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Coal mining in the Loess Plateau can very easily generate ground cracks, and these cracks can immediately result in ventilation trouble under the mine shaft, runoff disturbance, and vegetation destruction. Advanced UAV (Unmanned Aerial Vehicle) high-resolution mapping and DL (Deep Learning) are introduced as the key methods to quickly delineate coal mining ground surface cracks for disaster prevention. Firstly, the dataset named the Ground Cracks of Coal Mining Area Unmanned Aerial Vehicle (GCCMA-UAV) is built, with a ground resolution of 3 cm, which is suitable to make a 1:500 thematic map of the ground crack. This GCCMA-UAV dataset includes 6280 images of ground cracks, and the size of the imagery is 256 × 256 pixels. Secondly, the DRA-UNet model is built effectively for coal mining ground surface crack delineation. This DRA-UNet model is an improved UNet DL model, which mainly includes the DAM (Dual Dttention Dechanism) module, the RN (residual network) module, and the ASPP (Atrous Spatial Pyramid Pooling) module. The DRA-UNet model shows the highest recall rate of 77.29% when the DRA-UNet was compared with other similar DL models, such as DeepLabV3+, SegNet, PSPNet, and so on. DRA-UNet also has other relatively reliable indicators; the precision rate is 84.92% and the F1 score is 78.87%. Finally, DRA-UNet is applied to delineate cracks on a DOM (Digital Orthophoto Map) of 3 km2 in the mining workface area, with a ground resolution of 3 cm. There were 4903 cracks that were delineated from the DOM in the Huojitu Coal Mine Shaft. This DRA-UNet model effectively improves the efficiency of crack delineation.

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PURPOSE: Proliferative vitreoretinopathy (PVR) can cause blindness and the pathogenesis is unclear. Transforming growth factor (TGF)-ß-induced epithelial-mesenchymal transition (EMT) of RPE cells is vital. P53 protein 2 (ASPP2) was previously reported to inhibit EMT in PVR rats, but the specific mechanism is unveiled. METHODS: TGF-ß was used to induce EMT in ARPE-19 cells, and evaluated by immunofluorescence and western blot. ARPE-19 cells were transfected with scrambled/ASPP2-lentivirus, followed by TGF-ß treatment. After that, alterations of EMT and autophagy were measured by western blot and transmission electron microscopy. Moreover, TGF-ß and ARPE-19 cells treated with scrambled/ASPP2-lentivirus were employed to establish the PVR model via intravitreal injection to SD rats, and retinal changes as well as EMT and autophagy activity were evaluated accordingly. RESULTS: ASPP2 expression was decreased during TGF-ß-induced EMT in ARPE-19 cells. In vitro, EMT and autophagy was activated by TGF-ß, which could be partly reversed by ASPP2 upregulation. In vivo, ASPP2 upregulation protected against structural and functional changes in PVR retinas. Additionally, expressions of EMT and autophagy markers in retinas were inhibited by ASPP2 upregulation. CONCLUSIONS: ASPP2 upregulation inhibited the EMT and autophagy process caused by TGF-ß in ARPE-19 cells. Correspondingly, upregulation of ASPP2 alleviated intraocular fibrosis and protected visual function in PVR rats.

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BACKGROUND: In complex agricultural environments, the presence of shadows, leaf debris, and uneven illumination can hinder the performance of leaf segmentation models for cucumber disease detection. This is further exacerbated by the imbalance in pixel ratios between background and lesion areas, which affects the accuracy of lesion extraction. RESULTS: An original image segmentation framework, the LS-ASPP model, which utilizes a two-stage Atrous Spatial Pyramid Pooling (ASPP) approach combined with adaptive loss to address these challenges has been proposed. The Leaf-ASPP stage employs attention modules and residual structures to capture multi-scale semantic information and enhance edge perception, allowing for precise extraction of leaf contours from complex backgrounds. In the Spot-ASPP stage, we adjust the dilation rate of ASPP and introduce a Convolutional Attention Block Module (CABM) to accurately segment lesion areas. CONCLUSIONS: The LS-ASPP model demonstrates improved performance in semantic segmentation accuracy under complex conditions, providing a robust solution for precise cucumber lesion segmentation. By focusing on challenging pixels and adapting to the specific requirements of agricultural image analysis, our framework has the potential to enhance disease detection accuracy and facilitate timely and effective crop management decisions.

