In recent years, research on the antioxidant activity of natural antioxidants has become more and more popular. Polyphenols are a large number of natural antioxidants in plants. This paper selected ...three common polyphenols to study their antioxidant activity based on quantum chemistry theory. This experiment hopes to provide a theoretical basis for the further development of polyphenol health food with strong antioxidant activity. Three polyphenols resveratrol, liquiritigenin, and isoliquiritigenin were optimized at the level of B3lyp/6-311G (d, p), and the single point energy was calculated with B3lyp/6–311 + + G (2d, 2p). The phenol hydroxyl bond dissociation enthalpy (BDE), ionization potential (IP), proton dissociation enthalpy (PDE), proton affinity (PA), and electron transfer enthalpy (ETE) were calculated in different phase states study the antioxidant mechanism. Draw the frontier molecular orbital and conduct dynamic simulation analysis scavenging · OH and · OOH to explore the most possible active sites in different phenolic hydroxyl sites. The bond length, dihedral angle, BDE, IP, PDE, PA and ETE were compared to speculate the antioxidant activity: Resveratrol > isoliquiritigenin > liquiritigenin. By analyzing the frontier molecular orbital and dynamic simulation results, it is speculated that the phenolic hydroxyl groups at C4’, C4’, and C4 are the most likely active sites of resveratrol, liquiritigenin, and isoliquiritigenin, respectively. In different phase states, the three compounds showed the same antioxidant activity, and the phenolic hydroxyl activities of the three compounds were different at different sites.
Since milk yield is a highly important economic trait in dairy cattle, the genome-wide association study (GWAS) is vital to explain the genetic architecture underlying milk yield and to perform ...marker-assisted selection (MAS). In this study, we adopted a haplotype-based empirical Bayesian GWAS to identify the loci and candidate genes for milk yield. A total of 1 092 Holstein cows were sequenced by using the genotyping by genome reducing and sequencing (GGRS) method. After filtering, 164 312 high-confidence SNPs and 13 476 haplotype blocks were identified to use for GWAS. The results indicated that 17 blocks were significantly associated with milk yield. We further identified the nearest gene of each haplotype block and annotated the genes with milk-associated quantitative trait locus (QTL) intervals and ingenuity pathway analysis (IPA) networks. Our analysis showed that four genes, DLGAP1, AP2B1, ITPR2 and THBS4, have relationships with milk yield, while another three, ARHGEF4, TDRD1 and KIF19, were inferred to have potential relationships. Additionally, a network derived from the IPA containing one inferred (ARHGEF4) and all four confirmed genes likely regulates milk yield. Our findings add to the understanding of identifying the causal genes underlying milk production traits and could guide follow up studies for further confirmation of the associated genes, pathways and biological networks.
A novel electrochemical sensor using the molecularly imprinted (MIP) oxygen-containing polypyrrole (PPy) decorated carbon nanotubes (CNTs) composite was proposed for in vivo detection of dopamine ...(DA). The prepared sensor exhibits a remarkable sensitivity of (16.18μA/μM) with a linear range of 5.0×10−11–5.0×10−6M and limit of detection as low as 1.0×10−11M in the detection of DA, which might be due to the plenty cavities for binding DA through π–π stacking between aromatic rings and hydrogen bonds between amino groups of DA and oxygen-containing groups of the novel PPy.
•Novel strategy to improve the sensitivity of CNTs polymer composite sensors was proposed.•PPy/CNTs-MIPs with plenty oxygen-containing groups was facilely synthesized.•Oxygen-containing groups of PPy could establish strong hydrogen bonds with amino groups of DA.•PPy/CNTs-MIPs sensor offered a nanomole level detection for DA in biological systems.
