Tp47 induces cell death involving autophagy and mTOR in human microglial HMO6 cells

Abstract(#br)Background(#br)Tp47 can induce immune cells to produce numerous inflammatory factors, some of which can trigger autophagy. Increased autophagy has a dual effect on cell survival. However, whether Tp47 induces autophagy in microglia is unknown.(#br)Objective(#br)To evaluate the potential role of Tp47 in microglia.(#br)Methods(#br)After treatment with Tp47, autophagy-related proteins were assessed in HMO6 human microglial cells by flow cytometry, Western blotting and immunofluorescence. Cell death was assessed by flow cytometry and trypan blue staining. Changes in mTOR pathway proteins were explored by using Western blotting.(#br)Results(#br)After treatment with Tp47, a gradual increase in total LC3 expression was observed as a dose- and time-dependent accumulation of its active form, LC3-II ( P < 0.05), but P62 expression was downregulated ( P < 0.05). Moreover, microglial mortality gradually increased in a dose- and time-dependent manner. 3-Methyladenine (3-MA), a specific inhibitor of PI3KC3, reversed autophagy and cell death. The mortality rate of HMO6 microglial cells treated with Tp47 was approximately 13.7 ± 2%, and the basal expression of p-mTOR, p-p70s6k and p-S6 in these cells was significantly downregulated by Tp47. Moreover, the mortality rate of microglia was significantly reduced after mTOR agonist intervention.(#br)Conclusion(#br)In human microglial HMO6 cells, Tp47 induces autophagy- and mTOR pathway-dependent cell death

Lipoprotein‐Associated Phospholipase A2 Activity Is a Marker of Risk But Not a Useful Target for Treatment in Patients With Stable Coronary Heart Disease

Background: We evaluated lipoprotein‐associated phospholipase A2 (Lp‐PLA2) activity in patients with stable coronary heart disease before and during treatment with darapladib, a selective Lp‐PLA2 inhibitor, in relation to outcomes and the effects of darapladib in the STABILITY trial. Methods and Results: Plasma Lp‐PLA2 activity was determined at baseline (n=14 500); at 1 month (n=13 709); serially (n=100) at 3, 6, and 18 months; and at the end of treatment. Adjusted Cox regression models evaluated associations between Lp‐PLA2 activity levels and outcomes. At baseline, the median Lp‐PLA2 level was 172.4 μmol/min per liter (interquartile range 143.1–204.2 μmol/min per liter). Comparing the highest and lowest Lp‐PLA2 quartile groups, the hazard ratios were 1.50 (95% CI 1.23–1.82) for the primary composite end point (cardiovascular death, myocardial infarction, or stroke), 1.95 (95% CI 1.29–2.93) for hospitalization for heart failure, 1.42 (1.07–1.89) for cardiovascular death, and 1.37 (1.03–1.81) for myocardial infarction after adjustment for baseline characteristics, standard laboratory variables, and other prognostic biomarkers. Treatment with darapladib led to a ≈65% persistent reduction in median Lp‐PLA2 activity. There were no associations between on‐treatment Lp‐PLA2 activity or changes of Lp‐PLA2 activity and outcomes, and there were no significant interactions between baseline and on‐treatment Lp‐PLA2 activity or changes in Lp‐PLA2 activity levels and the effects of darapladib on outcomes. Conclusions: Although high Lp‐PLA2 activity was associated with increased risk of cardiovascular events, pharmacological lowering of Lp‐PLA2 activity by ≈65% did not significantly reduce cardiovascular events in patients with stable coronary heart disease, regardless of the baseline level or the magnitude of change of Lp‐PLA2 activity

The design of asynchronous learning environment /

Asynchronous Learning Environment (ALE) has the capability of providing learning to people anywhere and at any time for both to secure degree and to engage in continuing education throughout their lifetimes. The advance of communications and information technology will make students choose to purchase and enroll in open market, widely available networked courses regardless of institutional affiliation.Research results have found that success factors for asynchronous learning include whether students felt part of the online learning group, immediate feedback from instructors, automatic self-test, and indicating student's performance and progress in the course. These findings present basic requirement for the design of ALE. This paper explores all aspects of Asynchronous Learning Environment, including the architecture of ALE and complete database design. The modules of ALE include multimedia presentation, identity verification, intelligent agent, automatic test marking, computer conference, chat & whiteboard, and learning scheduling assistance. The purpose of this research is to make ALE a better way of education than traditional education. A database is designed to fully support these ALE functions.Guidelines of designing ALE are provided with implementation examples of intelligent agents that providing automatic reminders and learning progress report. Conclusion and further works are discussed at the end of the paper.The design described in this paper is intended for use by engineering courses. But it can be used by courses of other disciplines without much modification

Cooperative Multi-Node Jamming Recognition Method Based on Deep Residual Network

Anti-jamming is the core issue of wireless communication viability in complex electromagnetic environments, where jamming recognition is the precondition and foundation of cognitive anti-jamming. In the current jamming recognition methods, the existing convolutional networks are limited by the small number of layers and the extracted feature information. Simultaneously, simple stacking of layers will lead to the disappearance of gradients and the decrease in correct recognition rate. Meanwhile, most of the jamming recognition methods use single-node methods, which are easily affected by the channel and have a low recognition rate under the low jamming-to-signal ratio (JSR). To solve these problems, a multi-node cooperative jamming recognition method based on deep residual networks was proposed in this paper, and two data fusion algorithms based on hard fusion and soft fusion for jamming recognition were designed. Simulation results show that the use of deep residual networks to replace the original shallow CNN network structure can gain a 6–14% improvement in the correct recognition rate of jamming signals, and the hard and soft fusion-based methods can significantly improve the correct jamming recognition rate by about 3–7% and 5–12%, respectively, under low JSR conditions compared with the existing single-node method