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Background: Acute liver injury (ALI), which is a type of inflammation-mediated hepatocellular injury, is a clinical syndrome that results from hepatocellular apoptosis and hemorrhagic necrosis. Apoptosis stimulating protein of p53-2 (ASPP2) is a proapoptotic member of the p53 binding protein family. However, the role of ASPP2 in the pathogenesis of ALI and its regulatory mechanisms remain unclear. Methods: The expression of ASPP2 were compared between liver biopsies derived from patients with CHB, patients with ALI, and normal controls. Acute liver injury was modelled in mice by administration of D-GalN/LPS. Liver injury was demonstrated by serum transaminases and histological assessment of liver sections. ASPP2-knockdown mice (ASPP2+/-) were used to determine its role in acute liver injury. Mouse bone marrow macrophages (BMMs) were isolated from wildtype and ASPP2+/- mice and stimulated with LPS, and the supernatant was collected to incubate with the primary hepatocytes. Quantitative real-time PCR and western blot were used to analyze the expression level of target. Results: The expression of ASPP2 was significantly upregulated in the liver tissue of ALI patients and acute liver injury mice. ASPP2+/- mice significantly relieved liver injury through reducing liver inflammation and decreasing hepatocyte apoptosis. Moreover, the conditioned medium (CM) of ASPP2+/- bone marrow-derived macrophages (BMMs) protected hepatocytes against apoptosis. Mechanistically, we revealed that ASPP2 deficiency in BMMs specifically upregulated IL-6 through autophagy activation, which decreased the level of TNF-α to reduce hepatocytes apoptosis. Furthermore, up-regulation of ASPP2 sensitizes hepatocytes to TNF-α-induced apoptosis. Conclusion: Our novel findings show the critical role of ASPP2 in inflammatory immunoregulatory mechanism of ALI and provide a rationale to target ASPP2 as a refined therapeutic strategy to ameliorate acute liver injury.

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Cell polarity is crucial for gastric mucosal barrier integrity and mainly regulated by polarity-regulating kinase partitioning-defective 1b (Par1b). During infection, the carcinogen Helicobacter pylori hijacks Par1b via the bacterial oncoprotein CagA leading to loss of cell polarity, but the precise molecular mechanism is not fully clear. Here we discovered a novel function of the actin-binding protein cortactin in regulating Par1b, which forms a complex with cortactin and the tight junction protein zona occludens-1 (ZO-1). We found that serine phosphorylation at S405/418 and the SH3 domain of cortactin are important for its interaction with both Par1b and ZO-1. Cortactin knockout cells displayed disturbed Par1b cellular localization and exhibited morphological abnormalities that largely compromised transepithelial electrical resistance, epithelial cell polarity, and apical microvilli. H. pylori infection promoted cortactin/Par1b/ZO-1 abnormal interactions in the tight junctions in a CagA-dependent manner. Infection of human gastric organoid-derived mucosoids supported these observations. We therefore hypothesize that CagA disrupts gastric epithelial cell polarity by hijacking cortactin, and thus Par1b and ZO-1, suggesting a new signaling pathway for the development of gastric cancer by Helicobacter.

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C-type lectin proteins (CTLs), a group of pattern recognition receptors (PRRs), play pivotal roles in immune responses. However, the signal transduction and regulation of CTLs in cephalochordates have yet to be explored. In this study, we examined the composition of CTLs in Branchiostoma japonicum, identifying a total of 272 CTLs. These CTLs underwent further analysis concerning domain arrangement, tandem and segmental duplication events. A multidomain C-type lectin gene, designated as BjCTL5, encompassing CLECT, KR, CUB, MAM, and SR domains, was the focal point of our investigation. BjCTL5 exhibits ubiquitous expression across all detected tissues and is responsive to stimulation by LPS, mannose, and poly (I:C). The recombinant protein of BjCTL5 can bind to Escherichia coli and Staphylococcus aureus, inducing their agglutination and inhibiting the proliferation of S. aureus. Yeast two-hybrid, CoIP, and confocal immunofluorescence experiments revealed the interaction between BjCTL5 and apoptosis-stimulating proteins of p53, BjASPP. Intriguingly, BjCTL5 was observed to induce the luciferase activity of the NF-κB promoter in HEK293T cells. These results suggested a potential interaction between BjCTL5 and BjASPP, implicating that they involve in the activation of the NF-κB signaling pathway, which provides an evolutionary viewpoint on NF-κB signaling pathway in primitive chordate.

