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Abstract Background Previously, a chronic social defeat stress (CSDS) model has been widely‐adopted for assessing depressive‐like behaviors in animals. However, there is still room for improvement in the CSDS model to safeguard study accuracy and the welfare of lab rodents. Our study team developed a novel, standardized apparatus to induce CSDS in rodents and assessed the model's practical adaptability. Methods An innovative CSDS cage apparatus and water bottle was designed. To evaluate the effectiveness of the newly developed tools, a variety of animal models, including the tail suspension test (TST), sucrose preference test, forced swimming test (FST), novelty‐suppressed feeding test, female urine sniffing test, and open field test (OFT), were adopted to assess depressive‐like behaviors in mice. Fluoxetine treatment was also administered to observe the reversal effect, as part of the validation. Results The CSDS cage apparatus resulted in the manifestation of depressive‐like behaviors in the model mice. Significant reductions in sucrose preference and urine sniffing time were observed, while the OFT revealed decreased central zone total distance, residence time, and frequency of entry. Moreover, increased immobility was found in the FST and TST. Fluoxetine treatment was found to successfully reverse the modeling effect. Conclusion The CSDS cage apparatus was validated for enhanced usability and addressed the previous challenges of water bottle leakage and lab rodent welfare issues. The consistent results from multiple behavioral tests also supported real‐world application of the apparatus, offering researchers a promising alternative to conventional rodent cages.

Abstract Aiming at the shortcomings of seagull optimization algorithm in the process of searching for optimization, such as slow convergence speed, low precision, easy falling into local optimal, and performance dependent on the selection of parameters, this paper proposes an improved gull optimization algorithm based on multi-strategy fusion based on the analysis of gull population characteristics. Firstly, L–C cascade chaotic mapping is used to initialize the population so that seagulls are more evenly distributed in the initial solution space. Secondly, to improve the algorithm’s global exploration ability in the early stage, the nonlinear convergence factor is incorporated to adjust the position of seagulls in the migration stage. At the same time, the group learning strategy was introduced after the population position update to improve the population quality and optimization accuracy further. Finally, in the late stage of the algorithm, the golden sine strategy of the Levy flight guidance mechanism is used to update the population position to improve the population’s diversity and enhance the local development ability of the algorithm in the late stage. To verify the optimization performance of the improved algorithm, CEC2017 and CEC2022 test suites are selected for simulation experiments, and box graphs are drawn. The test results show that the proposed algorithm has apparent convergence speed, accuracy, and stability advantages. The engineering case results demonstrate the proposed algorithm’s advantages in solving complex problems with unknown search spaces.

Background: Curcumin is a natural compound with extensive pharmacological effects. This research is to verify the optimal dose and administration duration efficacy of curcumin in alleviating the movement symptoms of Parkinson's disease (PD). Methods: Wistar rats were divided into six groups including control, model, levodopa treatment and low/middle/high (40/80/160 mg/kg/d) curcumin treatment groups. After stereotactic brain injection of 6-hydroxydopamine (6-OHDA), curcumin was given by intragastric administration for 2 weeks. To evaluate the drug effect, the rats received behavioral tests including apomorphine (APO)-induced rotation test, rotarod test and open field test. Then the rats were sacrificed and the brain slices including substantia nigra pars compacta (SNc) were used for immunofluorescence staining. Results: After 6-OHDA injection, the model group showed typical movement symptoms including the severe APO-induced rotation to the healthy side, decreased latency in the rotarod with constant or accelerative mode, and decreased total distance and average speed in the open field test. In the results of immunofluorescence staining, the 6-OHDA induced a severe damage of dopaminergic neurons in SNc. The 160 mg/kg/d treatment of curcumin to intervene for 2 weeks alleviated most of the behavioral disorders but the 40/80 mg/kg/d treatment showed limitations. Then, we compared the effect of 1 week intervention to the 2 weeks with 160 mg/kg/d treatment of curcumin to intervene and results indicated that the treatment of 2 weeks could better alleviate the symptoms. Conclusions: Curcumin alleviated 6-OHDA-induced movement symptoms in a PD rat model. Additionally, the effect of curcumin against PD indicated dose and duration dependent and the intervention of 160 mg/kg/d for 2 weeks showed optimally therapeutic effect.