An Optimized Vector Tracking Architecture for Pseudo-Random Pulsing CDMA Signals

The vector tracking loop (VTL) has high tracking accuracy and a superior ability to track weak signals in GNSS. However, traditional VTL architecture is established on continuous Code Division Multiple Access (CDMA) signal and is incompatible with pseudolite positioning systems (PLPS) because PLPS generally adopts a pseudo-random pulsing CDMA signal structure to mitigate the near-far effect. Therefore, this paper proposes an optimized VTL architecture for pseudo-random pulsing CDMA signals. To avoid estimation biases in PLPS, the proposed VTL adopts irregular update periods (IUP) pre-filters which adjust the update cycles according to the active timeslot intervals. Meanwhile, as the active timeslots of different pseudolites do not overlap, the sampling time of the navigation filter inputs is inconsistent and time-varying, causing jitter degradation. Thus, the proposed VTL predicts the measurements so that they can be sampled at the same time, which improves tracking accuracy. Simulation is carried out to evaluate the performance of the proposed VTL. The results suggest that the proposed VTL outperforms the traditional pre-filter-based VTL and IUP pre-filter-based VTL

Cooperative Multi-Node Jamming Recognition Method Based on Deep Residual Network

Anti-jamming is the core issue of wireless communication viability in complex electromagnetic environments, where jamming recognition is the precondition and foundation of cognitive anti-jamming. In the current jamming recognition methods, the existing convolutional networks are limited by the small number of layers and the extracted feature information. Simultaneously, simple stacking of layers will lead to the disappearance of gradients and the decrease in correct recognition rate. Meanwhile, most of the jamming recognition methods use single-node methods, which are easily affected by the channel and have a low recognition rate under the low jamming-to-signal ratio (JSR). To solve these problems, a multi-node cooperative jamming recognition method based on deep residual networks was proposed in this paper, and two data fusion algorithms based on hard fusion and soft fusion for jamming recognition were designed. Simulation results show that the use of deep residual networks to replace the original shallow CNN network structure can gain a 6&ndash;14% improvement in the correct recognition rate of jamming signals, and the hard and soft fusion-based methods can significantly improve the correct jamming recognition rate by about 3&ndash;7% and 5&ndash;12%, respectively, under low JSR conditions compared with the existing single-node method

RTK with the Assistance of an IMU-Based Pedestrian Navigation Algorithm for Smartphones

Real-time kinematic (RTK) technique is widely used in modern society because of its high accuracy and real-time positioning. The appearance of Android P and the application of BCM47755 chipset make it possible to use single-frequency RTK and dual-frequency RTK on smartphones. The Xiaomi Mi 8 is the first dual-frequency Global Navigation Satellite System (GNSS) smartphone equipped with BCM47755 chipset. However, the performance of RTK in urban areas is much poorer compared with its performance under the open sky because the satellite signals can be blocked by the buildings and trees. RTK can\u27t provide the positioning results in some specific areas such as the urban canyons and the crossings under an overpass. This paper combines RTK with an IMU-based pedestrian navigation algorithm. We utilize attitude and heading reference system (AHRS) algorithm and zero velocity update (ZUPT) algorithm based on micro electro mechanical systems (MEMS) inertial measurement unit (IMU) in smartphones to assist RTK for the sake of improving positioning performance in urban areas. Some tests are carried out to verify the performance of RTK on the Xiaomi Mi 8 and we respectively assess the performances of RTK with and without the assistance of an IMU-based pedestrian navigation algorithm in urban areas. Results on actual tests show RTK with the assistance of an IMU-based pedestrian navigation algorithm is more robust and adaptable to complex environments than that without it

Regularity Index of Uncertain Random Graph

A graph containing some edges with probability measures and other edges with uncertain measures is referred to as an uncertain random graph. Numerous real-world problems in social networks and transportation networks can be boiled down to optimization problems in uncertain random graphs. Actually, information in optimization problems in uncertain random graphs is always asymmetric. Regularization is a common optimization problem in graph theory, and the regularity index is a fundamentally measurable indicator of graphs. Therefore, this paper investigates the regularity index of an uncertain random graph within the framework of chance theory and information asymmetry theory. The concepts of k-regularity index and regularity index of the uncertain random graph are first presented on the basis of the chance theory. Then, in order to compute the k-regularity index and the regularity index of the uncertain random graph, a simple and straightforward calculating approach is presented and discussed. Furthermore, we discuss the relationship between the regularity index and the k-regularity index of the uncertain random graph. Additionally, an adjacency matrix-based algorithm that can compute the k-regularity index of the uncertain random graph is provided. Some specific examples are given to illustrate the proposed method and algorithm. Finally, we conclude by highlighting some potential applications of uncertain random graphs in social networks and transportation networks, as well as the future vision of its combination with symmetry