Comprehensive Planning of Laboratory Equipment Based on Genetic Algorithms.

Laboratory equipment planning is a very important task in modern enterprise management. Laboratory equipment planning by computer algorithm is a very complex NP-hard combinatorial optimization problem, so it is impossible to find an accurate algorithm in polynomial time. In this study, an improved genetic algorithm is used to solve and analyze the comprehensive planning of laboratory equipment. After analyzing the traditional heuristic algorithm and genetic algorithm to solve the simple laboratory equipment planning problem, the simple laboratory equipment planning is designed and implemented according to the principle of the heuristic algorithm. Finally, the algorithm is implemented in Python. A general equipment planning based on genetic algorithm with two selection operators is realized. Two constraints of test start and completion time are given. In the scenario of using multiple test equipment for a test project, the possible solutions of laboratory equipment planning under given constraints are analyzed. The efficiency coefficient is not necessarily a constant, it is related to the output characteristics of energy equipment. Three independent planning algorithms are used to solve the actual test requirements. One is the planning method based on manual test scheduling in the test cycle of experimental instruments, the other is the genetic algorithm for gene location crossover operator, and the third is the genetic algorithm for experimental part crossover operator. The planning solutions obtained by the three algorithms are compared from three aspects: the shortest time to complete the test, the calculation time of the algorithm, and the utilization of the test equipment.

Progranulin deficiency promotes persistent neuroinflammation and causes regional pathology in the hippocampus following traumatic brain injury.

Traumatic brain injury (TBI) can be progressive and can lead to the development of a long-term complication termed chronic traumatic encephalopathy. The mechanisms underlying the progressive changes are still unknown; however, studies have suggested that microglia-mediated neuroinflammation in response to TBI may play a fundamental role. This study aimed to determine whether progranulin (PGRN), a major modulator of microglial activity, plays a role in the progressive damage following TBI. PGRN-deficient and wild-type mice were subjected to controlled cortical impact and were observed neuropathologically after 3 days, 7 days, and 5 months. Compared to sham and wild-type mice, the PGRN-deficient mice showed overall stronger microgliosis and astrocytosis. The astrocytosis involved broader areas than the microgliosis and was more prominent in the basal ganglia, hippocampus, and internal capsule in PGRN-deficient mice. Ongoing neuronal death was uniquely observed in the hippocampal CA3 region of PGRN-deficient mice at 5 months after TBI, accompanying the regional chronic microgliosis and astrocytosis involving the CA3 commissural pathway. In addition, there was M1 microglial polarization in the pericontusional area with activated TLR4/MyD88/NF-κB signaling; however, the hippocampus showed only mild M1 polarization 7 days after TBI. Lastly, Morris water maze tests showed PGRN-deficient mice had poorer spatial learning and memory 5 months after TBI than wild-type or sham mice. The data indicated the PGRN deficiency caused TBI progression by promoting persistent microgliosis with microglial polarization and astrocytosis, as well as regional pathology in the hippocampus. The study suggests that PGRN should be evaluated as a potential therapy for TBI.