Transcriptome analysis reveals miRNA expression profiles in hypothalamus tissues during the sexual development of Jining grey goats.

BACKGROUND: Exploring the physiological and molecular mechanisms underlying goat sexual maturation can enhance breeding practices and optimize reproductive efficiency and is therefore substantially important for practical breeding purposes. As an essential neuroendocrine organ in animals, the hypothalamus is involved in sexual development and other reproductive processes in female animals. Although microRNAs (miRNAs) have been identified as significant regulators of goat reproduction, there is a lack of research on the molecular regulatory mechanisms of hypothalamic miRNAs that are involved in the sexual development of goats. Therefore, we examined the dynamic changes in serum hormone profiles and hypothalamic miRNA expression profiles at four developmental stages (1 day (neonatal, D1, n = 5), 2 months (prepubertal, M2, n = 5), 4 months (sexual maturity, M4, n = 5), and 6 months (breeding period, M6, n = 5)) during sexual development in Jining grey goats. RESULTS: Transcriptome analysis revealed 95 differentially expressed miRNAs (DEMs) in the hypothalamus of goats across the four developmental stages. The target genes of these miRNAs were significantly enriched in the GnRH signalling pathway, the PI3K-Akt signalling pathway, and the Ras signalling pathway (P < 0.05). Additionally, 16 DEMs are common among the M2 vs. D1, M4 vs. D1, and M6 vs. D1 comparisons, indicating that the transition from D1 to M2 represents a potentially critical period for sexual development in Jining grey goats. The bioinformatics analysis results indicate that miR-193a/miR-193b-3p-Annexin A7 (ANXA7), miR-324-5p-Adhesion G protein-coupled receptor A1 (ADGRA1), miR-324-3p-Erbb2 receptor tyrosine kinase 2 (ERBB2), and miR-324-3p-Rap guanine nucleotide exchange factor 3 (RAPGEF3) are potentially involved in biological processes such as hormone secretion, energy metabolism, and signal transduction. In addition, we further confirmed that miR-324-3p targets the regulatory gene RAPGEF3. CONCLUSION: These results further enrich the expression profile of hypothalamic miRNAs in goats and provide important insights for studying the regulatory effects of hypothalamic miRNAs on the sexual development of goats after birth.

Study on the Influence of Label Image Accuracy on the Performance of Concrete Crack Segmentation Network Models.

A high-quality dataset is a basic requirement to ensure the training quality and prediction accuracy of a deep learning network model (DLNM). To explore the influence of label image accuracy on the performance of a concrete crack segmentation network model in a semantic segmentation dataset, this study uses three labelling strategies, namely pixel-level fine labelling, outer contour widening labelling and topological structure widening labelling, respectively, to generate crack label images and construct three sets of crack semantic segmentation datasets with different accuracy. Four semantic segmentation network models (SSNMs), U-Net, High-Resolution Net (HRNet)V2, Pyramid Scene Parsing Network (PSPNet) and DeepLabV3+, were used for learning and training. The results show that the datasets constructed from the crack label images with pix-el-level fine labelling are more conducive to improving the accuracy of the network model for crack image segmentation. The U-Net had the best performance among the four SSNMs. The Mean Intersection over Union (MIoU), Mean Pixel Accuracy (MPA) and Accuracy reached 85.47%, 90.86% and 98.66%, respectively. The average difference between the quantized width of the crack image segmentation obtained by U-Net and the real crack width was 0.734 pixels, the maximum difference was 1.997 pixels, and the minimum difference was 0.141 pixels. Therefore, to improve the segmentation accuracy of crack images, the pixel-level fine labelling strategy and U-Net are the best choices.

Metabolomic and Transcriptomic Analyses Reveal the Potential Mechanisms of Dynamic Ovarian Development in Goats during Sexual Maturation.

