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Impaired insulin release is the key feature of type 2 diabetes. Insulin secretion, mainly mediated by SNARE proteins, is closely related to the blood glucose level. However, the mechanism underlying how glucose controls SNARE proteins to regulate insulin release is largely unexplained. Herein, we investigated the effects of glucose on the subcellular localization and spatial distribution on the plasma membrane (PM) of t-SNAREs (SNAP-25 and STX-1A) using a live-cell confocal microscope and the single-molecule localization imaging technique. Live-cell confocal and dSTORM imaging first revealed that SNAP-25 was mostly localized to the PM as clusters under the basal glucose concentration condition and demonstrated significant colocalization with STX-1A clusters. Furthermore, our data showed that the elevated glucose concentration increased the expression of SNAP-25 and induced more and larger SNAP-25 clusters on the PM, whereas glucotoxicity severely inhibited SNAP-25 transport to the PM and caused fewer and smaller SNAP-25 clusters on the PM. Additionally, we found that glucotoxicity also had an inhibitory effect on the colocalization between SNAP-25 and STX-1A, indicating a decrease of their interactions. Our study sheds light on the regulatory effects of glucose on the functional organization of t-SNAREs at a subcellular and molecular level, thus providing new insights into the mechanisms by which SNAREs regulate insulin release.

In order to develop organic compounds for achieving highly efficient anticorrosion of mild steel in HCl solution, this study proposed to synthesize new dendritic molecules (DMs 1, 2) containing double norfloxacin skeletons. In addition, the linear molecule (LM) carrying a single norfloxacin framework was also prepared as a reference. The chemical structures of the studied molecules were fully characterized by nuclear magnetic resonance spectroscopy (NMR) (1D and 2D NMR spectroscopy, 1H, 13C, 19F), mass spectroscopy, Fourier-transform infrared spectroscopy. For this purpose, the adsorption of the studied molecules on mild steel was investigated by different means. Furthermore, the potential kinetic polarization and electrochemical impedance spectroscopy were used to survey the anticorrosion of the studied molecules in HCl solution at 298 K. It is shown that the DMs displayed superior corrosion inhibition effect on mild steel over the LM in acid medium at 298 K (the maximal corrosion inhibition efficiency, LM, 87.80%, DM1, 96.00%, DM2, 96.26% at 0.015 mM). The anticorrosion and adsorption mechanisms of the studied molecules for mild steel were further understood by material simulation and adsorption isotherms.

Gravity anchor blocks are a common type of ground anchor used for suspension bridges, whose bearing depends on its large body and gravity. As there is no stratum requirement, the safety of the anchor block is important for bridge stability. This study summarizes all available estimation indexes, calculating methods, and evaluation criteria for gravity anchor block safety for the Ruili bank of the Banjin Dam grand suspension bridge. The anti-overturning, anti-sliding, base stress, and deformation safeties were comprehensively evaluated using methods including the suggested specification method (SM), simplified mechanical method (SMM), and finite element method (FEM), the results of which were compared and analyzed. The reasons for errors and improved formulas and working conditions were presented. The main conclusions were as follows. 1) The methods for calculating different evaluation indexes according to specifications lack consistency. Moreover, FEM requires that designers have good computer skills and has low feasibility in practice. 2) The SMM for gravity anchor block safety estimation as described in this study, whose indexes cover systematic and overall, the computational formula is simple and speedy, with relatively conservative results and good practicability. 3) SM, SMM, and FEM were all used for the safety estimation of gravity anchor blocks in Ruili bank. The anti-overturning and anti-sliding stability coefficients must all meet the specification requirements of 2.0. SM cannot be used to estimate the base tensile stress under limited conditions. SMM denoted tensile stress at 2.5 times the main cable design force, compared to 3.2 P for FEM. Deformation calculation methods are not given by SM, but can be suggested by SMM based on the elastic mechanics. The horizontal displacements under design load conditions were 122 mm (SMM), 108 mm (FEM), and 44 mm (composite foundation treated by root piles), with a safety standard of

