MAK: a machine learning framework improved genomic prediction via multi-target ensemble regressor chains and automatic selection of assistant traits.

Incorporating the genotypic and phenotypic of the correlated traits into the multi-trait model can significantly improve the prediction accuracy of the target trait in animal and plant breeding, as well as human genetics. However, in most cases, the phenotypic information of the correlated and target trait of the individual to be evaluated was null simultaneously, particularly for the newborn. Therefore, we propose a machine learning framework, MAK, to improve the prediction accuracy of the target trait by constructing the multi-target ensemble regression chains and selecting the assistant trait automatically, which predicted the genomic estimated breeding values of the target trait using genotypic information only. The prediction ability of MAK was significantly more robust than the genomic best linear unbiased prediction, BayesB, BayesRR and the multi trait Bayesian method in the four real animal and plant datasets, and the computational efficiency of MAK was roughly 100 times faster than BayesB and BayesRR.

Dehydrogenation of Cyclohexanones to Phenols: A Mini Review.

BACKGROUND: Phenol and its derivatives are important intermediates in the chemical industry, especially the pharmaceutical and electronic industries. The synthesis of phenols has attracted the attention of scientists due to their importance. Dehydrogenation of cyclohexanones is one of the promising aromatization strategies for phenols manufacture because the raw materials are low cost and stable. In recent years, some efficient and green methods with the use of H2, O2 and air, alkene, H2 and O2-free are described. OBJECTIVE: This mini-review will summarize some recent developments relating to the dehydrogenation of cyclohexanones to phenols, along with their interesting mechanism aspects. The challenges and future trends of the transformation will be prospected. CONCLUSION: The synthesis of phenols from the dehydrogenation of cyclohexanones has recently attracted much attention. Some synthetic methods have been established, and interesting mechanisms have been proposed in some cases. Lots of catalysts were developed for the transformation to afford the corresponding product. Although the present methods still have drawbacks and limitations, it is supposed that many novel methods would probably be developed in the near future.

Improving Genomic Prediction with Machine Learning Incorporating TPE for Hyperparameters Optimization.

Depending on excellent prediction ability, machine learning has been considered the most powerful implement to analyze high-throughput sequencing genome data. However, the sophisticated process of tuning hyperparameters tremendously impedes the wider application of machine learning in animal and plant breeding programs. Therefore, we integrated an automatic tuning hyperparameters algorithm, tree-structured Parzen estimator (TPE), with machine learning to simplify the process of using machine learning for genomic prediction. In this study, we applied TPE to optimize the hyperparameters of Kernel ridge regression (KRR) and support vector regression (SVR). To evaluate the performance of TPE, we compared the prediction accuracy of KRR-TPE and SVR-TPE with the genomic best linear unbiased prediction (GBLUP) and KRR-RS, KRR-Grid, SVR-RS, and SVR-Grid, which tuned the hyperparameters of KRR and SVR by using random search (RS) and grid search (Gird) in a simulation dataset and the real datasets. The results indicated that KRR-TPE achieved the most powerful prediction ability considering all populations and was the most convenient. Especially for the Chinese Simmental beef cattle and Loblolly pine populations, the prediction accuracy of KRR-TPE had an 8.73% and 6.08% average improvement compared with GBLUP, respectively. Our study will greatly promote the application of machine learning in GP and further accelerate breeding progress.

Incorporating kernelized multi-omics data improves the accuracy of genomic prediction.

BACKGROUND: Genomic selection (GS) has revolutionized animal and plant breeding after the first implementation via early selection before measuring phenotypes. Besides genome, transcriptome and metabolome information are increasingly considered new sources for GS. Difficulties in building the model with multi-omics data for GS and the limit of specimen availability have both delayed the progress of investigating multi-omics. RESULTS: We utilized the Cosine kernel to map genomic and transcriptomic data as [Formula: see text] symmetric matrix (G matrix and T matrix), combined with the best linear unbiased prediction (BLUP) for GS. Here, we defined five kernel-based prediction models: genomic BLUP (GBLUP), transcriptome-BLUP (TBLUP), multi-omics BLUP (MBLUP, [Formula: see text]), multi-omics single-step BLUP (mssBLUP), and weighted multi-omics single-step BLUP (wmssBLUP) to integrate transcribed individuals and genotyped resource population. The predictive accuracy evaluations in four traits of the Chinese Simmental beef cattle population showed that (1) MBLUP was far preferred to GBLUP (ratio = 1.0), (2) the prediction accuracy of wmssBLUP and mssBLUP had 4.18% and 3.37% average improvement over GBLUP, (3) We also found the accuracy of wmssBLUP increased with the growing proportion of transcribed cattle in the whole resource population. CONCLUSIONS: We concluded that the inclusion of transcriptome data in GS had the potential to improve accuracy. Moreover, wmssBLUP is accepted to be a promising alternative for the present situation in which plenty of individuals are genotyped when fewer are transcribed.

