Search

Abstract Background Glioma is the most common brain tumor. IDH mutations occur frequently in glioma, indicating a more favorable prognosis. We aimed to explore energy metabolism-related genes in glioma to promote the research and treatment. Methods Datasets were obtained from TCGA and GEO databases. Candidate genes were screened by differential gene expression analysis, then functional enrichment analysis was conducted on the candidate genes. PPI was also carried out to help determine the target gene. GSEA and DO analysis were conducted in the different expression level groups of the target gene. Survival analysis and immune cell infiltrating analysis were performed as well. Results We screened 34 candidate genes and selected GLUD1 as the target gene. All candidate genes were significantly enriched in 10 KEGG pathways and 330 GO terms. GLUD1 expression was higher in IDH-mutant samples than IDH-wildtype samples, and higher in normal samples than tumor samples. Low GLUD1 expression was related to poor prognosis according to survival analysis. Most types of immune cells were negatively related to GLUD1 expression, but monocytes and activated mast cells exhibited significantly positive correlation with GLUD1 expression. GLUD1 expression was significantly related to 119 drugs and 6 immune checkpoint genes. GLUD1 was able to serve as an independent prognostic indicator of IDH-mutant glioma. Conclusion In this study, we identified an energy metabolism-related gene GLUD1 potentially contributing to favorable clinical outcomes of IDH-mutant glioma. In glioma, GLUD1 related clinical outcomes and immune landscape were clearer, and more valuable information was provided for immunotherapy.

Abstract The prevalence of malnutrition in diabetic patients and its influencing factors remain poorly described. We aim to investigate the prevalence of malnutrition and the influencing factors in diabetic patients through meta‐analysis. Utilizing search terms, such as diabetes, malnutrition, and prevalence, we systematically searched eight databases, including Embase, PubMed, Web of Science, The Cochrane Library, China Knowledge Resource Integrated Database (CNKI), Wanfang Database, Chinese Biomedical Database (CBM), and VIP Database, from inception to May 4, 2023. The search aimed to identify studies related to the prevalence of malnutrition and its influencing factors in adult patients with diabetes. Cohort studies, case–control studies, and cross‐sectional studies that met the inclusion criteria were included in the analysis. Stata 16.0 software was used for meta‐analysis. Quality of the evidence was assessed using Grading of Recommendations, Assessment, Development and Evaluation (GRADE). The study protocol is registered with Prospective Register of Systematic Reviews (PROSPERO), CRD42023443649. A total of 46 studies were included, involving 18 062 patients with ages ranging from 18 to 95 years. The overall malnutrition prevalence was 33% (95% confidence interval [CI]: 0.25–0.40), compared with an at‐risk prevalence of 44% (95% CI: 0.34–0.54). Sixteen factors associated with malnutrition in diabetic patients were identified. This meta‐analysis provides insights into the prevalence of malnutrition and its risk factors in diabetic patients. Regular nutritional screening for patients with risk factors is essential for early detection and intervention.

Cancer is a serious global public health issue, and a great deal of research has been made to treat cancer. Of these, discovery of promising compounds that effectively fight cancer always has been the main point of interest in pharmaceutical research. Carnosic acid (CA) is a phenolic diterpenoid compound widely present in Lamiaceae plants such as Rosemary (Rosmarinus officinalis L.). In recent years, there has been increasing evidence that CA has significant anti-cancer activity, such as leukaemia, colorectal cancer, breast cancer, lung cancer, liver cancer, pancreatic cancer, stomach cancer, lymphoma, prostate cancer, oral cancer, etc. The potential mechanisms involved by CA, including inhibiting cell proliferation, inhibiting metastasis, inducing cell apoptosis, stimulating autophagy, regulating the immune system, reducing inflammation, regulating the gut microbiota, and enhancing the effects of other anti-cancer drugs. This article reviews the biosynthesis, pharmacokinetics and metabolism, safety and toxicity, as well as the molecular mechanisms and signaling pathways of the anticancer activity of CA. This will contribute to the development of CA or CA-containing functional foods for the prevention and treatment of cancer, providing important advances in the advancement of cancer treatment strategies.

