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تحديث الاقتصاد الصيني
في كتابه \"تحديث الاقتصاد الصيني\" يتناول تشياو جنغ عملية الإصلاح الاقتصادي في الصين خلال ثلاثة عقود تم فيها وصف التطور السريع لاقتصاد الصين ب\"المعجزة الاقتصادية\"، ولكنه وصف غير دقيق -بتعبير المؤلف- وبمعنى أكثر دقة، يعتبر التطور السريع لاقتصاد الصين مجرد عملية انتعاش اقتصادي. إنها عملية مواكبة لاقتصادات الأسواق في المناطق المتقدمة.
Book
Changes in notifiable infectious disease incidence in China during the COVID-19 pandemic
Nationwide nonpharmaceutical interventions (NPIs) have been effective at mitigating the spread of the novel coronavirus disease (COVID-19), but their broad impact on other diseases remains under-investigated. Here we report an ecological analysis comparing the incidence of 31 major notifiable infectious diseases in China in 2020 to the average level during 2014-2019, controlling for temporal phases defined by NPI intensity levels. Respiratory diseases and gastrointestinal or enteroviral diseases declined more than sexually transmitted or bloodborne diseases and vector-borne or zoonotic diseases. Early pandemic phases with more stringent NPIs were associated with greater reductions in disease incidence. Non-respiratory diseases, such as hand, foot and mouth disease, rebounded substantially towards the end of the year 2020 as the NPIs were relaxed. Statistical modeling analyses confirm that strong NPIs were associated with a broad mitigation effect on communicable diseases, but resurgence of non-respiratory diseases should be expected when the NPIs, especially restrictions of human movement and gathering, become less stringent.
Non-pharmaceutical interventions implemented to mitigate COVID-19 transmission are likely to have impacted spread of other infectious diseases. Here, the authors investigate changes in the incidence of 31 notifiable infectious diseases using surveillance data from China.
Journal Article
Structure and mechanogating mechanism of the Piezo1 channel
The mechanosensitive Piezo channels function as key eukaryotic mechanotransducers. However, their structures and mechanogating mechanisms remain unknown. Here we determine the three-bladed, propeller-like electron cryo-microscopy structure of mouse Piezo1 and functionally reveal its mechanotransduction components. Despite the lack of sequence repetition, we identify nine repetitive units consisting of four transmembrane helices each—which we term transmembrane helical units (THUs)—which assemble into a highly curved blade-like structure. The last transmembrane helix encloses a hydrophobic pore, followed by three intracellular fenestration sites and side portals that contain pore-property-determining residues. The central region forms a 90?Å-long intracellular beam-like structure, which undergoes a lever-like motion to connect THUs to the pore via the interfaces of the C-terminal domain, the anchor-resembling domain and the outer helix. Deleting extracellular loops in the distal THUs or mutating single residues in the beam impairs the mechanical activation of Piezo1. Overall, Piezo1 possesses a unique 38-transmembrane-helix topology and designated mechanotransduction components, which enable a lever-like mechanogating mechanism.
The electron cryo-microscopy structure of full-length mouse Piezo1 reveals unique topological features such as the repetitive transmembrane helical units that constitute the highly curved transmembrane region, and identifies regions and single residues that are crucial for the mechanical activation of the channel.
Structure and mechanism of ion channel Piezo1
Mechanosensitive cation channels convert external mechanical stimuli into various biological actions, including touch, hearing, balance and cardiovascular regulation. The eukaryotic Piezo proteins are mechanotransduction channels, although their structure and gating mechanisms are not well elucidated. In related papers in this issue of
Nature
, two groups report cryo-electron microscopy structures of the full-length mouse Piezo1 and reveal three flexible propeller blades. Each blade is made up of at least 26 helices, forming a series of helical bundles, which adopt a curved transmembrane region. A kinked beam and anchor domain link these Piezo repeats to the pore, giving clues as to how the channel responds to membrane tension and mechanical force.
