DNA vaccination with a gene encoding Toxoplasma gondii Deoxyribose Phosphate Aldolase (TgDPA) induces partial protective immunity against lethal challenge in mice

BACKGROUND: Toxoplasma gondii is an obligate intracellular parasite that causes a pathological status known as toxoplasmosis, which has a huge impact on human and animal health. Currently, the main control strategy depends on the usage of drugs that target the acute stage of the infection, however, drawbacks were encountered while applying this method; therefore, development of an alternative effective method would be important progress. Deoxyribose Phosphate Aldolase (TgDPA) plays an important role supporting cell invasion and providing energy for the parasite. METHODS: TgDPA was expressed in Escherichia coli and the purified recombinant protein was used to immunize rats. The antibodies obtained were used to verify in vitro expression of TgDPA. The vector pVAX1 was utilized to formulate a DNA vaccine designated as pTgDPA, which was used to evaluate the immunological changes and the level of protection against challenge with the virulent RH strain of T. gondii. RESULTS: DNA vaccine, TgDPA revealed that it can induce a strong humoral as well as cellular mediated response in mice. These responses were a contribution of T(H)1, T(H)2 and T(H)17 type of responses. Following challenge, mice immunized with TgDPA showed longer survival rates than did those in control groups. CONCLUSIONS: Further investigation regarding TgDPA is required to shed more light on its immunogenicity and its possible selection as a vaccine candidate

Multi-objective optimization design for a battery pack of electric vehicle with surrogate models

In this investigation, a systematic surrogate-based optimization design framework for a battery pack is presented. An air-cooling battery pack equipped on electric vehicles is first designed. Finite element analysis (FEA) results of the baseline design show that global maximum stresses under x-axis and y-axis transient acceleration shock condition are both above the tensile limit of material. Selecting the panel and beam thickness of battery pack as design variables, with global maximum stress constraints in shock cases, a multi-objective optimization problem is implemented using metamodel technique and multi-objective particle-swarm-optimization (MOPSO) algorithm to simultaneously minimize the total mass and maximize the restrained basic frequency. It is found that 2nd order polynomial response surface (PRS), 3rd order PRS and radial basis function (RBF) are the most accurate and appropriate metamodels for restrained basic frequency, global maximum stresses under x-axis and y-axis shock conditions respectively. Results demonstrate that all the optimal solutions in Pareto Frontier have heavier weight and lower frequency compared with baseline design due to the restriction of global maximum stress response. Finally, two optimal schemes, “Knee Point” and “lightest weight”, satisfied both of the stress constraint conditions, show great consistency with FEA results and can be selected as alternative improved schemes

DYNAMIC ANALYSIS OF THE RIGID-FLEXIBLE EXCAVATOR MECHANISM BASED ON VIRTUAL PROTOTYPE

In this paper, the excavator’s dynamic performance is considered together with the study of its trajectory, stress distribution and vibration. Many researchers have focused their study on the kinematics principle while a few others focused their work on dynamic performance, especially the vibration analysis. Previous studies of dynamic performance analysis have ignored the vibration effects. To address these challenges, the rigid-flexible coupling model of the excavator attachment is established and carried out based on virtual prototyping in this study. The dipper handle, the boom and the hoist rope are modeled as a flexible multi-body system for structural strength. The other components are modeled as a rigid multi-body system to catch the dynamic characteristics. The results show that the number of flexible bodies has little effect on the excavation trajectory. The maximum stress determined for the dipper handle and the boom are 96.45 MPa and 212.24 MPa, respectively. The dynamic performance of the excavator is greatly influenced by the clearance and is characterized by two phases: as the clearance decreases, the dynamic response decreases at first and then increases

Iterative learning control for impulsive multi-agent systems with varying trial lengths

In this paper, we introduce iterative learning control (ILC) schemes with varying trial lengths (VTL) to control impulsive multi-agent systems (I-MAS). We use domain alignment operator to characterize each tracking error to ensure that the error can completely update the control function during each iteration. Then we analyze the system’s uniform convergence to the target leader. Further, we use two local average operators to optimize the control function such that it can make full use of the iteration error. Finally, numerical examples are provided to verify the theoretical results

Iterative learning control for multi-agent systems with impulsive consensus tracking

In this paper, we adopt D-type and PD-type learning laws with the initial state of iteration to achieve uniform tracking problem of multi-agent systems subjected to impulsive input. For the multi-agent system with impulse, we show that all agents are driven to achieve a given asymptotical consensus as the iteration number increases via the proposed learning laws if the virtual leader has a path to any follower agent. Finally, an example is illustrated to verify the effectiveness by tracking a continuous or piecewise continuous desired trajectory

A duplication-free quantum neural network for universal approximation

The universality of a quantum neural network refers to its ability to approximate arbitrary functions and is a theoretical guarantee for its effectiveness. A non-universal neural network could fail in completing the machine learning task. One proposal for universality is to encode the quantum data into identical copies of a tensor product, but this will substantially increase the system size and the circuit complexity. To address this problem, we propose a simple design of a duplication-free quantum neural network whose universality can be rigorously proved. Compared with other established proposals, our model requires significantly fewer qubits and a shallower circuit, substantially lowering the resource overhead for implementation. It is also more robust against noise and easier to implement on a near-term device. Simulations show that our model can solve a broad range of classical and quantum learning problems, demonstrating its broad application potential.Comment: 15 pages, 10 figure