Oyster Peptides Ameliorate Dextran Sulfate Sodium-Induced Ulcerative Colitis via Modulating the Gut Microbiota and Inhibiting the TLR4/NF-κB Pathway.

Ulcerative colitis (UC) is an inflammatory bowel disease with an increasing prevalence year over year, and the medications used to treat patients with UC clinically have severe side effects. Oyster peptides (OPs) have anti-inflammatory and antioxidant properties as functional foods that can alleviate a wide range of inflammatory conditions. However, the application of oyster peptides in ulcerative colitis is not well studied. In this work, an animal model of acute colitis was established using 3% dextran sulfate sodium (DSS), and the impact of OP therapy on colitis in mice was examined. Supplementing with OPs prevented DSS-induced colitis from worsening, reduced the expression of oxidative stress and inflammatory markers, and restored the intestinal barrier damage caused by DSS-induced colitis in mice. The 16S rDNA results showed that the OP treatment improved the gut microbiota structure of the UC mice, including increasing microbial diversity, increasing beneficial bacteria, and decreasing harmful bacteria. In the UC mice, the OP therapy decreased the relative abundance of Family_XIII_AD3011_group and Prevotella_9 and increased the relative abundance of Alistipes. In conclusion, OP treatment can inhibit the TLR4/NF-κB pathway and improve the intestinal microbiota in UC mice, which in turn alleviates DSS-induced colitis, providing a reference for the treatment of clinical UC patients.

GnRH-driven FTO-mediated RNA m6A modification promotes gonadotropin synthesis and secretion.

BACKGROUND: Gonadotropin precisely controls mammalian reproductive activities. Systematic analysis of the mechanisms by which epigenetic modifications regulate the synthesis and secretion of gonadotropin can be useful for more precise regulation of the animal reproductive process. Previous studies have identified many differential m6A modifications in the GnRH-treated adenohypophysis. However, the molecular mechanism by which m6A modification regulates gonadotropin synthesis and secretion remains unclear. RESULTS: Herein, it was found that GnRH can promote gonadotropin synthesis and secretion by promoting the expression of FTO. Highly expressed FTO binds to Foxp2 mRNA in the nucleus, exerting a demethylation function and reducing m6A modification. After Foxp2 mRNA exits the nucleus, the lack of m6A modification prevents YTHDF3 from binding to it, resulting in increased stability and upregulation of Foxp2 mRNA expression, which activates the cAMP/PKA signaling pathway to promote gonadotropin synthesis and secretion. CONCLUSIONS: Overall, the study reveals the molecular mechanism of GnRH regulating the gonadotropin synthesis and secretion through FTO-mediated m6A modification. The results of this study allow systematic interpretation of the regulatory mechanism of gonadotropin synthesis and secretion in the pituitary at the epigenetic level and provide a theoretical basis for the application of reproductive hormones in the regulation of animal artificial reproduction.

KLF4 promotes milk fat synthesis by regulating the PI3K-AKT-mTOR pathway and targeting FASN activation in bovine mammary epithelial cells.

Milk fat is an important indicator for evaluating the quality of cow's milk. In this study, we used bovine mammary epithelial cells (BMECs) to investigate the role and molecular mechanism of KLF4 in the regulation of milk fat synthesis. The results showed that KLF4 was more highly expressed in mammary tissues of high-fat cows compared with low-fat cows. KLF4 positively regulated the expression of genes related to milk fat synthesis in BMECs, increasing intracellular triglycerides content, and KLF4 promoted milk fat synthesis by activating the PI3K-AKT-mTOR signaling pathway. Furthermore, the results of animal experiments also confirmed that knockdown of KLF4 inhibited milk fat synthesis. In addition, yeast one-hybrid assays and dual-luciferase reporter gene assays confirmed that KLF4 directly targets and binds to the fatty acid synthase (FASN) promoter region to promote FASN transcription. These results demonstrate that KLF4 is a key transcription factor for milk fat synthesis in BMECs.