On 1 September 2022, a landslide in Hongya Village, Weiyuan Town, Huzhu Tu Autonomous County, Qinghai Province, caused significant casualties and economic losses. To mitigate such risks, InSAR technology is employed due to its wide coverage, all-weather operation, and cost-effectiveness in detecting landslides. In this study, focusing on the landslide in Hongya Village, SBAS-InSAR and Sentinel-1A satellite data from July 2021 to September/October 2022 were used to accurately identify the areas of active landslides and to analyze the landslide deformation trends, in combination with the geological characteristics of the landslides and rainfall data. The results showed that strong deformation was detected in the middle and back of the landslide in Hongya Village, with a maximum deformation rate of approximately -13 mm/year. The surface of the landslide consisted of mainly Upper Pleistocene wind-deposited loess, which is extremely sensitive to water. The deformation of the landslide was closely related to the rainfall, and the deformation of the landslide increased with the increase in rainfall. The research results prove that the combination of ascending and descending orbit data based on SBAS-InSAR technology is highly feasible in the field of landslide deformation monitoring and is of great practical significance for landslide disaster prevention and mitigation.

Landslides are most severe in the mountainous regions of southwestern China. While landslide identification provides a foundation for disaster prevention operations, methods for utilizing multi-source data and deep learning techniques to improve the efficiency and accuracy of landslide identification in complex environments are still a focus of research and a difficult issue in landslide research. In this study, we address the above problems and construct a landslide identification model based on the shifted window (Swin) transformer. We chose Ya’an, which has a complex terrain and experiences frequent landslides, as the study area. Our model, which fuses features from different remote sensing data sources and introduces a loss function that better learns the boundary information of the target, is compared with the pyramid scene parsing network (PSPNet), the unified perception parsing network (UPerNet), and DeepLab_V3+ models in order to explore the learning potential of the model and test the models’ resilience in an open-source landslide database. The results show that in the Ya’an landslide database, compared with the above benchmark networks (UPerNet, PSPNet, and DeepLab_v3+), the Swin Transformer-based optimization model improves overall accuracies by 1.7%, 2.1%, and 1.5%, respectively; the F1_score is improved by 14.5%, 16.2%, and 12.4%; and the intersection over union (IoU) is improved by 16.9%, 18.5%, and 14.6%, respectively. The performance of the optimized model is excellent.

In recent years, various kinds of civil explosive detonation accidents have occurred frequently around the world, resulting in substantial human casualties and significant property losses. It is generally believed that thermal stimulation plays a critical role in triggering the detonation of explosives; consequently, the study of the thermal hazards of explosives is of great significance to many aspects of safety emergency management practices in the production, transportation, storage, and use of explosives. It is known that the thermal stability of the ammonium perchlorate-aluminium system and the ammonium nitrate-aluminium system has been extensively investigated previously in the literature. However, there is a paucity of research on the thermal hazard characteristics of non-ideal explosives under varying oxygen balance conditions within the academic sphere. Therefore, this research focused on the study of the thermal hazards of non-ideal explosives based on thermokinetic analysis. The thermal hazards of non-ideal explosive mixtures of ammonium perchlorate and aluminium and of ammonium nitrate and aluminium were studied by thermal analysis kinetics. The thermokinetic parameters were meticulously studied through differential scanning calorimetry (DSC) analysis. The results showed that the peak reaction temperature and activation energy of the ammonium perchlorate-aluminium system were significantly higher than those of the ammonium nitrate-aluminium system. Under the condition of zero oxygen balance, the peak reaction temperature of the ammonium nitrate-aluminium system was 259 °C (heating rate 5 °C/min), and the activation energy was 84.7 kJ/mol. Under the same conditions, the peak reaction temperature and activation energy of the ammonium perchlorate-aluminium system were 292 °C (heating rate 5 °C/min) and 94.9 kJ/mol, respectively. These results indicate that the ammonium perchlorate-aluminium system has higher safety under the same thermal stimulation conditions. Furthermore, research on both non-ideal explosive systems reveals that the activation energy is at its peak under negative oxygen balance conditions, recorded at 104.2 kJ/mol (ammonium perchlorate-aluminium) and 86.2 kJ/mol (ammonium nitrate-aluminium), which indicates a higher degree of safety. Therefore, the investigation into the thermal hazards of non-ideal explosive systems under different oxygen balance conditions is of utmost importance for the enhancement and improvement of safety emergency management practices.

