(-)-Epicatechin gallate ameliorates cyprodinil-induced cardiac developmental defects through inhibiting aryl hydrocarbon receptor in zebrafish.

BACKGROUND: Cyprodinil is a widely used fungicide with broad-spectrum activity, but it has been associated with cardiac abnormalities. (-)-Epicatechin gallate (ECG), a natural polyphenolic compound, has been shown to possess protective properties in cardiac development. METHODS: In this study, we investigated whether ECG could mitigate cyprodinil-induced heart defects using zebrafish embryos as a model. Zebrafish embryos were exposed to cyprodinil with or without ECG. RESULTS: Our results demonstrated that ECG significantly improved the survival rate, embryo movement, and hatching delay induced by cyprodinil. Furthermore, ECG effectively ameliorated cyprodinil-induced cardiac developmental toxicity, including pericardial anomaly and impairment of cardiac function. Mechanistically, ECG attenuated the cyprodinil-induced alterations in mRNA expression related to cardiac development, such as amhc, vmhc, tbx5, and gata4, as well as calcium ion channels, such as ncx1h, atp2a2a, and cdh2. Additionally, ECG was found to inhibit the activity of the aryl hydrocarbon receptor (AhR) signaling pathways induced by cyprodinil. CONCLUSIONS: In conclusion, our findings provide evidence for the protective effects of ECG against cyprodinil-induced cardiac developmental toxicity, mediated through the inhibition of AhR activity. These findings contribute to a better understanding of the regulatory mechanisms and safe utilization of pesticide, such as cyprodinil.

Characterization and function of PTEN-induced putative kinase 1 (PINK1) in process of Zinc alleviates hepatic lipid deposition of yellow catfish (Pelteobagrus fulvidraco).

PTEN-induced putative kinase 1 (PINK1) is a key regulator of mitophagy, however, the relevant information remains poorly understood on aquatic animals. Here, a PINK1 gene was cloned, characterized and functionally studied in yellow catfish. PINK1 encoded a protein containing 570 amino acids, 2 functional domains. High fat (15.66%) fed fish showed a downregulation trend of liver PINK1 expression than that of normal fat (10.14%) group, and was reversed by the addition of Zn. In the in vitro study, high fat (HF) can increase lipid deposition and decrease by addition Zn (HFZ) in hepatocytes, whereas above phenomena reversed by overexpression/interference of PINK1, respectively. In addition, the addition of Zn can significantly affect mitochondrial activity, increase mitophagy, and improve the antioxidant activity of hepatocytes. Together, these findings illustrated that yellow catfish PINK1 is conserve, and it participated in mitochondria control of fish. These findings indicate Zn could alleviate high fat-induced hepatic lipid deposition of fish by activating PINK1-mediated mitophagy and provide basis for further exploring new approach for decreasing lipid deposition in fish products during aquaculture.

Grasping detection of dual manipulators based on Markov decision process with neural network.

With the development of artificial intelligence, robots are widely used in various fields, grasping detection has been the focus of intelligent robot research. A dual manipulator grasping detection model based on Markov decision process is proposed to realize the stable grasping with complex multiple objects in this paper. Based on the principle of Markov decision process, the cross entropy convolutional neural network and full convolutional neural network are used to parameterize the grasping detection model of dual manipulators which are two-finger manipulator and vacuum sucker manipulator for multi-objective unknown objects. The data set generated in the simulated environment is used to train the two grasping detection networks. By comparing the grasping quality of the detection network output the best grasping by the two grasping methods, the network with better detection effect corresponding to the two grasping methods of two-finger and vacuum sucker is determined, and the dual manipulator grasping detection model is constructed in this paper. Robot grasping experiments are carried out, and the experimental results show that the proposed dual manipulator grasping detection method achieves 90.6% success rate, which is much higher than the other groups of experiments. The feasibility and superiority of the dual manipulator grasping detection method based on Markov decision process are verified.

Local Path Planning for Mobile Robots Based on Fuzzy Dynamic Window Algorithm.