Background Community-acquired pneumonia (CAP) is a major public health challenge worldwide. However, the aetiological and disease severity-related pathogens associated with CAP in adults in China are not well established based on the detection of both viral and bacterial agents. Methods A multicentre, prospective study was conducted involving 10 hospitals located in nine geographical regions in China from 2014 to 2019. Sputum or bronchoalveolar lavage fluid (BALF) samples were collected from each recruited CAP patient. Multiplex real-time PCR and bacteria culture methods were used to detect respiratory pathogens. The association between detected pathogens and CAP severity was evaluated. Results Among the 3,403 recruited eligible patients, 462 (13.58%) had severe CAP, and the in-hospital mortality rate was 1.94% (66/3,403). At least one pathogen was detected in 2,054 (60.36%) patients, with two or more pathogens were co-detected in 725 patients. The ten major pathogens detected were Mycoplasma pneumoniae (11.05%), Haemophilus influenzae (10.67%), Klebsiella pneumoniae (10.43%), influenza A virus (9.49%), human rhinovirus (9.02%), Streptococcus pneumoniae (7.43%), Staphylococcus aureus (4.50%), adenovirus (2.94%), respiratory syncytial viruses (2.35%), and Legionella pneumophila (1.03%), which accounted for 76.06–92.52% of all positive detection results across sampling sites. Klebsiella pneumoniae (p p = 0.005) were more frequently detected in older patients, whereas Mycoplasma pneumoniae was more frequently detected in younger patients (p Klebsiella pneumoniae, Staphylococcus aureus, influenza viruses and respiratory syncytial viruses were risk factors for severe CAP. Conclusions The major respiratory pathogens causing CAP in adults in China were different from those in USA and European countries, which were consistent across different geographical regions over study years. Given the detection rate of pathogens and their association with severe CAP, we propose to include the ten major pathogens as priorities for clinical pathogen screening in China.

Decomposition hybrid algorithms with the recursive framework which recursively decompose the structural task into structural subtasks to reduce computational complexity are employed to learn Bayesian network (BN) structure. Merging rules are commonly adopted as the combination method in the combination step. The direction determination rule of merging rules has problems in using the idea of keeping v-structures unchanged before and after combination to determine directions of edges in the whole structure. It breaks down in one case due to appearances of wrong v-structures, and is hard to operate in practice. Therefore, we adopt a novel approach for direction determination and propose a two-stage combination method. In the first-stage combination method, we determine nodes, links of edges by merging rules and adopt the idea of permutation and combination to determine directions of contradictory edges. In the second-stage combination method, we restrict edges between nodes that do not satisfy the decomposition property and their parent nodes by determining the target domain according to the decomposition property. Simulation experiments on four networks show that the proposed algorithm can obtain BN structure with higher accuracy compared with other algorithms. Finally, the proposed algorithm is applied to the thickening process of gold hydrometallurgy to solve the practical problem.

Insulin receptor (INSR) is a key protein in the INSR signaling pathway and plays a critical role in biological processes, especially in the regulation of glucose homeostasis. Many metabolic diseases are often accompanied by abnormal INSR signaling. However, the specific effector mechanisms regulating insulin resistance and the distribution patterns of INSR during cell membrane activation remain unclear. Here, we investigated the changes in the distribution of INSR during activation using super-resolution imaging. By observing the connection between INSR activation and its distribution, we found that insulin resistance inhibits its receptor clustering. More importantly, we found that INSR has a highly co-localized relationship with the skeletal protein βII-spectrin. Specific knockout of βII-spectrin inhibited the interaction of INSR with GLUT4 and affected the normal metabolism of glucose. Our work elucidates the effects of insulin activation and insulin resistance on INSR distribution and reveals a potential relationship between INSR and cytoskeleton at the single molecule level, which promotes a deeper understanding of the roles associated with insulin signaling and insulin resistance.

