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Metabolic‐associated fatty liver disease (MAFLD) is a new emerging concept and is associated with metabolic dysfunction, generally replacing the name of nonalcoholic fatty liver disease (NAFLD) due to heterogeneous liver condition and inaccuracies in definition. The prevalence of MAFLD is rising by year due to dietary changes, metabolic disorders, and no approved therapy, affecting a quarter of the global population and representing a major economic problem that burdens healthcare systems. Currently, in addition to the common causative factors like insulin resistance, oxidative stress, and lipotoxicity, the role of immune cells, especially T cells, played in MAFLD is increasingly being emphasized by global scholars. Based on the diverse classification and pathophysiological effects of immune T cells, we comprehensively analyzed their bidirectional regulatory effects on the hepatic inflammatory microenvironment and MAFLD progression. This interaction between MAFLD and T cells was also associated with hepatic‐intestinal immune crosstalk and gut microbiota homeostasis. Moreover, we pointed out several T‐cell‐based therapeutic approaches including but not limited to adoptive transfer of T cells, fecal microbiota transplantation, and drug therapy, especially for natural products and Chinese herbal prescriptions. Overall, this study contributes to a better understanding of the important role of T cells played in MAFLD progression and corresponding therapeutic options and provides a potential reference for further drug development. Immune T cells are involved in metabolic‐associated fatty liver disease (MAFLD) progression either in a positive or negative manner and the therapeutic methods of MAFLD caused by disordered T cells include gut microbiota regulation, adoptive cell transfer, gene editor microRNAs, drugs (inhibitors/traditional Chinese medicine) therapy. Highlights Metabolic‐associated fatty liver disease (MAFLD) is the most common fatty liver disease caused by metabolic dysregulation. The maladjustment of T cell homeostasis including CD4/CD8+ T cells, Treg cells, γδ T cells, NKT cells, and MAIT cells give rise to severe hepatic steatosis and fat accumulation. Intestinal flora regulation and pharmacotherapy, such as traditional Chinese medicine, can restore the immune microenvironment of T cells in livers to improve MAFLD.

The treatment of ulcerative colitis (UC) presents an ongoing clinical challenge. Emerging research has implicated that the cGAS-STING pathway promotes the progression of UC, but conflicting results have hindered the development of STING as a therapeutic target. In the current study, we aim to comprehensively elucidate the origins, downstream signaling and pathogenic roles of myeloid STING in colitis and colitis-associated carcinoma (CAC). mice were constructed for inducible myeloid-specific deletion of STING. RNA-sequencing, flow cytometry, and multiplex immunohistochemistry were employed to investigate immune responses in DSS-induced colitis or AOM/DSS-induced carcinogenesis. Colonic organoids, primary bone marrow derived macrophages and dendritic cells, and splenic T cells were used for studies. We observed that myeloid STING knockout in adult mice inhibited macrophage maturation, reduced DC cell activation, and suppressed pro-inflammatory Th1 and Th17 cells, thereby protecting against both acute and chronic colitis and CAC. However, myeloid STING deletion in neonatal or tumor-present mice exhibited impaired immune tolerance and anti-tumor immunity. Furthermore, we found that TFAM-associated mtDNA released from damaged colonic organoids, rather than bacterial products, activates STING in dendritic cells in an extracellular vesicle-independent yet endocytosis-dependent manner. Both IRF3 and NF-κB are required for STING-mediated expression of IL-12 family cytokines, promoting Th1 and Th17 differentiation and contributing to excessive inflammation in colitis. Detection of the TFAM-mtDNA complex from damaged intestinal epithelium by myeloid STING exacerbates colitis through IL-12 cytokines, providing new evidence to support the development of STING as a therapeutic target for UC and CAC.