The ovary is a crucial reproductive organ in mammals, and its development directly influences an individual's sexual maturity and reproductive capacity. To comprehensively describe ovarian sexual maturation in goats, we integrated phenotypic, hormonal, metabolomic, and transcriptomic data from four specific time points: after birth (D1), at 2 months old (M2), at 4 months old (M4), and at 6 month old (M6). The study showed that during the early stage (D1-M2), ovarian growth was the most rapid, with weight and morphology increasing by 284% and 65%, respectively, and hormone levels rose significantly, with estradiol increasing by 57%. Metabolomic analysis identified 1231 metabolites, primarily lipids, lipid molecules, and organic acids, which can support hormone balance and follicle development by providing energy and participating in signaling transduction. Transcriptomic analysis identified 543 stage-specific differentially expressed genes, mainly enriched in steroid biosynthesis, amino acid metabolism, and the PI3K/AKT pathway, which are key factors influencing ovarian cell proliferation, apoptosis, hormone secretion, and metabolism. The integrated analysis revealed the key processes in the ovarian steroid hormone biosynthesis pathway and gene/metabolite networks associated with ovarian phenotypes and hormone levels, ultimately highlighting scavenger receptor class B type 1 (SCARB1), Cytochrome P450 Family 1 Subfamily A Member 1 (CYP11A1), 3beta-hydroxysteroid dehydrogenase (3BHSD), progesterone, estradiol, and L-phenylalanine as key regulators of ovarian morphological and functional changes at different developmental stages. This study is the first to reveal the metabolic changes and molecular regulatory mechanisms during ovarian sexual maturation in goats, providing valuable insights for understanding reproductive system development and optimizing reproductive performance and breeding efficiency.

The Transcriptome Characterization of the Hypothalamus and the Identification of Key Genes during Sexual Maturation in Goats.

Sexual maturation in goats is a dynamic process regulated precisely by the hypothalamic-pituitary-gonadal axis and is essential for reproduction. The hypothalamus plays a crucial role in this process and is the control center of the reproductive activity. It is significant to study the molecular mechanisms in the hypothalamus regulating sexual maturation in goats. We analyzed the serum hormone profiles and hypothalamic mRNA expression profiles of female goats during sexual development (1 day old (neonatal, D1, n = 5), 2 months old (prepuberty, M2, n = 5), 4 months old (sexual maturity, M4, n = 5), and 6 months old (breeding period, M6, n = 5)). The results indicated that from D1 to M6, serum hormone levels, including FSH, LH, progesterone, estradiol, IGF1, and leptin, exhibited an initial increase followed by a decline, peaking at M4. Furthermore, we identified a total of 508 differentially expressed genes in the hypothalamus, with a total of four distinct expression patterns. Nuclear receptor subfamily 1, group D, member 1 (NR1D1), glucagon-like peptide 1 receptor (GLP1R), and gonadotropin-releasing hormone 1 (GnRH-1) may contribute to hormone secretion, energy metabolism, and signal transduction during goat sexual maturation via circadian rhythm regulation, ECM receptor interactions, neuroactive ligand-receptor interactions, and Wnt signaling pathways. This investigation offers novel insights into the molecular mechanisms governing the hypothalamic regulation of goat sexual maturation.

Blending Aryl Ketone in Covalent Organic Frameworks to Promote Photoinduced Electron Transfer.

Aryl-ketone derivatives have been acknowledged as promising organic photocatalysts for photosynthesis. However, they are limited by their photostability and have been less explored for photoinduced electron transfer (PET) applications. Herein we demonstrate a novel strategy to cover the shortage of aryl-ketone photocatalysts and control the photoreactivity by implanting symmetric aryl ketones into the conjugated covalent organic frameworks (COFs). To prove the concept, three comparative materials with the same topology and varied electronic structures were built, adopting truxenone knot and functionalized terephthalaldehyde linkers. Spectroscopic investigation and excited carrier dynamics analysis disclosed improvements in the photostability and electronic transfer efficiency as well as the structure-performance relationships toward N-aryl tetrahydroisoquinoline oxidation. This system provides a robust rule of thumb for designing new-generation aryl-ketone photocatalysts.