Wild Horse Optimizer (WHO), a new metaheuristic algorithm proposed in recent years, has some weaknesses in solving practical problems, such as low searching accuracy and slow convergence speed. Herein, a Hybrid Multi‐Strategy improved Wild Horse Optimizer (HMSWHO) is proposed, which includes four strategies to improve the optimization capability. The Halton sequence is used to initialize the foal population to make the population more diverse. The adaptive parameter TDR is improved to balance the global exploration and local exploitation. The simplex method is used to improve the worst position of the population. Wild horse escaping behavior is added to improve search efficiency and optimization accuracy. The main innovation strategies are the improvement of TDR and the addition of escaping behavior. To verify the effectiveness of the improved strategies, 12 benchmark test functions, CEC2017, and CEC2021 test functions are selected for simulation experiments. Mechanical design examples are added for optimization, and the optimization results are 16.61%, 1.65%, and 0.21% less than that of WHO. The results show that the improved algorithm has obvious advantages in convergence speed, accuracy, and stability. HMSWHO can be applied to more practical engineering optimization problems and provide new ideas for structural optimization methods.

Background: Agarwood, a type of herbal medicine widely used in Asian countries, is noted in traditional medicine for its intelligence-enhancing effects. Agarwood incense is traditionally administered by oral and nasal inhalation. To verify whether agarwood incense can exert its intelligence-enhancing effects in this way to rescue learning and memory impairment, typical clinical manifestations of dementia, we conducted a set of behavioral tests related to learning and memory.Methods: C57BL/6 mice were divided into six groups. In addition to the control and model groups, we added a donepezil treatment group to evaluate the effect of three different agarwood administration doses. After a week of administration, scopolamine was injected 30 min before each behavioral test to create a learning and memory impairment model. A series of behavioral tests [the Morris water maze test (MWM), the novel object recognition test (NOR), and the step-down test (SDT)] were used to assess their learning ability, as well as their spatial and recognition memory.Results: After scopolamine injection, the model group showed significant learning and memory impairment (i.e., longer latencies, lower crossing times, and lesser distance travelled in the target quadrant in MWM; a lower recognition index in NOR; and longer latencies and higher error times in SDT). The other four treatment groups all showed improvements in these indicators, and the overall therapeutic effect of agarwood was superior.Conclusion: The inhalation administration of agarwood can significantly improve the learning and memory impairment caused by scopolamine in mice, and the therapeutic effect varied between doses.

Abstract To improve the efficiency of the structural optimization design in truss calculation, an improved fruit fly optimization algorithm was proposed for truss structure optimization. The fruit fly optimization algorithm was a novel swarm intelligence algorithm. In the standard fruit fly optimization algorithm, it is difficult to solve the high-dimensional nonlinear optimization problem and easy to fall into the local optimum. To overcome the shortcomings of the basic fruit fly optimization algorithm, the immune algorithm self–non-self antigen recognition mechanism and the immune system learn–memory–forgetting knowledge processing mechanism were employed. The improved algorithm was introduced to the structural optimization. Optimization results and comparison with other algorithms show that the stability of improved fruit fly optimization algorithm is apparently improved and the efficiency is obviously remarkable. This study provides a more effective solution to structural optimization problems.

To improve the efficiency of the structural optimization design in truss calculation, an improved chicken swarm optimization algorithm was proposed for truss structure optimization. The chicken swarm optimization is a novel swarm intelligence algorithm. In the basic chicken swarm optimization algorithm, the concept of combining chaos strategy and reverse learning strategy was introduced in the initialization to ensure the global search ability. And the inertia weighting factor and the learning factor were introduced into the chick position update process, so as to better combine the global and local search. Finally, the overall individual position of the algorithm was optimized by the differential evolution algorithm. The improved algorithm was tested by multipeak function and applied to the truss simulation experiment. The study provided a new method for the truss structure optimization.