Integrating genomics and transcriptomics to identify candidate genes for subcutaneous fat deposition in beef cattle.

Fat deposition is a complex economic trait regulated by polygenic genetic basis and environmental factors. Therefore, integrating multi-omics data to uncover its internal regulatory mechanism has attracted extensive attention. Here, we performed genomics and transcriptomics analysis to detect candidates affecting subcutaneous fat (SCF) deposition in beef cattle. The association of 770K SNPs with the backfat thickness captured nine significant SNPs within or near 11 genes. Additionally, 13 overlapping genes regarding fat deposition were determined via the analysis of differentially expressed genes and weighted gene co-expression network analysis (WGCNA). We then calculated the correlations of these genes with BFT and constructed their interaction network. Finally, seven biomarkers including ACACA, SCD, FASN, ACOX1, ELOVL5, HACD2, and HSD17B12 were screened. Notably, ACACA, identified by the integration of genomics and transcriptomics, was more likely to exert profound effects on SCF deposition. These findings provided novel insights into the regulation mechanism underlying bovine fat accumulation.

A high-performance and stable Cu/Beta for adsorption-catalytic oxidation in-situ destruction of low concentration toluene.

Finding a cost-effective treatment to remove of low concentrations of volatile organic compounds (VOCs) is still a challenge. In this study, a Cu/Beta material was developed for in situ adsorption-catalytic oxidation of low concentrations of toluene. The results showed that the addition of Cu enhanced the adsorption and catalytic oxidation of toluene by Beta zeolite. Cu7/Beta with a Cu+ ratio of close to 50% performed best. The high adsorption of Cu7/Beta was mainly attributed to the abundant Cu+ species and the micro-mesoporous structure of the Beta zeolite, and the high catalytic oxidation was attributed to the lattice oxygen in the uniformly dispersed CuO. Finally, the adsorption intermediates and reaction pathways in the catalytic oxidation of toluene were clarified using XPS and DRIFTS spectra. This work provides new strategies for the development of efficient and stable adsorption-catalytic oxidation in situ destruction materials.

Incorporating genome-wide and transcriptome-wide association studies to identify genetic elements of longissimus dorsi muscle in Huaxi cattle.

Locating the genetic variation of important livestock and poultry economic traits is essential for genetic improvement in breeding programs. Identifying the candidate genes for the productive ability of Huaxi cattle was one crucial element for practical breeding. Based on the genotype and phenotype data of 1,478 individuals and the RNA-seq data of 120 individuals contained in 1,478 individuals, we implemented genome-wide association studies (GWAS), transcriptome-wide association studies (TWAS), and Fisher's combined test (FCT) to identify the candidate genes for the carcass trait, the weight of longissimus dorsi muscle (LDM). The results indicated that GWAS, TWAS, and FCT identified seven candidate genes for LDM altogether: PENK was located by GWAS and FCT, PPAT was located by TWAS and FCT, and XKR4, MTMR3, FGFRL1, DHRS4, and LAP3 were only located by one of the methods. After functional analysis of these candidate genes and referring to the reported studies, we found that they were mainly functional in the progress of the development of the body and the growth of muscle cells. Combining advanced breeding techniques such as gene editing with our study will significantly accelerate the genetic improvement for the future breeding of Huaxi cattle.

Transcriptomics and Lipid Metabolomics Analysis of Subcutaneous, Visceral, and Abdominal Adipose Tissues of Beef Cattle.

Fat deposition traits are influenced by genetics and environment, which affect meat quality, growth rate, and energy metabolism of domestic animals. However, at present, the molecular mechanism of fat deposition is not entirely understood in beef cattle. Therefore, the current study conducted transcriptomics and lipid metabolomics analysis of subcutaneous, visceral, and abdominal adipose tissue (SAT, VAT, and AAT) of Huaxi cattle to investigate the differences among these adipose tissues and systematically explore how candidate genes interact with metabolites to affect fat deposition. These results demonstrated that compared with SAT, the gene expression patterns and metabolite contents of VAT and AAT were more consistent. Particularly, SCD expression, monounsaturated fatty acid (MUFA) and triglyceride (TG) content were higher in SAT, whereas PCK1 expression and the contents of saturated fatty acid (SFA), diacylglycerol (DG), and lysoglycerophosphocholine (LPC) were higher in VAT. Notably, in contrast to PCK1, 10 candidates including SCD, ELOVL6, ACACA, and FABP7 were identified to affect fat deposition through positively regulating MUFA and TG, and negatively regulating SFA, DG, and LPC. These findings uncovered novel gene resources and offered a theoretical basis for future investigation of fat deposition in beef cattle.