Background Spinal cord injury (SCI) is a serious injury with high mortality and disability rates, and there is no effective treatment at present. It has been reported that some treatments have positive effects in promoting neurological recovery, such as drug intervention and stem cell transplantation. Although those treatments are effective for nerve regeneration, many drawbacks have limited their development, such as low stem cell survival rates and side effects caused by systemic medication. In recent years, injectable hydrogel materials have been widely used in tissue engineering due to their good biocompatibility, biodegradability, controllable properties, and low invasiveness. The treatment strategy of injectable hydrogels combined with stem cells or drugs has made some progress in SCI repair, which can overcome the defects existing in traditional drugs and stem cell therapy. Method In this study, a novel injectable electroactive hydrogel (NGP) based on sodium hyaluronate oxide (SAO) and polyaniline-grafted gelatin (NH2-Gel-PANI) was developed to load neural stem cells (NSCs) and donepezil (DPL) to facilitate nerve regeneration post-SCI. To evaluate the potential of the prepared NGP hydrogel in SCI repair application, the surface morphology, self-repairing properties, electrical conductivity and cytocompatibility of the resulting hydrogel were analyzed. Meanwhile, we evaluated the neural repair ability of NGP hydrogels loaded with DPL and NSCs using a rat model of spinal cord injury. Result The NGP hydrogel has a suitable pore size, good biocompatibility, excellent conductivity, injectable and self-repairing properties, and its degradation rate matches the repair cycle of spinal cord injury. At the same time, DPL could be released continuously and slowly in the NGP hydrogel; thus, the NGP hydrogel could be used as an excellent carrier of drugs and cells. The results of in vitro cell experiments showed that the NGP hydrogel had good cytocompatibility and could significantly promote neuronal differentiation and axon growth of NSCs, and the hydrogel loaded with DPL could significantly enhance this effect. More importantly, the NGP hydrogel loaded with DPL showed a significant inhibitory effect on astrocyte differentiation of NSCs in vitro. Animal experiments show that the combination of NGP hydrogel + DPL + NSCs group showed the best therapeutic effect on the recovery of motor function and nerve conduction function in rats. NGP hydrogel-loaded NSCs and DPL not only significantly increased the myelin sheath area, number of new neurons and axon area but also minimized the area of the cystic cavity and glial scar and promoted neural circuit reconstruction. Conclusions The DPL and NSCs-laden electroactive hydrogel developed in this study is an ideal biomaterial for the treatment of traumatic spinal cord injury.

ObjectiveThis study attempted to explore the difference of clinical characteristics in H1N1 influenza infection and SARS-CoV-2 Omicron infection in people younger than 65 years old, in order to better identify the two diseases.MethodsA total of 127 H1N1 influenza patients diagnosed from May 2009 to July 2009 and 3265 patients diagnosed and identified as SARS-CoV-2 Omicron BA.2 variant from March 2022 to May 2022 were admitted in this study. Through the 1 : 2 match based on age (The difference is less than 2 years), gender and underlying diseases, 115 patients with H1N1 infection and 230 patients with SARS-CoV-2 Omicron BA.2 infection(referred to as H1N1 group and Omicron group) were included in the statistics. The clinical manifestations of H1N1 group were compared with those of Omicron group. Logistic regression was performed to analyze the possible independent risk factors of H1N1 group and Omicron group. And multiple linear regression was used to analyze the factors for time for nucleic acid negativization (NAN).ResultsThe median age of the two groups was 21 [11,26] years. Compared with the H1N1 group, the Omicron group had lower white blood cell count and CRP levels, less fever, nasal congestion, sore throat, cough, sputum and headache, while more olfactory loss, muscle soreness and LDH abnormalities. The Omicron group used less antibiotics and antiviral drugs, and the NAN time was longer (17 [14,20] VS 4 [3,5], P < 0.001). After logistic regression, it was found that fever, cough, headache, and increased white blood cell count were more correlated with the H1N1 group, while muscle soreness and LDH abnormalities were more correlated with the Omicron group. After analyzing the factors of NAN time, it was found that fever (B 1.529, 95 % CI [0.149,2.909], P = 0.030) significantly predicted longer NAN time in Omicron patients.ConclusionThis study comprehensively evaluated the similarities and differences in clinical characteristics between SARS-CoV-2 Omicron infection and 2009 H1N1 influenza infection, which is of great significance for a better understanding for these diseases.