Due to the increased employment of robots in modern society, path planning methods based on human-robot collaborative mobile robots have been the subject of research in both academia and industry. The dynamic window approach used in the research of the robot local path planning problem involves a mixture of fixed weight coefficients, which makes it hard to deal with the changing dynamic environment and the issue of the sub-optimal global planning paths that arise after local obstacle avoidance. By dynamically modifying the combination of weight coefficients, we propose, in this research, the use of fuzzy control logic to optimize the evaluation function's sub-functions and enhance the algorithm's performance through the safe and dynamic avoidance of obstacles. The global path is introduced to enhance the dynamic window technique's ability to plan globally, and important points on the global path are selected as key sub-target sites for the local motion planning phase of the dynamic window technique. The motion position changes after local obstacle avoidance to keep the mobile robot on the intended global path. According to the simulation results, the enhanced dynamic window algorithm cuts planning time and path length by 16% and 5%, respectively, while maintaining good obstacle avoidance and considering a better global path in the face of various dynamic environments. It is difficult to achieve a local optimum using this algorithm.

Multi-Objective Location and Mapping Based on Deep Learning and Visual Slam.

Simultaneous localization and mapping (SLAM) technology can be used to locate and build maps in unknown environments, but the constructed maps often suffer from poor readability and interactivity, and the primary and secondary information in the map cannot be accurately grasped. For intelligent robots to interact in meaningful ways with their environment, they must understand both the geometric and semantic properties of the scene surrounding them. Our proposed method can not only reduce the absolute positional errors (APE) and improve the positioning performance of the system but also construct the object-oriented dense semantic point cloud map and output point cloud model of each object to reconstruct each object in the indoor scene. In fact, eight categories of objects are used for detection and semantic mapping using coco weights in our experiments, and most objects in the actual scene can be reconstructed in theory. Experiments show that the number of points in the point cloud is significantly reduced. The average positioning error of the eight categories of objects in Technical University of Munich (TUM) datasets is very small. The absolute positional error of the camera is also reduced with the introduction of semantic constraints, and the positioning performance of the system is improved. At the same time, our algorithm can segment the point cloud model of objects in the environment with high accuracy.

A siamese network-based approach for vehicle pose estimation.

We propose a deep learning-based vehicle pose estimation method based on a monocular camera called FPN PoseEstimateNet. The FPN PoseEstimateNet consists of a feature extractor and a pose calculate network. The feature extractor is based on Siamese network and a feature pyramid network (FPN) is adopted to deal with feature scales. Through the feature extractor, a correlation matrix between the input images is obtained for feature matching. With the time interval as the label, the feature extractor can be trained independently of the pose calculate network. On the basis of the correlation matrix and the standard matrix, the vehicle pose changes can be predicted by the pose calculate network. Results show that the network runs at a speed of 6 FPS, and the parameter size is 101.6 M. In different sequences, the angle error is within 8.26° and the maximum translation error is within 31.55 m.

Improved Multi-Stream Convolutional Block Attention Module for sEMG-Based Gesture Recognition.

As a key technology for the non-invasive human-machine interface that has received much attention in the industry and academia, surface EMG (sEMG) signals display great potential and advantages in the field of human-machine collaboration. Currently, gesture recognition based on sEMG signals suffers from inadequate feature extraction, difficulty in distinguishing similar gestures, and low accuracy of multi-gesture recognition. To solve these problems a new sEMG gesture recognition network called Multi-stream Convolutional Block Attention Module-Gate Recurrent Unit (MCBAM-GRU) is proposed, which is based on sEMG signals. The network is a multi-stream attention network formed by embedding a GRU module based on CBAM. Fusing sEMG and ACC signals further improves the accuracy of gesture action recognition. The experimental results show that the proposed method obtains excellent performance on dataset collected in this paper with the recognition accuracies of 94.1%, achieving advanced performance with accuracy of 89.7% on the Ninapro DB1 dataset. The system has high accuracy in classifying 52 kinds of different gestures, and the delay is less than 300 ms, showing excellent performance in terms of real-time human-computer interaction and flexibility of manipulator control.

Photoelastic Stress Field Recovery Using Deep Convolutional Neural Network.

