Roles of bacterial extracellular vesicles in systemic diseases

Accumulating evidence suggests that in various systems, not all bidirectional microbiota–host interactions involve direct cell contact. Bacterial extracellular vesicles (BEVs) may be key participants in this interkingdom crosstalk. BEVs mediate microbiota functions by delivering effector molecules that modulate host signaling pathways, thereby facilitating host–microbe interactions. BEV production during infections by both pathogens and probiotics has been observed in various host tissues. Therefore, these vesicles released by microbiota may have the ability to drive or inhibit disease pathogenesis in different systems within the host. Here, we review the current knowledge of BEVs and particularly emphasize their interactions with the host and the pathogenesis of systemic diseases

Using Trajectory Clusters to Define the Most Relevant Features for Transient Stability Prediction Based on Machine Learning Method

To achieve rapid real-time transient stability prediction, a power system transient stability prediction method based on the extraction of the post-fault trajectory cluster features of generators is proposed. This approach is conducted using data-mining techniques and support vector machine (SVM) models. First, the post-fault rotor angles and generator terminal voltage magnitudes are considered as the input vectors. Second, we construct a high-confidence dataset by extracting the 27 trajectory cluster features obtained from the chosen databases. Then, by applying a filter–wrapper algorithm for feature selection, we obtain the final feature set composed of the eight most relevant features for transient stability prediction, called the global trajectory clusters feature subset (GTCFS), which are validated by receiver operating characteristic (ROC) analysis. Comprehensive simulations are conducted on a New England 39-bus system under various operating conditions, load levels and topologies, and the transient stability predicting capability of the SVM model based on the GTCFS is extensively tested. The experimental results show that the selected GTCFS features improve the prediction accuracy with high computational efficiency. The proposed method has distinct advantages for transient stability prediction when faced with incomplete Wide Area Measurement System (WAMS) information, unknown operating conditions and unknown topologies and significantly improves the robustness of the transient stability prediction system

A Hierarchical Method for Transient Stability Prediction of Power Systems Using the Confidence of a SVM-Based Ensemble Classifier

Machine learning techniques have been widely used in transient stability prediction of power systems. When using the post-fault dynamic responses, it is difficult to draw a definite conclusion about how long the duration of response data used should be in order to balance the accuracy and speed. Besides, previous studies have the problem of lacking consideration for the confidence level. To solve these problems, a hierarchical method for transient stability prediction based on the confidence of ensemble classifier using multiple support vector machines (SVMs) is proposed. Firstly, multiple datasets are generated by bootstrap sampling, then features are randomly picked up to compress the datasets. Secondly, the confidence indices are defined and multiple SVMs are built based on these generated datasets. By synthesizing the probabilistic outputs of multiple SVMs, the prediction results and confidence of the ensemble classifier will be obtained. Finally, different ensemble classifiers with different response times are built to construct different layers of the proposed hierarchical scheme. The simulation results show that the proposed hierarchical method can balance the accuracy and rapidity of the transient stability prediction. Moreover, the hierarchical method can reduce the misjudgments of unstable instances and cooperate with the time domain simulation to insure the security and stability of power systems

Ultra-Fast Polarity Switching, Non-Radioactive Drift Tube for the Miniaturization of Drift-Time Ion Mobility Spectrometer

Drift-time ion mobility spectrometer (DT-IMS) is a promising technology for gas detection and analysis in the form of miniaturized instrument. Analytes may exist in the form of positively or negatively charged ions according to their chemical composition and ionization condition, and therefore require both polarity of electric field for the detection. In this work the polarity switching of a drift-time ion mobility spectrometer based on a direct current (DC) corona discharge ionization source was investigated, with novel solutions for both the control of ion shutter and the stabilization of aperture grid. The drift field is established by employing a switchable high voltage power supply and a serial of voltage regulator diode, with optocouplers to drive the ion shutter when the polarity is switched. The potential of aperture grid is stabilized during the polarity switching by the use of four diodes to avoid unnecessary charging cycle of the aperture grid capacitor. Based on the proposed techniques, the developed DT-IMS with 50 mm drift path is able to switch its polarity in 10 ms and acquire mobility spectrum after 10 ms of stabilization. Coupled with a thermal desorption sampler, limit of detection (LoD) of 0.1 ng was achieved for ketamine and TNT. Extra benefits include single calibration substance for both polarities and largely simplified pneumatic design, together with the reduction of second drift tube and its accessories. This work paved the way towards further miniaturization of DT-IMS without compromise of performance

Educational Evaluation in the PKU SPOC Course "Data Structures and Algorithms"

In order to learn the impact of MOOCs, we conducted a SPOC experiment on the course of Data Structures and Algorithms in Peking University. In this paper, we analyze student online activities, test scores, and two surveys using statistical methods (t-test, analysis of variance, correlation analysis and OLS regression) to understand what factors will foster improvements in student learning. We find that the 'SPOC + Flipped' is a helpful mode to teach algorithm, time spent on the course and students' confidence had a positive impact on learning effect, and SPOC resource should be made full use of. Copyright ? 2015 ACM.EI