Alveolar echinococcosis (AE) is a parasitic disease caused by E. multilocularis metacestodes and it is highly prevalent in the northern hemisphere. We have previously found that vaccination with E. multilocularis Leucine aminopeptidase (EM-LAP) induced specific immune response and had an inhibiting effect on the parasites. In this study, the therapeutic effect of recombinant EM-LAP (rEM-LAP) on AE was evaluated and verified using Ubenimex, a broad-spectrum inhibitor of LAP. The results reveal that rEM-LAP could inhibit cyst growth and invasion and induce specific immunity response in BALB/c mice infected with E. multilocularis protoscoleces. The ultrasonic, MRI, and morphological results show that treatment with rEM-LAP inhibits E. multilocularis infection and reduces cyst weight, number, fibrosis and invasion. The same effect is observed for the treatment with Ubenimex by inhibiting LAP activity. The indirect ELISA shows that rEM-LAP could induce specific immunity response and produce high levels of IgG, IgG1, IgG2a, IgM, and IgA, and the serum levels of IFN-γ and IL-4 are significantly increased compared to the control groups, indicating that treatment with rEM-LAP leads to a Th1 and Th2 mixed-type immune response. This study suggests that EM-LAP could be a potential therapeutic target of E. multilocularis infection.

The explosive growth of users and applications in IoT environments has promoted the development of cloud computing. In the cloud computing environment, task scheduling plays a crucial role in optimizing resource utilization and improving overall performance. However, effective task scheduling remains a key challenge. Traditional task scheduling algorithms often rely on static heuristics or manual configuration, limiting their adaptability and efficiency. To overcome these limitations, there is increasing interest in applying reinforcement learning techniques for dynamic and intelligent task scheduling in cloud computing. How can reinforcement learning be applied to task scheduling in cloud computing? What are the benefits of using reinforcement learning-based methods compared to traditional scheduling mechanisms? How does reinforcement learning optimize resource allocation and improve overall efficiency? Addressing these questions, in this paper, we propose a Q-learning-based Multi-Task Scheduling Framework (QMTSF). This framework consists of two stages: First, tasks are dynamically allocated to suitable servers in the cloud environment based on the type of servers. Second, an improved Q-learning algorithm called UCB-based Q-Reinforcement Learning (UQRL) is used on each server to assign tasks to a Virtual Machine (VM). The agent makes intelligent decisions based on past experiences and interactions with the environment. In addition, the agent learns from rewards and punishments to formulate the optimal task allocation strategy and schedule tasks on different VMs. The goal is to minimize the total makespan and average processing time of tasks while ensuring task deadlines. We conducted simulation experiments to evaluate the performance of the proposed mechanism compared to traditional scheduling methods such as Particle Swarm Optimization (PSO), random, and Round-Robin (RR). The experimental results demonstrate that the proposed QMTSF scheduling framework outperforms other scheduling mechanisms in terms of the makespan and average task processing time.