Huaier Polysaccharide Alleviates Dextran Sulphate Sodium Salt-Induced Colitis by Inhibiting Inflammation and Oxidative Stress, Maintaining the Intestinal Barrier, and Modulating Gut Microbiota.

The incidence of ulcerative colitis (UC) is increasing annually, and UC has a serious impact on patients' lives. Polysaccharides have gained attention as potential drug candidates for treating ulcerative colitis (UC) in recent years. Huaier (Trametes robiniophila Murr) is a fungus that has been used clinically for more than 1000 years, and its bioactive polysaccharide components have been reported to possess immunomodulatory effects, antitumour potential, and renoprotective effects. In this study, we aimed to examine the protective effects and mechanisms of Huaier polysaccharide (HP) against UC. Based on the H2O2-induced oxidative stress model in HT-29 cells and the dextran sulphate sodium salt (DSS)-induced UC model, we demonstrated that Huaier polysaccharides significantly alleviated DSS-induced colitis (weight loss, elevated disease activity index (DAI) scores, and colonic shortening). In addition, HP inhibited oxidative stress and inflammation and alleviated DSS-induced intestinal barrier damage. It also significantly promoted the expression of the mucin Muc2. Furthermore, HP reduced the abundance of harmful bacteria Escherichia-Shigella and promoted the abundance of beneficial bacteria Muribaculaceae_unclassified, Anaerotruncus, and Ruminococcaceae_unclassified to regulate the intestinal flora disturbance caused by DSS. Nontargeted metabolomics revealed that HP intervention would modulate metabolism by promoting levels of 3-hydroxybutyric acid, phosphatidylcholine (PC), and phosphatidylethanolamine (PE). These results demonstrated that HP had the ability to mitigate DSS-induced UC by suppressing oxidative stress and inflammation, maintaining the intestinal barrier, and modulating the intestinal flora. These findings will expand our knowledge of how HP functions and offer a theoretical foundation for using HP as a potential prebiotic to prevent UC.

Turtle peptide and its derivative peptide ameliorated DSS-induced ulcerative colitis by inhibiting inflammation and modulating the composition of the gut microbiota.

Ulcerative colitis (UC) is a recurrent intestinal disease with an increasing incidence worldwide that seriously affects the life of patients. Turtle peptide (TP) is a bioactive peptide extracted from turtles that has anti-inflammatory, antioxidant and anti-aging properties. However, studies investigating the effect of TP on the progression of UC are lacking. The aim of this study was to investigate effects and underlying mechanisms of TP and its derivative peptide GPAGPIGPV (GP-9) in alleviating UC in mice. The results showed that 500 mg/kg TP treatment significantly ameliorated colitis symptoms and oxidative stress in UC mice. TP alleviated intestinal barrier damage in UC mice by promoting mucosal repair and increasing the expression of tight junction proteins (ZO1, occludin and claudin-1). TP also modulated the composition of the gut microbiota by increasing the abundance of the beneficial bacteria Anaerotignum, Prevotellaceae_UCG-001, Alistipes, and Lachno-spiraceae_NK4A136_group and decreasing the abundance of the harmful bacteria Prevotella_9 and Parasutterella. Furthermore, we characterized the peptide composition of TP and found that GP-9 ameliorated the symptoms of dextran sodium sulfate (DSS)-induced colitis in mice by inhibiting the TLR4/NF-κB signaling pathway. In conclusion, TP and its derivative peptides ameliorated DSS-induced ulcerative colitis by inhibiting the expression of inflammatory factors and modulating the composition of the intestinal microbiota; this study provides a theoretical basis for the application of TP and its derivative peptides for their anti-inflammatory activity.

Hybridized gated recurrent unit with variational mode decomposition and an error compensation mechanism for multi-step-ahead monthly rainfall forecasting.

