The outcomes of hypertension refer to the death or serious complications (such as myocardial infarction or stroke) that may occur in patients with hypertension. The outcomes of hypertension are very concerning for patients and doctors, and are ideally avoided. However, there is no satisfactory method for predicting the outcomes of hypertension. Therefore, this paper proposes a prediction method for outcomes based on physical examination indicators of hypertension patients. In this work, we divide the patients’ outcome prediction into two steps. The first step is to extract the key features from the patients’ many physical examination indicators. The second step is to use the key features extracted from the first step to predict the patients’ outcomes. To this end, we propose a model combining recursive feature elimination with a cross-validation method and classification algorithm. In the first step, we use the recursive feature elimination algorithm to rank the importance of all features, and then extract the optimal features subset using cross-validation. In the second step, we use four classification algorithms (support vector machine (SVM), C4.5 decision tree, random forest (RF), and extreme gradient boosting (XGBoost)) to accurately predict patient outcomes by using their optimal features subset. The selected model prediction performance evaluation metrics are accuracy, F1 measure, and area under receiver operating characteristic curve. The 10-fold cross-validation shows that C4.5, RF, and XGBoost can achieve very good prediction results with a small number of features, and the classifier after recursive feature elimination with cross-validation feature selection has better prediction performance. Among the four classifiers, XGBoost has the best prediction performance, and its accuracy, F1, and area under receiver operating characteristic curve (AUC) values are 94.36%, 0.875, and 0.927, respectively, using the optimal features subset. This article’s prediction of hypertension outcomes contributes to the in-depth study of hypertension complications and has strong practical significance.

Timely and accurate state detection and fault diagnosis of rolling element bearings are very critical to ensuring the reliability of rotating machinery. This paper proposes a novel method of rolling bearing fault diagnosis based on a combination of ensemble empirical mode decomposition (EEMD), weighted permutation entropy (WPE) and an improved support vector machine (SVM) ensemble classifier. A hybrid voting (HV) strategy that combines SVM-based classifiers and cloud similarity measurement (CSM) was employed to improve the classification accuracy. First, the WPE value of the bearing vibration signal was calculated to detect the fault. Secondly, if a bearing fault occurred, the vibration signal was decomposed into a set of intrinsic mode functions (IMFs) by EEMD. The WPE values of the first several IMFs were calculated to form the fault feature vectors. Then, the SVM ensemble classifier was composed of binary SVM and the HV strategy to identify the bearing multi-fault types. Finally, the proposed model was fully evaluated by experiments and comparative studies. The results demonstrate that the proposed method can effectively detect bearing faults and maintain a high accuracy rate of fault recognition when a small number of training samples are available.

Drug-resistant pathogens have presented increasing challenges to the discovery and development of new antibacterial agents. The type III secretion system (T3SS), existing in bacterial chromosomes or plasmids, is one of the most complicated protein secretion systems. T3SSs of animal and plant pathogens possess many highly conserved main structural components comprised of about 20 proteins. Many Gram-negative bacteria carry T3SS as a major virulence determinant, and using the T3SS, the bacteria secrete and inject effector proteins into target host cells, triggering disease symptoms. Therefore, T3SS has emerged as an attractive target for antimicrobial therapeutics. In recent years, many T3SS-targeting small-molecule inhibitors have been discovered; these inhibitors prevent the bacteria from injecting effector proteins and from causing pathophysiology in host cells. Targeting the virulence of Gram-negative pathogens, rather than their survival, is an innovative and promising approach that may greatly reduce selection pressures on pathogens to develop drug-resistant mutations. This article summarizes recent progress in the search for promising small-molecule T3SS inhibitors that target the secretion and translocation of bacterial effector proteins.

The search for electron sources with simultaneous optimal spatial and temporal resolution has become an area of intense activity for a wide variety of applications in the emerging fields of lightwave electronics and attosecond science. Most recently, increasing efforts are focused on the investigation of ultrafast field-emission phenomena of nanomaterials, which not only are fascinating from a fundamental scientific point of view, but also are of interest for a range of potential applications. Here, the current state-of-the-art in ultrafast field-emission, particularly sub-optical-cycle field emission, based on various nanostructures (e.g., metallic nanotips, carbon nanotubes) is reviewed. A number of promising nanomaterials and possible future research directions are also established.