Recent work has shown that deep convolutional neural network is capable of solving inverse problems in computational imaging, and recovering the stress field of the loaded object from the photoelastic fringe pattern can also be regarded as an inverse problem solving process. However, the formation of the fringe pattern is affected by the geometry of the specimen and experimental configuration. When the loaded object produces complex fringe distribution, the traditional stress analysis methods still face difficulty in unwrapping. In this study, a deep convolutional neural network based on the encoder-decoder structure is proposed, which can accurately decode stress distribution information from complex photoelastic fringe images generated under different experimental configurations. The proposed method is validated on a synthetic dataset, and the quality of stress distribution images generated by the network model is evaluated using mean squared error (MSE), structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and other evaluation indexes. The results show that the proposed stress recovery network can achieve an average performance of more than 0.99 on the SSIM.

Time Optimal Trajectory Planing Based on Improved Sparrow Search Algorithm.

Complete trajectory planning includes path planning, inverse solution solving and trajectory optimization. In this paper, a highly smooth and time-saving approach to trajectory planning is obtained by improving the kinematic and optimization algorithms for the time-optimal trajectory planning problem. By partitioning the joint space, the paper obtains an inverse solution calculation based on the partitioning of the joint space, saving 40% of the inverse kinematics solution time. This means that a large number of computational resources can be saved in trajectory planning. In addition, an improved sparrow search algorithm (SSA) is proposed to complete the solution of the time-optimal trajectory. A Tent chaotic mapping was used to optimize the way of generating initial populations. The algorithm was further improved by combining it with an adaptive step factor. The experiments demonstrated the performance of the improved SSA. The robot's trajectory is further optimized in time by an improved sparrow search algorithm. Experimental results show that the method can improve convergence speed and global search capability and ensure smooth trajectories.

Cloning, tissue distribution and mRNA expression of type I collagen alpha 1 gene from Chu's croaker (Nibea coibor).

The demand for collagen has been increasing over years due to its wide application in food, cosmetics and biomedicine industries. The synthesis of collagen protein in fish depends on instructions provided by collagen, type I, alpha 1 (COL1A1) gene. However, cloning, tissue distribution and mRNA expression of COL1A1 gene in a gel-producing Chu's croaker (Nibea coibor) is currently unknown. This study cloned the cDNA of COL1A1 gene (GenBank accession number: MK641512) from six N. coibor fish. The distribution and mRNA expression pattern of COL1A1 was analyzed in eight tissues of N. coibor. The COL1A1 cDNA had a full length of 6130 bp and contained a 4344 bp open reading frame (ORF) encoding a polypeptide of 1448 amino acids. The homology of N. coibor COL1A1 amino acid had 98% similarity with Larimichthys crocea, indicating conservatism with other members in same family (Sciaenidae). The deduced polypeptide contained the same signal peptides, C-propeptide and N-propeptide domains, and triple helix domains, which are the characteristics of type I collagen in vertebrates. The mRNA of COL1A1 gene was expressed significantly higher in the spine of N. coibor than in all other tissues (P < 0.05), followed by swim bladder, skin and scales. The swim bladder had higher collagen and hydroxyproline contents than other tissues, followed by spine >, scales > and > skin (P < 0.05). Our study successfully cloned the COL1A1 gene from N. coibor for the first time. The COL1A1 gene contained all the features of collagen pro-α1(I) chain proteins, and shared high homology with other marine teleost. COL1A1 gene in N. coibor is highly expressed in spine and swim bladder, consistent with collagen distribution. Our study contributes to better understanding on collagen biosynthesis in N. coibor tissues for various industrial uses.

Dynamic Gesture Recognition Using Surface EMG Signals Based on Multi-Stream Residual Network.