Highly accurate monthly rainfall predictions can provide early warnings for rain-related disasters, such as floods and droughts, and allow governments to make timely decisions. This paper proposes a two-phase error compensation model based on a gated recurrent unit (GRU), variational mode decomposition (VMD), and error compensation mechanism (ECM) (GRU-VMD-ECM) for accurate multi-step-ahead monthly rainfall forecasts. In the first phase, the GRU model is used to make an initial monthly rainfall prediction, and the error series is extracted. In the second phase, the error series is decomposed into eight subseries using the VMD method. Each subseries is then input into the GRU model to build different forecasting models. These predicted error sequences are added to the initial prediction results to obtain the final forecast. The model's performance is tested using six evaluation indicators based on Beijing's monthly rainfall data from 1951 to 2018. The results show that the error compensation mechanism significantly improved the prediction accuracy, particularly in the Nash-Sutcliffe efficiency (NSE) of single-step-ahead prediction which recorded a substantial increase of 281.16% from 0.259981 to 0.990944, as well as a decrease in root mean square error (RMSE) from 2.257580 to 0.249746. Furthermore, the GRU-VMD-ECM model outperforms the RF, GRU-CNN, and VMD-GRU models in terms of precision across all forecasting horizons. These findings highlight the potential of the GRU-VMD-ECM model in providing highly accurate monthly rainfall predictions for early warnings and informed decision-making by governments.

Sea Cucumber Peptides Ameliorate DSS-Induced Ulcerative Colitis: The Role of the Gut Microbiota, the Intestinal Barrier, and Macrophage Polarization.

The incidence of ulcerative colitis (UC) is increasing annually. There are few treatments for UC patients, and some drugs have serious side effects. Sea cucumber peptide (SCP) has anti-inflammatory, antioxidant and other biological activities, and various sea cucumber species are in pharmaceutical development. However, relevant studies on the effects of SCP on UC progression are still lacking. In this study, a mouse model of acute colitis was induced by 3% dextran sulfate (DSS), and the effect of 500 mg/kg SCP on colitis was investigated. The results showed that SCP can alleviate DSS-induced colon damage and intestinal barrier damage. SCP significantly inhibited the expression of inflammatory factors and oxidative stress in UC mice. SCP reversed the intestinal microbiota dysregulation induced by DSS, inhibited the growth of Sutterella, Prevotella_9 and Escherichia-Shigella harmful bacteria, and increased the abundance of Lachnospiraceae_NK4A136_group. At the same time, SCP treatment significantly inhibited the LPS-induced polarization of M1 macrophages, which may be mediated by two monopeptides, IPGAPGVP and TGPIGPPGSP, via FPR2. In conclusion, SCP can protect against colitis by modulating the intestinal microbiota composition and the intestinal barrier and inhibiting the polarization of M1 macrophages.

Flood risk assessment of Wuhan, China, using a multi-criteria analysis model with the improved AHP-Entropy method.

Floods are one of the most frequent global natural hazards resulting in significant human and economic losses. Therefore, assessing and mapping flood hazard levels is essential to reduce the severity of future flood disasters. This study developed an integrated methodology to evaluate flood risk using an improved Analytic Hierarchy Process (AHP) and Entropy Weight (AHP-EW) method based on cosine similarity (COS-AHP-EW). This method has more scientific results because it combines subjective and objective information. The proposed method's viability was then tested in Wuhan, China. Fourteen flood-inducing indicators were identified for the flood hazard, vulnerability, and restorability index system, with the indicator weights calculated using the COS-AHP-EW. This study utilized the Jenks method to develop the Wuhan flood risk map. We observed that the very high risk and high-risk areas covered 2.43% and 11.54% of the total study area and were mainly distributed in the highest economic and urbanization development and low-permeability districts, respectively. The validation with the historical waterlogging points reflected the accuracy and reliability of the COS-AHP-EW. The superiority of the proposed method was further verified by comparing it with single-evaluation methods (AHP and Entropy Weight) and another combined weight method (combined AHP-EW based on ideal point theory, namely, Ideal-AHP-EW). The comparison results indicated that the COS-AHP-EW was more accurate at predicting the risk in flood-prone area. Flood risk maps generated using the COS-AHP-EW could be applied to improve flood risk assessments, and the proposed method could be extended to other study areas to provide reliable flood management information.

Hydroxycitric acid prevents hyperoxaluric-induced nephrolithiasis and oxidative stress via activation of the Nrf2/Keap1 signaling pathway.