Landslide susceptibility prediction usually involves the comprehensive analysis of terrain and other factors that may be distributed with spatial patterns. Without considering the spatial correlation and mutual influence between pixels, conventional prediction methods often focus only on information from individual pixels. To address this issue, the present study proposes a new strategy for neighboring pixel collaboration based on the Unified Perceptual Parsing Network (UPerNet), the Vision Transformer (ViT), and Vision Graph Neural Networks (ViG). This strategy efficiently utilizes the strengths of deep learning in feature extraction, sequence modeling, and graph data processing. By considering the information from neighboring pixels, this strategy can more accurately identify susceptible areas and reduce misidentification and omissions. The experimental results suggest that the proposed strategy can predict landslide susceptibility zoning more accurately. These predictions can identify flat areas such as rivers and distinguish between areas with high and very high landslide susceptibility. Such refined zoning outcomes are significant for landslide prevention and mitigation and can help decision-makers formulate targeted response measures.

The delayed surface subsidence caused by coal seam mining is a problem that cannot be ignored, while accurate prediction of the surface subsidence provides a guarantee of the safety and stability of the relevant areas. However, the traditional Knothe model has limitations in considering delayed surface subsidence. Because of this, the Knothe-n time function model is segmented and improved by using the data of the subsidence area obtained from a FLAC3D-based numerical model, and the maximum delayed surface subsidence in different periods is calculated. The analytical results are compared with the numerical results to validate the effectiveness of the improved segmented time function model in predicting delayed surface subsidence. The improved model is applied to predict the surface subsidence in the Yutianbao subsidence area. The root-mean-square error between the predicted and measured values for the maximum subsidence monitoring point is 1.12, and the root-mean-square error between the average predicted and measured values for the surface monitoring points is 0.37, which verifies the accuracy of the improved model. The prediction model provides a scientific basis for environmental protection and safety management after coal seam mining.

This paper mainly explores the deformation instability mechanism and influencing factors of landslide in a town called Fa'er, in southwestern China’s Guizhou Province. Drawing on the theories of engineering geological mechanics, the exploration was carried out through the analysis of geological data, field investigation, and drone survey. The results show that: The mining of underground coal seams led to multiple tensile cracks on the trailing edge; the fragmented rock and soil mass, as well as the free face on the front edge create a favorable landform for initiating the landslide; the landslide is mainly induced by the heavy rainfall for several consecutive days; under the continuous heavy rainfall, the creep shear zone was penetrated by cracks, and the landslide was pulled by gravity to slide; the deformation mode shifted from creeping-tensile cracking to plastic flow-tensile cracking.

Abstract Current strategies for porcine reproductive and respiratory syndrome (PRRS) control are inadequate and mainly restricted to immunization using different PRRS virus (PPRSV) vaccines. Although there are no safety concerns, the poor performance of inactivated PRRSV vaccines has restricted their practical application. In this research, we employed the novel PRRSV-specific IgM monoclonal antibody (Mab)-PR5nf1 as a vaccine adjuvant for the formulation of a cocktail composed of inactivated PRRSV (KIV) and Mab-PR5nf1 along with a normal adjuvant to enhance PRRSV-KIV vaccine-mediated protection and further compared it with a normal KIV vaccine and modified live virus vaccine (MLV). After challenge with highly pathogenic (HP)-PRRSV, our results suggested that the overall survival rate (OSR) and cell-mediated immunity (CMI), as determined by serum IFN-γ quantification and IFN-γ ELISpot assay, were significantly improved by adding PRRSV-specific IgM to the PRRSV-KIV vaccine. It was also notable that both the OSR and CMI in the Mab-PR5nf1-adjuvanted KIV group were even higher than those in the MLV group, whereas the CMI response is normally poorly evoked by KIV vaccines or subunit vaccines. Compared with those in piglets immunized with the normal KIV vaccine, viral shedding and serum neutralizing antibody levels were also improved, and reduced viral shedding appeared to be a result of enhanced CMI caused by the inclusion of IgM as an adjuvant. In conclusion, our data provide not only a new formula for the development of an effective PRRSV-KIV vaccine for practical use but also a novel method for improving antigen-specific CMI induction by inactivated vaccines and subunit vaccines.