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Introduction: Early detection of leaf diseases is necessary to control the spread of plant diseases, and one of the important steps is the segmentation of leaf and disease images. The uneven light and leaf overlap in complex situations make segmentation of leaves and diseases quite difficult. Moreover, the significant differences in ratios of leaf and disease pixels results in a challenge in identifying diseases. Methods: To solve the above issues, the residual attention mechanism combined with atrous spatial pyramid pooling and weight compression loss of UNet is proposed, which is named RAAWC-UNet. Firstly, weights compression loss is a method that introduces a modulation factor in front of the cross-entropy loss, aiming at solving the problem of the imbalance between foreground and background pixels. Secondly, the residual network and the convolutional block attention module are combined to form Res_CBAM. It can accurately localize pixels at the edge of the disease and alleviate the vanishing of gradient and semantic information from downsampling. Finally, in the last layer of downsampling, the atrous spatial pyramid pooling is used instead of two convolutions to solve the problem of insufficient spatial context information. Results: The experimental results show that the proposed RAAWC-UNet increases the intersection over union in leaf and disease segmentation by 1.91% and 5.61%, and the pixel accuracy of disease by 4.65% compared with UNet. Discussion: The effectiveness of the proposed method was further verified by the better results in comparison with deep learning methods with similar network architectures.

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The spine is one of the most important structures in the human body, serving to support the body, organs, protect nerves, etc. Medical image segmentation for the spine can help doctors in their clinical practice for rapid decision making, surgery planning, skeletal health diagnosis, etc. The current difficulty is mainly the poor segmentation accuracy of skeletal Magnetic Resonance Imaging (MRI) images. To address the problem, we propose a spine MRI image segmentation method, Atrous Spatial Pyramid Pooling (ASPP)-U-shaped network (UNet), which combines an ASPP structure with a U-Net network. This approach improved the network feature extraction by introducing an ASPP structure into the U-Net network down-sampling structure. The medical image segmentation models are trained and tested on publicly available datasets and obtained the Dice coefficient and Mean Intersection over Union coefficients with 0.866 and 0.755, respectively. The experimental results show that ASPP-UNet has higher accuracy for spine MRI image segmentation compared with other mainstream networks.

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(1) Background: pancreatic cancer is highly lethal. The role of apoptosis-stimulating protein of p53-2 (ASPP2) in this lethal disease remains unclear. This protein belongs to the ASPP family of p53 interacting proteins. Previous studies in this lab used phosphate-binding tag (Phos-tag) sodium dodecyl sulfate (SDS) polyacrylamide gels and identified a motility upshift of the ASPP family of proteins during mitosis. (2) Purpose: this study expands on previous findings to identify the detailed phosphorylation regulation of ASPP2 during mitosis, as well as the function of ASPP2 in pancreatic cancer. (3) Methods: the Phos-tag technique was used to investigate the phosphorylation mechanism of ASPP2 during mitosis. Phospho-specific antibodies were generated to validate the phosphorylation of ASPP2, and ASPP2-inducible expression cell lines were established to determine the role of ASPP2 in pancreatic cancer. RNA sequencing (RNA-Seq) was used to uncover the downstream targets of ASPP2. (4) Results: results demonstrate that ASPP2 is phosphorylated during mitosis by cyclin-dependent kinase 1 (CDK1) at sites S562 and S704. In vitro and in vivo results show that ASPP2 is required for pancreatic cancer growth. Furthermore, the expressions of yes-associated protein (YAP)-related genes are found to be dramatically altered by ASPP2 depletion. Together, these findings reveal the phosphorylation mechanism of ASPP2 during mitosis. Collectively, results strongly indicate that ASPP2 is a potential target for abating tumor cell growth in pancreatic cancer.

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Hepatitis C virus (HCV) infects millions of people worldwide and is a leading cause of liver disease. Despite recent advances in antiviral therapies, viral resistance can limit drug efficacy and understanding the mechanisms that confer viral escape is important. We employ an unbiased interactome analysis to discover host binding partners of the HCV non-structural protein 5A (NS5A), a key player in viral replication and assembly. We identify ASPP2, apoptosis-stimulating protein of p53, as a new host co-factor that binds NS5A via its SH3 domain. Importantly, silencing ASPP2 reduces viral replication and spread. Our study uncovers a previously unknown role for ASPP2 to potentiate HCV RNA replication.

