The pandemic of coronavirus disease 2019 (COVID‐19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), has caused an unprecedented global social and economic impact, and ...high numbers of deaths. Many risk factors have been identified in the progression of COVID‐19 into a severe and critical stage, including old age, male gender, underlying comorbidities such as hypertension, diabetes, obesity, chronic lung diseases, heart, liver and kidney diseases, tumors, clinically apparent immunodeficiencies, local immunodeficiencies, such as early type I interferon secretion capacity, and pregnancy. Possible complications include acute kidney injury, coagulation disorders, thoromboembolism. The development of lymphopenia and eosinopenia are laboratory indicators of COVID‐19. Laboratory parameters to monitor disease progression include lactate dehydrogenase, procalcitonin, high‐sensitivity C‐reactive protein, proinflammatory cytokines such as interleukin (IL)‐6, IL‐1β, Krebs von den Lungen‐6 (KL‐6), and ferritin. The development of a cytokine storm and extensive chest computed tomography imaging patterns are indicators of a severe disease. In addition, socioeconomic status, diet, lifestyle, geographical differences, ethnicity, exposed viral load, day of initiation of treatment, and quality of health care have been reported to influence individual outcomes. In this review, we highlight the scientific evidence on the risk factors of severity of COVID‐19.
Bactericidal/permeability-increasing protein (BPI) is an important factor of innate immunity that in mammals is known to take part in the clearance of invading Gram-negative bacteria. In teleost, the ...function of BPI is unknown. In the present work, we studied the function of tongue sole (Cynoglossus semilaevis) BPI, CsBPI. We found that CsBPI was produced extracellularly by peripheral blood leukocytes (PBL). Recombinant CsBPI (rCsBPI) was able to bind to a number of Gram-negative bacteria but not Gram-positive bacteria. Binding to bacteria led to bacterial death through membrane permeabilization and structural destruction, and the bound bacteria were more readily taken up by PBL. In vivo, rCsBPI augmented the expression of a wide arrange of genes involved in antibacterial and antiviral immunity. Furthermore, rCsBPI enhanced the resistance of tongue sole against bacterial as well as viral infection. These results indicate for the first time that a teleost BPI possesses immunoregulatory effect and plays a significant role in antibacterial and antiviral defense.
Accurate and reliable relative navigation is the prerequisite to guarantee the effectiveness and safety of various multiple Unmanned Aerial Vehicles (UAVs) cooperation tasks, when absolute position ...information is unavailable or inaccurate. Among the UAV navigation techniques, Global Navigation Satellite System (GNSS) is widely used due to its worldwide coverage and simplicity in relative navigation. However, the observations of GNSS are vulnerable to different kinds of faults arising from transmission degradation, ionospheric scintillations, multipath, spoofing, and many other factors. In an effort to improve the reliability of multi-UAV relative navigation, an autonomous integrity monitoring method is proposed with a fusion of double differenced GNSS pseudoranges and Ultra Wide Band (UWB) ranging units. Specifically, the proposed method is designed to detect and exclude the fault observations effectively through a consistency check algorithm in the relative positioning system of the UAVs. Additionally, the protection level for multi-UAV relative navigation is estimated to evaluate whether the performance meets the formation flight and collision avoidance requirements. Simulated experiments derived from the real data are designed to verify the effectiveness of the proposed method in autonomous integrity monitoring for multi-UAV relative navigation.
Objective
Although several individual nutrients/foods are associated with uric acid status, the association of overall diet quality with hyperuricemia remains unclear. The current study was ...undertaken to examine the association between adherence to the Dietary Approaches to Stop Hypertension (DASH) diet and the odds of having hyperuricemia in a Chinese adult population.
Methods
Included were 71,893 Chinese participants in the Kailuan I study and the Kailuan II study (mean age 51.4 years) who were free of gout prior to or in 2014. Dietary intakes were assessed using a validated food frequency questionnaire, and the DASH diet score was calculated based on consumptions of vegetables, fruit, dairy, beans, whole grains, meat, fat, sodium, and sugar‐sweetened beverages. Fasting blood samples were collected in 2014, and hyperuricemia was defined as serum uric acid concentrations of ≥7 mg/dl for men, and of ≥6 mg/dl for women. The association between DASH diet score and hyperuricemia was assessed using multiple logistic regression models, adjusting for age, sex, total energy, obesity, physical activity, education, smoking, alcohol drinking, blood pressure, fasting glucose, lipid profiles, renal function, and presence of cardiovascular disease.
Results
A High DASH diet score was associated with low odds of having hyperuricemia (adjusted odds ratio for quartile 4 versus quartile 1 0.70 95% confidence interval 0.66, 0.75, P for trend < 0.001) after adjusting for potential confounders. The association between the DASH diet and hyperuricemia was more pronounced among older individuals (age ≥50 years), women, and physically inactive participants compared with their counterparts (P for interaction < 0.01 for all).
Conclusion
The DASH diet was associated with a low likelihood of having hyperuricemia in Chinese adults.
Hyperspectral images (HSIs) usually have high spectral and low spatial resolution. Conversely, multispectral images (MSIs) usually have low spectral and high spatial resolution. The fusion of HSI and ...MSI aims to create spectral images with high spectral and spatial resolution. In this paper, we propose a fusion algorithm by combining linear spectral unmixing with the local low-rank property. By taking advantage of the local low-rank property, we first partition the corresponding spectral image into patches. For each patch pair, we cast the fusion problem as a coupled spectral unmixing problem that extracts the abundance and the endmembers of MSI and HSI, respectively. It then updates the abundance and the endmember through an alternating update algorithm. In fact, the convergence of the alternative update algorithm can be mathematically and empirically supported. We also propose a multiscale postprocessing procedure to combine fusion results obtained under different patch sizes. In experiments on three data sets, the proposed fusion algorithms outperformed state-of-the-art fusion algorithms in both spatial and spectral domains.