With the development of augmented reality supporting manual assembly collaboration, it is particularly important to explore the transformation from traditional “human-machine” cooperation mode to smart “human-machine” cooperation mode. Early studies have shown that user cues (i.e., head, gesture, eye) and scene cues (i.e., objects, tools, space) are intuitive and highly expressive for traditional AR collaborative mode. However, how to integrate these cues into an assembly system, reasonably infer an operator’s work intention, and then give an appropriate rendering scheme is one of the problems in collaborative assembly. This paper describes a AR collaborative assembly platform: SHARIDEAS. It uses a generalized grey correlation method to integrate user cues and scene cues. The results of data fusion can provide appropriate and intuitive assembly guidance for local workers. A formal user study is to explore the usability and feasibility of SHAREDEAS in a manual assembly task. The experimental data show that SHAREDEAS is more conducive than traditional one to improve the efficiency of human-machine cooperation. Finally, some conclusions of SHARIDEAS are given and the future research direction has prospected.

In recent years, the increase of satellites and UAV (unmanned aerial vehicles) has multiplied the amount of remote sensing data available to people, but only a small part of the remote sensing data has been properly used; problems such as land planning, disaster management and resource monitoring still need to be solved. Buildings in remote sensing images have obvious positioning characteristics; thus, the detection of buildings can not only help the mapping and automatic updating of geographic information systems but also have guiding significance for the detection of other types of ground objects in remote sensing images. Aiming at the deficiency of traditional building remote sensing detection, an improved Faster R-CNN (region-based Convolutional Neural Network) algorithm was proposed in this paper, which adopts DRNet (Dense Residual Network) and RoI (Region of Interest) Align to utilize texture information and to solve the region mismatch problems. The experimental results showed that this method could reach 82.1% mAP (mean average precision) for the detection of landmark buildings, and the prediction box of building coordinates was relatively accurate, which improves the building detection results. Moreover, the recognition of buildings in a complex environment was also excellent.

Text recognition in natural scene images has always been a hot topic in the field of document-image related visual sensors. The previous literature mostly solved the problem of horizontal text recognition, but the text in the natural scene is usually inclined and irregular, and there are many unsolved problems. For this reason, we propose a scene text recognition algorithm based on a text position correction (TPC) module and an encoder-decoder network (EDN) module. Firstly, the slanted text is modified into horizontal text through the TPC module, and then the content of horizontal text is accurately identified through the EDN module. Experiments on the standard data set show that the algorithm can recognize many kinds of irregular text and get better results. Ablation studies show that the proposed two network modules can enhance the accuracy of irregular scene text recognition.

Signal‐free crosswalks are a high incidence area for pedestrian–autonomous vehicles (AV) conflicts, but there is no comprehensive and reasonable solution for AVs to safely and efficiently navigate through these conflict scenarios. To address this problem, this study proposes a responsibility‐sensitive safety (RSS) model specifically for pedestrian–AV conflicts in signal‐free crosswalks. The model is based on the principles and contents of existing RSS models and proposes a safe AV access strategy for hazardous scenarios. The effectiveness of the strategy is verified by an integrated SUMO simulation taking into account the vehicle motion state, driving conservatism, and safety. The results show that the proposed automatic driving access strategy based on the improved RSS model effectively improves the driving stability and safety of the AV through the signal‐free crosswalk. This study provides a solution to the pedestrian–AV conflict in signal‐free crosswalks on road sections, which can provide a reference for the further promotion and application of the RSS model in the field of autonomous driving.

As a promising medical imaging modality, electrical impedance tomography (EIT) can image the electrical properties within a region of interest using electrical measurements applied at electrodes on the region boundary. This paper proposes to combine frequency and time difference imaging methods in EIT to simultaneously image bio- and non-conductive targets, where the image fusion is accomplished by applying a wavelet-based technique. To enable image fusion, both time and frequency difference imaging methods are investigated regarding the reconstruction of bio- or non-conductive inclusions in the target region at varied excitation frequencies, indicating that none of those two methods can tackle with the scenarios where both bio- and non-conductive inclusions exist. This dilemma can be resolved by fusing the time difference (td) and appropriate frequency difference (fd) EIT images since they are complementary to each other. Through simulation and in vitro experiment, it is demonstrated that the proposed fusion method can reasonably reconstruct both the bio- and non-conductive inclusions within the lung models established to simulate the ventilation process, which is expected to be beneficial for the diagnosis of lung-tissue related diseases by EIT.