Journal Article
Detection and Classification of Cotton Foreign Fibers Based on Polarization Imaging and Improved YOLOv5
It is important to detect and classify foreign fibers in cotton, especially white and transparent foreign fibers, to produce subsequent yarn and textile quality. There are some problems in the actual cotton foreign fiber removing process, such as some foreign fibers missing inspection, low recognition accuracy of small foreign fibers, and low detection speed. A polarization imaging device of cotton foreign fiber was constructed based on the difference in optical properties and polarization characteristics between cotton fibers. An object detection and classification algorithm based on an improved YOLOv5 was proposed to achieve small foreign fiber recognition and classification. The methods were as follows: (1) The lightweight network Shufflenetv2 with the Hard-Swish activation function was used as the backbone feature extraction network to improve the detection speed and reduce the model volume. (2) The PANet network connection of YOLOv5 was modified to obtain a fine-grained feature map to improve the detection accuracy for small targets. (3) A CA attention module was added to the YOLOv5 network to increase the weight of the useful features while suppressing the weight of invalid features to improve the detection accuracy of foreign fiber targets. Moreover, we conducted ablation experiments on the improved strategy. The model volume, mAP@0.5, mAP@0.5:0.95, and FPS of the improved YOLOv5 were up to 0.75 MB, 96.9%, 59.9%, and 385 f/s, respectively, compared to YOLOv5, and the improved YOLOv5 increased by 1.03%, 7.13%, and 126.47%, respectively, which proves that the method can be applied to the vision system of an actual production line for cotton foreign fiber detection.
Journal Article
CD147 deficiency in T cells prevents thymic involution by inhibiting the EMT process in TECs in the presence of TGFβ
Thymic involution during aging is a major cause of decreased T-cell production and reduced immunity. Here, we show that the loss of CD147 on T cells prevents thymic senescence, resulting in slowed shrinkage of the thymus with age and increased production of naive T cells. This phenotype is the result of slowing of the epithelial–mesenchymal transition (EMT) process in thymic epithelial cells (TECs), which eventually leads to reduced adipocyte accumulation. In an in vitro coculture system, we found that TGFβ is an important factor in the EMT process in TECs and that it can reduce the expression of E-cadherin through p-Smad2/FoxC2 signaling. Moreover, CD147 on T cells can accelerate the decline in E-cadherin expression by interacting with Annexin A2 on TECs. In the presence of TGFβ, Annexin A2 and E-cadherin colocalize on TECs. However, CD147 on T cells competitively binds to Annexin A2 on TECs, leading to the isolation of E-cadherin. Then, the isolated E-cadherin is easily phosphorylated by phosphorylated Src kinase, the phosphorylation of which was induced by TGFβ, and finally, p-E-cadherin is degraded. Thus, in the thymus, the interaction between T cells and TECs contributes to thymic involution with age. In this study, we illuminate the mechanism underlying the triggering of the EMT process in TECs and show that inhibiting TGFβ and/or CD147 may serve as a strategy to hinder age-related thymic involution.
Journal Article
CD147-spike protein is a novel route for SARS-CoV-2 infection to host cells
In face of the everlasting battle toward COVID-19 and the rapid evolution of SARS-CoV-2, no specific and effective drugs for treating this disease have been reported until today. Angiotensin-converting enzyme 2 (ACE2), a receptor of SARS-CoV-2, mediates the virus infection by binding to spike protein. Although ACE2 is expressed in the lung, kidney, and intestine, its expressing levels are rather low, especially in the lung. Considering the great infectivity of COVID-19, we speculate that SARS-CoV-2 may depend on other routes to facilitate its infection. Here, we first discover an interaction between host cell receptor CD147 and SARS-CoV-2 spike protein. The loss of CD147 or blocking CD147 in Vero E6 and BEAS-2B cell lines by anti-CD147 antibody, Meplazumab, inhibits SARS-CoV-2 amplification. Expression of human CD147 allows virus entry into non-susceptible BHK-21 cells, which can be neutralized by CD147 extracellular fragment. Viral loads are detectable in the lungs of human CD147 (hCD147) mice infected with SARS-CoV-2, but not in those of virus-infected wild type mice. Interestingly, virions are observed in lymphocytes of lung tissue from a COVID-19 patient. Human T cells with a property of ACE2 natural deficiency can be infected with SARS-CoV-2 pseudovirus in a dose-dependent manner, which is specifically inhibited by Meplazumab. Furthermore, CD147 mediates virus entering host cells by endocytosis. Together, our study reveals a novel virus entry route, CD147-spike protein, which provides an important target for developing specific and effective drug against COVID-19.
Journal Article
Effect of propofol, sevoflurane, and isoflurane on postoperative cognitive dysfunction following laparoscopic cholecystectomy in elderly patients: A randomized controlled trial
To compare the incidence of postoperative cognitive dysfunction (POCD) in elderly surgical patients (>60years) receiving different anesthetics (propofol, sevoflurane, or isoflurane) and to identify potential biomarkers of POCD in this patient population.
Prospective, randomized, double-blind clinical trial.
