UvHOS3-mediated histone deacetylation is essential for virulence and negatively regulates ustilaginoidin biosynthesis in Ustilaginoidea virens.

Ustilaginoidea virens is the causal agent of rice false smut, which has recently become one of the most important rice diseases worldwide. Ustilaginoidins, a major type of mycotoxins produced in false smut balls, greatly deteriorates grain quality. Histone acetylation and deacetylation are involved in regulating secondary metabolism in fungi. However, little is yet known on the functions of histone deacetylases (HDACs) in virulence and mycotoxin biosynthesis in U. virens. Here, we characterized the functions of the HDAC UvHOS3 in U. virens. The ΔUvhos3 deletion mutant exhibited the phenotypes of retarded growth, increased mycelial branches and reduced conidiation and virulence. The ΔUvhos3 mutants were more sensitive to sorbitol, sodium dodecyl sulphate and oxidative stress/H2 O2 . ΔUvhos3 generated significantly more ustilaginoidins. RNA-Seq and metabolomics analyses also revealed that UvHOS3 is a key negative player in regulating secondary metabolism, especially mycotoxin biosynthesis. Notably, UvHOS3 mediates deacetylation of H3 and H4 at H3K9, H3K18, H3K27 and H4K8 residues. Chromatin immunoprecipitation assays indicated that UvHOS3 regulates mycotoxin biosynthesis, particularly for ustilaginoidin and sorbicillinoid production, by modulating the acetylation level of H3K18. Collectively, this study deepens the understanding of molecular mechanisms of the HDAC UvHOS3 in regulating virulence and mycotoxin biosynthesis in phytopathogenic fungi.

Quantitative association between gene expression and blood cell production of individual hematopoietic stem cells in mice.

Individual hematopoietic stem cells (HSCs) produce different amounts of blood cells upon transplantation. Taking advantage of the intercellular variation, we developed an experimental and bioinformatic approach to evaluating the quantitative association between gene expression and blood cell production across individual HSCs. We found that most genes associated with blood production exhibit the association only at some levels of blood production. By mapping gene expression with blood production, we identified four distinct patterns of their quantitative association. Some genes consistently correlate with blood production over a range of levels or across all levels, and these genes are found to regulate lymphoid but not myeloid production. Other genes exhibit one or more clear peaks of association. Genes with overlapping peaks are found to be coexpressed in other tissues and share similar molecular functions and regulatory motifs. By dissecting intercellular variations, our findings revealed four quantitative association patterns that reflect distinct dose-response molecular mechanisms modulating the blood cell production of HSCs.

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.

Multi-view fusion network-based gesture recognition using sEMG data.

sEMG(surface electromyography) signals have been widely used in rehabilitation medicine in the past decades because of their non-invasive, convenient and informative features, especially in human action recognition, which has developed rapidly. However, the research on sparse EMG in multi-view fusion has made less progress compared to high-density EMG signals, and for the problem of how to enrich sparse EMG feature information, a method that can effectively reduce the information loss of feature signals in the channel dimension is needed. In this paper, a novel IMSE (Inception-MaxPooling-Squeeze- Excitation) network module is proposed to reduce the loss of feature information during deep learning. Then, multiple feature encoders are constructed to enrich the information of sparse sEMG feature maps based on the multi-core parallel processing method in multi-view fusion networks, while SwT (Swin Transformer) is used as the classification backbone network. By comparing the feature fusion effects of different decision layers of the multi-view fusion network, it is experimentally obtained that the fusion of decision layers can better improve the classification performance of the network. In NinaPro DB1, the proposed network achieves 93.96% average accuracy in gesture action classification with the feature maps obtained in 300ms time window, and the maximum variation range of action recognition rate of individuals is less than 11.2%. The results show that the proposed framework of multi-view learning plays a good role in reducing individuality differences and augmenting channel feature information, which provides a certain reference for non-dense biosignal pattern recognition.

RNA splicing factor Rbm25 underlies heterogeneous preleukemic clonal expansion in mice.

Clonal expansion sets the stage for cancer genesis by allowing for the accumulation of molecular alterations. Although genetic mutations such as Tet2 that induce clonal expansion and malignancy have been identified, these mutations are also frequently found in healthy individuals. Here, we tracked preleukemic clonal expansion using genetic barcoding in an inducible Tet2 knockout mouse model and found that only a small fraction of hematopoietic stem cells (HSCs) expanded excessively upon Tet2 knockout. These overexpanded HSCs expressed significantly lower levels of genes associated with leukemia and RNA splicing than nonoverexpanded Tet2 knockout HSCs. Knocking down Rbm25, an identified RNA splicing factor, accelerated the expansion of Tet2-knockout hematopoietic cells in vitro and in vivo. Our data suggest that mutations of an epigenetic factor Tet2 induce variability in the expression of an RNA splicing factor Rbm25, which subsequently drives heterogeneous preleukemic clonal expansion. This heterogeneous clonal expansion could contribute to the variable disease risks across individuals.

