Numerical analysis of drainage rate in multi-layer coalbed methane development in Western Guizhou, Southern China

In Western Guizhou, China, multi-layer development is a successful way for CBM development, with drainage rate control being the essential technology. In this article, a coupled hydraulic-mechanical numerical model considering permeability velocity-sensitive damage was established to analyze the impact of drainage rate on the gas production and reservoir parameters of multi-layer CBM development. The CBM development in the study area can be divided into four stages. The permeability velocity-sensitive damage and Jamin effect mainly occurred in the first two stages. Increasing the drainage rate during the first two stages will cause more serious permeability velocity-sensitive damage. Reducing the drainage rate in the first two stages could alleviate the permeability velocity-sensitive damage and Jamin effect. During the stable production stage II, the gas seepage being dominant in the c409 coal seam, the gas production would be significantly reduced under the permeability stress-sensitive damage as the drainage rate increasing. Based on the simulation results, three recommendations concerning the drainage rate optimization of multi-layer CBM development were advanced, and gas production was successfully improved. This study has important theoretical and practical significance for guiding the multi-layer CBM development in Western Guizhou and Southern China.</p

Data_Sheet_1_Allium Vegetables, Garlic Supplements, and Risk of Cancer: A Systematic Review and Meta-Analysis.docx

PurposeThe role of allium vegetables or garlic supplements on reducing cancer risk was inconsistent between laboratory study findings and related epidemiologic studies.MethodsStudies assessing the effect of allium vegetables and garlic supplement consumption on cancer risk were included in our meta-analysis. We used fixed- or random-effects models to pool effect measures to evaluate the highest and lowest consumption. A dose-response regression analysis was used to assess the association between allium vegetables, garlic supplements, and cancer risk.ResultsIn a pooled analysis of 22 studies with 25 reports on allium vegetables, a high consumption of allium vegetables showed no significant association with cancer risk (relative risk [RR] = 0.97, 95% confidence interval [CI] 0.92–1.03) in a fixed-effects model. Similarly, garlic supplements were not found to be significantly associated with an increased risk of cancer (RR = 0.97, 95% CI 0.84–1.12) in a random-effects model involving a pooled analysis of 10 studies with 11 reports. Consumption of allium vegetables did not significantly correspond with cancer risk (P for nonlinearity = 0.958, P for linearity = 0.907).ConclusionIn this meta-analysis, we found no evidence that higher consumption of allium vegetables or garlic supplements reduced the risk of cancer; however, this finding requires further validation.Systematic Review Registrationhttps://www.crd.york.ac.uk/prospero/#recordDetails, identifier: CRD42021246947.</p

Image_1_Genomic Prediction for 25 Agronomic and Quality Traits in Alfalfa (Medicago sativa).TIF

Agronomic and quality traits in alfalfa are very important to forage industry. Genomic prediction (GP) based on genotyping-by-sequencing (GBS) data could shorten the breeding cycles and accelerate the genetic gains of these complex traits, if they display moderate to high prediction accuracies. The aim of this study was to investigate the predictive potentials of these traits in alfalfa. A total of 322 genotypes from 75 alfalfa accessions were used for GP of the agronomic and quality traits, which were related to yield and nutrition value, respectively, using BayesA, BayesB, and BayesCπ methods. Ten-fold cross validation was used to evaluate the accuracy of GP represented by the correlation between genomic estimated breeding value (GEBV) and estimated breeding value (EBV). The accuracies ranged from 0.0021 to 0.6485 for different traits. For each trait, three GP methods displayed similar prediction accuracies. Among 15 quality traits, mineral element Ca had a moderate and the highest prediction accuracy (0.34). NDF digestibility after 48 h (NDFD 48 h) and 30 h (NDFD 30 h) and mineral element Mg had prediction accuracies varying from 0.20 to 0.25. Other traits, for example, fat and crude protein, showed low prediction accuracies (0.05 to 0.19). Among 10 agronomic traits, however, some displayed relatively high prediction accuracies. Plant height (PH) in fall (FH) had the highest prediction accuracy (0.65), followed by flowering date (FD) and plant regrowth (PR) with accuracies at 0.52 and 0.51, respectively. Leaf to stem ratio (LS), plant branch (PB), and biomass yield (BY) reached to moderate prediction accuracies ranging from 0.25 to 0.32. Our results revealed that a few agronomic traits, such as FH, FD, and PR, had relatively high prediction accuracies, therefore it is feasible to apply genomic selection (GS) for these traits in alfalfa breeding programs. Because of the limitations of population size and density of SNP markers, several traits displayed low accuracies which could be improved by a bigger reference population, higher density of SNP markers, and more powerful statistic tools.</p

