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Simple Summary: Ketosis (KET), a metabolic disorder frequently observed in dairy cows, is characterized by increased levels of ketone bodies. The "gold standard" for diagnosing ketosis is the concentration of blood β-hydroxybutyrate (BHB). The increasing number of studies focusing on BHB highlights the increasing significance of metabolic disorders in both the dairy industry and the scientific community. Additionally, the surge in research on the genetic and economic facets of the KET is likely correlated with the recent accessibility of extensive datasets containing blood BHB concentrations as routinely recorded traits in certain production systems. Such data are essential to accurately estimating the genetic parameters associated with these traits. Consequently, the objective of this study was to estimate genetic parameters and identify candidate genes related to blood BHB through genome-wide association analysis to provide research directions for dairy ketosis. Ketosis is a common metabolic disorder in the early lactation of dairy cows. It is typically diagnosed by measuring the concentration of β-hydroxybutyrate (BHB) in the blood. This study aimed to estimate the genetic parameters of blood BHB and conducted a genome-wide association study (GWAS) based on the estimated breeding value. Phenotypic data were collected from December 2019 to August 2023, comprising blood BHB concentrations in 45,617 Holstein cows during the three weeks post-calving across seven dairy farms. Genotypic data were obtained using the Neogen Geneseek Genomic Profiler (GGP) Bovine 100 K SNP Chip and GGP Bovine SNP50 v3 (Illumina Inc., San Diego, CA, USA) for genotyping. The estimated heritability and repeatability values for blood BHB levels were 0.167 and 0.175, respectively. The GWAS result detected a total of ten genome-wide significant associations with blood BHB. Significant SNPs were distributed in Bos taurus autosomes (BTA) 2, 6, 9, 11, 13, and 23, with 48 annotated candidate genes. These potential genes included those associated with insulin regulation, such as INSIG2, and those linked to fatty acid metabolism, such as HADHB, HADHA, and PANK2. Enrichment analysis of the candidate genes for blood BHB revealed the molecular functions and biological processes involved in fatty acid and lipid metabolism in dairy cattle. The identification of novel genomic regions in this study contributes to the characterization of key genes and pathways that elucidate susceptibility to ketosis in dairy cattle. [ABSTRACT FROM AUTHOR]

Changes in the habitats of species can provide insights into the impact of climate change on their habitats. Species in the genus Morina (Morinoideae) are perennial herbaceous plants that are mainly distributed in the South Asian Mountains and Eastern Mediterranean. In China, there are four species and two varieties of this genus distributed across the Yunnan, Sichuan, Qinghai, and Gansu provinces. This study used the optimal MaxEnt model to simulate past, current, and future potentially suitable habitats of Morina kokonorica and Morina chinensis. Seventy data of M. kokonorica occurrences and 3 of M. chinensis were used in the model to predict potentially suitable habitats. The model prediction results indicated that both M. kokonorica and M. chinensis exhibited trends of northward migration to higher latitudes and westward migration along the Himalayas to higher elevations, suggesting that the northern valleys of Hengduan Mountains and northern and eastern parts of the Himalayas were potential refugia for M. kokonorica, and the potential refugia for M. chinensis was located in the eastern part of Qinghai‐Tibet Plateau. The results of this niche analysis showed that the two species had higher levels of interspecific competition and that the environmental adaptability of M. chinensis was stronger. This research could help further understand the response pattern of Morina to environmental change, to understand the adaptability of species to the environment, and promote the protection of species. [ABSTRACT FROM AUTHOR]

