Angiogenesis and Endothelial Dysfunction: Insights of Autophagy Machinery in Regulating Endothelial cell Biology

Autophagy is an intracellular degradation system that delivers cytoplasmic components to the lysosome for degradation. Autophagy is essential for cellular homeostasis and provides a mechanism to adapt to metabolic and stress cues. Endothelial autophagy regulates the response of ECs to a variety of stress factors related to EC homeostasis and plasticity. However, the precise role of autophagy in angiogenesis requires more detailed research. Although autophagy-related 7 (ATG7) is essential for classical degradative autophagy and cell cycle regulation, whether and how ATG7 influences endothelial cell (EC) function and regulates post-ischemic angiogenesis remain unknown. Endothelial dysfunction is a potential contributor to the pathogenesis of diabetic cardiovascular complications. However, little is known about disruptions of endothelial autophagy contributing to diabetes-induced endothelial dysfunction. This dissertation aims to address how ATG7 influences endothelial cell (EC) function and regulates post-ischemic angiogenesis, and to determine the role of autophagy in the development of endothelial dysfunction. EC-specific deletion of Atg7 significantly impaired angiogenesis, delayed the recovery of blood flow reperfusion, and displayed reduction in hypoxia inducible factor 1 subunit alpha (HIF1A) expression. Mechanistically, lack of ATG7 in the cytoplasm disrupted the association between ATG7 and transcription factor ZNF148/ZBP-89 that is required for STAT1 (signal transducer and activator of transcription1) constitutive expression, increased the binding between ZNF148/ZBP-89 and importin-β1 (KPNB1), which promoted ZNF148/ZBP-89 nuclear translocation, and increased STAT1 expression. STAT1 bond to HIF1A promotor and suppressed HIF1A mRNA expression, thereby preventing ischemia-induced angiogenesis. These results demonstrate that ATG7 deficiency is a novel suppressor of ischemia-induced angiogenesis. In addition, streptozotocin (STZ)-induced type 1 diabetes inhibits autophagic flux and reduced protein levels of autophagy gene related protein, including ULK1, ATG7, ATG5, and Beclin1, which was accompanied by an impairment of acetylcholine-induced relaxation of isolated mouse aortas. Inhibition of endothelial autophagy by the deletion of endothelial ULK1 exacerbated diabetes-induced endothelial dysfunction, reactive oxygen species (ROS) overproduction and impeded endothelial nitric oxide synthase (eNOS) phosphorylation. Mechanistically, suppression of autophagy by diabetes aggravated ROS overproduction. Downregulation of ULK1 reduced eNOS phosphorylation. Thus, promoting autophagy activity may be a potential strategy to prevent endothelial dysfunction in diabetes

Serum level of S100A8/A9 as a biomarker for establishing the diagnosis and severity of community-acquired pneumonia in children

BackgroundS100A8/A9, which is a member of S100 proteins, may be involved in the pathophysiology of Community-acquired pneumonia (CAP) that seriously threatens children’s health. However, circulating markers to assess the severity of pneumonia in children are yet to be explored. Therefore, we aimed to investigate the diagnostic performance of serum S100A8/A9 level in determining the severity of CAP in children.MethodsIn this prospective and observational study, we recruited 195 in-hospital children diagnosed with CAP. In comparison, 63 healthy children (HC) and 58 children with non-infectious pneumonia (pneumonitis) were included as control groups. Demographic and clinical data were collected. Serum S100A8/A9 levels, serum pro-calcitonin concentrations, and blood leucocyte counts were quantified.ResultsThe serum S100A8/A9 levels in patients with CAP was 1.59 ± 1.32 ng/mL, which was approximately five and two times higher than those in healthy controls and those in children with pneumonitis, respectively. Serum S100A8/A9 was elevated parallelly with the clinical pulmonary infection score. The sensitivity, specificity, and Youden’s index of S100A8/A9 ≥1.25 ng/mL for predicting the severity of CAP in children was optimal. The area under the receiver operating characteristic curve of S100A8/A9 was the highest among the indices used to evaluate severity.ConclusionsS100A8/A9 may serve as a biomarker for predicting the severity of the condition in children with CAP and establishing treatment grading

Composite Clustering Sampling Strategy for Multiscale Spectral-Spatial Classification of Hyperspectral Images

In recent years, many high-performance spectral-spatial classification methods were proposed in the field of hyperspectral image classification. At present, a great quantity of studies has focused on developing methods to improve classification accuracy. However, some research has shown that the widely adopted pixel-based random sampling strategy is not suitable for spectral-spatial hyperspectral image classification algorithms. Therefore, a composite clustering sampling strategy is proposed, which can greatly reduce the overlap between the training set and the test set, while making sample points in the training set sufficiently representative in the spectral domain. At the same time, in order to solve problems of a three-dimensional Convolutional Neural Network which is commonly used in spectral-spatial hyperspectral image classification methods, such as long training time and large computing resource requirements, a multiscale spectral-spatial hyperspectral image classification model based on a two-dimensional Convolutional Neural Network is proposed, which effectively reduces the training time and computing resource requirements

The Relationship between Preoperative Urine Culture and Post-Percutaneous Nephrostolithotomy Systemic Inflammatory Response Syndrome: A Single-Center Retrospective Study