Nephrolithiasis is a common and frequently-occurring disease in the urinary system with high recurrence. The present study aimed to explore the protective effect and underlying mechanism of hydroxycitric acid (HCA) in hyperoxaluria-induced nephrolithiasis in vitro and in vivo. Crystal deposition and pathophysiological injury in rat models of glyoxylate-induced nephrolithiasis were examined using H&E staining. Cell models of nephrolithiasis were established by oxalate-treated renal tubular epithelial cells. The levels of oxidative stress indexes were determined by ELISA kits. Cell proliferation in vivo and in vitro was evaluated using a cell counting kit-8 (CCK-8) assay and Ki-67 cell proliferation detection kit. Cell apoptosis was measured by flow cytometry and TUNEL staining. The protein levels were examined by western blotting. Our results showed that HCA administration significantly reduced crystal deposition and kidney injury induced by glyoxylate. HCA also alleviated oxidative stress via upregulating the antioxidant enzyme activities of superoxide dismutase (SOD) and catalase (CAT) and reducing the malondialdehyde (MDA) content. Moreover, HCA treatment promoted cell proliferation and inhibited apoptosis of renal tubular epithelial cells exposed to hyperoxaluria. Of note, Nrf2 activator dimethyl fumarate (DMF) exerted the same beneficial effects as HCA in nephrolithiasis. Mechanistically, HCA prevented crystal deposition and oxidative stress induced by hyperoxaluria through targeting the Nrf2/Keap1 antioxidant defense pathway, while knockdown of Nrf2 significantly abrogated these effects. Taken together, HCA exhibited antioxidation and anti-apoptosis activities in nephrolithiasis induced by hyperoxaluria via activating Nrf2/Keap1 pathway, suggesting that it may be an effective therapeutic agent for the prevention and treatment of nephrolithiasis.

Spatial evolution and decomposition of energy-related CO2 emissions in China's mining industry: from the perspective of regional heterogeneity.

To ensure China's energy security, the mining industry faces increasing emissions reduction and energy conservation pressures. This study combined index and production-theoretical decomposition analyses to decompose the energy-related CO2 emissions in mining industry (ERCEMI) influencing factors into seven major effects and adopted a gravity model to dynamically visualize the transfer path and gravity distribution from 2000 to 2015. As investment effects were introduced into the decomposition analysis, the results fully considered the regional heterogeneity and spatiotemporal dynamics. The main findings were as follows: (i) a typical heavy emissions trend along the Heihe-Tengchong line, with a concentration of large ERCEMI values; (ii) the gravity center of ERCEMI had shifted to the southwest, and the migration trends were divided into three stages; (iii) the ERCEMI had strong regional heterogeneity, with a diffusion trend from north to south and shrinking from east to west; (iv) the potential energy intensity and investment efficiency effects had significantly inhibited the ERCEMI, while the investment scale had boosted it. Implications for regional layouts, energy intensity reductions, and investment optimization are discussed. This research provides a comprehensive regional analysis for ERCEMI reductions and the sustainable development of the mining industry and provides a reference for local industrial development planning.

Characterization of metabolism-associated molecular patterns in prostate cancer.