Gesture recognition technology is widely used in the flexible and precise control of manipulators in the assisted medical field. Our MResLSTM algorithm can effectively perform dynamic gesture recognition. The result of surface EMG signal decoding is applied to the controller, which can improve the fluency of artificial hand control. Much current gesture recognition research using sEMG has focused on static gestures. In addition, the accuracy of recognition depends on the extraction and selection of features. However, Static gesture research cannot meet the requirements of natural human-computer interaction and dexterous control of manipulators. Therefore, a multi-stream residual network (MResLSTM) is proposed for dynamic hand movement recognition. This study aims to improve the accuracy and stability of dynamic gesture recognition. Simultaneously, it can also advance the research on the smooth control of the Manipulator. We combine the residual model and the convolutional short-term memory model into a unified framework. The architecture extracts spatiotemporal features from two aspects: global and deep, and combines feature fusion to retain essential information. The strategy of pointwise group convolution and channel shuffle is used to reduce the number of network calculations. A dataset is constructed containing six dynamic gestures for model training. The experimental results show that on the same recognition model, the gesture recognition effect of fusion of sEMG signal and acceleration signal is better than that of only using sEMG signal. The proposed approach obtains competitive performance on our dataset with the recognition accuracies of 93.52%, achieving state-of-the-art performance with 89.65% precision on the Ninapro DB1 dataset. Our bionic calculation method is applied to the controller, which can realize the continuity of human-computer interaction and the flexibility of manipulator control.

Dynamic Gesture Recognition Algorithm Based on 3D Convolutional Neural Network.

Gesture recognition is one of the important ways of human-computer interaction, which is mainly detected by visual technology. The temporal and spatial features are extracted by convolution of the video containing gesture. However, compared with the convolution calculation of a single image, multiframe image of dynamic gestures has more computation, more complex feature extraction, and more network parameters, which affects the recognition efficiency and real-time performance of the model. To solve above problems, a dynamic gesture recognition model based on CBAM-C3D is proposed. Key frame extraction technology, multimodal joint training, and network optimization with BN layer are used for making the network performance better. The experiments show that the recognition accuracy of the proposed 3D convolutional neural network combined with attention mechanism reaches 72.4% on EgoGesture dataset, which is improved greatly compared with the current main dynamic gesture recognition methods, and the effectiveness of the proposed algorithm is verified.

Genetic Algorithm-Based Trajectory Optimization for Digital Twin Robots.

Mobile robots have an important role in material handling in manufacturing and can be used for a variety of automated tasks. The accuracy of the robot's moving trajectory has become a key issue affecting its work efficiency. This paper presents a method for optimizing the trajectory of the mobile robot based on the digital twin of the robot. The digital twin of the mobile robot is created by Unity, and the trajectory of the mobile robot is trained in the virtual environment and applied to the physical space. The simulation training in the virtual environment provides schemes for the actual movement of the robot. Based on the actual movement data returned by the physical robot, the preset trajectory of the virtual robot is dynamically adjusted, which in turn enables the correction of the movement trajectory of the physical robot. The contribution of this work is the use of genetic algorithms for path planning of robots, which enables trajectory optimization of mobile robots by reducing the error in the movement trajectory of physical robots through the interaction of virtual and real data. It provides a method to map learning in the virtual domain to the physical robot.

Intelligent Detection of Steel Defects Based on Improved Split Attention Networks.

The intelligent monitoring and diagnosis of steel defects plays an important role in improving steel quality, production efficiency, and associated smart manufacturing. The application of the bio-inspired algorithms to mechanical engineering problems is of great significance. The split attention network is an improvement of the residual network, and it is an improvement of the visual attention mechanism in the bionic algorithm. In this paper, based on the feature pyramid network and split attention network, the network is improved and optimised in terms of data enhancement, multi-scale feature fusion and network structure optimisation. The DF-ResNeSt50 network model is proposed, which introduces a simple modularized split attention block, which can improve the attention mechanism of cross-feature graph groups. Finally, experimental validation proves that the proposed network model has good performance and application prospects in the intelligent detection of steel defects.

Effects of dietary lipid sources on the intestinal microbiome and health of golden pompano (Trachinotus ovatus).

