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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

In order to study the leaching of exogenous nitrogen during green space management and maintenance, the parameters of the model were calibrated through field monitoring and grow box simulation experiments, and the Model for Studying Nitrogen Transport in Green Space Ecosystems was established by using HYDRUS-2D software. Results showed that the model is highly reliable for simulating nitrogen transport in microtopography, with R2 values greater than 0.9 and RMSE values below 5. Slope gradient was positively correlated with horizontal nitrogen differences (ammonium and nitrate nitrogen) and negatively correlated with vertical differences (p < 0.05), while nitrogen application was positively correlated with both horizontal and vertical differences in nitrate nitrogen and negatively correlated with ammonium nitrogen (p < 0.05). The vertical differences of soil ammonium nitrogen exhibited a significant negative correlation with slope (−0.837 to −0.851), while the horizontal differences of nitrate nitrogen showed a significant positive correlation, with correlation coefficients of 0.965 and 0.967 for surface and subsurface soils, respectively. The increasing nitrogen application rate exacerbated these discrepancies, with the highest nitrogen treatment (0.312 g) exhibiting the most pronounced differential effects. Notably, the horizontal variation in nitrate nitrogen reached 6.9-fold that of ammonium nitrogen, while the vertical discrepancy demonstrated a 7.0-fold magnitude relative to ammonium nitrogen levels.

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.

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 n × n 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,  M = ratio × G + ( 1 - ratio ) × T ), 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.

Mitocbondrial fusion is a highly coordinated process that mixes and unifies the mitochondrial compartment for normal mitochondrial functions and mitochondrial DNA inheritance. Dysregulated mitochondrial fusion causes mitochondrial fragmentation, abnormal mitochondrial physiology and inheritance, and has been causally linked with a number of neuronal diseases. Here, we identified a diterpenoid derivative 15-oxospiramilactone （$3） that potently induced mitochondrial fusion to restore the mitochondrial network and oxidative respiration in cells that are deficient in either Mfnl or Mfn2. A mitochondria-localized deubiquitinase USP30 is a target of $3. The inhibition of USP30 by $3 leads to an increase of non-degradative ubiquitination of Mfnl/2, which enhances Mfnl and Mfn2 activity and promotes mitochondrial fusion. Thus, through the use of an inhibitor of USP30, our study uncovers an unconventional function of non-degradative ubiquitination of Mfns in promoting mitochondrial fusion.

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.

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 n × n 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, M = ratio × G + (1 ? ratio) × T ), 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.

Wide-bandgap (WBG) perovskite solar cells (PSCs) can exceed the Shockley–Queisser limit in tandem solar cells (TSCs), but phase segregation under continuous illumination limits their stability. Using in-situ microscopic characterizations, we investigate the dynamics of photon-induced phase segregation. Initial light soaking drives iodide diffusion into a metastable state, but continued redistribution increases the phase separation energy barrier, resulting in a more stable, segregation-resistant state. Inspired by stabilization methods in silicon photovoltaics, we develop the Photo-Homogenization Assisted Segregation Easing Technique (PHASET), which combines light soaking with 2-ThEABr surface passivation to suppress halide segregation. PHASET enhances efficiency and stability, enabling an efficiency of 20.23% for 1.79 eV WBG-PSCs, with 97% of the initial efficiency retained after 1200 hours of continuous illumination. Integration with a 1.25 eV narrow-bandgap subcell results in a two-terminal all-perovskite TSC with 28.64% efficiency, retaining 77% of its initial performance after 1200 hours of maximum power point tracking. Wide-bandgap perovskite solar cells often suffer from phase segregation under continuous illumination. Here, authors combine light soaking with small-molecule surface passivation to suppress halide segregation, achieving efficiency of 28.64% in stable two-terminal all-perovskite tandem solar cells.

