The optimal systemic therapy strategy for advanced epithelial ovarian cancer (EOC) remains unclear. We performed a systematic review and meta-analysis to assess oncologic outcomes and toxicity of bevacizumab combination treatment in advanced EOC.
 
  We conducted an electronic search of all phase 2 and 3 clinical trials involving bevacizumab combination therapy in advanced-stage EOC between 2010 and March 2020, using Embase, Medline, Epub Ahead of Print, Cochrane for clinical trials, Cochrane Database of Systematic Reviews, Web of Science and clinicaltrials.gov databases. Progression-free survival (PFS), overall survival (OS), and their hazard ratios (HR) when available were extracted. Pooled HR were calculated for each efficacy endpoint in the meta-analysis using inverse variance weighted method. Bias was assessed using the Cochrane Collaboration Risk of Bias I (ROB1) tool for randomized controlled trials.
 
  Thirty-five studies were included in the qualitative analysis and eight studies in the quantitative sne setting, bevacizumab was associated with improved survival in the recurrent setting.This paper investigates the adaptive neural tracking control of the strict-feedback nonlinear systems, where the states are measured in an event-triggered manner so as to save the communication resources. As the neural networks (NNs) account for the unknown dynamics of the system, the minimum learning parameters (MLPs) are extracted from the weights of the NNs and the upper bounds of the disturbances. The estimates of the MLPs are updated in an event-triggered manner to ensure the approximation ability of the NNs and the stability of the closed-loop system. An adaptive neural model is established to substitute for the original strict-feedback system and direct the design of the backstepping-based control laws.  https://www.selleckchem.com/products/sulfopin.html  The states of this adaptive model are reset to the measured states of the original system when the triggering condition is violated. The triggering condition is constructed in the compound form and with the adaptive threshold. The dead-zone operator is involved to avoid the accumulation of triggering instants. In this paper, we notice the problem of "jumps of virtual control laws" for the event-triggered control (ETC) in the backstepping frame, and a detailed formulaic definition is given in section 2.2. To solve this problem, the first-order filters are fabricated to provide the continuous substitutes for virtual control laws. In addition, the "complexity explosion" generated by direct differentiating of virtual control laws can be averted. Through the proposed scheme, the closed-loop system can be viewed as an impulsive dynamic system, and the semi-globally uniformly ultimate boundedness (SGUUB) of all the errors is proved. Finally, two examples validate the feasibility of the proposed control scheme.This paper focuses on the mean square cluster consensus of nonlinear multi-agent systems with Markovian switching topologies and communication noise via pinning control technique. Network topology can take weaker conditions in each cluster but an extra balanced condition is also needed. A time-varying control gain will be introduced to eliminate the effect of stochastic noise. For the case of fixed topology, if the induced digraph of each cluster has a directed spanning tree, the sufficient conditions for the mean square cluster consensus can be obtained. For the case of Markovian switching topologies, if the induced digraph of union of the Laplacian matrix of each mode has a directed spanning tree, the mean square cluster consensus conclusion can be derived. Particularly, if the elements of transition probability of Markov chain are partly unknown, we can also obtain the same conclusion under the same conditions. Finally, two examples are given to illustrate our results.The main purpose of this paper is design and implementation of a new linear observer for an attitude and heading reference system (AHRS), which includes three-axis accelerometers, gyroscopes, and magnetometers in the presence of sensors and modeling uncertainties. Since the increase of errors over time is the main difficulty of low-cost micro electro mechanical systems (MEMS) sensors producing instable on-off bias, scale factor (SF), nonlinearity and random walk errors, development of a high-precision observer to improve the accuracy of MEMS-based navigation systems is considered. First, the duality between controller and estimator in a linear system is presented as the base of design method. Next, Legendre polynomials together with block-pulse functions are applied for the solution of a common linear time-varying control problem. Through the duality theory, the obtained control solution results in the block-pulse functions and Legendre polynomials observer (BPLPO). According to product properties of the hybrid functions in addition to the operational matrices of integration, the optimal control problem is simplified to some algebraic equations which particularly fit with low-cost implementations. The improved performance of the MEMS AHRS owing to implementation of BPLPO has been assessed through vehicle field tests in urban area compared with the extended Kalman filter (EKF).The adaptive integrated guidance and control issue of missiles with less sensor requirement is investigated in the optimal stabilization problem of an uncertain nonlinear system subjected to state and input constraints. The nonlinear system with partially unmeasurable states is transformed into the non-strict feedback form, firstly. Then, an adaptive observer is designed to approximate the full states, where a disturbance estimator is incorporated to suppress the unmatched external disturbances. Next, by employing a Barrier Lyapunov Function (BLF) and an auxiliary system to tackle the multiple constraints, an adaptive feedforward controller is raised to reduce the stabilization issue of the nonlinear system in non-strict feedback form to the equivalent control problem of an affine nonlinear system. Subsequently, an optimal controller is derived by utilizing adaptive dynamic programming (ADP) theory. The system stability is rigorously proved by using Lyapunov theory. Finally, simulations are performed to validate the effectiveness of the proposed control strategy.