Lineage tracking to reveal the fate of hematopoietic stem cells influenced by Flk2- multipotent progenitors after transplantation.

After transplantation, hematopoietic stem cells (HSCs) sustain blood cell regeneration throughout the patient's life. Recent studies suggest that several types of mature blood cells provide feedback signals to regulate HSC fate. However, the potential feedback effect of hematopoietic progenitor cells has not been characterized to date. The present investigation demonstrated that multipotent progenitors (MPPs) promoted T cell production of HSCs when both cell types were cotransplanted in mice. Using genetic barcodes to track individual HSCs in mice, we found that the increased T cell production by HSCs was associated with the combined effects of altered lineage bias and clonal expansion during HSC differentiation. We showed that MPP and HSC co-transplantation promoted the multilineage differentiation of HSCs in the short term while preserving lymphoid-specialized HSC differentiation in the long term. Our findings indicate that MPPs derived from HSCs regulate the fate of HSCs after bone marrow transplantation.

Quantitative analysis of macular microcirculation changes in diabetic retinopathy patients by optical coherence tomography angiography / 国际眼科杂志(Guoji Yanke Zazhi)

AIM: To evaluate the macular microcirculation changes in patients with diabetic retinopathy(DR)by optical coherence tomography angiography(OCTA), and correlate the risk factors that may affect the macular microcirculation, so as to provide a clinical basis for early screening, diagnosis and therapeutic intervention for DR.METHODS: Retrospective study. A total of 75 patients(75 eyes)with type 2 diabetes mellitus(DM)who came to the ophthalmology outpatient clinic and ward of Xi'an Central Hospital from May to October 2022 were collected, and the DM patients were further divided into non-DR(NDR)group, non-proliferative DR(NPDR)group and proliferative DR(PDR)group, with 28 eyes, 25 eyes and 22 eyes in each group respectively. OCTA was applied to perform a 3mm×3mm blood flow imaging scan of the macular retina to automatically obtain the vascular density(VD)of the superficial retinal macular foveal, the parafoveal(1～3mm), as well as the macular 3mm×3mm and the area of macular foveal avascular zone(FAZ). Furthermore, the alteration of these parameters in patients with different degrees of DR was analyzed.RESULTS: Compared with the NDR group, the VD of the 3mm×3mm superficial retinal capillary(SCP)in the macular region was decreased in both PDR and NPDR group(all P<0.017), and the VD of the parafoveal was decreased in each quadrant(P<0.017), with the most significant decrease in the PDR group. The FAZ area of patients in the PDR and NPDR groups was significantly larger than that in the NDR group(P<0.017). Age, homocysteine, best corrected visual acuity(LogMAR), axial length and glycosylated hemoglobin(HbA1c)of patients with DR were negatively correlated with VD in the macular foveal(P<0.05), while homocysteine, best corrected visual acuity(LogMAR), axial length, and the duration of diabetes were negatively correlated with VD in the macular 3mm×3mm(P<0.05). Triglycerides, best corrected visual acuity(LogMAR), and HbA1c were negatively correlated with VD in the parafoveal(P<0.05), while total cholesterol and central macular thickness had no significant correlation with VD(P>0.05).CONCLUSION: The microcirculation changes in the macular area of DR patients can be monitored by OCTA, and the systemic condition of DR patients is closely related to their macular microcirculation.

Real-Time Target Detection Method Based on Lightweight Convolutional Neural Network.

The continuous development of deep learning improves target detection technology day by day. The current research focuses on improving the accuracy of target detection technology, resulting in the target detection model being too large. The number of parameters and detection speed of the target detection model are very important for the practical application of target detection technology in embedded systems. This article proposed a real-time target detection method based on a lightweight convolutional neural network to reduce the number of model parameters and improve the detection speed. In this article, the depthwise separable residual module is constructed by combining depthwise separable convolution and non-bottleneck-free residual module, and the depthwise separable residual module and depthwise separable convolution structure are used to replace the VGG backbone network in the SSD network for feature extraction of the target detection model to reduce parameter quantity and improve detection speed. At the same time, the convolution kernels of 1 × 3 and 3 × 1 are used to replace the standard convolution of 3 × 3 by adding the convolution kernels of 1 × 3 and 3 × 1, respectively, to obtain multiple detection feature graphs corresponding to SSD, and the real-time target detection model based on a lightweight convolutional neural network is established by integrating the information of multiple detection feature graphs. This article used the self-built target detection dataset in complex scenes for comparative experiments; the experimental results verify the effectiveness and superiority of the proposed method. The model is tested on video to verify the real-time performance of the model, and the model is deployed on the Android platform to verify the scalability of the model.