Image2_Comparison of Genotype Imputation for SNP Array and Low-Coverage Whole-Genome Sequencing Data.TIF

Genotype imputation is the term used to describe the process of inferring unobserved genotypes in a sample of individuals. It is a key step prior to a genome-wide association study (GWAS) or genomic prediction. The imputation accuracy will directly influence the results from subsequent analyses. In this simulation-based study, we investigate the accuracy of genotype imputation in relation to some factors characterizing SNP chip or low-coverage whole-genome sequencing (LCWGS) data. The factors included the imputation reference population size, the proportion of target markers /SNP density, the genetic relationship (distance) between the target population and the reference population, and the imputation method. Simulations of genotypes were based on coalescence theory accounting for the demographic history of pigs. A population of simulated founders diverged to produce four separate but related populations of descendants. The genomic data of 20,000 individuals were simulated for a 10-Mb chromosome fragment. Our results showed that the proportion of target markers or SNP density was the most critical factor affecting imputation accuracy under all imputation situations. Compared with Minimac4, Beagle5.1 reproduced higher-accuracy imputed data in most cases, more notably when imputing from the LCWGS data. Compared with SNP chip data, LCWGS provided more accurate genotype imputation. Our findings provided a relatively comprehensive insight into the accuracy of genotype imputation in a realistic population of domestic animals.</p

Table1_Comparison of Genotype Imputation for SNP Array and Low-Coverage Whole-Genome Sequencing Data.DOCX

Genotype imputation is the term used to describe the process of inferring unobserved genotypes in a sample of individuals. It is a key step prior to a genome-wide association study (GWAS) or genomic prediction. The imputation accuracy will directly influence the results from subsequent analyses. In this simulation-based study, we investigate the accuracy of genotype imputation in relation to some factors characterizing SNP chip or low-coverage whole-genome sequencing (LCWGS) data. The factors included the imputation reference population size, the proportion of target markers /SNP density, the genetic relationship (distance) between the target population and the reference population, and the imputation method. Simulations of genotypes were based on coalescence theory accounting for the demographic history of pigs. A population of simulated founders diverged to produce four separate but related populations of descendants. The genomic data of 20,000 individuals were simulated for a 10-Mb chromosome fragment. Our results showed that the proportion of target markers or SNP density was the most critical factor affecting imputation accuracy under all imputation situations. Compared with Minimac4, Beagle5.1 reproduced higher-accuracy imputed data in most cases, more notably when imputing from the LCWGS data. Compared with SNP chip data, LCWGS provided more accurate genotype imputation. Our findings provided a relatively comprehensive insight into the accuracy of genotype imputation in a realistic population of domestic animals.</p

Table2_Comparison of Genotype Imputation for SNP Array and Low-Coverage Whole-Genome Sequencing Data.DOCX

Genotype imputation is the term used to describe the process of inferring unobserved genotypes in a sample of individuals. It is a key step prior to a genome-wide association study (GWAS) or genomic prediction. The imputation accuracy will directly influence the results from subsequent analyses. In this simulation-based study, we investigate the accuracy of genotype imputation in relation to some factors characterizing SNP chip or low-coverage whole-genome sequencing (LCWGS) data. The factors included the imputation reference population size, the proportion of target markers /SNP density, the genetic relationship (distance) between the target population and the reference population, and the imputation method. Simulations of genotypes were based on coalescence theory accounting for the demographic history of pigs. A population of simulated founders diverged to produce four separate but related populations of descendants. The genomic data of 20,000 individuals were simulated for a 10-Mb chromosome fragment. Our results showed that the proportion of target markers or SNP density was the most critical factor affecting imputation accuracy under all imputation situations. Compared with Minimac4, Beagle5.1 reproduced higher-accuracy imputed data in most cases, more notably when imputing from the LCWGS data. Compared with SNP chip data, LCWGS provided more accurate genotype imputation. Our findings provided a relatively comprehensive insight into the accuracy of genotype imputation in a realistic population of domestic animals.</p

Image1_Comparison of Genotype Imputation for SNP Array and Low-Coverage Whole-Genome Sequencing Data.TIF