Heart diseases represent a serious medical condition that can be fatal. Therefore, it is critical to investigate the measures of its early prevention. The Mel-scale frequency cepstral coefficients (MFCC) feature has been widely used in the early diagnosis of heart abnormity and achieved promising results. During feature extraction, the Mel-scale triangular overlapping filter set is applied, which makes the frequency response more in line with the human auditory property. However, the frequency of the heart sound signals has no specific relationship with the human auditory system, which may not be suitable for processing of heart sound signals. To overcome this issue and obtain a more objective feature that can better adapt to practical use, in this work, we propose an equal scale frequency cepstral coefficients (EFCC) feature based on replacing the Mel-scale filter set with a set of equally spaced triangular overlapping filters. We further designed classifiers combining convolutional neural network (CNN), recurrent neural network (RNN) and random forest (RF) layers, which can extract both the spatial and temporal information of the input features. We evaluated the proposed algorithm on our database and the PhysioNet Computational Cardiology (CinC) 2016 Challenge Database. Results from ten-fold cross-validation reveal that the EFCC-based features show considerably better performance and robustness than the MFCC-based features on the task of classifying heart sounds from novel patients. Our algorithm can be further used in wearable medical devices to monitor the heart status of patients in real time with high precision, which is of great clinical importance.

Layers with low permeability are an important factor affecting the stability of tailings dams. In order to analyze the influence of the weak permeability interlayer on the seepage stability of the dam, this paper establishes a numerical analysis model of the tailings dam with the weak permeability interlayer. The seepage stability of the dam slope is calculated and analyzed for the location, thickness, number, and intervals of mud intercalation on the seepage stability of the dam slope. The weakly permeable interlayers at different locations form different saturation lines in the dam. When the weak permeability interlayer is located in the middle of the dam, the height of the saturation line is the highest, and the corresponding stability safety factor is the smallest. When the weak permeability interlayer moves up or down, the buried depth of the saturation line increases, and the safety factor increases. However, when the weak permeability interlayer is located above the drainage prism, the overall slip along the weak permeability interlayer is incidental to the increase in dam height. When the thickness of the weak permeability interlayer changes, the safety factor of the dam has a positively high significance related to the leakage. When the location of the weak permeability interlayer changes, the safety factor of the dam has no obvious correlation with the leakage. With the increase of the thickness and number of weak pemeability interlayer and the decrease of the intervals of mud intercalation, the height of the saturation line gradually increases, and the safety factor of the dam gradually decreases. Through the location of the sliding surface, it is found that the sliding surface eventually destabilizes through the lowest low-permeability interlayer.

In this article, a multiscale generative adversarial network (MS-GAN) is proposed for generating high-quality crowd density maps of arbitrary crowd density scenes. The task of crowd counting has many challenges, such as severe occlusions in extremely dense crowd scenes, perspective distortion, and high visual similarity between the pedestrians and background elements. To address these problems, the proposed MS-GAN combines a multiscale convolutional neural network (generator) and an adversarial network (discriminator) to generate a high-quality density map and accurately estimate the crowd count in complex crowd scenes. The multiscale generator utilizes the fusion features from multiple hierarchical layers to detect people with large-scale variation. The resulting density map produced by the multiscale generator is processed by a discriminator network trained to solve a binary classification task between a poor quality density map and real ground-truth ones. The additional adversarial loss can improve the quality of the density map, which is critical to accurately estimate the crowd counts. The experiments were conducted on multiple datasets with different crowd scenes and densities. The results showed that the proposed method provided better performance compared to current state-of-the-art methods.