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The coronavirus disease 2019, initially named 2019-nCOV (COVID-19) has been declared a global pandemic by the World Health Organization in March 2020. Because of the growing number of COVID patients, the world's health infrastructure has collapsed, and computer-aided diagnosis has become a necessity. Most of the models proposed for the COVID-19 detection in chest X-rays do image-level analysis. These models do not identify the infected region in the images for an accurate and precise diagnosis. The lesion segmentation will help the medical experts to identify the infected region in the lungs. Therefore, in this paper, a UNet-based encoder-decoder architecture is proposed for the COVID-19 lesion segmentation in chest X-rays. To improve performance, the proposed model employs an attention mechanism and a convolution-based atrous spatial pyramid pooling module. The proposed model obtained 0.8325 and 0.7132 values of the dice similarity coefficient and jaccard index, respectively, and outperformed the state-of-the-art UNet model. An ablation study has been performed to highlight the contribution of the attention mechanism and small dilation rates in the atrous spatial pyramid pooling module.

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Prostate cancer is a common disease that seriously endangers the health of middle-aged and elderly men. MRI images are the gold standard for assessing the health status of the prostate region. Segmentation of the prostate region is of great significance for the diagnosis of prostate cancer. In the past, some methods have been used to segment the prostate region, but segmentation accuracy still has room for improvement. This study has proposed a new image segmentation model based on Attention UNet. The model improves Attention UNet by using GN instead of BN, adding dropout to prevent overfitting, introducing the ASPP module, adding channel attention to the attention gate module, and using different channels to output segmentation results of different prostate regions. Finally, we conducted comparative experiments using five existing UNet-based models, and used the dice coefficient as the metric to evaluate the segmentation result. The proposed model achieves dice scores of 0.807 and 0.907 in the transition region and the peripheral region, respectively. The experimental results show that the proposed model is better than other UNet-based models.

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Heart chamber quantification is an essential clinical task to analyze heart abnormalities by evaluating the heart volume estimated through the endocardial border of the chambers. A precise heart chamber segmentation algorithm using echocardiography is essential for improving the diagnosis of cardiac disease. This paper proposes a robust two chamber segmentation network (TC-SegNet) for echocardiography which follows a U-Net architecture and effectively incorporates the proposed modified skip connection, Atrous Spatial Pyramid Pooling (ASPP) modules and squeeze and excitation modules. The TC-SegNet is evaluated on the open-source fully annotated dataset of cardiac acquisitions for multi-structure ultrasound segmentation (CAMUS). The proposed TC-SegNet obtained an average value of F1-score of 0.91, an average Dice score of 0.9284 and an IoU score of 0.8322 which are higher than the reference models used here for comparison. Further, Pixel error (PE) of 1.5109 which are significantly less than the comparison models. The segmentation results and metrics show that the proposed model outperforms the state-of-the-art segmentation methods.

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Intravascular Ultrasound (IVUS) is a medical imaging modality widely used for the detection and treatment of coronary heart disease. The detection of vascular structures is extremely important for accurate treatment procedures. Manual detection of lumen and calcification is very time-consuming and requires technical experience. Ultrasound imaging suffers from the generation of artifacts which obstructs the clear delineation among structures. Considering, the need, to provide special attention to crucial areas, convolutional block attention modules (CBAM) is integrated into an encoder-decoder-based U-Net architecture along with Atrous Spatial Pyramid Pooling (ASPP) to detect vessel components: lumen, calcification and shadow borders. The attention modules prove effective in dealing with areas of special attention by assigning additional weights to crucial channels and preserving spatial features. The IVUS data of 12 patients undergoing the treatment is taken for this study. The novelty of the model design is such that it is able to detect the lumen area in the presence/absence of calcification and bifurcation artifacts too. Also, the model efficiently detects the calcification area even in case of severely complex lesions with shadows behind them. The main contribution of the work is that IVUS images of varying degrees of calcification till 360° are also considered in this work, which is usually neglected in previous studies. The experimental results of 1097 IVUS images of 12 patients resulted in meanIoU (0.7894 ± 0.011), Dice Coefficient (0.8763 ± 0.070), precision (0.8768 ± 0.069) and recall (0.8774 ± 0.071) of the proposed model CADNet which show the model's effectiveness relative to other state-of-the art methods.