BACKGROUND: Metabolism is a hallmark of cancer and it involves in resistance to antitumor treatment. Therefore, the purposes of this study are to classify metabolism-related molecular pattern and to explore the molecular and tumor microenvironment characteristics for prognosis predicting in prostate cancer. METHODS: The mRNA expression profiles and the corresponding clinical information for prostate cancer patients from TCGA, cBioPortal, and GEO databases. Samples were classified using unsupervised non-negative matrix factorization (NMF) clustering based on differentially expressed metabolism-related genes (MAGs). The characteristics of disease-free survival (DFS), clinicopathological characteristics, pathways, TME, immune cell infiltration, response to immunotherapy, and sensitivity to chemotherapy between subclusters were explored. A prognostic signature was constructed by LASSO cox regression analysis based on differentially expressed MAGs and followed by the development for prognostic prediction. RESULTS: A total of 76 MAGs between prostate cancer samples and non-tumorous samples were found, then 489 patients were divided into two metabolism-related subclusters for prostate cancer. The significant differences in clinical characteristics (age, T/N stage, Gleason) and DFS between two subclusters. Cluster 1 was associated with cell cycle and metabolism-related pathways, and epithelial-mesenchymal transition (EMT), etc., involved in cluster 2. Moreover, lower ESTIMATE/immune/stromal scores, lower expression of HLAs and immune checkpoint-related genes, and lower half-maximal inhibitory concentration (IC50) values in cluster 1 compared with cluster 2. The 10 MAG signature was identified and constructed a risk model for DFS predicting. The patients with high-risk scores showed poorer DFS. The area under the curve (AUC) values for 1-, 3-, 5-year DFS were 0.744, 0.731, 0.735 in TCGA-PRAD dataset, and 0.668, 0.712, 0.809 in GSE70768 dataset, 0.763, 0.802, 0.772 in GSE70769 dataset. Besides, risk score and Gleason score were identified as independent factors for DFS predicting, and the AUC values of risk score and Gleason score were respectively 0.743 and 0.738. The nomogram showed a favorable performance in DFS predicting. CONCLUSION: Our data identified two metabolism-related molecular subclusters for prostate cancer that were distinctly characterized in prostate cancer. Metabolism-related risk profiles were also constructed for prognostic prediction.

Sequencing of the Pituitary Transcriptome after GnRH Treatment Uncovers the Involvement of lncRNA-m23b/miR-23b-3p/CAMK2D in FSH Synthesis and Secretion.

The pituitary gland is a key participant in the hypothalamic-pituitary-gonadal axis, as it secretes a variety of hormones and plays an important role in mammalian reproduction. Gonadotrophin-releasing hormone(GnRH) signaling molecules can bind to GnRH receptors on the surfaces of adenohypophysis gonadotropin cells and regulate the expression of follicle-stimulating hormone(FSH) and luteinizing hormone(LH) through various pathways. An increasing number of studies have shown that noncoding RNAs mediate the regulation of GnRH signaling molecules in the adenohypophysis. However, the expression changes and underlying mechanisms of genes and noncoding RNAs in the adenohypophysis under the action of GnRH remain unclear. In the present study, we performed RNA sequencing (RNA-seq) of the rat adenohypophysis before and after GnRH treatment to identify differentially expressed mRNAs, lncRNAs, and miRNAs. We found 385 mRNAs, 704 lncRNAs, and 20 miRNAs that were significantly differentially expressed in the rat adenohypophysis. Then, we used a software to predict the regulatory roles of lncRNAs as molecular sponges that compete with mRNAs to bind miRNAs, and construct a GnRH-mediated ceRNA regulatory network. Finally, we enriched the differentially expressed mRNAs, lncRNA target genes, and ceRNA regulatory networks to analyze their potential roles. Based on the sequencing results, we verified that GnRH could affect FSH synthesis and secretion by promoting the competitive binding of lncRNA-m23b to miR-23b-3p to regulate the expression of Calcium/Calmodulin Dependent Protein Kinase II Delta(CAMK2D). Our findings provide strong data to support exploration of the physiological processes in the rat adenohypophysis under the action of GnRH. Furthermore, our profile of lncRNA expression in the rat adenohypophysis provides a theoretical basis for research on the roles of lncRNAs in the adenohypophysis.

Transcriptome sequencing of black and white hair follicles in the giant panda.

With the completion of the draft assembly of the giant panda genome sequence, RNA sequencing technology has been widely used in genetic research on giant pandas. We used RNA-seq to examine black and white hair follicle samples from adult pandas. By comparison with the giant panda genome, 75 963 SNP loci were labeled, 2426 differentially expressed genes (DEGs) were identified, and 2029 new genes were discovered, among which 631 were functionally annotated. A cluster analysis of the DEGs showed that they were mainly related to the Wnt signaling pathway, ECM-receptor interaction, the p53 signaling pathway, and ribosome processing. The enrichment results showed that there were significant differences in the regulatory networks of hair follicles with different colors during the transitional stage of hair follicle resting growth, which may play a regulatory role in melanin synthesis during growth. In conclusion, our results provide new insights and more data support for research on the color formation in giant pandas.