Background: The role of the cellular level in kidney transplant rejection is unclear, and single-cell RNA sequencing (scRNA-seq) can reveal the single-cell landscape behind rejection of human kidney allografts at the single-cell level.Methods: High-quality transcriptomes were generated from scRNA-seq data from five human kidney transplantation biopsy cores. Cluster analysis was performed on the scRNA-seq data by known cell marker genes in order to identify different cell types. In addition, pathways, pseudotime developmental trajectories and transcriptional regulatory networks involved in different cell subpopulations were explored. Next, we systematically analyzed the scoring of gene sets regarding single-cell expression profiles based on biological processes associated with oxidative stress.Results: We obtained 81,139 single cells by scRNA-seq from kidney transplant tissue biopsies of three antibody-mediated rejection (ABMR) patients and two acute kidney injury (AKI) patients with non-rejection causes and identified 11 cell types, including immune cells, renal cells and several stromal cells. Immune cells such as macrophages showed inflammatory activation and antigen presentation and complement signaling, especially in rejection where some subpopulations of cells specifically expressed in rejection showed specific pro-inflammatory responses. In addition, patients with rejection are characterized by an increased number of fibroblasts, and further analysis of subpopulations of fibroblasts revealed their involvement in inflammatory and fibrosis-related pathways leading to increased renal rejection and fibrosis. Notably, the gene set score for response to oxidative stress was higher in patients with rejection.Conclusion: Insight into histological differences in kidney transplant patients with or without rejection was gained by assessing differences in cellular levels at single-cell resolution. In conclusion, we applied scRNA-seq to rejection after renal transplantation to deconstruct its heterogeneity and identify new targets for personalized therapeutic approaches.

Considering the great time and labor consumption involved in conventional hazard assessment methods in compiling landslide inventory, the construction of a transferable landslide susceptibility prediction model is crucial. This study employs UAV images as data sources to interpret the typical alpine valley area of Beichuan County. Eight environmental factors including a digital elevation model (DEM) are extracted to establish a pixel-wise dataset, along with interpreted landslide data. Two landslide susceptibility models were built, each with a deep neural network (DNN) and a support vector machine (SVM) as the learner, and the DNN model was determined to have the best pre-training performance (accuracy = 88.6%, precision = 91.3%, recall = 94.8%, specificity = 87.8%, F1-score = 93.0%, and area under curve = 0.943), with higher parameters in comparison to the SVM model (accuracy = 77.1%, precision = 80.9%, recall = 87.8%, specificity = 73.9%, F1-score = 84.2%, and area under curve = 0.878). The susceptibility model of Beichuan County is then transferred to Mao County (which has no available dataset) to realize cross-regional landslide susceptibility prediction. The results suggest that the model predictions accomplish susceptibility zoning principles and that the DNN model can more precisely distinguish between high and very-high susceptibility areas in relation to the SVM model.

Currently, the application of remote sensing technology in landslide identification and investigation is an important research direction in the field of landslides. To address the errors arising from the inaccurate extraction of texture and location information in landslide intelligent recognition, we developed a new network, the dual path attention network (DPANet), and performed experiments in a typical alpine canyon area (Wenchuan County). The results show that the new network recognizes landslide areas with an overall accuracy (OA) and pixel accuracy (PA) of 0.93 and 0.87, respectively, constituting an overall improvement of 4% and 18% compared to the base pyramid scene parsing network (PSPNet). We applied our knowledge of the landslide image features to other areas in the upper reaches of the Minjiang River to enrich the landslide database for this region. Our evaluation of the results shows that the proposed network framework has good robustness and can accurately identify some complex landslides, providing an excellent contribution to the intelligent recognition of landslides.