Path Planning Optimization of Intelligent Vehicle Based on Improved Genetic and Ant Colony Hybrid Algorithm.

Intelligent vehicles were widely used in logistics handling, agriculture, medical service, industrial production, and other industries, but they were often not smooth enough in planning the path, and the number of turns was large, resulting in high energy consumption. Aiming at the unsmooth path planning problem of four-wheel intelligent vehicle path planning algorithm, this article proposed an improved genetic and ant colony hybrid algorithm, and the physical model of intelligent vehicle was established. This article first improved ant colony optimization algorithm about heuristic function with the adaptive change of evaporation factor. Then, it improved the genetic algorithm on fitness function, adaptive adjustment of crossover factor, and mutation factor. Last, this article proposed the improved hybrid algorithm with the addition of a deletion operator, adoption of an elite retention strategy, and addition of suboptimal solutions obtained from the improved ant colony algorithm to improved genetic algorithm to obtain optimized new populations. The simulation environment for this article is windows 10, the processor is Intel Core i5-5257U, the running memory is 4GB, the compilation environment is MATLAB2018b, the number of ant samples is 50, the maximum number of iterations is 100, the initial population size of the genetic algorithm is 200, and the maximum number of iterations is 50. Simulation and physical experiments show that the improved hybrid algorithm is effective. Compared with the traditional hybrid algorithm, the improved hybrid algorithm reduced by 46% in the average number of iterations and 75% in the average number of turns in a simple grid. The improved hybrid algorithm reduced by 47% in the average number of iterations and 21% in the average number of turns in a complex grid. The improved hybrid algorithm works better to reduce the number of turns in simple maps.

A Tandem Robotic Arm Inverse Kinematic Solution Based on an Improved Particle Swarm Algorithm.

The analysis of robot inverse kinematic solutions is the basis of robot control and path planning, and is of great importance for research. Due to the limitations of the analytical and geometric methods, intelligent algorithms are more advantageous because they can obtain approximate solutions directly from the robot's positive kinematic equations, saving a large number of computational steps. Particle Swarm Algorithm (PSO), as one of the intelligent algorithms, is widely used due to its simple principle and excellent performance. In this paper, we propose an improved particle swarm algorithm for robot inverse kinematics solving. Since the setting of weights affects the global and local search ability of the algorithm, this paper proposes an adaptive weight adjustment strategy for improving the search ability. Considering the running time of the algorithm, this paper proposes a condition setting based on the limit joints, and introduces the position coefficient k in the velocity factor. Meanwhile, an exponential product form modeling method (POE) based on spinor theory is chosen. Compared with the traditional DH modeling method, the spinor approach describes the motion of a rigid body as a whole and avoids the singularities that arise when described by a local coordinate system. In order to illustrate the advantages of the algorithm in terms of accuracy, time, convergence and adaptability, three experiments were conducted with a general six-degree-of-freedom industrial robotic arm, a PUMA560 robotic arm and a seven-degree-of-freedom robotic arm as the research objects. In all three experiments, the parameters of the robot arm, the range of joint angles, and the initial attitude and position of the end-effector of the robot arm are given, and the attitude and position of the impact point of the end-effector are set to verify whether the joint angles found by the algorithm can reach the specified positions. In Experiments 2 and 3, the algorithm proposed in this paper is compared with the traditional particle swarm algorithm (PSO) and quantum particle swarm algorithm (QPSO) in terms of position and direction solving accuracy, operation time, and algorithm convergence. The results show that compared with the other two algorithms, the algorithm proposed in this paper can ensure higher position accuracy and orientation accuracy of the robotic arm end-effector. the position error of the algorithm proposed in this paper is 0 and the maximum orientation error is 1.29 × 10-8. while the minimum position error of the other two algorithms is -1.64 × 10-5 and the minimum orientation error is -4.03 × 10-6. In terms of operation time, the proposed algorithm in this paper has shorter operation time compared with the other two algorithms. In the last two experiments, the computing time of the proposed algorithm is 0.31851 and 0.30004s respectively, while the shortest computing time of the other two algorithms is 0.33359 and 0.30521s respectively. In terms of algorithm convergence, the proposed algorithm can achieve faster and more stable convergence than the other two algorithms. After changing the experimental subjects, the proposed algorithm still maintains its advantages in terms of accuracy, time and convergence, which indicates that the proposed algorithm is more applicable and has certain potential in solving the multi-arm inverse kinematics solution. This paper provides a new way of thinking for solving the multi-arm inverse kinematics solution problem.