Public attitudes toward the whole life cycle management of plastics: A text-mining study in China.

Plastic pollution control, involving the whole life cycle management of plastic production, consumption, sorting, recycling, and disposal, has become necessary for global sustainable development. Research on public attitudes is vital to understanding whether plastic pollution control policies are being successfully implemented and the degree to which the public is involved. However, few studies have assessed public attitudes toward plastic pollution control from the whole life cycle perspective, especially using big data. Based on China's whole life cycle management policy of plastics, this study collected more than 200,000 relevant comments and user information from Sina Weibo to analyze and evaluate public attitudes and opinions toward plastic pollution control. Spatial-temporal analysis was conducted to discover the regional and temporal differences in public attention. Using a sentiment classification method based on semantic analysis, the emotional tendencies of the public attitudes toward ten subdivided plastic pollution control links were studied. It was found that more people held a positive attitude and paid more attention to reusing and sorting links, while the negative emotions were concentrated on the collection and sorting links. Using a topic modeling method, the negative opinions in various links were revealed, such as lack of supervision and industry standards; over packaging or insufficient packaging; food safety problems caused by the reuse; high costs, poor use and possibly greater waste of substitutes; unclear sorting rules and insufficient supporting measures. Graph theory was applied to display these opinions. Finally, some policy implications derived from the discussions are given.

The effectiveness of traffic and production restrictions on urban air quality: A rare opportunity for investigation.

Traffic and production restrictions are two important emergency measures for controlling urban air pollution. The lockdown policies implemented during the COVID-19 pandemic period are nearly equivalent to the policies of traffic and production restriction, which provides a rare opportunity to quantitatively evaluate the effectiveness of these emergency measures. Taking Wuhan, China as the study area, this paper firstly verified the changes in six air pollutants and analyzed their change rules in different lockdown periods using statistical methods. Then the structural breakpoints in air pollutants were detected via regression discontinuity design model. To comprehensively understand the effects of restrictions on air pollution, the influences of meteorological conditions on air pollution were also investigated. The results illustrated that the concentrations of PM2.5, PM10 and NO2 decreased significantly during lockdown period. By comparing with the RDD coefficients of PM2.5 (-34.46), PM10 (-37.11) and NO2 (-19.15), the lockdown had little effect on CO (-0.32). The traffic and production restrictions had no apparent effects on SO2. Although O3 showed an increasing trend, the increase was not limited to the lockdown period, meaning that the traffic and production restrictions had less effect on the increasing trend of O3 concentration. Moreover, the structural breakpoints were verified in four air pollutants (PM2.5, PM10, NO2, and CO), and the structural breakpoints were caused by lockdown instead of the Spring Festival. The results also indicated that the meteorological conditions were not the main reasons for the changes in air pollutants during the lockdown period. This paper reveals how the traffic and production restrictions affect urban air pollution and provides a strong implementation basis for the air pollution control policy.Implications: The traffic and production restrictions are two important emergency measures for controlling heavy urban air pollution. However, these two measures have never been implemented in a large area like a city for a long enough period, so the effectiveness of these two measures has never been estimated quantitatively at a city level. The lockdown policies implemented during the COVID-19 pandemic are nearly equivalent to the policies of traffic and production restriction, which provides a rare opportunity to quantitatively evaluate the effectiveness of these emergency measures. Thus, this study measured the effectiveness of production and traffic restrictions on different air pollutants. This study provides the following implications: (1) the dominant factors for air pollution changes during the lockdown are traffic and production restriction instead of meteorological conditions; (2) the production and traffic restriction policies are effective for reducing concentrations of PM2.5, PM10 and NO2, while having less effect on O3 and CO concentrations; (3) the sharp changes in air pollutants in 2020 are unlikely to be caused by the Spring Festival. These findings are crucial for making more comprehensive policies for protecting urban air quality.

