Explore the vast range of titles available.

Considering that serious hand function damage will greatly affect the daily life of patients, its recovery mainly depends on the regular inspection and manual training of medical staff, and medical monitoring based on bioelectric signals can largely replace manual re-examination as autonomous rehabilitation technology. So, for the rationality of feature selection and the diversity of classifier design in the gesture recognition process based on electromyography (EMG) signals, this paper proposes a hand medical monitoring system based on feature selection method of feature subset average recognition rate and optimal machine learning algorithm selection, which mainly depends on the prediction of hand movement. At the same time, since most experiments are conducted in different non-public proprietary databases, the comparison between various gesture recognition methods can only be analyzed to a certain extent. Therefore, this paper uses the DB1 dataset in the large publicly available NinaPro database and combines with presently well-known 11 time-domain (TD) features and 5 frequency domain (FD) features, then uses the support vector machine (SVM) classifier to comparative analysis total 136 feature combinations under various feature numbers. Under the premise of ensuring the overall recognition rate of electromyography gesture, this method will be able to reduce the number of features in feature set, according to the change of the average remove redundant features, and construct an optimal reduced EMG feature set. Finally, through the four common hand motion classifiers based on machine learning: SVM, back propagation neural network, linear discriminant analysis, and K-nearest neighbor, this paper tests and verifies the separability of the optimal reduced EMG feature set, and based on this, selects the optimal hand motion classifier to build the optimal hand motion recognition system, improve the hand medical monitoring system, and provide technical reference for the construction of real-time medical monitoring system.

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.

Background Circular RNAs (circRNAs) mediate the infiltration of tumor-associated macrophages (TAMs) to facilitate carcinogenesis and development of various types of cancers. However, the role of circRNAs in regulating macrophages in prostate cancer (PCa) remains uncertain. Methods Differentially expressed circRNAs in PCa were identified by RNA sequencing. The expression of circSMARCC1 was recognized and evaluated using fluorescence in situ hybridization and quantitative real-time PCR. The oncogenic role of circSMARCC1 in PCa tumor proliferation and metastasis was investigated through a series of in vitro and in vivo assays. Finally, Western blot, biotin-labeled RNA pulldown, luciferase assay, rescue experiments, and co-culture experiments with TAMs were conducted to reveal the mechanistic role of circSMARCC1. Results CircSMARCC1 was dramatically up-regulated in PCa cells, plasma and tissues. Overexpression of circSMARCC1 promotes tumor proliferation and metastasis both in vitro and in vivo, whereas knockdown of circSMARCC1 exerts the opposite effects. Mechanistically, circSMARCC1 regulates the expression of CC-chemokine ligand 20 (CCL20) via sponging miR-1322 and activate PI3K-Akt signaling pathway involved in the proliferation and epithelial mesenchymal transformation. More importantly, high expression of circSMARCC1 was positively associated with colonization of CD68 + /CD163 + /CD206 + TAMs in tumor microenvironment. In addition, overexpression of circSMARCC1 facilitates the expression of CD163 in macrophages through the CCL20-CCR6 axis, induces TAMs infiltration and M2 polarization, thereby leading to PCa progression. Conclusions CircSMARCC1 up-regulates the chemokine CCL20 secretion by sponging miR-1322, which is involved in the crosstalk between tumor cells and TAMs by targeting CCL20/CCR6 signaling to promote progression of PCa.

With the manipulator performs fixed-point tasks, it becomes adversely affected by external disturbances, parameter variations, and random noise. Therefore, it is essential to improve the robust and accuracy of the controller. In this article, a self-tuning particle swarm optimization (PSO) fuzzy PID positioning controller is designed based on fuzzy PID control. The quantization and scaling factors in the fuzzy PID algorithm are optimized by PSO in order to achieve high robustness and high accuracy of the manipulator. First of all, a mathematical model of the manipulator is developed, and the manipulator positioning controller is designed. A PD control strategy with compensation for gravity is used for the positioning control system. Then, the PID controller parameters dynamically are minute-tuned by the fuzzy controller 1. Through a closed-loop control loop to adjust the magnitude of the quantization factors–proportionality factors online. Correction values are outputted by the modified fuzzy controller 2. A quantization factor–proportion factor online self-tuning strategy is achieved to find the optimal parameters for the controller. Finally, the control performance of the improved controller is verified by the simulation environment. The results show that the transient response speed, tracking accuracy, and follower characteristics of the system are significantly improved.

In the field of human-computer interaction, vision-based gesture recognition methods are widely studied. However, its recognition effect depends to a large extent on the performance of the recognition algorithm. The skeletonization algorithm and convolutional neural network (CNN) for the recognition algorithm reduce the impact of shooting angle and environment on recognition effect, and improve the accuracy of gesture recognition in complex environments. According to the influence of the shooting angle on the same gesture recognition, the skeletonization algorithm is optimized based on the layer-by-layer stripping concept, so that the key node information in the hand skeleton diagram is extracted. The gesture direction is determined by the spatial coordinate axis of the hand. Based on this, gesture segmentation is implemented to overcome the influence of the environment on the recognition effect. In order to further improve the accuracy of gesture recognition, the ASK gesture database is used to train the convolutional neural network model. The experimental results show that compared with SVM method, dictionary learning + sparse representation, CNN method and other methods, the recognition rate reaches 96.01%.