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.

Low-Illumination Image Enhancement Algorithm Based on Improved Multi-Scale Retinex and ABC Algorithm Optimization.

In order to solve the problems of poor image quality, loss of detail information and excessive brightness enhancement during image enhancement in low light environment, we propose a low-light image enhancement algorithm based on improved multi-scale Retinex and Artificial Bee Colony (ABC) algorithm optimization in this paper. First of all, the algorithm makes two copies of the original image, afterwards, the irradiation component of the original image is obtained by used the structure extraction from texture via relative total variation for the first image, and combines it with the multi-scale Retinex algorithm to obtain the reflection component of the original image, which are simultaneously enhanced using histogram equalization, bilateral gamma function correction and bilateral filtering. In the next part, the second image is enhanced by histogram equalization and edge-preserving with Weighted Guided Image Filtering (WGIF). Finally, the weight-optimized image fusion is performed by ABC algorithm. The mean values of Information Entropy (IE), Average Gradient (AG) and Standard Deviation (SD) of the enhanced images are respectively 7.7878, 7.5560 and 67.0154, and the improvement compared to original image is respectively 2.4916, 5.8599 and 52.7553. The results of experiment show that the algorithm proposed in this paper improves the light loss problem in the image enhancement process, enhances the image sharpness, highlights the image details, restores the color of the image, and also reduces image noise with good edge preservation which enables a better visual perception of the image.

Attitude Stabilization Control of Autonomous Underwater Vehicle Based on Decoupling Algorithm and PSO-ADRC.

Autonomous Underwater Vehicle are widely used in industries, such as marine resource exploitation and fish farming, but they are often subject to a large amount of interference which cause poor control stability, while performing their tasks. A decoupling control algorithm is proposed and A single control volume-single attitude angle model is constructed for the problem of severe coupling in the control system of attitude of six degrees of freedom Autonomous Underwater Vehicle. Aiming at the problem of complex Active Disturbance Rejection Control (ADRC) adjustment relying on manual experience, the PSO-ADRC algorithm is proposed to realize the automatic adjustment of its parameters, which improves the anti-interference ability and control accuracy of Autonomous Underwater Vehicle in dynamic environment. The anti-interference ability and control accuracy of the method were verified through experiments.

Development of human alveolar epithelial cell models to study distal lung biology and disease.

Many acute and chronic diseases affect the distal lung alveoli. Alveolar epithelial cell (AEC) lines are needed to better model these diseases. We used de-identified human remnant transplant lungs to develop a method to establish AEC lines. The lines grow well in 2-dimensional (2D) culture as epithelial monolayers expressing lung progenitor markers. In 3-dimensional (3D) culture with fibroblasts, Matrigel, and specific media conditions, the cells form alveolar-like organoids expressing mature AEC markers including aquaporin 5 (AQP5), G-protein-coupled receptor class C group 5 member A (GPRC5A), and surface marker HTII280. Single-cell RNA sequencing of an AEC line in 2D versus 3D culture revealed increased cellular heterogeneity and induction of cytokine and lipoprotein signaling in 3D organoids. Our approach yields lung progenitor lines that retain the ability to differentiate along the alveolar cell lineage despite long-term expansion and provides a valuable system to model and study the distal lung in vitro.

Thymosin alpha 1 for the adjuvant treatment of coronavirus disease 2019: a retrospective cohort study / 中华危重病急救医学