Genotype imputation is the term used to describe the process of inferring unobserved genotypes in a sample of individuals. It is a key step prior to a genome-wide association study (GWAS) or genomic prediction. The imputation accuracy will directly influence the results from subsequent analyses. In this simulation-based study, we investigate the accuracy of genotype imputation in relation to some factors characterizing SNP chip or low-coverage whole-genome sequencing (LCWGS) data. The factors included the imputation reference population size, the proportion of target markers /SNP density, the genetic relationship (distance) between the target population and the reference population, and the imputation method. Simulations of genotypes were based on coalescence theory accounting for the demographic history of pigs. A population of simulated founders diverged to produce four separate but related populations of descendants. The genomic data of 20,000 individuals were simulated for a 10-Mb chromosome fragment. Our results showed that the proportion of target markers or SNP density was the most critical factor affecting imputation accuracy under all imputation situations. Compared with Minimac4, Beagle5.1 reproduced higher-accuracy imputed data in most cases, more notably when imputing from the LCWGS data. Compared with SNP chip data, LCWGS provided more accurate genotype imputation. Our findings provided a relatively comprehensive insight into the accuracy of genotype imputation in a realistic population of domestic animals.</p

Image1_Integrating genome-wide association studies and population genomics analysis reveals the genetic architecture of growth and backfat traits in pigs.pdf

Growth and fat deposition are complex traits, which can affect economical income in the pig industry. Due to the intensive artificial selection, a significant genetic improvement has been observed for growth and fat deposition in pigs. Here, we first investigated genomic-wide association studies (GWAS) and population genomics (e.g., selection signature) to explore the genetic basis of such complex traits in two Large White pig lines (n = 3,727) with the GeneSeek GGP Porcine HD array (n = 50,915 SNPs). Ten genetic variants were identified to be associated with growth and fatness traits in two Large White pig lines from different genetic backgrounds by performing both within-population GWAS and cross-population GWAS analyses. These ten significant loci represented eight candidate genes, i.e., NRG4, BATF3, IRS2, ANO1, ANO9, RNF152, KCNQ5, and EYA2. One of them, ANO1 gene was simultaneously identified for both two lines in BF100 trait. Compared to single-population GWAS, cross-population GWAS was less effective for identifying SNPs with population-specific effect, but more powerful for detecting SNPs with population-shared effects. We further detected genomic regions specifically selected in each of two populations, but did not observe a significant enrichment for the heritability of growth and backfat traits in such regions. In summary, the candidate genes will provide an insight into the understanding of the genetic architecture of growth-related traits and backfat thickness, and may have a potential use in the genomic breeding programs in pigs.</p

<i>Pax7</i> expression is affected by <i>Src</i>, <i>Ezh2</i>, and <i>Akt</i> suppression by siRNA in the sheep primary myoblasts.

<p>The sheep primary myoblasts were transfected with <i>Akt</i>-siRNA, <i>Ezh2</i>-siRNA, <i>Src</i>-siRNA, and the controls in serum-free medium using Lipofectamine 2000. After cultured 3 days in GM, cell lysates were used for gene expression analysis by qPCR in each group. For qPCR analysis, gene expression was quantified relative to <i>GADPH</i> expression using the 2-△△Ct method. The expression data in every group are shown as the mean + SD (n = 3) * P < 0.05, *** P < 0.001.</p

Identification and Characterization of the miRNA Transcriptome of <em>Ovis aries</em>

<div><p>The discovery and identification of <i>Ovis aries</i> (sheep) miRNAs will further promote the study of miRNA functions and gene regulatory mechanisms. To explore the microRNAome (miRNAome) of sheep in depth, samples were collected that included eight developmental stages: the longissimus dorsi muscles of Texel fetuses at 70, 85, 100, 120, and 135 days, and the longissimus dorsi muscles of Ujumqin fetuses at 70, 85, 100, 120, and 135 d, and lambs at 0 (birth), 35, and 70 d. These samples covered all of the representative periods of <i>Ovis aries</i> growth and development throughout gestation (about 150 d) and 70 d after birth. Texel and Ujumqin libraries were separately subjected to Solexa deep sequencing; 35,700,772 raw reads were obtained overall. We used ACGT101-miR v4.2 to analyze the sequence data. Following meticulous comparisons with mammalian mature miRNAs, precursor hairpins (pre-miRNAs), and the latest sheep genome, we substantially extended the <i>Ovis aries</i> miRNAome. The list of pre-miRNAs was extended to 2,319, expressing 2,914 mature miRNAs. Among those, 1,879 were genome mapped to unique miRNAs, representing 2,436 genome locations, and 1,754 pre-miRNAs were mapped to chromosomes. Furthermore, the <i>Ovis aries</i> miRNAome was processed using an elaborate bioinformatic analysis that examined multiple end sequence variation in miRNAs, precursors, chromosomal localizations, species-specific expressions, and conservative properties. Taken together, this study provides the most comprehensive and accurate exploration of the sheep miRNAome, and draws conclusions about numerous characteristics of <i>Ovis aries</i> miRNAs, including miRNAs and isomiRs.</p> </div