Ex Vivo Expanded Hematopoietic Stem Cells Overcome the MHC Barrier in Allogeneic Transplantation

SummaryThe lack of understanding of the interplay between hematopoietic stem cells (HSCs) and the immune system has severely hampered the stem cell research and practice of transplantation. Major problems for allogeneic transplantation include low levels of donor engraftment and high risks of graft-versus-host disease (GVHD). Transplantation of purified allogeneic HSCs diminishes the risk of GVHD but results in decreased engraftment. Here we show that ex vivo expanded mouse HSCs efficiently overcame the major histocompatibility complex barrier and repopulated allogeneic-recipient mice. An 8-day expansion culture led to a 40-fold increase of the allograft ability of HSCs. Both increased numbers of HSCs and culture-induced elevation of expression of the immune inhibitor CD274 (B7-H1 or PD-L1) on the surface of HSCs contributed to the enhancement. Our study indicates the great potential of utilizing ex vivo expanded HSCs for allogeneic transplantation and suggests that the immune privilege of HSCs can be modulated

Three-Dimensional Localization Algorithm of WSN Nodes Based on RSSI-TOA and Single Mobile Anchor Node

Aimed at the shortcomings of low localization accuracy of the fixed multianchor method, a three-dimensional localization algorithm for wireless sensor network nodes is proposed in this paper, which combines received signal strength indicator (RSSI) and time of arrival (TOA) ranging information and single mobile anchor node. A mobile anchor node was introduced in the proposed three-dimensional localization algorithm for wireless sensor networks firstly, and the mobile anchor node moves according to the Gauss–Markov three-dimensional mobility model. Then, based on the idea of using RSSI ranging in the near end and TOA ranging in the far end, a ranging method combining RSSI and TOA ranging information is proposed to obtain the precise distance between the anchor node and the unknown node. Finally, the maximum-likelihood estimation method is used to estimate the position of unknown nodes based on the obtained ranging values. The MATLAB simulation results show that the proposed algorithm had a higher localization accuracy and lower localization energy consumption compared with the traditional RSSI localization method or TOA localization method

A Maximum Power Point Tracking Algorithm of Load Current Maximization-Perturbation and Observation Method with Variable Step Size

A photovoltaic power supply with a simple structure and high tracking efficiency is needed in self-powered, wireless sensor networks. First, a maximum power point tracking (MPPT) algorithm, including the load current maximization-perturbation and observation (LCM-P&O) methods, with a fixed step size, is proposed by integrating the traditional load current maximization (LCM) method and perturbation and observation (P&O) method. By sampling the changes of load current and photovoltaic cell input current once the disturbance is applied, the pulse width modulation (PWM) regulation mode, i.e., increasing or reducing, can be determined in the next process. Then, the above algorithm is improved by using the variable step size strategy. By comparing the difference between the absolute value of the observed current value and the theoretical current value at the maximum power point of the photovoltaic cell with the set threshold value, the variable step size for perturbation is determined. MATLAB simulation results show that the LCM-P&O method, with a variable step size, has faster convergence speed and higher tracking accuracy. Finally, the two MPPT algorithms are tested and analyzed under constant voltage source input and indoor fluorescent lamp illumination through an actual circuit, respectively. The experimental results show that the LCM-P&O method with variable step size has a higher tracking efficiency, about 90%–92%, and has higher stability and lower power consumption

Ant colony optimization for Cuckoo Search algorithm for permutation flow shop scheduling problem

A Cuckoo Search (CS) algorithm based on ant colony algorithm is proposed for scheduling problem in permutation flow shop scheduling problem (PFSP). When the raised CS algorithm obtains the position of the bird nest to be updated, it is used as a set of initial solution of the ant colony optimization algorithm (ACO), and ACO algorithm search optimization is performed in a very small range. After that, the solution obtained by the ACO search is taken as a new candidate solution, compared with the candidate bird nest according to the fitness degree. When the candidate solution of the ACO search optimization is better than the one generated by the Lévy flight, the latter is replaced. Finally, the CS algorithm is selected, changing the new bird nest position according to the abandonment probability. The updated position tends to be more optimal, which improves the quality of the solution as well as the convergence speed and accuracy of the algorithm. Comparing the performance of the proposed algorithm with the standard Cuckoo one, by testing function, the optimized performance was verified. Finally, the Car benchmark test served as test data, and the performance in the PFSP was compared. The effectiveness and superiority in the algorithm in solving problem were confirmed

UWB Positioning Algorithm Based on Fuzzy Inference and Adaptive Anti-NLOS Kalman Filtering