The depth and plane position of the goaf control the deformation characteristics of the mining slope with gently inclined layered structure and directly affect the overall stability of the mining slope. In recent years, scholars have carried out a large number of studies on the deformation of the overlying rock mass caused by underground coal mining, but the influence of goaf characteristics on the key positions of slopes has not been revealed deeply enough. The Jianshanying deformation body in the Faer Coal Mine in Guizhou Province is a typical gently-inclined layered mining slope, which has the steep upward and gentle downward, and hard upward and soft downward characteristics. The Jianshanying Deformation Body is chosen as the subject of the study in this paper. The effects of multi-layer mining and single-layer mining at different depths on the slope deformation are studied by qualitative analyses of the geological process mechanism and numerical simulation method. The width, depth and depth ratio of goaf have an important influence on the slope deformation characteristics. The results show that the multi-layer mining leads to the destruction of the slope toe and the overall collapse, which will cause the slope to produce a significant horizontal displacement, resulting in the vertical displacement of the multi-layer mining being much greater than the total thickness of the coal seam mining. The goaf crossing the slope toe, which makes the depth-to-thickness ratio decrease sharply and the mining cracks are more likely to extend to the surface, is an important factor for the destruction of the rock mass near the slope toe. When the width of the goaf increases or the multi-layer mining operation is carried out, the slope deformation increases significantly. When the depth of the goaf increases, the slope deformation value decreases, but the deformation range increases. It is beneficial to slope stability by optimizing the layout of the working face to prevent the sharp decrease of the depth-to-thickness ratio, controlling the width of the goaf, avoiding repeated mining and increasing the depth of mining, when mining in the strong terrain areas.

It is important to study the instability law of typical landslides to understand the evolution of regional landslides and for disaster prevention. This study uses the Zhangjiawan landslide in Xining City, Qinghai Province, as an example. Through field surveys and an analysis of the geological environmental conditions, the formation and evolution mechanisms of the Zhangjiawan landslide are summarized. The stability of the Zhangjiawan landslide was evaluated using limit equilibrium and numerical simulation methods to provide theoretical support for the susceptibility of the Zhangjiawan landslide. The results show that the main reasons for the occurrence of multi-grade landslides are the steep and gentle terrain changes, the poor permeability of the mudstone lithology, the excavation of the front anti-slide section, and rainfall. The whole deformation and failure mechanism of the landslide is the “unloading rebound-tension deformation-creep deformation.” The results of the numerical simulation and the limit equilibrium methods are nearly the same, and the difference in the stability coefficients is small. Under rainstorm conditions, the displacement and the maximum shear strain increments are mainly concentrated on the rear edge of the landslide mass, and the front edge is pushed, which is a typical push-type landslide.

Areas with vulnerable ecological environments often breed many geological disasters, especially landslides, which pose a severe threat to the safety of people’s lives and property in these areas. To aid in landslide prevention and mitigation, an approach combining the coefficient of determination method (CF) and a deep neural network (DNN) were proposed in this study for landslide susceptibility evaluation. The deep neural network can excavate the deep features of samples and improve the accuracy of the susceptibility model. In addition, the logistic regression model (LRM) and support vector machine (SVM) were selected to create landslide susceptibility maps for comparison, which also involved the coefficient of determination method (CF). Based on landslide remote sensing interpretation and field investigations, a spatial database of mudstone landslides in the Xining area was established. Eight different conditional factors, including the elevation, slope, slope aspect, undulation, curvature, watershed, distance from a fault, and distance from a road, in the study area were selected as the evaluation factors to evaluate the susceptibility. The results revealed that four factors (i.e., the ground elevation, curvature, distance from a fault, and distance from a road) had relatively significant influences on the landslide susceptibility in the study area. Finally, the confusion matrix was used to evaluate the accuracy of the results obtained using the three methods, and the optimal result was selected to evaluate the landslide susceptibility in the study area. It was found that the combined CF-DNN method was more suitable for evaluating the landslide susceptibility in this area. Landslide susceptibility zoning was conducted to divide the study area into four sensitivity levels: low (32.65%), medium (35.12%), high (22.44%), and extremely high (9.79%) susceptibility. The high-risk areas were primarily distributed in the high-elevation areas along the eastern edge of the Huangshui Basin.