Objective:To evaluate the effect of thymosin alpha 1 on the prognosis of patients with coronavirus disease 2019 (COVID-19).Methods:A retrospective cohort study was performed to collect clinical data of 95 patients treated by Shanghai Aid Medical Team in Wuhan Third Hospital during January 31, 2020 and March 4, 2020, who were confirmed COVID-19. They were divided into two groups according to whether they were treated with thymosin alpha 1 after admission. The 28-day mortality (primary outcome), and 28-ventilator-free-day, lymphocyte count (LYM) level, C-reactive protein (CRP) level (secondary outcomes) were compared between two groups. Survival analysis was performed using the Kaplan-Meier curve. The effect of thymosin alpha 1 on 28-day survival was evaluated with Cox regression model.Results:Among the 95 patients, there were 31 cases in thymosin group and 64 cases in non-thymosin group; 29 patients died 28 days after admission, including 11 cases (35.5%) in thymosin group and 18 cases (28.1%) in non-thymosin group. Kaplan-Meier survival curve showed that thymosin alpha 1 could improve the 28-day survival of patients with COVID-19, but the univariate Cox model analysis showed that the difference was not statistically significant [hazard ratio ( HR) = 0.48, 95% confidence interval (95% CI) was 0.20-1.14, P = 0.098]; multivariate Cox model analysis showed that thymosin alpha 1 was the factor to improve the 28-day mortality ( HR = 0.15, 95% CI was 0.04-0.55, P = 0.004), old age ( HR = 1.10, 95% CI was 1.05-1.15, P < 0.001), accompanied by chronic renal dysfunction ( HR = 42.35, 95% CI was 2.77-648.64, P = 0.007), decrease of LYM at admission ( HR = 0.15, 95% CI was 0.04-0.60, P = 0.007) and the use of methylprednisolone ( HR = 4.59, 95% CI was 1.26-16.67, P = 0.021) were also risk factors for the increase of 28-day mortality. The use of immunoglobulin and antiviral drugs abidol and ganciclovir did not affect the 28-day mortality. After adjustment for age, gender, LYM and other factors, weighted multivariate Cox analysis model showed thymosin alpha 1 could significantly improve the 28-day survival of COVID-19 patients ( HR = 0.45, 95% CI was 0.25-0.84, P = 0.012). In terms of secondary outcomes, no statistical difference (all P > 0.05) was found between two groups in days without ventilator at 28 days after admission (days: 17.97±13.56 vs. 20.09±12.67) and the increase of LYM at 7 days after admission [×10 9/L: -0.07 (-0.23, 0.43) vs. 0.12 (-0.54, 0.41)]. But the decrease of CRP at 7 days after admission in thymosin alpha group was significantly greater than that in non-thymosin group [mg/L: 39.99 (8.44, 82.22) vs. 0.53 (-7.78, 22.93), P < 0.05]. Conclusion:Thymosin alpha 1 may improve 28-day mortality and inflammation state in COVID-19 patients.

Differences and causes of myopia among students in different stages in the same region in Xi'an / 国际眼科杂志(Guoji Yanke Zazhi)

AIM: To gather baseline data on visual acuity by screening students from different stages in the same administrative district of Xi'an, as well as to analyze and investigate disparities in myopia rates among students from different stages in this area.METHODS: A total of 13 707 students from 21 elementary schools, 3 middle schools, 3 general high schools and 1 vocational high school were randomly selected for uncorrected distance visual acuity and computerized refraction testing in 28 schools with a random sampling approach.RESULTS: The detection rate of myopia in elementary, middle, high and vocational high schools in the same region of Xi'an were 32.27%, 72.07%, 81.22% and 65.12%, respectively; The total myopia rate of students was 47.81%; The myopia rate of girls was higher than that of boys in all grades; The myopia rates of elementary and middle schools increased with the growth of grades; The percentage of high myopia increased from 2.40% in elementary schools to 16.51% in high schools with the growth of grades.CONCLUSION: The myopia rate in different stages of the same region is different, and it tends to rise with the gronth of grades; Girls have a higher myopia rate than boys; Compared to the results of the national survey on myopia in different grades of children and adolescents in 2018, the myopia rate in the same region has decreased significantly in 2021 of the elementary school level, although myopia rate of middle school and high school stage do not rise, the decline is not obvious.

Deciphering intratumoral heterogeneity using integrated clonal tracking and single-cell transcriptome analyses.

Cellular heterogeneity is a major cause of treatment resistance in cancer. Despite recent advances in single-cell genomic and transcriptomic sequencing, it remains difficult to relate measured molecular profiles to the cellular activities underlying cancer. Here, we present an integrated experimental system that connects single cell gene expression to heterogeneous cancer cell growth, metastasis, and treatment response. Our system integrates single cell transcriptome profiling with DNA barcode based clonal tracking in patient-derived xenograft models. We show that leukemia cells exhibiting unique gene expression respond to different chemotherapies in distinct but consistent manners across multiple mice. In addition, we uncover a form of leukemia expansion that is spatially confined to the bone marrow of single anatomical sites and driven by cells with distinct gene expression. Our integrated experimental system can interrogate the molecular and cellular basis of the intratumoral heterogeneity underlying disease progression and treatment resistance.

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.