ABSTRACT The main objective of this study was observe the distribution and expression of tau in the distal colonic muscle layers using opioid-induced constipation (OIC) rat model and to explore the mechanistic correlation between the tau phosphorylation and occurrence of OIC. The rat model of OIC was generated by intraperitoneal (i.p) injection of loperamide hydrochloride (Lop; 5mg/kg), and was evaluated by examining the fecal properties, intestinal propulsion mobility, immunohistochemistry (IHC), western blot (WB) and ELISA for detecting levels of Tau, P-Ser396-tau, P-Ser404-tau, protein kinase C (PKC), p38 MAPK (p38), p-p38 MAPK (p-p38) and µ-opioid receptors (MORs) in the colonic samples. The gene sequencing approach was used to detect alternatively spliced tau isoforms in the distal colonic muscle layer. Sequencing analysis revealed that 3 isoforms of tau, namely 2N4R, 1N4R and 0N4R were expressed in the distal colonic myocardium tissue. OIC rats had small, dry and hard feces, and intestinal propulsion velocity was significantly slower, especially in the distal colon. P-Ser396-Tau, P-Ser404-Tau, PKC, p-p38, p38, and Tau positive cells were distributed in the intestinal muscle plexus of the distal colon, and all of them were colocalized with MORs; though the expressions levels of tau and p38 remained unchanged, others indexes above significantly increased in the distal colonic muscle layer of OIC rats. However, after the administration of p38 blocker SB203580, OIC rats had salami-like feces, with an increase in colonic propulsion velocities; although the expressions levels of Tau and p38 didn't change significantly in distal colonic muscle layers, the expressions levels of P-Ser396-Tau, P-Ser404-Tau, and p-p38 significantly reduced. To conclude there is a unique distribution pattern of tau within the distal colonic musculature of OIC rats. The abnormal activation of MORs contributes to the hyperphosphorylation of tau protein, which in turn induces the development of OIC.

Based on the principal-agent theory, we give a theoretical interpretation on evolution of the complex partnerships between the online SCF (supply chain finance) providers in China. First, we describe the principal-agent relationships and analyze the optimal profit-sharing contracts between the banks and the B2B platforms. Then, from a dual perspective of leadership transfer and absolute benefit change, we explain the behavioral choices of the banks in the cooperation. Results show that, at the initial stage of growth of the platforms’ abilities to rate online borrowers, the leadership and the absolute benefit of the banks will suffer a “double decline,” which explains why the leading banks in China “divorced” the B2B platforms during 2011 to 2013. However, as the platforms’ rating abilities grow to “maturity,” the absolute benefit of the banks will finally exceed its original level, and then the rational banks would cooperate with the platforms again even at the expense of losing a portion of their leadership, which answers why the banks in China have come back to “remarry” the B2B platforms since 2014.

The application of deep convolutional neural networks (CNN) in the field of medical image processing has attracted extensive attention and demonstrated remarkable progress. An increasing number of deep learning methods have been devoted to classifying ChestX-ray (CXR) images, and most of the existing deep learning methods are based on classic pretrained models, trained by global ChestX-ray images. In this paper, we are interested in diagnosing ChestX-ray images using our proposed Fusion High-Resolution Network (FHRNet). The FHRNet concatenates the global average pooling layers of the global and local feature extractors—it consists of three branch convolutional neural networks and is fine-tuned for thorax disease classification. Compared with the results of other available methods, our experimental results showed that the proposed model yields a better disease classification performance for the ChestX-ray 14 dataset, according to the receiver operating characteristic curve and area-under-the-curve score. An ablation study further confirmed the effectiveness of the global and local branch networks in improving the classification accuracy of thorax diseases.