Study on the correlation between bioelectrical impedance analysis index and protein energy consumption in maintenance dialysis patients.

BACKGROUND: Protein-energy wasting (PEW) has been reported to be pretty common in maintenance dialysis patients. However, the existing PEW diagnostic standard is limited in clinical use due to the complexity of it. Bioelectrical impedance analysis (BIA), as a non-invasive nutritional assessment method, can objectively and quantitatively analyze the changes of body tissue components under different nutritional states. We aim to explore the association between PEW and BIA and establish a reliable diagnostic model of PEW. METHODS: We collected cross-sectional data of 609 maintenance dialysis patients at the First Affiliated Hospital, College of Medicine, Zhejiang University. PEW was diagnosed according to International Society of Renal Nutrition and Metabolism (ISRNM) criteria. Among them, 448 consecutive patients were included in the training set for the establishment of a diagnostic nomogram. 161 consecutive patients were included for internal validation. 52 patients from Zhejiang Hospital were included for external validation of the diagnostic model. Correlation analysis of BIA indexes with other nutritional indicators was performed. Logistic regression was used to examine the association of BIA indexes with PEW. 12 diagnostic models of PEW in maintenance dialysis patients were developed and the performance of them in terms of discrimination and calibration was evaluated using C statistics and Hosmer-Lemeshow-type χ2 statistics. After comparing to existing diagnostic models, and performing both internal and external validation, we finally established a simple but reliable PEW diagnostic model which may have great value of clinical application. RESULTS: A total of 609 individuals from First Affiliated Hospital, College of Medicine, Zhejiang University and 52 individuals from Zhejiang Hospital were included. After full adjustment, age, peritoneal dialysis (compared to hemodialysis), subjective global assessment (SGA, compared to non-SGA) and water ratio were independent risk factors, while triglyceride, urea nitrogen, calcium, ferritin, BCM, VFA and phase angle were independent protective factors of PEW. The model incorporated water ratio, VFA, BCM, phase angle and cholesterol revealed best performance. A nomogram was developed according to the results of model performance. The model achieved high C-indexes of 0.843 in the training set, 0.841 and 0.829 in the internal and external validation sets, respectively, and had a well-fitted calibration curve. The net reclassification improvement (NRI) showed 8%, 13%, 2%, 38%, 36% improvement of diagnostic accuracy of our model compared with "PEW score model", "modified PEW score model", "3-index model", "SGA model" and "BIA decision tree model", respectively. CONCLUSIONS: BIA can be used as an auxiliary tool to evaluate PEW risk and may have certain clinical application value.

Research on the Efficiency of Bridge Crack Detection by Coupling Deep Learning Frameworks with Convolutional Neural Networks.

Bridge crack detection based on deep learning is a research area of great interest and difficulty in the field of bridge health detection. This study aimed to investigate the effectiveness of coupling a deep learning framework (DLF) with a convolutional neural network (CNN) for bridge crack detection. A dataset consisting of 2068 bridge crack images was randomly split into training, verification, and testing sets with a ratio of 8:1:1, respectively. Several CNN models, including Faster R-CNN, Single Shot MultiBox Detector (SSD), You Only Look Once (YOLO)-v5(x), U-Net, and Pyramid Scene Parsing Network (PSPNet), were used to conduct experiments using the PyTorch, TensorFlow2, and Keras frameworks. The experimental results show that the Harmonic Mean (F1) values of the detection results of the Faster R-CNN and SSD models under the Keras framework are relatively large (0.76 and 0.67, respectively, in the object detection model). The YOLO-v5(x) model of the TensorFlow2 framework achieved the highest F1 value of 0.67. In semantic segmentation models, the U-Net model achieved the highest detection result accuracy (AC) value of 98.37% under the PyTorch framework. The PSPNet model achieved the highest AC value of 97.86% under the TensorFlow2 framework. These experimental results provide optimal coupling efficiency parameters of a DLF and CNN for bridge crack detection. A more accurate and efficient DLF and CNN model for bridge crack detection has been obtained, which has significant practical application value.