In this note, we study consensus problems for continuous-time multi-agent systems in directed networks with dynamically changing topologies and nonuniform time-varying delays. We have analyzed ...consensus problems in the following three cases: 1) directed networks with dynamically changing topologies and nonuniform time-varying delays; 2) directed networks with intermittent communication and data packet dropout; and 3) finite-time consensus in directed networks with dynamically changing topologies and nonuniform time-varying delays. We propose a new approach based on a tree-type transformation to investigate consensus problems in all three cases. Some necessary and/ or sufficient conditions are established. Simulation results are also given to demonstrate the theoretical results.
With the continuous popularization of Global Navigation Satellite System (GNSS) in various applications, the performance requirement for integrity is also increasing, especially in the field of ...safety-of-life. Although the existing Receiver Autonomous Integrity Monitoring (RAIM) algorithm has been embedded in the GNSS receiver as a standard method, it might still suffer from small fault detection and delay alarm problem for time series fault models. In an effort to solve this problem, a Deep Neural Network (DNN) for RAIM, named RAIM-NET, is investigated in this paper. The main idea of RAIM-NET is to propose a combination of feature vector extraction and DNN model to improve the performance of integrity monitoring, with a problem specifically designed for loss function, obtaining the model parameters. Inspired by the powerful advantages of Recurrent Neural Network (RNN) in time series data processing, a multilayer RNN is applied to build the DNN model structure and improve the detection rate for small faults and reduce the alarm delay for the time series fault event. Finally, real GNSS data experiments are designed to verify the performance of RAIM-NET in fault detection and time delay for integrity monitoring.
We introduce a novel soft antenna selection approach for multiple antenna systems through a joint design of both RF (radio frequency) and baseband signal processing. When only a limited number of ...frequency converters are available, conventional antenna selection schemes show severe performance degradation in most fading channels. To alleviate those degradations, we propose to adopt a transformation of the signals in the RF domain that requires only simple, variable phase shifters and combiners to reduce the number of RF chains. The constrained optimum design of these shifters, adapting to the channel state, is given in analytical form, which requires no search or iterations. The resulting system shows a significant performance advantage for both correlated and uncorrelated channels. The technique works for both transmitter and receiver design, which leads to the joint transceiver antenna selection. When only a single information stream is transmitted through the channel, the new design can achieve the same SNR gain as the full-complexity system while requiring, at most, two RF chains. With multiple information streams transmitted, it is demonstrated by computer experiments that the capacity performance is close to optimum.
Autophagic dysfunction is observed in diabetes mellitus. Resveratrol has a beneficial effect on diabetic cardiomyopathy. Whether the resveratrol‐induced improvement in cardiac function in diabetes is ...via regulating autophagy remains unclear. We investigated the mechanisms underlying resveratrol‐mediated protection against heart failure in diabetic mice, with a focus on the role of sirtuin 1 (SIRT1) in regulating autophagic flux. Diabetic cardiomyopathy in mice was induced by streptozotocin (STZ). Long‐term resveratrol treatment improved cardiac function, ameliorated oxidative injury and reduced apoptosis in the diabetic mouse heart. Western blot analysis revealed that resveratrol decreased p62 protein expression and promoted SIRT1 activity and Rab7 expression. Inhibiting autophagic flux with bafilomycin A1 increased diabetic mouse mortality and attenuated resveratrol‐induced down‐regulation of p62, but not SIRT1 activity or Rab7 expression in diabetic mouse hearts. In cultured H9C2 cells, redundant or overactive H2O2 increased p62 and cleaved caspase 3 expression as well as acetylated forkhead box protein O1 (FOXO1) and inhibited SIRT1 expression. Sirtinol, SIRT1 and Rab7 siRNA impaired the resveratrol amelioration of dysfunctional autophagic flux and reduced apoptosis under oxidative conditions. Furthermore, resveratrol enhanced FOXO1 DNA binding at the Rab7 promoter region through a SIRT1‐dependent pathway. These results highlight the role of the SIRT1/FOXO1/Rab7 axis in the effect of resveratrol on autophagic flux in vivo and in vitro, which suggests a therapeutic strategy for diabetic cardiomyopathy.
Recently, differentiable neural architecture search (NAS) methods have made significant progress in reducing the computational costs of NASs. Existing methods search for the best architecture by ...choosing candidate operations with higher architecture weights. However, architecture weights cannot accurately reflect the importance of each operation, that is, the operation with the highest weight might not be related to the best performance. To circumvent this deficiency, we propose a novel indicator that can fully represent the operation importance and, thus, serve as an effective metric to guide the model search. Based on this indicator, we further develop a NAS scheme for "exploiting operation importance for effective NAS" (EoiNAS). More precisely, we propose a high-order Markov chain-based strategy to slim the search space to further improve search efficiency and accuracy. To evaluate the effectiveness of the proposed EoiNAS, we applied our method to two tasks: image classification and semantic segmentation. Extensive experiments on both tasks provided strong evidence that our method is capable of discovering high-performance architectures while guaranteeing the requisite efficiency during searching.
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