Membrane proteins tend to interact with each other in the cell membranes to form protein clusters and perform the corresponding physiological functions. However, because channel proteins are involved in many biological functions, their distribution and nano-organization in these protein clusters are unclear. To study the distribution patterns and relationships between the different channel proteins, we identified the locations of glucose transporter 1 (Glut1) and Band3 (anion transporter 1) precisely in the topography of the cytoplasmic side of the human red blood cell (hRBC) membranes using combined atomic force microscopy (AFM) and single-molecule localization microscopy (SMLM). The AFM results revealed that membrane proteins interacted with each other and aggregated into protein islands. The SMLM results showed that Glut1 and Band3 tended to form protein clusters in the hRBC membranes, and there was a strong colocalization between the two proteins. The results of the combined AFM and SMLM method indicated that the protein clusters of Glut1 and Band3 were mainly located in the protein islands of topography, and the protein islands in topography also interacted with each other to assemble into larger protein clusters or functional microdomains.

Background We compared the clinical characteristics of infections caused by different pathogens and established a viral/bacterial infection prediction model to guide early clinical identification of pathogens among inpatients with community-acquired pneumonia (CAP). Methods Data were analysed to establish a prediction model for the early treatment of bacterial or viral infections. Basic data, clinical symptoms, laboratory examinations, and imaging of patients were collected and compared, and the virus/bacteria prediction equation was established. Results The proportion of patients with muscle soreness and headaches was significantly higher in the viral infection group than in the bacterial infection group. Procalcitonin (PCT) levels, erythrocyte sedimentation rate (ESR), and neutrophil alkaline phosphatase (NAP) scores were significantly higher in the bacterial infection group than in the viral infection group. The creatine kinase level was significantly higher in the viral infection group than in the bacterial infection group (P P

Cells can regulate a variety of behaviors by sensing mechanical signals, including growth, differentiation, apoptosis and so on. Yes-associated protein (YAP) is a mechanically sensitive protein that can be used as an indicator of mechanosignaling transduction. Unlike macroscopic statistical analysis, single-cell analysis is more demanding and challenging in terms of mechanistic regulation. Here, we quantified the location, amplitude and duration of single-cell mechanical stimulation by precise mechanical modulation, and simultaneously observed the mechanical force induced YAP nuclear and cytoplasmic distribution translocation using the AFM-dSTORM coupled techniques. Additionally, we investigated the regulation of YAP translocation according to the physical factors (cytoskeletal destruction and osmotic pressure) and biochemical factors (nuclear active transport protein inhibiter and starvation). Our study revealed that mechanical signals were transferred from the cytoskeleton to the nucleus through the synergistic action of microfilaments and microtubules, and then induced YAP translocation from the nucleus to the cytoplasm under the cooperation of nuclear export proteins. This conclusion deepens the understanding of the signaling pathway by which mechanical signals are transmitted from the plasma membrane to the cytoplasm and then to the nucleus to determine the cell's fate.

The performance of feature is essential to the degradation state identification for hydraulic pumps. The initial feature set extracted from the vibration signal of the hydraulic pump is often high-dimensional and contains redundant information, which undermines the effectiveness of the feature set. The novel three-stage feature fusion scheme proposed in this paper aims to enhance the performance of the original features extracted from the vibration signal. First, sparse local Fisher discriminant analysis (SLFDA) performs intra-set fusion within the two original feature sets, respectively. SLFDA has a good effect on samples with intra-class multimodality, and the feature set fused by it has obvious multivariate normal distribution characteristics, which is conducive to the next fusion. Second, our modified intra-class correlation analysis (MICA) is used to fuse two feature sets in the second stage. MICA is a CCA (Canonical correlation analysis) -based method. A new class matrix is used to modify the covariance matrix between two feature sets, which allows MICA to conveniently inherit the discriminating structure while fusing features. Finally, we propose a feature selection algorithm based on kernel local Fisher discriminant analysis (KLFDA) and kernel canonical correlation analysis (KCCA) to select the desired features. This algorithm based on Max-Relevance and Min-Redundancy (mRMR) framework solves the problem that CCA cannot properly evaluate the correlation between features and the class variable, as well as accurately evaluates the correlation among features. Based on the experimental data, the proposed method is compared with several popular methods, and the feature fusion methods used in some previous studies related to the fault diagnosis of rotating machinery are compared with it as well. The results show that the fusion effectiveness of our method is better than other methods, which obtains higher recognition accuracy.