Machine Learning Models for Slope Stability Classification of Circular Mode Failure: An Updated Database and Automated Machine Learning (AutoML) Approach.

Slope failures lead to large casualties and catastrophic societal and economic consequences, thus potentially threatening access to sustainable development. Slope stability assessment, offering potential long-term benefits for sustainable development, remains a challenge for the practitioner and researcher. In this study, for the first time, an automated machine learning (AutoML) approach was proposed for model development and slope stability assessments of circular mode failure. An updated database with 627 cases consisting of the unit weight, cohesion, and friction angle of the slope materials; slope angle and height; pore pressure ratio; and corresponding stability status has been established. The stacked ensemble of the best 1000 models was automatically selected as the top model from 8208 trained models using the H2O-AutoML platform, which requires little expert knowledge or manual tuning. The top-performing model outperformed the traditional manually tuned and metaheuristic-optimized models, with an area under the receiver operating characteristic curve (AUC) of 0.970 and accuracy (ACC) of 0.904 based on the testing dataset and achieving a maximum lift of 2.1. The results clearly indicate that AutoML can provide an effective automated solution for machine learning (ML) model development and slope stability classification of circular mode failure based on extensive combinations of algorithm selection and hyperparameter tuning (CASHs), thereby reducing human efforts in model development. The proposed AutoML approach has the potential for short-term severity mitigation of geohazard and achieving long-term sustainable development goals.

Circ-ERC2 Is Involved in Melatonin Synthesis by Regulating the miR-125a-5p/MAT2A Axis.

The circadian rhythm of melatonin secretion in the pineal gland is highly conserved in vertebrates. Melatonin levels are always elevated at night. Acetylserotonin O-methyltransferase (ASMT) is the last enzyme in the regulation of melatonin biosynthesis (N-acetyl-5-hydroxytryptamine-melatonin). S-adenosylmethionine (SAM) is an important methyl donor in mammals and can be used as a substrate for the synthesis of melatonin. Methionine adenosyltransferase (MAT) catalyzes the synthesis of SAM from methionine and ATP and has a circadian rhythm. CircRNA is an emerging type of endogenous noncoding RNA with a closed loop. Whether circRNAs in the pineal gland can participate in the regulation of melatonin synthesis by binding miRNAs to target mat2a as part of the circadian rhythm is still unclear. In this study, we predicted the targeting relationship of differentially expressed circRNAs, miRNAs and mRNAs based on the results of rat pineal RNA sequencing. Mat2a siRNA transfection confirmed that mat2a is involved in the synthesis of melatonin. Circ-ERC2 and miR-125a-5p were screened out by software prediction, dual-luciferase reporter experiments, cell transfection, etc. Finally, we constructed a rat superior cervical ganglionectomy model (SCGx), and the results showed that circ-ERC2 could participate in the synthesis of melatonin through the miR-125a-5p/MAT2A axis. The results of the study revealed that circ-ERC2 can act as a molecular sponge of miR-125a-5p to regulate the synthesis of melatonin in the pineal gland by targeting mat2a. This experiment provides a basis for research on the circadian rhythm of noncoding RNA on pineal melatonin secretion.

TRH Regulates the Synthesis and Secretion of Prolactin in Rats with Adenohypophysis through the Differential Expression of miR-126a-5p.

Prolactin (PRL) is an important hormone that is secreted by the pituitary gland and plays an important role in the growth, development and reproduction of organisms. Thyrotropin-releasing hormone (TRH) is a common prolactin-releasing factor that regulates the synthesis and secretion of prolactin. In recent studies, microRNAs (miRNAs) have been found to play a key role in the regulation of pituitary hormones. However, there is a lack of systematic studies on the regulatory role that TRH plays on the pituitary transcriptome, and the role of miRNAs in the regulation of PRL synthesis and secretion by TRH lacks experimental evidence. In this study, we first investigated the changes in PRL synthesis and secretion in the rat pituitary gland after TRH administration. The results of transcriptomic analysis after TRH treatment showed that 102 genes, including those that encode Nppc, Fgf1, PRL, Cd63, Npw, and Il23a, were upregulated, and 488 genes, including those that encode Lats1, Cacna2d1, Top2a, and Tfap2a, were downregulated. These genes are all involved in the regulation of prolactin expression. The gene expression of miR-126a-5p, which regulates the level of PRL in the pituitary gland, was screened by analysis prediction software and by a dual luciferase reporter system. The data presented in this study demonstrate that TRH can regulate prolactin synthesis and secretion through miR-126a-5p, thereby improving our understanding of the molecular mechanism of TRH-mediated PRL secretion and providing a theoretical basis for the role of miRNAs in regulating the secretion of pituitary hormones.