Excessive use of chemical fertilizers and extensive farming can degrade soil properties so that leading to decline in crop yields. Combining plant growth-promoting rhizobacteria (PGPR) with biochar (BC) may be an alternative way to mitigate this situation. However, the proportion of PGPR and BC at which crop yield can be improved, as well as the improvement effect extent on different eco-geographic region and crops, remain unclear. This research used cabbage [ Brassica pekinensis (Lour.) Rupr.] as the target crop and established as treatment conventional fertilization as a control and a 50% reduction in nitrogen fertilizer at the Yunnan-Guizhou Plateau of China, adding BC or PGPR to evaluate the effects of different treatments on cabbage yield and the soil physicochemical properties. Specifically, high-throughput sequencing probed beneficial soil microbial communities and investigated the impact of BC and PGPR on cabbage yield and soil properties. The results revealed that the soil alkaline hydrolyzable nitrogen (AH-N), available phosphorus (AP), and available potassium (AK) contents were higher in the BC application than in control. The BC application or mixed with PGPR significantly increased the soil organic matter (OM) content (P<0.05), with a maximum of 42.59 g/kg. Further, applying BC or PGPR significantly increased the abundance of beneficial soil microorganisms in the whole growth period of cabbage (P<0.05), such as Streptomyces , Lysobacter , and Bacillus . Meanwhile, the co-application of BC and PGPR increased the abundance of Pseudomonas , and also significantly enhanced the Shannon index and Simpson index of bacterial community (P<0.05). Combined or not with PGPR, the BC application significantly enhanced cabbage yield (P<0.05), with the highest yield reached 1.41 fold of the control. Our research indicated that BC is an suitable and promising carrier of PGPR for soil improvement, combining BC and PGPR can effectively ameliorate the diversity of bacterial community even in acid red soil rhizosphere, and the most direct reflection is to improve soil fertility and cabbage yield.

Background Maintaining the repair phenotype of denervated Schwann cells in the injured distal nerve is crucial for promoting peripheral nerve regeneration. However, when chronically denervated, the capacity of Schwann cells to support repair and regeneration deteriorates, leading to peripheral nerve regeneration and poor functional recovery. Herein, we investigated whether neurotrophin-3 (NT-3) could sustain the reparative phenotype of Schwann cells and promote peripheral nerve regeneration after chronic denervation and aimed to uncover its potential molecular mechanisms. Methods Western blot was employed to investigate the relationship between the expression of c-Jun and the reparative phenotype of Schwann cells. The inducible expression of c-Jun by NT-3 was examined both in vitro and in vivo with western blot and immunofluorescence staining. A chronic denervation model was established to study the role of NT-3 in peripheral nerve regeneration. The number of regenerated distal axons, myelination of regenerated axons, reinnervation of neuromuscular junctions, and muscle fiber diameters of target muscles were used to evaluate peripheral nerve regeneration by immunofluorescence staining, transmission electron microscopy (TEM), and hematoxylin and eosin (H&E) staining. Adeno-associated virus (AAV) 2/9 carrying shRNA, small molecule inhibitors, and siRNA were employed to investigate whether NT-3 could signal through the TrkC/ERK pathway to maintain c-Jun expression and promote peripheral nerve regeneration after chronic denervation. Results After peripheral nerve injury, c-Jun expression progressively increased until week 5 and then began to decrease in the distal nerve following denervation. NT-3 upregulated the expression of c-Jun in denervated Schwann cells, both in vitro and in vivo. NT-3 promoted peripheral nerve regeneration after chronic denervation, mainly by upregulating or maintaining a high level of c-Jun rather than NT-3 itself. The TrkC receptor was consistently presented on denervated Schwann cells and served as NT-3 receptors following chronic denervation. NT-3 mainly upregulated c-Jun through the TrkC/ERK pathway. Conclusion NT-3 promotes peripheral nerve regeneration by maintaining the repair phenotype of Schwann cells after chronic denervation via the TrkC/ERK/c-Jun pathway. It provides a potential target for the clinical treatment of peripheral nerve injury after chronic denervation.