Previous studies have shown that water can reduce the acoustic emission (AE) energy and other parameters during rock failure. However, the fracture mechanism of rock can be better reflected by analyzing the AE waveform. Therefore, this paper conducted experiments of uniaxial compression on sandstone samples of various water contents and collected AE signals simultaneously. Analyses of fast Fourier transform (FFT) and Hilbert-Huang transform (HHT) were performed on the AE waveform when the sample failed. The results show that as the water content increases, the frequency and intensity of the AE signal will decrease. The influence of water on the intensity of the AE signal is greater than that on the frequency. Through the analysis of the energy mechanism of rock failure, it is pointed out that the frequency and intensity of AE signal are closely related to elastic energy index W ET and burst energy index K E . The research results have guiding significance for the monitoring of rockburst.

The interaction between the double-track shield tunnel and the integrated pipe gallery is complex, and there is still a lack of systematic research. Based on the numerical simulation of the Xi’an case, this paper studies the influence of the construction of the integrated pipe gallery on the deformation characteristics of the existing double-line subway tunnels with different buried depths and clear distances and finds the safe buried depth and clear distance of the tunnel. The results indicated that, in the loess area, the maximum deformation of the existing subway has a linear and inverse relationship with the buried depth and spacing, respectively. Increasing the buried depth can reduce the impact of the upper construction disturbance more than increasing the clear distance ratio. Moreove, under the design conditions of constructing a utility tunnel by the open-cut method, when the buried depth of the tunnel is 4.5 D and the spacing is greater than 4.5 D, or the buried depth is 5.0 D and the spacing is greater than 2.5 D, the combination of subway tunnels can meet the deformation control standards.

ObjectIt remains unelucidated regarding the effects of vaccination on disease severity and factors for viral clearance and hospitalization in omicron-infected patients.MethodsThe clinical manifestations of 3,265 Omicron-infected patients (BA.2 variant; the Omicron group) were compared with those of 226 Delta-infected patients (the Delta group).A Multi-class logistic regression model was employed to analyze the impacts of vaccination doses and intervals on disease severity; a logistic regression model to evaluate the risk factors for hospitalization; R 4.1.2 data analysis to investigate the factors for time for nucleic acid negativization (NAN).ResultsCompared with the Delta group, the Omicron group reported a fast transmission, mild symptoms, and lower severity incidence, and a significant inverse correlation of vaccination dose with clinical severity (OR: 0.803, 95%CI: 0.742-0.868, pConclusionBooster vaccination should be advocated for COVID-19 pandemic-related control and prevention policies and adequate precautions should be taken for patients with underlying conditions.