Replacement of fish oil (FO) with vegetable oils (VO) in diets is economically desirable for the sustainable development of the aquaculture industry. However, inflammation provoked by FO replacement limited its widely application in fish industry. In order to understand the mechanism of VO-induced inflammation, this study investigated the impact of different dietary vegetable oils on the intestinal health and microbiome in carnivorous marine fish golden pompano (Trachinotus ovatus). Three diets supplemented with fish oil (FO, rich in long-chain polyunsaturated fatty acids), soybean oil (SO, rich in 18:2n-6) and linseed oil (LO, rich in 18:3n-3), respectively, were fed on juvenile golden pompano for 8 weeks, and the intestinal histology, digestive enzymes activities, immunity and antioxidant indices as well as intestinal microbiome were determined. The results showed that dietary SO significantly impaired intestinal health, and decreased the number and height of intestinal folds, and muscle thickness, as well as the zonula occludens-1 (zo-1) mRNA expression in intestine. Moreover, the two dietary VO significantly decreased the amylase and lipase activities in intestine, and reduced the trypsin activity in the dietary SO group. Furthermore, the two VO diets increased intestinal acid phosphatase (ACP) activity, while intestinal lysozyme (LZM) activity and serum diamine oxidase (DAO) activity in the SO group were also significantly increased (P < 0.05). Analysis of the intestinal microbiota showed that the two VO diets significantly increased the abundance of intestinal potentially pathogenic bacteria (Mycoplasma and Vibrio) and decreased proportions of intestinal probiotics (Bacillus and Lactococcus), especially in the dietary SO group. These results indicate that complete replacement of FO with VO in diets would induce intestinal inflammation and impair intestinal function, which might be due to changes in intestinal microbiota profiles, and that dietary SO would have a more negative effect compared to dietary LO on intestinal health in T. ovatus.

miR-24 is involved in vertebrate LC-PUFA biosynthesis as demonstrated in marine teleost Siganus canaliculatus.

Recently, microRNAs (miRNAs) have emerged as crucial regulators of lipid metabolism. However, the miRNA-mediated regulatory mechanism on long-chain (≥C20) polyunsaturated fatty acids (LC-PUFA) biosynthesis in vertebrates remains largely unknown. Here, we address a potentially important role of miRNA-24 (miR-24) in the regulation of LC-PUFA biosynthesis in rabbitfish Siganus canaliculatus. miR-24 showed significantly higher abundance in liver of rabbitfish reared in brackish water than in seawater for fish fed vegetable oil diets and in S. canaliculatus hepatocyte line (SCHL) cells incubated with alpha-linolenic acid (ALA) than the control group. Similar expression patterns were also observed on the expression of sterol regulatory element-binding protein-1 (srebp1) and LC-PUFA biosynthesis related genes. While opposite results were observed on the expression of insulin-induced gene 1 (insig1), an endoplasmic reticulum membrane protein blocking Srebp1 proteolytic activation. Luciferase reporter assays revealed rabbitfish insig1 as a target of miR-24. Knockdown of miR-24 in SCHL cells resulted in increased Insig1 protein, and subsequently reduced mature Srebp1 protein and expression of genes required for LC-PUFA biosynthesis, and these effects could be attenuated after additional insig1 knockdown. Opposite results were observed with overexpression of miR-24. Moreover, increasing endogenous insig1 by knockdown of miR-24 inhibited Srebp1 processing and consequently suppressed LC-PUFA biosynthesis in rabbitfish hepatocytes. These results indicate a potentially critical role for miR-24 in regulating LC-PUFA biosynthesis through the Insig1/Srebp1 pathway by targeting insig1. This is the first report of miR-24 involved in LC-PUFA biosynthesis and thus may provide knowledge on the regulatory mechanisms of LC-PUFA biosynthesis in vertebrates.

Operation reliability assessment for cutting tools by applying a proportional covariate model to condition monitoring information.

The reliability of cutting tools is critical to machining precision and production efficiency. The conventional statistic-based reliability assessment method aims at providing a general and overall estimation of reliability for a large population of identical units under given and fixed conditions. However, it has limited effectiveness in depicting the operational characteristics of a cutting tool. To overcome this limitation, this paper proposes an approach to assess the operation reliability of cutting tools. A proportional covariate model is introduced to construct the relationship between operation reliability and condition monitoring information. The wavelet packet transform and an improved distance evaluation technique are used to extract sensitive features from vibration signals, and a covariate function is constructed based on the proportional covariate model. Ultimately, the failure rate function of the cutting tool being assessed is calculated using the baseline covariate function obtained from a small sample of historical data. Experimental results and a comparative study show that the proposed method is effective for assessing the operation reliability of cutting tools.