Background: To predict the occurrence of systemic inflammatory response syndrome (SIRS) after percutaneous nephrostrolithotomy(PCNL), preoperative urine culture is a popular method, but the debate about its predictive value is ongoing. In order to better evaluate the value of urine culture before percutaneous nephrolithotomy, we conducted a single-center retrospective study. Methods: A total of 273 patients who received PCNL in Shanghai Tenth People’s Hospital from January 2018 to December 2020 were retrospectively evaluated. Urine culture results, bacterial profiles, and other clinical information were collected. The primary outcome observed was the occurrence of SIRS after PCNL. Univariate and multivariate logistic regression analysis was performed to determine the predictive factors of SIRS after PCNL. A nomogram was constructed using the predictive factors, and the receiver operating characteristic (ROC) curves and calibration plot were drawn. Results: Our results showed that there was a significant correlation between positive preoperative urine cultures and the occurrence of postoperative systemic inflammatory response syndrome. Meanwhile, diabetes, staghorn calculi, and operation time were also risk factors for postoperative systemic inflammatory response syndrome. Our results suggest that among the positive bacteria in urine culture before percutaneous nephrolithotomy, Enterococcus faecalis has become the dominant strain. Conclusion: Urine culture is still an important method of preoperative evaluation. A comprehensive evaluation of multiple risk factors should be undertaken and heeded to before percutaneous nephrostrolithotomy. In addition, the impact of changes in bacterial drug resistance is also worthy of attention

Runoff Prediction Method Based on Adaptive Elman Neural Network

The prediction of medium- and long-term runoff is of great significance to the comprehensive utilization of water resources. Building an adaptive data-driven runoff prediction model by automatic identification of multivariate time series change in runoff forecasting and identifying its influence degree is an attractive and intricate task. At present, the commonly used screening factor method is correlational analysis; others offer multi-collinearity. If these factors are directly input into the model, the parameters of the model tend to increase, and the excessive redundancy and noise adversely affects the prediction results of the model. On the basis of previous studies on medium- and long-term runoff prediction methods, this paper proposes an Elman Neural Network (ENN) adaptive runoff prediction method based on normalized mutual information (NMI) and kernel principal component analysis (KPCA). In this method, the features of the screening factors are extracted automatically by using the mutual information automatic screening factor, and then input into the Elman Neural Network for training. With less features, the parameters of the Elman Neural Network model can be reduced, and the problem of overfitting of the Elman Neural Network model is effectively alleviated. The method is evaluated by using the annual average runoff data of Jinping hydropower station in Chengdu, China, from 2007 to 2011. The maximum relative error of multiple forecasts was found to be less than 16%, and forecast effect was good. The accuracy of prediction is further improved by averaging the results of multiple forecasts

Joint Alternate Small Convolution and Feature Reuse for Hyperspectral Image Classification

A hyperspectral image (HSI) contains fine and rich spectral information and spatial information of ground objects, which has great potential in applications. It is also widely used in precision agriculture, marine monitoring, military reconnaissance and many other fields. In recent years, a convolutional neural network (CNN) has been successfully used in HSI classification and has provided it with outstanding capacity for improving classification effects. To get rid of the bondage of strong correlation among bands for HSI classification, an effective CNN architecture is proposed for HSI classification in this work. The proposed CNN architecture has several distinct advantages. First, each 1D spectral vector that corresponds to a pixel in an HSI is transformed into a 2D spectral feature matrix, thereby emphasizing the difference among samples. In addition, this architecture can not only weaken the influence of strong correlation among bands on classification, but can also fully utilize the spectral information of hyperspectral data. Furthermore, a 1 × 1 convolutional layer is adopted to better deal with HSI information. All the convolutional layers in the proposed CNN architecture are composed of small convolutional kernels. Moreover, cascaded composite layers of the architecture consist of 1 × 1 and 3 × 3 convolutional layers. The inputs and outputs of each composite layer are stitched as the inputs of the next composite layer, thereby accomplishing feature reuse. This special module with joint alternate small convolution and feature reuse can extract high-level features from hyperspectral data meticulously and comprehensively solve the overfitting problem to an extent, in order to obtain a considerable classification effect. Finally, global average pooling is used to replace the traditional fully connected layer to reduce the model parameters and extract high-dimensional features from the hyperspectral data at the end of the architecture. Experimental results on three benchmark HSI datasets show the high classification accuracy and effectiveness of the proposed method

Multidisciplinary Design Optimization of Crankshaft Structure Based on Cooptimization and Multi-Island Genetic Algorithm

The feasibility design method with multidisciplinary and multiobjective optimization is applied in the research of lightweight design and NVH performances of crankshaft in high-power marine reciprocating compressor. Opt-LHD is explored to obtain the experimental scheme and perform data sampling. The elliptical basis function neural network (EBFNN) model considering modal frequency, static strength, torsional vibration angular displacement, and lightweight design of crankshaft is built. Deterministic optimization and reliability optimization for lightweight design of crankshaft are operated separately. Multi-island genetic algorithm (MIGA) combined with multidisciplinary cooptimization method is used to carry out the multiobjective optimization of crankshaft structure. Pareto optimal set is obtained. Optimization results demonstrate that the reliability optimization which considers the uncertainties of production process can ensure product stability compared with deterministic optimization. The coupling and decoupling of structure mechanical properties, NVH, and lightweight design are considered during the multiobjective optimization of crankshaft structure. Designers can choose the optimization results according to their demands, which means the production development cycle and the costs can be significantly reduced