nonalcoholic steatohepatitis are a worldwide epidemic, although how these syndromes are regulated with respect to lncRNAs remains largely unknown. Our previous studies have revealed important pathological features and molecular characteristics of nonalcoholic steatohepatitis in the minipig model, and in this study, we analyze the features of lncRNAs and their potential target genes. Minipig samples only from liver were analyzed using next-generation deep sequencing. In total, we obtained 585 million raw reads approximately 70.4 Gb of high quality data. After a strict five-step filtering process, 1,179 lncRNAs were identified, including 89 differentially expressed lncRNAs (P < 0.05) in the experiment group relative to the control group. The cis and trans analysis identified target genes that were enriched for specific GO terms (P < 0.01), including immune processes, chemokine activity, cytokine activity, and G-protein coupled receptor binding, which are closely related to nonalcoholic steatohepatitis. The predicted protein-coding targets of the differentially expressed lncRNAs were further analyzed, such as PPAR, FADS2, DGAT2, ACAA2, CYP2E1, ADH4, and Fos. This study reveals a wealth of candidate lncRNAs involved in NASH and their regulated pathways, which should facilitate further research into the molecular mechanisms of this disorder.

Based on thermal simulation experiment, SEM analysis and mathematical simulation, limestone dissolution and decomposition mechanism in steelmaking slag were studied. The results showed that limestone decomposition and dissolution happen simultaneously in molten slag, and influence each other. Owing to high-activity lime product and CO 2 from limestone decomposition, the dissolution rate of limestone is greater than that of lime under the same conditions in slag, and the calculated activation energy of limestone dissolution is 226.8 kJ mol −1 . On the other hand, as decomposition product lime dissolves into slag, a sloughing-type unreacted shrinking core model was proposed to describe limestone decomposition behaviour in slag. In addition, mathematical simulation results showed that heat transfer is the rate-controlling step for limestone decomposition in slag.

Obesity is the major risk factor for type 2 diabetes, cardiovascular disorders, and many other diseases. Adipose tissue inflammation is frequently associated with obesity and contributes to the morbidity and mortality. Dedicator of cytokinesis 2 (DOCK2) is involved in several inflammatory diseases, but its role in obesity remains unknown. To explore the function of DOCK2 in obesity and insulin resistance, WT and DOCK2-deficient (DOCK2) mice were given chow or high-fat diet (HFD) for 12 weeks followed by metabolic, biochemical, and histologic analyses. DOCK2 was robustly induced in adipose tissues of WT mice given HFD. DOCK2 mice with HFD showed decreased body weight gain and improved metabolic homeostasis and insulin resistance compared with WT mice. DOCK2 deficiency also attenuated adipose tissue and systemic inflammation accompanied by reduced macrophage infiltration. Moreover, DOCK2 mice exhibited increased expression of metabolic genes in adipose tissues with greater energy expenditure. Mechanistically, DOCK2 appeared to regulate brown adipocyte differentiation because increased preadipocyte differentiation to brown adipocytes in interscapular and inguinal fat was observed in DOCK2 mice, as compared with WT. These data indicated that DOCK2 deficiency protects mice from HFD-induced obesity, at least in part, by stimulating brown adipocyte differentiation. Therefore, targeting DOCK2 may be a potential therapeutic strategy for treating obesity-associated diseases.

Difluoromethylation is of prime importance for its applicability in functionalizing diverse fluorine‐containing heterocycles, which are core groups in diverse biologically and pharmacologically active ingredients. Herein, we report a novel transition metal‐ and oxidant‐free visible light‐photoinduced protocol for direct C(sp2)‐H difluoromethylation of heterocycles. The reaction afforded difluoromethyl heterocycles without using colored organic dyes and metal catalysts in good yields and showed a broad substrate tolerance. Moreover, the representative products exhibited potential drug activity, and one product showed good antifungal activities against Rhizoctorzia solani (62.7 %).

Field weeds identification is challenging for precision spraying, i.e., the automation identification of the weeds from the crops. For rapidly obtaining weed distribution in field, this study developed a weed density detection method based on absolute feature corner point (AFCP) algorithm for the first time. For optimizing the AFCP algorithm, image preprocessing was firstly performed through a sub-module processing capable of segmenting and optimizing the field images. The AFCP algorithm improved Harris corner to extract corners of single crop and weed and then sub-absolute corner classifier as well as absolute corner classifier were proposed for absolute corners detection of crop rows. Then, the AFCP algorithm merged absolute corners to identify crop and weed position information. Meanwhile, the weed distribution was obtained based on two weed density parameters (weed pressure and cluster rate). At last, the AFCP algorithm was validated based on the images that were obtained using one typical digital camera mounted on the tractor in field. The results showed that the proposed weed detection method manifested well given its ability to process an image of 2748 × 576 pixels using 782 ms as well as its accuracy in identifying weeds reaching 90.3%. Such results indicated that the weed detection method based on AFCP algorithm met the requirements of practical weed management in field, including the real-time images computation processing and accuracy, which provided the theoretical base for the precision spraying operations.

No abstract