Background More and more studies have shown that circular RNAs (circRNAs) play a critical regulatory role in many cancers. However, the potential molecular mechanism of circRNAs in prostate cancer (PCa) remains largely unknown. Methods Differentially expressed circRNAs were identified by RNA sequencing. The expression of hsa_circ_0003258 was evaluated using quantitative real-time PCR and RNA in situ hybridization. The impacts of hsa_circ_0003258 on the metastasis of PCa cells were investigated by a series of in vitro and in vivo assays. Lastly, the underlying mechanism of hsa_circ_0003258 was revealed by Western blot, biotin-labeled RNA pulldown, RNA immunoprecipitation, luciferase assays and rescue experiments. Results Increased expression of hsa_circ_0003258 was found in PCa tissues and was associated with advanced TNM stage and ISUP grade. Overexpression of hsa_circ_0003258 promoted PCa cell migration by inducing epithelial mesenchymal transformation (EMT) in vitro as well as tumor metastasis in vivo , while knockdown of hsa_circ_0003258 exerts the opposite effect. Mechanistically, hsa_circ_0003258 could elevate the expression of Rho GTPase activating protein 5 (ARHGAP5) via sponging miR-653-5p. In addition, hsa_circ_0003258 physically binds to insulin like growth factor 2 mRNA binding protein 3 (IGF2BP3) in the cytoplasm and enhanced HDAC4 mRNA stability, in which it activates ERK signalling pathway, then triggers EMT programming and finally accelerates the metastasis of PCa. Conclusions Upregulation of hsa_circ_0003258 drives tumor progression through both hsa_circ_0003258/miR-653-5p/ARHGAP5 axis and hsa_circ_0003258/IGF2BP3 /HDAC4 axis. Hsa_circ_0003258 may act as a promising biomarker for metastasis of PCa and an attractive target for PCa intervention.

Fucoidan, a sulfated polysaccharide purified from brown algae, has a variety of immune-modulation effects, including promoting antigen uptake and enhancing anti-viral and anti-tumor effects. However, the effect of fucoidan in vivo, especially its adjuvant effect on in vivo anti-tumor immune responses, was not fully investigated. In this study, we investigated the effect of fucoidan on the function of spleen dendritic cells (DCs) and its adjuvant effect in vivo. Systemic administration of fucoidan induced up-regulation of CD40, CD80 and CD86 expression and production of IL-6, IL-12 and TNF-α in spleen cDCs. Fucoidan also promoted the generation of IFN-γ-producing Th1 and Tc1 cells in an IL-12-dependent manner. When used as an adjuvant in vivo with ovalbumin (OVA) antigen, fucoidan promoted OVA-specific antibody production and primed IFN-γ production in OVA-specific T cells. Moreover, fucoidan enhanced OVA-induced up-regulation of MHC class I and II on spleen cDCs and strongly prompted the proliferation of OVA-specific CD4 and CD8 T cells. Finally, OVA immunization with fucoidan as adjuvant protected mice from the challenge with B16-OVA tumor cells. Taken together, these results suggest that fucoidan can function as an adjuvant to induce Th1 immune response and CTL activation, which may be useful in tumor vaccine development.

Caudal autotomy is a phenomenon observed in many reptile taxa, and tail loss is a pivotal functional trait for reptiles, with potentially negative implications for organism fitness due to its influence on locomotion. Some lizard species can regenerate a lost tail, which sometimes can lead to the development of more than one tail (i.e., abnormal tail regeneration) in the process. However, little is currently known about the impact of abnormal tail regeneration on locomotor performance. In this study, we document abnormal tail regeneration in Eremias yarkandensis, a reptile species native to northwestern China. Additionally, we investigated the sprint speed and endurance performance of these lizards. This study provides the first report on abnormal tail regeneration and its locomotor performance on a Chinese reptile. We suggest that the abnormal regeneration of tails may contribute to the accumulation of food reserves in the species. In light of our findings, we propose that herpetologists continue to share their sporadic observations and assess the locomotor performance of species experiencing abnormal tail regeneration, further expanding our understanding of this intriguing phenomenon. Little is currently known about the impact of abnormal tail regeneration on locomotor performance. In this study, we document abnormal tail regeneration in Eremias yarkandensis, a reptile species native to northwestern China.

A weighted fusion method of D-S evidence theory in decision making is proposed to aim at the problem of lacking in the distribution of trust, data processing and precision in D-S evidential theory. The method of gesture recognition based on Kinect and sEMG signal are established. Weighted D-S evidence theory is used to fuse Kinect and sEMG signals and the simulation experiment is made respectively. The stimulation results show that comparing with other experimental methods, the decision fusion method based on weighted D-S evidence theory has higher utilization efficiency and recognition rate.

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.