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Abstract Rejection seriously affects the success of kidney transplantations. However, the molecular mechanisms underlying this rejection remain unclear. The GSE21374 and GSE36059 datasets were downloaded from the Gene Expression Omnibus (GEO) database. Next, the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to infer the proportions of 22 immune cells. Moreover, infiltrating immune cell-related genes were identified using weighted gene co-expression network analysis (WGCNA), and enrichment analysis was conducted to observe their biological functions. Extreme Gradient Boosting (XGBoost) and Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression algorithms were used to screen hub genes. Quantitative real-time PCR was conducted to verify the number of immune cells and hub gene expression levels. The rejection and non-rejection groups showed significantly different distributions (P

Abstract Previous studies that focused on univariate correlations between neuroanatomy and cognition in schizophrenia identified some inconsistent findings. Moreover, antipsychotic medication may impact the brain-behavior profiles in affected individuals. It remains unclear whether unmedicated and medicated individuals with schizophrenia would share common neuroanatomy-cognition associations. Therefore, we aimed to investigate multivariate neuroanatomy-cognition relationships in both groups. A sample of 59 drug-naïve individuals with first-episode schizophrenia (FES) and a sample of 115 antipsychotic-treated individuals with schizophrenia were finally included. Multivariate modeling was conducted in the two patient samples between multiple cognitive domains and neuroanatomic features, such as cortical thickness (CT), cortical surface area (CSA), and subcortical volume (SV). We observed distinct multivariate correlational patterns between the two samples of individuals with schizophrenia. In the FES sample, better performance in token motor, symbol coding, and verbal fluency tests was associated with greater thalamic volumes but lower CT in the prefrontal and anterior cingulate cortices. Two significant multivariate correlations were identified in antipsychotic-treated individuals: 1) worse verbal memory performance was related to smaller volumes for the most subcortical structures and smaller CSA mainly in the temporal regions and inferior parietal lobule; 2) a lower symbol coding test score was correlated with smaller CSA in the right parahippocampal gyrus but greater volume in the right caudate. These multivariate patterns were sample-specific and not confounded by imaging quality, illness duration, antipsychotic dose, or psychopathological symptoms. Our findings may help to understand the neurobiological basis of cognitive impairments and the development of cognition-targeted interventions.

Artificial tracheal substitutes encounter significant challenges during long-segmental tracheal defects (LSTD) reconstruction, notably early postoperative anastomotic stenosis and tracheal chondromalacia. Mitigating early anastomotic stenosis by creating a compliant sutureless substitute is pivotal. Enhancing its chondrogenic capacity is equally critical for sustained healthy tracheal cartilage regeneration. This study proposes a self-healing hydrogel for sutureless tracheal anastomosis to mitigate anastomotic stenosis, enriched with kartogenin (KGN) and transforming growth factor-β1 (TGFβ1) to bolster chondrogenic properties. Initially, two precursor solutions were prepared: 1) aldehyde-modified hyaluronic acid with sulfonation and β-cyclodextrin-CHO loaded with KGN; 2) hydrazide-grafted gelatin loaded with TGFβ1. Coextrusion of these solutions resulted in a gelated G + TGFβ1/sH-CD + KGN hydrogel, characterized by a robust covalent bonding network of acylhydrazones between hydrazide and aldehyde groups, imparting excellent self-healing properties. The G + TGFβ1/sH-CD + KGN hydrogels, showcasing favorable cytocompatibility, excellent injectability, and rapid gelation, were loaded with bone marrow stem cells. These were customized into O-shaped rings and assembled into a malleable tracheal substitute using our established ring-to-tube method. This resultant compliant substitute facilitated sutureless anastomosis of LSTD in a rabbit model, attributed to the Schiff base reaction between the hydrogel's carbonyl group and the tissue's amino group. Notably, the tracheal substitute reduced early postoperative anastomotic stenosis, maintained tracheal patency, alleviated sputum blockage, promoted reepithelization, and increased the survival rate of the experimental rabbits. The sustained release of chondrocytokines resulted in excellent tracheal cartilage regeneration. Employing chondrocytokines-loaded hydrogels with self-healing properties represents a significant advancement in sutureless tracheal anastomosis and tracheal cartilage regeneration, holding promising potential in inhibiting early postoperative anastomotic stenosis and tracheal chondromalacia when treating LSTD.

Promoting osteogenic differentiation and inhibiting osteoclast formation remain significant challenges in the treatment of osteoporosis. With the growing understanding of osteoporosis, increasing literature has highlighted the regulatory role of m6A methylation in this condition. However, there is currently no reliable method to stably regulate cellular m6A methylation levels. Here, we report a novel approach utilizing alendronate (aln)-modified mesoporous silica nanoparticles (MSNs) to deliver sodium bicarbonate and piR7472, modulating cellular behavior. Our experimental results demonstrate that Aln modification enables the nanoparticles to stably target hydroxyapatite, thereby accumulating in osteoporotic regions. Sodium bicarbonate suppresses osteoclastogenesis, while piR7472 enhances m6A methylation, promoting osteogenic differentiation of bone marrow stromal cells (BMSCs). Computed tomography (CT) and hematoxylin and eosin (HE) staining showed that after 2 weeks of treatment with MSNs-Na@piR7472, cortical bone thickened, trabecular bone density increased, collagen fiber thickness improved, and both the number and staining area of osteoclasts were significantly reduced. These findings indicate a marked improvement in osteoporosis.