Genome-wide association studies (GWAS) based on high throughput SNP genotyping technologies open a broad avenue for exploring genes associated with milk production traits in dairy cattle. Motivated ...by pinpointing novel quantitative trait nucleotide (QTN) across Bos Taurus genome, the present study is to perform GWAS to identify genes affecting milk production traits using current state-of-the-art SNP genotyping technology, i.e., the Illumina BovineSNP50 BeadChip. In the analyses, the five most commonly evaluated milk production traits are involved, including milk yield (MY), milk fat yield (FY), milk protein yield (PY), milk fat percentage (FP) and milk protein percentage (PP). Estimated breeding values (EBVs) of 2,093 daughters from 14 paternal half-sib families are considered as phenotypes within the framework of a daughter design. Association tests between each trait and the 54K SNPs are achieved via two different analysis approaches, a paternal transmission disequilibrium test (TDT)-based approach (L1-TDT) and a mixed model based regression analysis (MMRA). In total, 105 SNPs were detected to be significantly associated genome-wise with one or multiple milk production traits. Of the 105 SNPs, 38 were commonly detected by both methods, while four and 63 were solely detected by L1-TDT and MMRA, respectively. The majority (86 out of 105) of the significant SNPs is located within the reported QTL regions and some are within or close to the reported candidate genes. In particular, two SNPs, ARS-BFGL-NGS-4939 and BFGL-NGS-118998, are located close to the DGAT1 gene (160bp apart) and within the GHR gene, respectively. Our findings herein not only provide confirmatory evidences for previously findings, but also explore a suite of novel SNPs associated with milk production traits, and thus form a solid basis for eventually unraveling the causal mutations for milk production traits in dairy cattle.
To investigate the potential mechanism of resveratrol in anti-fatigue by network pharmacology and molecular docking, and to investigate the anti-fatigue efficacy of resveratrol through in vitro ...animal experiments. Resveratrol action targets and fatigue-related targets were obtained using various databases. The anti-fatigue targets of resveratrol were obtained using the Venn diagram, uploaded to the String database, imported into Cytoscape 3.7.1, and constructed into a Protein-protein interaction network. The target genes were then subjected to Gene ontology and Kyoto encyclopedia of gene and genome enrichment analysis. Molecular docking verification was performed on the binding ability of the core target to resveratrol. Using swimming-trained mice as exercise models, exhaustive swimming time and fatigue-related biochemical parameters were used as indicators to investigate the effects of resveratrol on exercise endurance and energy metabolism. 104 anti-fatigue targets and 10 core target genes of resveratrol were obtained. KEGG analysis enrichment included AGE-RAGE signaling pathway in diabetic complications, Human cytomegalovirus infection, and Pathways in cancer. Molecular docking showed that the core target genes TP53, PIK3R1, AKT1, PIK3CA, and MAPK1 had good binding activity to resveratrol. Animal experiments showed that resveratrol could prolong the exhaustive swimming time of endurance-trained mice (P < 0.01), decrease aspartate aminotransferase, alanine aminotransferase, uric acid, blood lactate (P < 0.01), decrease blood urea nitrogen (P < 0.05), increase the liver glycogen, muscle glycogen (P < 0.01). Conclusion: Resveratrol has the characteristics of multiple targets and multiple pathways in anti-fatigue; resveratrol can enhance exercise endurance in mice.
Over several decades, a wide range of natural and artificial selection events in response to subtropical environments, intensive pasture and intensive feedlot systems have greatly changed the ...customary behaviour, appearance, and important economic traits of Shanghai Holstein cattle. In particular, the longevity of the Shanghai Holstein cattle population is generally short, approximately the 2nd to 3rd lactation. In this study, two complementary approaches, integrated haplotype score (iHS) and runs of homozygosity (ROH), were applied for the detection of selection signatures within the genome using genotyping by genome-reduced sequence data from 1092 cows.