Abstract Neutralizing antibodies (NAbs) have attracted attention as tools for achieving PRRSV control and prevention, but viral antigenic variation undermines the abilities of NAbs elicited by attenuated PRRSV vaccines to confer full protection against heterogeneous PRRSV field isolates. As demonstrated in this study, the monoclonal antibody (mAb) mAb-PN9cx3 exhibited broad-spectrum recognition and neutralizing activities against PRRSV-1 and PRRSV-2 strains in vitro. Furthermore, in vivo experiments revealed that the administration of two 10-mg doses of mAb-PN9cx3 before and after the inoculation of piglets with heterologous PRRSV isolates (HP-PRRSV-JXA1 or PRRSV NADC30-like strain HNhx) resulted in significant reduction of the PRRSV-induced pulmonary pathological changes and virus loads in porcine alveolar macrophages (PAMs) compared with the results obtained with mAb-treated isotype controls. Moreover, minimal hilar lymph node PRRSV antigen levels were observed in mAb-PN9cx3-treated piglets. A transcriptome profile analysis of PAMs extracted from lung tissues of piglets belonging to different groups (except for antibody-isotype controls) indicated that mAb-PN9cx3 treatment reversed the PRRSV infection-induced alterations in expression profiles. A gene ontology (GO) enrichment analysis of these genes traced their functions to pathways that included the immune response, inflammatory response, and response to steroid hormone, and their functions in oogenesis and positive regulation of angiogenesis have been implicated in PRRSV pathogenesis. Overall, NADC30-like HNhx infection affected more gene pathways than HP-PRRSV infection. In conclusion, our research describes a novel immunologic approach involving the use of mAbs that confer cross-protection against serious illness resulting from infection with heterogeneous PRRSV-2 isolates, which is a feat that has not yet been achieved through vaccination. Ultimately, mAb-PN9cx3 will be a powerful addition to our current arsenal for achieving PRRSV prevention and eradication.

Landslides are geohazards of major concern that can cause casualties and property damage. Short-term landslide displacement prediction is one of the most critical and challenging tasks in landslide deformation analysis, and is beneficial for future hazard mitigation. In this research, a novel short-term displacement prediction approach using spatial-temporal correlation and a gated recurrent unit (GRU) is proposed. The proposed approach is a unified framework that integrates time-series instant displacements collected from multiple monitoring points on a failing slope. First, a spatial-temporal correlation matrix, including the pairwise Pearson’s correlation coefficients, was studied based on the temporal instant displacement data. Then, the extracted spatial features were integrated into the time-series prediction model using GRU. This approach combines both spatial and temporal features simultaneously and provides enhanced prediction performance. In the last step, a comparative analysis against other benchmark algorithms is performed in two case studies including the conventional time-series modeling approach and the spatial-temporal modeling approach. The computational results show that the proposed model performs best in terms of performance evaluation metrics.

