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Abstract Early social isolation (SI) leads to various abnormalities in emotion and behavior during adulthood. However, the negative impact of SI on offspring remains unclear. This study has discovered that paternal early SI causes social memory deficits and anxiety-like behavior in F1 young adult mice, with alterations of myelin and synapses in the medial prefrontal cortex (mPFC). The 2-week SI in the F1 progeny exacerbates social memory impairment and hypomyelination in the mPFC. Furthermore, the down-regulation of miR-124, a key inhibitor of myelinogenesis, or over-expression of its target gene Nr4a1 in the mPFC of the F1 mice improves social interaction ability and enhances oligodendrocyte maturation and myelin formation. Mechanistically, elevated levels of miR-124 in the sperm of paternal SI mice are transmitted epigenetically to offspring, altering the expression levels of miR-124/Nr4a1/glucocorticoid receptors in mPFC oligodendrocytes. This, in turn, impedes the establishment of myelinogenesis-dependent social behavior. This study unveils a novel mechanism through which miR-124 mediates the intergenerational effects of early isolation stress, ultimately impairing the establishment of social behavior and neurodevelopment.

Summary: Social cooperation is fundamentally important for group animals but rarely studied in mice because of their natural aggressiveness. Here, we present a new water-reward assay to investigate mutualistic cooperative behavior in mice. We describe the construction of the apparatus and provide details of the procedures and analysis for investigators to characterize and quantify the mutualistic cooperative behavior. This protocol has been validated in mice and can be used for investigating mechanisms of cooperation.For complete details on the use and execution of this protocol, please refer to Zhang et al. and Wang et al.1,2 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

As a novel building forms, prefabricated buildings offer unique advantages in terms of safety and speed. Focusing on the problems of target tracking in the threaded sleeve production line of prefabricated buildings, such as low accuracy rate, slow recognition speed, and difficulty in dealing with object occlusion and environment changes, a target tracking and detection approach based on the improved DeepSort algorithm is proposed. The real-time tracking of the equipment of the prefabricated building threaded sleeve production line is realized. The improved YOLOv5 replaces the detection algorithm in the track phase of the original DeepSort algorithm, which is used for real-time tracking and detecting the equipment. The experimental results show that the tracking accuracy of the improved algorithm is 88%, 70.5%, and 64.8% for three videos of different lengths, respectively. The tracking detection frame rate reached 71.95 fps in the three experiments of video tracking. The improvement of the frame rate of the improved algorithm is over 100% compared with the traditional tracking algorithms. The improved algorithm enhances the accuracy of target tracking in prefabricated building threaded sleeve production line and improves the production efficiency of prefabricated building threaded sleeve production line effectively.

This paper proposed a lightweight design method for orthotropic steel bridge deck U-ribs. Firstly, the U-rib is analyzed in static structural properties. An analytic hierarchy model was constructed to maximize the stiffness for multi-objective topology optimization. Secondly, a parametric reconfiguration model of the topologically optimized U-rib was established. Finally, static tests are carried out and the performance of the 3D printing scale model was tested. The test results show that the method is effective. Weight of the original model with U-rib is 99.8 kg, and the weight of its optimized model is 76.8 kg. The method achieved a weight reduction of 23% for the U-ribs, and the stiffness still met the permitted conditions. The orthotropic steel bridge deck composed of the optimized U-ribs effectively reduced the weight by 150 kg on the basis of basically unchanged stiffness. The method provides an important reference for the lightweight transformation of assembled bridge structures.

Owing to their excellent elasticities and adaptability as sensing materials, ionic hydrogels exhibit significant promise in the field of intelligent wearable devices. Nonetheless, molecular chains within the polymer network of hydrogels are susceptible to damage, leading to crack extension. Hence, we drew inspiration from the composite structure of the human dermis to engineer a composite hydrogel, incorporating dopamine-modified elastic fibers as a reinforcement. This approach mitigates crack expansion and augments sensor sensitivity by fostering intermolecular forces between the dopamine on the fibers, the hydrogel backbone, and water molecules. The design of this composite hydrogel elevates its breaking tensile capacity from 35 KJ to 203 KJ, significantly enhancing the fatigue resistance of the hydrogel. Remarkably, its electrical properties endure stability even after 2000 cycles of testing, and it manifests heightened sensitivity compared to conventional hydrogel configurations. This investigation unveils a novel method for crafting composite-structured hydrogels.

Mature broccoli has large flower balls and thick stems. Therefore, manual broccoli picking is laborious and energy-consuming. However, the big spheroid vegetable-picking manipulator has a complex structure and poor enveloping effect and easily causes mechanical damage. Therefore, a broccoli flower ball-picking manipulator with a compact structure and simple control system was designed. The manipulator was smart in structure and stable in configuration when enveloped in flower balls. First, a physical damage test was carried out on broccoli according to the underactuated manipulator’s design scheme. The maximum surface pressure of the flower ball was 30 N, and the maximum cutting force of the stem was 35 N. Then, kinematic analysis was completed, and the statical model of the underactuated mechanism was established. The dimension of the underactuated mechanism for each connecting rod was determined based on the damage test results and design requirements. The sizes of each connecting rod were 50 cm, 90 cm, 50 cm, 90 cm, 50 cm, 60 cm, and 65 cm. The statical model calculated the required thrust of the underactuated mechanism as 598.66–702.88 N. Then, the manipulator was simulated to verify its reliability of the manipulator. Finally, the manipulator’s motion track, speed, and motor speed were determined in advance in the laboratory environment. One-hundred picking tests were carried out on mature broccoli with a 135–185 mm diameter. Results showed that the manipulator had an 84% success rate in picking and a 100% lossless rate. The fastest single harvest time in the test stand was 11.37 s when the speed of the robot arm was 3.4 m/s, and the speed of the stepper motor was 60 r/min.

Intelligent video surveillance based on artificial intelligence, image processing, and other advanced technologies is a hot topic of research in the upcoming era of Industry 5.0. Currently, low recognition accuracy and low location precision of devices in intelligent monitoring remain a problem in production lines. This paper proposes a production line device recognition and localization method based on an improved YOLOv5s model. The proposed method can achieve real-time detection and localization of production line equipment such as robotic arms and AGV carts by introducing CA attention module in YOLOv5s network model architecture, GSConv lightweight convolution method and Slim-Neck method in Neck layer, add Decoupled Head structure to the Detect layer. The experimental results show that the improved method achieves 93.6% Precision, 85.6% recall, and 91.8% mAP@0.5, and the Pascal VOC2007 public dataset test shows that the improved method effectively improves the recognition accuracy. The research results can substantially improve the intelligence level of production lines and provide an important reference for manufacturing industries to realize intelligent and digital transformation.