Lithium–sulfur (Li–S) batteries have gained considerable attention for high theoretical specific capacity and energy density. However, their development is hampered by the poor electrical conductivity of sulfur and the shuttle of polysulfides. Herein, the acidified bamboo-structure carbon nanotubes (BCNTs) were mixed with polyvinylidene difluoride and pyrolyzed at high-temperature to obtain the fluorinated bamboo-structure carbon nanotubes (FBCNTs), which were compounded with sulfur as the cathode. The prepared S@FBCNTs with sulfur loading reaching 74.2 wt.% shows a high initial specific capacity of 1407.5 mAh·g−1 at the discharge rate of 0.1 C. When the discharge rate was increased to 5 C, the capacity could be maintained at 622.3 mAh·g−1. The electrical conductivity of carbon nanotubes is effectively improved by semi-ionic C–F bonds formed by the doped F atoms and carbon atoms. Simultaneously, the surface of the F-containing carbon tubes exhibits strong polarity and strong chemisorption effect on polysulfides, which inhibits the shuttle effect of Li–S batteries. [ABSTRACT FROM AUTHOR]

Nitrogen (N) application significantly increases tiller numbers and is accompanied by changes in endogenous hormone content. We treated seedlings of Festuca kirilowii—a perennial forage grass—with nitrogen, determined the endogenous hormone content in the tiller buds, and performed a transcriptome analysis. The application of N reduced GA3, ABA, and 5-DS content and increased ZT and IAA content. By screening DEGs in the transcriptome results, we obtained DEGs annotated to 25 GO entries and 8 KEGG pathways associated with endogenous hormones. Most of these GO entries and KEGG pathways were associated with IAA, GAS, and ABA. We conducted a validation analysis of hormone-related DEGs using qRT-PCR to demonstrate that nitrogen controls the content of endogenous hormones by regulating the expression of these DEGs, which further affects tillering in F. kirilowii. [ABSTRACT FROM AUTHOR]

The deep shale of the Wufeng–Longmaxi Formation in the southern Sichuan Basin has high gas potential. The development characteristics of lamination could significantly impact reservoir property. Samples were investigated using microscopic observation, element analysis, organic petrology analysis, mineralogy analysis, and pore structure analysis to determine the types of laminae and laminasets, clarify the formation conditions of argillaceous lamina and silty lamina as well as their relationships with the sedimentary environment, and explore the influence of laminae on shale reservoir property. Results indicate that the Wufeng Formation shale exhibits weak development of laminae due to bioturbation, while the Longmaxi Formation shale develops continuous, parallel, and plate-like laminae. Compared with light silty lamina-rich shale, dark argillaceous lamina-rich shale usually develops in an anoxic reduction environment, with higher total organic carbon content, porosity, pore volume, specific surface area, and more developed organic matter pores, which can provide greater space for shale gas adsorption and storage. Shales in the middle section of the Longmaxi Formation are characterized by the development of silty-argillaceous interbedded type laminaset, which have good reservoir performance, making them the primary target for deep shale gas exploration and development. [ABSTRACT FROM AUTHOR]

The marine protease fermentation process is a highly nonlinear, time-varying, multivariable, and strongly coupled complex biochemical reaction process. Due to the growth and reproduction of living organisms, the internal mechanism is very complicated. Some key variables (such as cell concentration, substrate concentration, and enzyme activity) that directly reflect the fermentation process's quality are difficult to measure in real-time by traditional measurement methods. A soft sensor model based on a support vector regression (SVR) is proposed in this paper to resolve this problem. To further improve the model's prediction accuracy, the grey wolf optimization (GWO) algorithm is used to optimize the critical parameters (kernel function width σ, penalty factor c, and insensitivity coefficient ε) of the SVR model. To study the influence of selecting auxiliary variables on soft sensor modeling, the successive projection algorithm (SPA) is used to determine the characteristic variables and compare them with grey relation analysis (GRA) algorithm. Finally, the Excel spreadsheet data was called by MATLAB programming, and the established SPA-GWO-SVR soft sensor model predicted crucial biological variables. The simulation results show that the SPA-GWO-SVR model has higher prediction accuracy and generalization ability than the traditional SPA-SVR model. The real-time monitoring was processed by MATLAB software for the marine protease fermentation process, which met the requirements of optimal control of the marine protease fermentation process.