Spatio-temporal changes in cultivated land have a profound impact on food security and sustainable development. However, existing studies on spatio-temporal changes in cultivated land mostly focus on single factors, for instance quantity, quality and ecology, that cannot comprehensively reflect the changes in total production capacity and the sustainability of cultivated land. This study aims to construct a comprehensive analysis approach and to provide a reference basis for a comprehensive analysis of the extent of changes in overall cultivated land food-production capacity and the formulation of cultivated land conservation-related policies. This comprehensive analysis method constructed from three dimensions: quantity, production capacity and ecology, fully reflects the changes in the total amount, structure, rate of change, spatial distribution, quality, total production capacity and sustainability of cultivated land. The results from the application of this approach to Long’an County, Guangxi Province, China demonstrate that: (a) from 2010 to 2020, the total amount of cultivated land in Long’an County decreased sharply by 30.83%, accounted for mainly by the conversion into orchards, forest land and other garden land; (b) the quality of cultivated land improved by 2.71% on average, mostly in relation to natural factors; (c) the total food-production capacity of cultivated land decreased by 28.96% on average, mainly due to the decrease in the area of cultivated land; (d) both the ecological grade and the sustainability of cultivated land decreased slightly; (e) the barycenter of cultivated land migrated 3.3 km to the ecologically sensitive areas in the west, and the patch size of cultivated land decreased from an average of 2.60 hectares/pc in 2010 to that of 1.34 hectares/pc in 2020, suggesting increased fragmentation of cultivated land; and (f) the patch regularity of cultivated land decreased from 2.08 in 2010 to 1.76 in 2020, showing improved patch regularity and slightly better adaptability to mechanization. There were two main reasons for the lower, total food production capacity in Long’an County: first, the low comparative income of grain cultivation, because of which farmers spontaneously adjusted the agricultural cultivation structure to pursue high returns; and second, the lack of targeted government policies to protect cultivated land. In general, this comprehensive analysis method is applicable to other provinces in China or other regions abroad to provide a reference basis for a comprehensive understanding of changes in the food production capacity of cultivated land and the formulation of policies on cultivated land protection.

Myeloblastosis (MYB) proteins constitute one of the largest transcription factor (TF) families in plants. They are functionally diverse in regulating plant development, metabolism, and multiple stress responses. However, the function of watermelon MYB proteins remains elusive to date. Here, a genome-wide identification of watermelon MYB TFs was performed by bioinformatics analysis. A total of 162 MYB genes were identified from watermelon (ClaMYB). A comprehensive overview of the ClaMYB genes was undertaken, including the gene structures, chromosomal distribution, gene duplication, conserved protein motif, and phylogenetic relationship. According to the analyses, the watermelon MYB genes were categorized into three groups (R1R2R3-MYB, R2R3-MYB, and MYB-related). Amino acid alignments for all MYB motifs of ClaMYBs demonstrated high conservation. Investigation of their chromosomal localization revealed that these ClaMYB genes distributed across the 11 watermelon chromosomes. Gene duplication analyses showed that tandem duplication events contributed predominantly to the expansion of the MYB gene family in the watermelon genome. Phylogenetic comparison of the ClaMYB proteins with Arabidopsis MYB proteins revealed that watermelon MYB proteins underwent a more diverse evolution after divergence from Arabidopsis. Some watermelon MYBs were found to cluster into the functional clades of Arabidopsis MYB proteins. Expression analysis under different stress conditions identified a group of watermelon MYB proteins implicated in the plant stress responses. The comprehensive investigation of watermelon MYB genes in this study provides a useful reference for future cloning and functional analysis of watermelon MYB proteins. Keywords: watermelon, MYB transcription factor, abiotic stress, phylogenetic analysis

Rhythmic incremental growth lines and the presence of melatonin receptors were discovered in tooth enamel, suggesting possible role of circadian rhythm. We therefore hypothesized that circadian rhythm may regulate enamel formation through melatonin receptors. To test this hypothesis, we examined expression of melatonin receptors (MTs) and amelogenin (AMELX), a maker of enamel formation, during tooth germ development in mouse. Using qRT-PCR and immunocytochemistry, we found that mRNA and protein levels of both MTs and AMELX in normal mandibular first molar tooth germs increased gradually after birth, peaked at 3 or 4 day postnatal, and then decreased. Expression of MTs and AMELX by immunocytochemistry was significantly delayed in neonatal mice raised in all-dark or all-light environment as well as the enamel development. Furthermore, development of tooth enamel was also delayed showing significant immature histology in those animals, especially for newborn mice raised in all daylight condition. Interestingly, disruption in circadian rhythm in pregnant mice also resulted in delayed enamel development in their babies. Treatment with melatonin receptor antagonist 4P-PDOT in pregnant mice caused underexpression of MTs and AMELX associated with long-lasting deficiency in baby enamel tissue. Electromicroscopic evidence demonstrated increased necrosis and poor enamel mineralization in ameloblasts. The above results suggest that circadian rhythm is important for normal enamel development at both pre- and postnatal stages. Melatonin receptors were partly responsible for the regulation.