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In a previous study, our team found that ASPP2 overexpression increases the sensitivity of liver cancer cells to sorafenib. ASPP2 is an important target in the study of drug therapy for hepatocellular carcinoma. In this study, we demonstrated that ASPP2 altered the response of HepG2 cells to usnic acid (UA) by using mRNA sequencing and CyTOF. CCK8 assay was used to detect cytotoxicity of UA on HepG2 cells. Annexin V-RPE assay, TUNEL assay, and cleaved caspase 3 assay were performed to examine the apoptotic cell death induced by UA. Transcriptomic sequencing and a single-cell mass cytometry were used to analyze the dynamic response of HepG2shcon and HepG2shASPP2 cells to UA treatment. We have demonstrated that UA could inhibit proliferation in HepG2 cells in a concentration-dependent manner. Apoptotic cell death was significantly induced by UA in HepG2 cells, while knocking down ASPP2 could increase the resistance of HepG2 cells to UA. Data from mRNA-Seq indicated that knockout of ASPP2 in HepG2 cells affected cell proliferation, cycle, and metabolism. ASPP2 knockdown resulted in increased stemness and decreased apoptosis of HepG2 cells under the action of UA. CyTOF analysis confirmed the above results, ASPP2 knockdown increased oncoproteins in HepG2 cells and altered response patterns of HepG2 cells to UA. Our data suggested that the natural compound UA could inhibit liver cancer HepG2 cells; meanwhile, ASPP2 knockdown could affect response patterns of HepG2 cells to UA. The above results indicate that ASPP2 could be a research target in the chemoresistance of liver cancer.

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In recent years, with the rapid development of single-cell sequencing technology, this brings new opportunities and challenges to reconstruct gene regulatory networks. On the one hand, scRNA-seq data reveal statistical information of gene expression at single-cell resolution, which is beneficial to construct gene expression regulatory networks. On the other hand, the noise and dropout of single-cell data bring great difficulties to the analysis of scRNA-seq data, resulting in lower accuracy of gene regulatory networks reconstructed by traditional methods. In this article, we propose a novel supervised convolutional neural network (CNNSE), which can extract gene expression information from 2D co-expression matrices of gene doublets and identify interactions between genes. Our method can avoid the loss of extreme point interference by constructing a 2D co-expression matrix of gene pairs and significantly improve the regulation precision between gene pairs. And the CNNSE model is able to obtain detailed and high-level semantic information from the 2D co-expression matrix. Our method achieves satisfactory results on simulated data [accuracy (ACC): 0.712, F1: 0.724]. On two real scRNA-seq datasets, our method exhibits higher stability and accuracy in inference tasks compared with other existing gene regulatory network inference algorithms.

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Abnormal energy metabolism is one of the characteristics of tumours. In the last few years, more and more attention is being paid to the role and regulation of tumour aerobic glycolysis. Cancer cells display enhanced aerobic glycolysis, also known as the Warburg effect, whereby tumour cells absorb glucose to produce a large amount of lactic acid and energy under aerobic conditions to favour tumour proliferation and metastasis. In this study, we report that the haploinsufficient tumour suppressor ASPP2, can inhibit HCC growth and stemness characteristics by regulating the Warburg effect through the WNT/ß-catenin pathway. we performed glucose uptake, lactate production, pyruvate production, ECAR and OCR assays to verify ASPP2 can inhibit glycolysis in HCC cells. The expression of ASPP2 and HK2 was significantly inversely correlated in 80 HCC tissues. Our study reveals downregulation of ASPP2 can promote the aerobic glycolysis metabolism pathway, increasing HCC proliferation, glycolysis metabolism, stemness and drug resistance. This ASPP2-induced inhibition of glycolysis metabolism depends on the WNT/ß-catenin pathway. ASPP2-regulated Warburg effect is associated with tumour progression and provides prognostic value. and suggest that may be promising as a new therapeutic strategy in HCC.