To reduce the influence of non-line-of-sight (NLOS) errors in the ultra-wideband (UWB) positioning process, a UWB positioning algorithm based on fuzzy inference and adaptive anti-NLOS Kalman filtering (KF) was proposed in this paper. First of all, the NLOS errors of the channel impulse response (CIR) signal characteristics were estimated by the fuzzy inference algorithm and then initially mitigated. Next, an adaptive anti-NLOS KF algorithm was developed to perform a second mitigation on the ranging errors after mitigation of the NLOS errors with the fuzzy inference, thereby further raising the range estimation accuracy. At last, the range estimation information after error mitigation was taken as the ranging information of the LS positioning algorithm for target localization. In the static positioning experiment, the probability of producing an error range of less than 19.1 cm with the positioning algorithm combining fuzzy inference with adaptive anti-NLOS KF was 0.93, which was much better than the positioning algorithm based on fuzzy inference and the adaptive anti-NLOS KF positioning algorithm. In the dynamic positioning experiment, compared with the adaptive anti-NLOS KF positioning algorithm, the RMSE was reduced by 43.31% in the overall positioning. Furthermore, compared with those of the positioning algorithm based on fuzzy inference, the RMSEs in overall positioning were lowered by 12.89%. The positioning accuracy was improved significantly

A DDQN Path Planning Algorithm Based on Experience Classification and Multi Steps for Mobile Robots

Constrained by the numbers of action space and state space, Q-learning cannot be applied to continuous state space. Targeting this problem, the double deep Q network (DDQN) algorithm and the corresponding improvement methods were explored. First of all, to improve the accuracy of the DDNQ algorithm in estimating the target Q value in the training process, a multi-step guided strategy was introduced into the traditional DDQN algorithm, for which the single-step reward was replaced with the reward obtained in continuous multi-step interactions of mobile robots. Furthermore, an experience classification training method was introduced into the traditional DDQN algorithm, for which the state transition generated by the mobile robot–environment interaction was divided into two different types of experience pools, and experience pools were trained by the Q network, and the sampling proportions of the two experience pools were updated through the training loss. Afterward, the advantages of a multi-step guided DDQN (MS-DDQN) algorithm and experience classification DDQN (EC-DDQN) algorithm were combined to develop a novel experience classification multi-step DDQN (ECMS-DDQN) algorithm. Finally, the path planning of these four algorithms, including DDQN, MS-DDQN, EC-DDQN, and ECMS-DDQN, was simulated on the OpenAI Gym platform. The simulation results revealed that the ECMS-DDQN algorithm outperforms the other three in the total return value and generalization in path planning

An Improved Three-dimensional DV-Hop Localization Algorithm Optimized by Adaptive Cuckoo Search Algorithm

Aiming at the low accuracy of DV-Hop localization algorithm in three-dimensional localization of wireless sensor network, a DV-Hop localization algorithm optimized by adaptive cuckoo search algorithm was proposed in this paper. Firstly, an improved DV-Hop algorithm was proposed, which can reduce the localization error of DV-Hop algorithm by controlling the network topology and improving the method for calculating average hop distance. Meanwhile, aiming at the slow convergence in traditional cuckoo search algorithm, the adaptive strategy was improved for the step search strategy and the bird's nest recycling strategy. And the adaptive cuckoo search algorithm was introduced to the process of node localization to optimize the unknown node position estimation. The experiment results show that compared with the improved DV-Hop algorithm and the traditional DV-Hop algorithm, the DV-Hop algorithm optimized by adaptive cuckoo search algorithm improved the localization accuracy and reduced the localization errors

UWB Positioning Algorithm Based on Fuzzy Inference and Adaptive Anti-NLOS Kalman Filtering

To reduce the influence of non-line-of-sight (NLOS) errors in the ultra-wideband (UWB) positioning process, a UWB positioning algorithm based on fuzzy inference and adaptive anti-NLOS Kalman filtering (KF) was proposed in this paper. First of all, the NLOS errors of the channel impulse response (CIR) signal characteristics were estimated by the fuzzy inference algorithm and then initially mitigated. Next, an adaptive anti-NLOS KF algorithm was developed to perform a second mitigation on the ranging errors after mitigation of the NLOS errors with the fuzzy inference, thereby further raising the range estimation accuracy. At last, the range estimation information after error mitigation was taken as the ranging information of the LS positioning algorithm for target localization. In the static positioning experiment, the probability of producing an error range of less than 19.1 cm with the positioning algorithm combining fuzzy inference with adaptive anti-NLOS KF was 0.93, which was much better than the positioning algorithm based on fuzzy inference and the adaptive anti-NLOS KF positioning algorithm. In the dynamic positioning experiment, compared with the adaptive anti-NLOS KF positioning algorithm, the RMSE was reduced by 43.31% in the overall positioning. Furthermore, compared with those of the positioning algorithm based on fuzzy inference, the RMSEs in overall positioning were lowered by 12.89%. The positioning accuracy was improved significantly