Enantioselective analysis of lansoprazole in rat plasma by LC-MS/MS: Application to a stereoselective pharmacokinetic study.

A rapid, sensitive and enantioselective method was developed and fully validated for the separation and determination of lansoprazole enantiomers in rat plasma by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The analytes and the internal standard (esomeprazole) were both extracted from plasma samples by liquid-liquid extraction with diethyl ether-dichloromethane (70:30; v/v). Satisfactory resolution (Rs = 2.0) was achieved within 7.3 min on a Chiralpak ID column (250 × 4.6 mm, 5 µm) employing acetonitrile-water (60:40, v/v) as the mobile phase at a flow rate of 0.6 mL/min. The acquisition of mass spectrometric data was performed in the multiple reaction monitoring mode coupled with a positive electrospray ionization source. A comprehensive validation of this method was rigorously conducted over the concentration range of 1.00-500.0 ng/mL for both enantiomers. All of the validation data demonstrated that the desirable linearity, sensitivity, accuracy, precision, recovery and stability were attained from the proposed approach. The established method was successfully applied to a stereoselective pharmacokinetic study of lansoprazole enantiomers in rat plasma after oral administration of 3 mg/kg racemic lansoprazole or dexlansoprazole. No chiral inversion was observed during the experimental procedure.

Enantioselective separation and determination of miconazole in rat plasma by chiral LC-MS/MS: application in a stereoselective pharmacokinetic study.

Miconazole has one chiral center, and consists of two enantiomers. In this study, a novel chiral liquid chromatography-tandem mass spectrometry method was developed for enantioselective separation and determination of miconazole in rat plasma. For the first time, the enantioselective pharmacokinetics of miconazole was investigated by the current method. Firstly, attempts were made to separate the enantiomers in reversed-phase mode with a mobile phase that was mass spectrometry compatible. Baseline separation was achieved on a Chiralpak IC column with a mobile phase composed of acetonitrile and aqueous ammonium hydrogen carbonate (5 mM; 80:20, v/v). Data were acquired in multiple reaction monitoring mode with positive electrospray ionization by triple-quadrupole mass spectrometry. Then, overall method validation regarding the linearity, accuracy, precision, extraction recovery, matrix effect, and stability of each enantiomer was performed, and acceptable results were obtained for all of these. Finally, the method developed was applied in an enantioselective pharmacokinetic study of miconazole enantiomers in rats after oral administration of racemic miconazole at doses of 5 and 10 mg/kg. The results demonstrated that (-)-(R)-miconazole had a higher concentration than (+)-(S)-miconazole in plasma, with a ratio of 1.3-1.7 for both doses. This is the first experimental evidence of enantioselective behavior of miconazole in vivo, and provides a reference for clinical practice and encourages further research into miconazole enantioselective metabolism and drug interactions. Graphical Abstract A stereoselective pharmacokinetic study of the miconazole enantiomers was investigated using a novel chiral liquid chromatography-tandem mass spectrometry method. Baseline separation was achieved on Chiralpak IC column, and Chiralcel OJ column was used to collect single enantiomer. A significant difference between the two enantiomers was observed in view of the plasma concentration.

Carboxymethyl ß-cyclodextrin as chiral selector in capillary electrophoresis: Enantioseparation of 16 basic chiral drugs and its chiral recognition mechanism associated with drugs' structural features.

Herein we present the enantioseparation of 10 cardiovascular agents and six bronchiectasis drugs including propranolol, carteolol, metoprolol, atenolol, pindolol, esmolol, bisoprolol, bevantolol, arotinolol, sotalol, clenbuterol, procaterol, bambuterol, tranterol, salbutamol and terbutaline sulfate using carboxymethyl-ß-cyclodextrin (CM-ß-CD) as chiral selector. To our knowledge, there is no literature about using CM-ß-CD for separating carteolol, esmolol, bisoprolol, bevantolol, arotinolol, procaterol, bambuterol and tranterol. During the course of work, changes in pH, CM-ß-CD concentration, buffer type and concentration were studied in relation to chiral resolution. Excellent enantiomeric separations were obtained for all 16 compounds, especially for procaterol. An impressive resolution value, up to 17.10, was obtained. In particular, most of them achieved rapid separations within 20 min. Given the fact that enantioseparation results rely on analytes' structural characters, the possible separation mechanisms were discussed. In addition, in order to obtain faster separation for propranolol enantiomers in practical application, the effective length of capillary was innovatively shortened from 45 to 30 cm. After the validation, the method was successfully applied to the enantiomeric purity determination of propranolol in the formulation of drug substances.