In total, 101 significant iHS genomic regions containing selection signatures encompassing a total of 256 candidate genes were detected. There were 27 significant |iHS| genomic regions with a mean |iHS| score > 2. The average number of ROH per individual was 42.15 ± 25.47, with an average size of 2.95 Mb. The length of 78 % of the detected ROH was within the range of 1-2 MB and 2-4 MB, and 99 % were shorter than 8 Mb. A total of 168 genes were detected in 18 ROH islands (top 1 %) across 16 autosomes, in which each SNP showed a percentage of occurrence > 30 %. There were 160 and 167 genes associated with the 52 candidate regions within health-related QTL intervals and 59 candidate regions within reproduction-related QTL intervals, respectively. Annotation of the regions harbouring clustered |iHS| signals and candidate regions for ROH revealed a panel of interesting candidate genes associated with adaptation and economic traits, such as IL22RA1, CALHM3, ITGA9, NDUFB3, RGS3, SOD2, SNRPA1, ST3GAL4, ALAD, EXOSC10, and MASP2. In a further step, a total of 1472 SNPs in 256 genes were matched with 352 cis-eQTLs in 21 tissues and 27 trans-eQTLs in 6 tissues. For SNPs located in candidate regions for ROH, a total of 108 cis-eQTLs in 13 tissues and 4 trans-eQTLs were found for 1092 SNPs. Eighty-one eGenes were significantly expressed in at least one tissue relevant to a trait (P value < 0.05) and matched the 256 genes detected by iHS. For the 168 significant genes detected by ROH, 47 gene-tissue pairs were significantly associated with at least one of the 37 traits.
We provide a comprehensive overview of selection signatures in Shanghai Holstein cattle genomes by combining iHS and ROH. Our study provides a list of genes associated with immunity, reproduction and adaptation. For functional annotation, the cGTEx resource was used to interpret SNP-trait associations. The results may facilitate the identification of genes relevant to important economic traits and can help us better understand the biological processes and mechanisms affected by strong ongoing natural or artificial selection in livestock populations.
This paper presents an online supervisory control strategy for commuter hybrid electric vehicles (HEVs) based on driving condition learning and prediction. The aim is to provide an online ...self-learning-based framework to keep Pontryagin's minimum principle (PMP)-based control adapting to the time-varying driving condition on commuting routes and minimize fuel consumption. There are three steps to realizing this strategy. First, two novel statistical features are proposed to describe the frequency distribution of achievable working points of the hybrid powertrain under a driving condition. Second, based on the characteristic that commuting trips with similar trip start time, direction, and weather condition have similar driving conditions, we develop an instance-based machine learning algorithm to learn the driving condition. A k-nearest neighbor (k-NN) prediction algorithm is used to predict future driving conditions. Third, we establish an online supervisory control strategy, together with rolling driving condition prediction and optimal costate approximation. The approximation algorithm can approximate the optimal constant costate for the entire prediction horizon just based on the proposed two features. Simulation study and bench tests are conducted on a parallel hybrid powertrain of a city bus using standard driving cycles and real-world sampled commuting trips with different trip start times and weather conditions. The results show that the proposed driving condition learning and prediction method is effective for predicting the upcoming driving conditions. Meanwhile, the proposed strategy shows substantial improvement of fuel economy compared with the rule-based (RB) strategy and the adaptive equivalent consumption minimization strategy (A-ECMS).