Landslide Susceptibility Evaluation Based on Potential Disaster Identification and Ensemble Learning.

Catastrophic landslides have much more frequently occurred worldwide due to increasing extreme rainfall events and intensified human engineering activity. Landslide susceptibility evaluation (LSE) is a vital and effective technique for the prevention and control of disastrous landslides. Moreover, about 80% of disastrous landslides had not been discovered ahead and significantly impeded social and economic sustainability development. However, the present studies on LSE mainly focus on the known landslides, neglect the great threat posed by the potential landslides, and thus to some degree constrain the precision and rationality of LSE maps. Moreover, at present, potential landslides are generally identified by the characteristics of surface deformation, terrain, and/or geomorphology. The essential disaster-inducing mechanism is neglected, which has caused relatively low accuracies and relatively high false alarms. Therefore, this work suggests new synthetic criteria of potential landslide identification. The criteria involve surface deformation, disaster-controlling features, and disaster-triggering characteristics and improve the recognition accuracy and lower the false alarm. Furthermore, this work combines the known landslides and discovered potential landslides to improve the precision and rationality of LSE. This work selects Chaya County, a representative region significantly threatened by landslides, as the study area and employs multisource data (geological, topographical, geographical, hydrological, meteorological, seismic, and remote sensing data) to identify potential landslides and realize LSE based on the time-series InSAR technique and XGBoost algorithm. The LSE precision indices of AUC, Accuracy, TPR, F1-score, and Kappa coefficient reach 0.996, 97.98%, 98.77%, 0.98, and 0.96, respectively, and 16 potential landslides are newly discovered. Moreover, the development characteristics of potential landslides and the cause of high landslide susceptibility are illuminated. The proposed synthetic criteria of potential landslide identification and the LSE idea of combining known and potential landslides can be utilized to other disaster-serious regions in the world.

Scientometric Analysis of Artificial Intelligence (AI) for Geohazard Research.

Geohazard prevention and mitigation are highly complex and remain challenges for researchers and practitioners. Artificial intelligence (AI) has become an effective tool for addressing these challenges. Therefore, for decades, an increasing number of researchers have begun to conduct AI research in the field of geohazards leading to rapid growth in the number of related papers. This has made it difficult for researchers and practitioners to grasp information on cutting-edge developments in the field, thus necessitating a comprehensive review and analysis of the current state of development in the field. In this study, a comprehensive scientometric analysis appraising the state-of-the-art research for geohazard was performed based on 9226 scientometric records from the Web of Science core collection database. Multiple types of scientometric techniques, including coauthor analysis, co-citation analysis, and cluster analysis were employed to identify the most productive researchers, institutions, and hot research topics. The results show that research related to the application of AI in the field of geohazards experienced a period of rapid growth after 2000, with major developments in the field occurring in China, the United States, and Italy. The hot research topics in this field are ground motion, deep learning (DL), and landslides. The commonly used AI algorithms include DL, support vector machine (SVM), and decision tree (DT). The obtained visualization on research networks offers valuable insights and an in-depth understanding of the key researchers, institutions, fundamental articles, and salient topics through animated maps. We believe that this scientometric review offers useful reference points for early-stage researchers and provides valuable in-depth information to experienced researchers and practitioners in the field of geohazard research. This scientometric analysis and visualization are promising for reflecting the global picture of AI-based geohazard research comprehensively and possess potential for the visualization of the emerging trends in other research fields.