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OBJECTIVE: ASPP1 (apoptosis stimulating of p53 protein 1) is critical in regulating cell apoptosis as a cofactor of p53 to promote its transcriptional activity in the nucleus. However, whether cytoplasmic ASPP1 affects p53 nuclear trafficking and its role in cardiac diseases remains unknown. This study aims to explore the mechanism by which ASPP1 modulates p53 nuclear trafficking and the subsequent contribution to cardiac ischemia/reperfusion (I/R) injury. METHODS AND RESULTS: The immunofluorescent staining showed that under normal condition ASPP1 and p53 colocalized in the cytoplasm of neonatal mouse ventricular cardiomyocytes, while they were both upregulated and translocated to the nuclei upon hypoxia/reoxygenation treatment. The nuclear translocation of ASPP1 and p53 was interdependent, as knockdown of either ASPP1 or p53 attenuated nuclear translocation of the other one. Inhibition of importin-ß1 resulted in the cytoplasmic sequestration of both p53 and ASPP1 in neonatal mouse ventricular cardiomyocytes with hypoxia/reoxygenation stimulation. Overexpression of ASPP1 potentiated, whereas knockdown of ASPP1 inhibited the expression of Bax (Bcl2-associated X), PUMA (p53 upregulated modulator of apoptosis), and Noxa, direct apoptosis-associated targets of p53. ASPP1 was also increased in the I/R myocardium. Cardiomyocyte-specific transgenic overexpression of ASPP1 aggravated I/R injury as indicated by increased infarct size and impaired cardiac function. Conversely, knockout of ASPP1 mitigated cardiac I/R injury. The same qualitative data were observed in neonatal mouse ventricular cardiomyocytes exposed to hypoxia/reoxygenation injury. Furthermore, inhibition of p53 significantly blunted the proapoptotic activity and detrimental effects of ASPP1 both in vitro and in vivo. CONCLUSIONS: Binding of ASPP1 to p53 triggers their nuclear cotranslocation via importin-ß1 that eventually exacerbates cardiac I/R injury. The findings imply that interfering the expression of ASPP1 or the interaction between ASPP1 and p53 to block their nuclear trafficking represents an important therapeutic strategy for cardiac I/R injury.

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Background: Growing evidence indicates that lipid metabolism disorders and gut microbiota dysbiosis were related to the progression of non-alcoholic fatty liver disease (NAFLD). Apoptosis-stimulating p53 protein 2 (ASPP2) has been reported to protect against hepatocyte injury by regulating the lipid metabolism, but the mechanisms remain largely unknown. In this study, we investigate the effect of ASPP2 deficiency on NAFLD, lipid metabolism and gut microbiota using ASPP2 globally heterozygous knockout (ASPP2+/-) mice. Methods: ASPP2+/- Balb/c mice were fed with methionine and choline deficient diet for 3, 10 and 40 day to induce an early and later-stage of NAFLD, respectively. Fresh fecal samples were collected and followed by 16S rRNA sequencing. HPLC-MRM relative quantification analysis was used to identify changes in hepatic lipid profiles. The expression level of innate immunity-, lipid metabolism- and intestinal permeability-related genes were determined. A spearman's rank correlation analysis was performed to identify possible correlation between hepatic medium and long-chain fatty acid and gut microbiota in ASPP2-deficiency mice. Results: Compared with the WT control, ASPP2-deficiency mice developed moderate steatosis at day 10 and severe steatosis at day 40. The levels of hepatic long chain omega-3 fatty acid, eicosapentaenoic (EPA, 20:5 n-3) and docosahexaenoic (DHA, 22:6 n-3), were decreased at day 10 and increased at day 40 in ASPP+/- mice. Fecal microbiota analysis showed significantly increased alpha and beta diversity, as well as the composition of gut microbiota at the phylum, class, order, family, genus, species levels in ASPP2+/- mice. Moreover, ASPP-deficiency mice exhibited impaired intestinal barrier function, reduced expression of genes associated with chemical barrier (REG3B, REG3G, Lysozyme and IAP), and increased expression of innate immune components (TLR4 and TLR2). Furthermore, correlation analysis between gut microbiota and fatty acids revealed that EPA was significantly negatively correlated with Bifidobacterium family. Conclusion: Our findings suggested that ASPP2-deficiency promotes the progression of NAFLD, alterations in fatty acid metabolism and gut microbiota dysbiosis. The long chain fatty acid EPA was significantly negatively correlated with Bifidobacterial abundance, which is a specific feature of NAFLD in ASPP2-deficiency mice. Totally, the results provide evidence for a mechanism of ASPP2 on dysregulation of fatty acid metabolism and gut microbiota dysbiosis.

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The tooth arrangements of human beings are challenging to accurately observe when relying on dentists' naked eyes, especially for dental caries in children, which is difficult to detect. Cone-beam computer tomography (CBCT) is used as an auxiliary method to measure patients' teeth, including children. However, subjective and irreproducible manual measurements are required during this process, which wastes much time and energy for the dentists. Therefore, a fast and accurate tooth segmentation algorithm that can replace repeated calculations and annotations in manual segmentation has tremendous clinical significance. This study proposes a local contextual enhancement model for clinical dental CBCT images. The local enhancement model, which is more suitable for dental CBCT images, is proposed based on the analysis of the existing contextual models. Then, the local enhancement model is fused into an encoder-decoder framework for dental CBCT images. At last, extensive experiments are conducted to validate our method.