Genome-wide association studies (GWAS) significantly enhance our ability to identify trait-associated genomic variants by considering the host genome. Moreover, the hologenome refers to the host ...organism’s collective genetic material and its associated microbiome. In this study, we utilized the hologenome framework, called Hologenome-wide association studies (HWAS), to dissect the architecture of complex traits, including milk yield, methane emissions, rumen physiology in cattle, and gut microbial composition in pigs. We employed four statistical models: (1) GWAS, (2) Microbial GWAS (M-GWAS), (3) HWAS-CG (hologenome interaction estimated using COvariance between Random Effects Genome-based restricted maximum likelihood (CORE-GREML)), and (4) HWAS-H (hologenome interaction estimated using the Hadamard product method). We applied Bonferroni correction to interpret the significant associations in the complex traits. The GWAS and M-GWAS detected one and sixteen significant SNPs for milk yield traits, respectively, whereas the HWAS-CG and HWAS-H each identified eight SNPs. Moreover, HWAS-CG revealed four, and the remaining models identified three SNPs each for methane emissions traits. The GWAS and HWAS-CG detected one and three SNPs for rumen physiology traits, respectively. For the pigs’ gut microbial composition traits, the GWAS, M-GWAS, HWAS-CG, and HWAS-H identified 14, 16, 13, and 12 SNPs, respectively. We further explored these associations through SNP annotation and by analyzing biological processes and functional pathways. Additionally, we integrated our GWA results with expression quantitative trait locus (eQTL) data using transcriptome-wide association studies (TWAS) and summary-based Mendelian randomization (SMR) methods for a more comprehensive understanding of SNP-trait associations. Our study revealed hologenomic variability in agriculturally important traits, enhancing our understanding of host-microbiome interactions.
•We evaluated key factors influencing joint breeding accuracy.•Each factor contributes to the accuracy of joint evaluation.•Casual variant effect size is the primary influencing factor.•There is a ...weak genetic correlation between two Duroc populations.•Multi-trait model is a promising method for joint evaluation.
Genomic prediction (GP) has greatly advanced animal and plant breeding over the past two decades. GP in joint populations is a feasible method to improve the accuracy of genomic estimated breeding values in small populations. However, there is still a need to understand the factors that influence GP in joint populations. This study used simulated data and real data from Duroc pig populations to examine the impact of linkage disequilibrium (LD), causal variants effect sizes (CVESs), and minor allele frequencies (MAF) of SNPs on the accuracy of genomic prediction in joint populations. Three prediction methods were used: genomic best linear unbiased prediction (GBLUP), single-step GBLUP and multi-trait GBLUP. Results from the simulated datasets showed that the accuracies of GP in joint populations were always higher than those in a single population when only LD inconsistencies existed. However, single-step GBLUP accuracy in joint populations decreased as the correlation of MAF between populations decreased, while the accuracy of GBLUP is consistently higher in joint populations than in a single population. As the correlation of CVES between populations decreased, the accuracy of both GBLUP and single-step GBLUP in joint populations declined. Analysis of real Duroc populations showed low genetic correlation, similar to the simulated relationship between the most distant populations. In most cases in Duroc populations, GP have higher accuracies in joint populations than in individual population. In conclusion, the consistency of CVES plays a more important role in multi-population GP. The genetic relatedness of the Duroc populations is so weak that the prediction accuracy of GP in joint populations is reduced in some traits. Multi-trait GBLUP is a competitive method for the joint breeding evaluation.
This study aimed to investigate the effects of incorporating different concentrations of flaxseed gum (FG) into acid-induced soy protein isolate (SPI) gels. The investigation focused on assessing the ...effects of FG on the textural, rheological, and tribological properties of the resultant SPI gels. The results showed that adding a small amount of FG (0.05%) to the SPI gel system increased the storage modulus (G') and enhanced gelation while improving textural properties including hardness, viscosity, elasticity, and adhesion. Moreover, these gels exhibited strong water-holding capacity, a desirable property in various food products. However, when the concentration was increased to 0.3%, the WHC of the gel decreased, as did the hardness and cohesiveness. The particle size of the gel also increased with increasing concentration. Tribological investigations revealed that at 0.05-0.2% FG addition, the coefficient of friction (μ) of the composite gel was decreased compared to the pure SPI gel. In the sliding speed range of 1-100 mm/s, the coefficient of friction gradually increased with increasing concentration. When the FG concentration was 0.05%, the μ of the gel system was the lowest. In summary, low concentration of FG (0.05%) was found to play an important role in improving the properties of SPI gel, including enhancing textural, rheological, and lubricating properties.
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