University-affiliated teaching hospital.
One hundred and fifty elderly patients scheduled for laparoscopic cholecystectomy.
Elderly patients undergoing laparoscopic cholecystectomy were randomly assigned to receive propofol, sevoflurane, or isoflurane anesthesia. Measurements: Cognitive function was assessed using neuropsychological tests at baseline (1day before surgery [D0]), and on postoperative day 1 (D1) and day 3 (D3). Plasma S-100β and Aβ1–40 protein, IL-1β, IL-6 and TNF-α concentrations were assessed before induction of anesthesia (T0), after extubation (T1), and 1h (T2) and 24h (T3) postoperatively.
The incidence of POCD was significantly lower in the propofol group compared to the isoflurane group and the sevoflurane group at D1 and D3 (propofol vs. isoflurane: D1 and D3, P<0.001; propofol vs. sevoflurane: D1, P=0.012; D3, P=0.013). The incidence of POCD was significantly lower in the sevoflurane group compared to the isoflurane group at D1 (P=0.041), but not at D3. Postoperatively, plasma S-100β and Aβ1–40 protein, IL-1β, IL-6, and TNF-α concentrations were significantly decreased in the propofol group compared to the isoflurane group.
Propofol anesthesia may be an option for elderly surgical patients.
•Postoperative cognitive dysfunction is commonly occurred in elderly.•Anesthesia impaired the cognitive function.•Propofol had little effect then inhaled anesthetics.
Journal Article
Prototype Calibration with Feature Generation for Few-Shot Remote Sensing Image Scene Classification
Few-shot classification of remote sensing images has attracted attention due to its important applications in various fields. The major challenge in few-shot remote sensing image scene classification is that limited labeled samples can be utilized for training. This may lead to the deviation of prototype feature expression, and thus the classification performance will be impacted. To solve these issues, a prototype calibration with a feature-generating model is proposed for few-shot remote sensing image scene classification. In the proposed framework, a feature encoder with self-attention is developed to reduce the influence of irrelevant information. Then, the feature-generating module is utilized to expand the support set of the testing set based on prototypes of the training set, and prototype calibration is proposed to optimize features of support images that can enhance the representativeness of each category features. Experiments on NWPU-RESISC45 and WHU-RS19 datasets demonstrate that the proposed method can yield superior classification accuracies for few-shot remote sensing image scene classification.
Journal Article
Joint Classification of Hyperspectral and LiDAR Data Based on Position-Channel Cooperative Attention Network
Remote sensing image classification is a prominent topic in earth observation research, but there is a performance bottleneck when classifying single-source objects. As the types of remote sensing data are gradually diversified, the joint classification of multi-source remote sensing data becomes possible. However, the existing classification methods have limitations in heterogeneous feature representation of multimodal remote sensing data, which restrict the collaborative classification performance. To resolve this issue, a position-channel collaborative attention network is proposed for the joint classification of hyperspectral and LiDAR data. Firstly, in order to extract the spatial, spectral, and elevation features of land cover objects, a multiscale network and a single-branch backbone network are designed. Then, the proposed position-channel collaborative attention module adaptively enhances the features extracted from the multi-scale network in different degrees through the self-attention module, and exploits the features extracted from the multiscale network and single-branch network through the cross-attention module, so as to capture the comprehensive features of HSI and LiDAR data, narrow the semantic differences of heterogeneous features, and realize complementary advantages. The depth intersection mode further improves the performance of collaborative classification. Finally, a series of comparative experiments were carried out in the 2012 Houston dataset and Trento dataset, and the effectiveness of the model was proved by qualitative and quantitative comparison.
Journal Article
Multi-Scale Graph-Based Feature Fusion for Few-Shot Remote Sensing Image Scene Classification
Remote sensing image scene classification has drawn extensive attention for its wide application in various scenarios. Scene classification in many practical cases faces the challenge of few-shot conditions. The major difficulty of few-shot remote sensing image scene classification is how to extract effective features from insufficient labeled data. To solve these issues, a multi-scale graph-based feature fusion (MGFF) model is proposed for few-shot remote sensing image scene classification. In the MGFF model, a graph-based feature construction model is developed to transform traditional image features into graph-based features, which aims to effectively represent the spatial relations among images. Then, a graph-based feature fusion model is proposed to integrate graph-based features of multiple scales, which aims to enhance sample discrimination based on different scale information. Experimental results on two public remote sensing datasets prove that the MGFF model can achieve superior accuracy than other few-shot scene classification approaches.
Journal Article