Succinic anhydride-based chemical modification making laccase@Cu3(PO4)2 hybrid nanoflowers robust in removing bisphenol A in wastewater.

To prepare a robust biocatalyst and enhance the removal of bisphenol A in wastewater, succinic anhydride was reacted with laccase to obtain succinic anhydride-modified laccase (SA-laccase) and then co-crystallized with Cu3(PO4)2 to form SA-laccase@Cu3(PO4)2 hybrid nanoflowers (hNFs). The activity of SA-laccase@Cu3(PO4)2 reached 5.27 U/mg, 1.86-, 2.88- and 2.15-fold those of bare laccase@Cu3(PO4)2, laccase@Ca3(PO4)2 and laccase@epoxy resin, respectively. Compared with free laccase, the obtained hNFs present enhanced activity and tolerance to pH and high temperature in the removal of BPA. Under the optimum conditions of pH 6.0 and 35 °C, BPA removal reached 93.2% using SA-laccase@Cu3(PO4)2 hNFs, which was 1.21-fold of that using free laccase. In addition, the obtained SA-laccase@Cu3(PO4)2 hNFs retained nearly 90% of their initial catalytic activity for BPA removal after 8 consecutive batch cycles. This efficient method for preparing immobilized laccase can also be further developed and improved to acquire green biocatalysts for removing persistent organic pollutants in wastewater.

The role of agricultural green production technologies in improving low-carbon efficiency in China: Necessary but not effective.

The effects of agricultural green production technologies (AGPTs) on agricultural productivity and the environment have received increasing attention. With the panel data of agricultural production of mainland China from 2000 to 2017, this study investigates the role of AGPTs adoption rates in improving low-carbon efficiency by adopting a random-effects panel Tobit model. Results indicate that average adoption rates of AGPTs are less than 20% and unbalanced adoptions vary between the main and non-main grain-producing areas, as well as the northern and southern main grain-producing areas. Furthermore, AGPTs adoption reduces low-carbon efficiency at nationwide and main grain-producing areas. In the northern main grain-producing areas, water-saving irrigation and no-tillage seeding reduce low-carbon efficiency, while mechanized returning straw crushing promotes it. In the southern main grain-producing areas, deep tillage with fertilizer application and no-tillage seeding decrease low-carbon efficiency, while mechanized deep ploughing and scarification increases it. We also find that AGPTs can promote low-carbon efficiency through comprehensive mechanization level and mechanical input density. To improve low-carbon efficiency, we suggest that the improvement and diffusion of applicable AGPTs should be adapted to the local production conditions, and the agricultural machinery service, research and development system should be improved as well.

A Lacunary Polyoxovanadate Precursor and Transition-Metal-Sandwiched Derivatives for Catalytic Oxidation of Sulfides.

A SeO3 -centered lacunary Keggin-type heteropolyoxovanadate (hetero-POV) K6 H2 [SeV10 O28 (SeO3 )3 ]⋅14 H2 O (1) was isolated by one-pot reaction of KVO3 and SeO2 under acidic conditions. X-ray studies revealed that it comprised a single {VO5 }-capped trivacant B-α-type Keggin ion [SeV9 O33 (VO)]14- with its lacunary sites decorated by three {SeO3 } pyramids. Interestingly, this new basic hetero-POV building block was further used as a precursor to assemble with different transition-metal (TM) ions, yielding a series of TM-sandwiched POVs K6 H8 [(SeV10 O28 (SeO3 )3 )2 (M(H2 O)4 )]⋅24 H2 O (M2+ =Mn2+ (2), Co2+ (3), Zn2+ (4)). All four compounds were characterized by single-crystal X-ray structure analysis, IR, X-ray photoelectron spectroscopy (XPS), EPR, and 51 V NMR spectroscopy. Importantly, three TM-sandwiched derivatives exhibited effective catalytic activity for the heterogeneous oxidative desulfurization of sulfides at room temperature.