Hong Xue,1,2 Baosong Xie,1,2 Nengluan Xu,1,2 Hongru Li,1,2 Qianshun Chen,2,3 Weiping Xie,4 Hong Wang4 1Department of Respiratory and Critical Care Medicine, Fujian Provincial Hospital, Fuzhou, Fujian, People’s Republic of China; 2Provincial School of Clinical Medicine, Fujian Medical University, Fuzhou, Fujian, People’s Republic of China; 3Department of Thoracic Surgery, Fujian Provincial Hospital, Fuzhou, Fujian, People’s Republic of China; 4Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital with Nanjing Medical University, Nanjing, Jiangsu, People’s Republic of ChinaCorrespondence: Baosong Xie; Hong XueDepartment of Respiratory and Critical Care Medicine, Fujian Provincial Hospital, No. 134, East Street, Fuzhou, Fujian, 350000, People’s Republic of ChinaTel +86 591-88217531Email xiebaos_baosong@163.com; xuehong_xhx@163.comPurpose: Etanercept (ETN), a tumor necrosis factor-α (TNF-α) inhibitor, has been applied in the treatment of many diseases. However, whether it has effects on chronic obstructive pulmonary disease (COPD) and its interaction with tumor necrosis factor receptor 1 (TNFR1) remained unknown.Methods: Histopathological analysis of lung tissues from non-smokers and smokers with or without COPD was conducted using hematoxylin–eosin (H&E) staining, Van Gieson (VG) staining, and terminal transferase-mediated biotin dUTP nick end labeling (TUNEL). TNF-α content was measured using Immunohistochemistry. Correlation analysis among apoptosis rate, smoke index, the FEV1/FVC ratio, and TNF-α-positive cells was performed. After ETN treatment and transfection of overexpressed or silenced TNFR1, levels of inflammatory cytokines, apoptosis and related genes expressions in cigarette smoke extract (CSE)-treated human pulmonary artery endothelial cells (HPAECs) were detected using enzyme-linked immunosorbent assay (ELISA), Hoechst 33342 staining, flow cytometry, quantitative real-time PCR (qRT-PCR) and Western blot.Results: Pulmonary arterial remodeling and increased apoptotic and TNF-α+ HPAECs were found in lung tissue of smokers with or without COPD, with higher degrees in smokers with COPD. The numbers of apoptotic and TNF-α+ HPAECs were positively correlated with smoke index, while the FEV1/FVC ratio was negatively correlated with apoptotic HPAECs. In HPAECs, ETN downregulated the expressions of proteins related to CSE-induced apoptosis and the TNF receptor family, decreased CSE-induced cell apoptosis and inflammatory cytokine levels, and inhibited TNFR1 expression and p65 phosphorylation. Overexpressed TNFR1 reversed the effects of ETN on CSE-treated HPAECs, whereas silencing TNFR1 did the opposite.Conclusion: ETN protected HPAECs against CSE-induced inflammation and apoptosis via downregulating TNFR1, thus providing a potential therapy for smoking-induced COPD.Keywords: chronic obstructive pulmonary disease, etanercept, apoptosis, cigarette smoke extract, human pulmonary artery endothelial cells, tumor necrosis factor receptor 1

Blood glucose (BG) prediction is an effective approach to avoid hyper- and hypoglycemia, and achieve intelligent glucose management for patients with type 1 or serious type 2 diabetes. Recent studies have tended to adopt deep learning networks to obtain improved prediction models and more accurate prediction results, which have often required significant quantities of historical continuous glucose-monitoring (CGM) data. However, for new patients with limited historical dataset, it becomes difficult to establish an acceptable deep learning network for glucose prediction. Consequently, the goal of this study was to design a novel prediction framework with instance-based and network-based deep transfer learning for cross-subject glucose prediction based on segmented CGM time series. Taking the effects of biodiversity into consideration, dynamic time warping (DTW) was applied to determine the proper source domain dataset that shared the greatest degree of similarity for new subjects. After that, a network-based deep transfer learning method was designed with cross-domain dataset to obtain a personalized model combined with improved generalization capability. In a case study, the clinical dataset demonstrated that, with additional segmented dataset from other subjects, the proposed deep transfer learning framework achieved more accurate glucose predictions for new subjects with type 2 diabetes.

As a kind of renewable energy with economic and environmental friendliness, wind energy is widely used, although the high uncertainty of wind speed has significant impact on the operation and planning of wind power systems. Thus, scenario generation of wind speed is the primary step to obtain optimal decisions. However, as to the wind power plants to be newly-built or expanded, adequate wind data may not be available, or even the wind data are missing or invalid, which may lead to the inaccuracy of data-driven scenario generation. In this paper, considering that multiple wind plants in neighboring areas may have similar wind patterns, a novel scenario generation framework is proposed to transfer the knowledge extracted from existing data-rich plants to help establish the uncertainty model of newly-built plant. Transfer component analysis (TCA) is used to minimize the distribution gap between multiple source plants and target plant, and the results of maximum mean discrepancy (MMD) can be obtained to construct the mixture distribution model of target wind speed. Experimental results show that the scenarios generated by transferring the knowledge of existing wind plants can better reflect the real characteristics of the target wind speed in the case of insufficient historical data.