Abstract Tumour segmentation in medical images (especially 3D tumour segmentation) is highly challenging due to the possible similarity between tumours and adjacent tissues, occurrence of multiple tumours and variable tumour shapes and sizes. The popular deep learning‐based segmentation algorithms generally rely on the convolutional neural network (CNN) and Transformer. The former cannot extract the global image features effectively while the latter lacks the inductive bias and involves the complicated computation for 3D volume data. The existing hybrid CNN‐Transformer network can only provide the limited performance improvement or even poorer segmentation performance than the pure CNN. To address these issues, a short‐term and long‐term memory self‐attention network is proposed. Firstly, a distinctive self‐attention block uses the Transformer to explore the correlation among the region features at different levels extracted by the CNN. Then, the memory structure filters and combines the above information to exclude the similar regions and detect the multiple tumours. Finally, the multi‐layer reconstruction blocks will predict the tumour boundaries. Experimental results demonstrate that our method outperforms other methods in terms of subjective visual and quantitative evaluation. Compared with the most competitive method, the proposed method provides Dice (82.4% vs. 76.6%) and Hausdorff distance 95% (HD95) (10.66 vs. 11.54 mm) on the KiTS19 as well as Dice (80.2% vs. 78.4%) and HD95 (9.632 vs. 12.17 mm) on the LiTS.

Deep learning is becoming the most widely used technology for multi-sensor data fusion. Semantic correspondence has recently emerged as a foundational task, enabling a range of downstream applications, such as style or appearance transfer, robot manipulation, and pose estimation, through its ability to provide robust correspondence in RGB images with semantic information. However, current representations generated by self-supervised learning and generative models are often limited in their ability to capture and understand the geometric structure of objects, which is significant for matching the correct details in applications of semantic correspondence. Furthermore, efficiently fusing these two types of features presents an interesting challenge. Achieving harmonious integration of these features is crucial for improving the expressive power of models in various tasks. To tackle these issues, our key idea is to integrate depth information from depth estimation or depth sensors into feature maps and leverage learnable weights for feature fusion. First, depth information is used to model pixel-wise depth distributions, assigning relative depth weights to feature maps for perceiving an object’s structural information. Then, based on a contrastive learning optimization objective, a series of weights are optimized to leverage feature maps from self-supervised learning and generative models. Depth features are naturally embedded into feature maps, guiding the network to learn geometric structure information about objects and alleviating depth ambiguity issues. Experiments on the SPair-71K and AP-10K datasets show that the proposed method achieves scores of 81.8 and 83.3 on the percentage of correct keypoints (PCK) at the 0.1 level, respectively. Our approach not only demonstrates significant advantages in experimental results but also introduces the depth awareness module and a learnable feature fusion module, which enhances the understanding of object structures through depth information and fully utilizes features from various pre-trained models, offering new possibilities for the application of deep learning in RGB and depth data fusion technologies. We will also continue to focus on accelerating model inference and optimizing model lightweighting, enabling our model to operate at a faster speed.

The inverse kinematics of robotic manipulators involves determining an appropriate joint configuration to achieve a specified end-effector position. This problem is challenging because the inverse kinematics of manipulators are highly nonlinear and complexly coupled. To address this challenge, the bald eagle search optimization algorithm is introduced. This algorithm combines the advantages of evolutionary and swarm techniques, making it more effective at solving nonlinear problems and improving search efficiency. Due to the tendency of the algorithm to fall into local optima, the Lévy flight strategy is introduced to enhance its performance. This strategy adopts a heavy-tailed distribution to generate long-distance jumps, thereby preventing the algorithm from becoming trapped in local optima and enhancing its global search efficiency. The experiments first evaluated the accuracy and robustness of the proposed algorithm based on the inverse kinematics problem of manipulators, achieving a solution accuracy of up to 10−18 m. Subsequently, the proposed algorithm was compared with other algorithms using the CEC2017 test functions. The results showed that the improved algorithm significantly outperformed the original in accuracy, convergence speed, and stability. Specifically, it achieved over 70% improvement in both standard deviation and mean for several test functions, demonstrating the effectiveness of the Lévy flight strategy in enhancing global search capabilities. Furthermore, the practicality of the proposed algorithm was verified through two real engineering optimization problems.

The seven-degree-of-freedom space manipulator, characterized by its redundant and aspheric wrist structure, is extensively used in space missions due to its exceptional dexterity and multi-joint capabilities. However, the non-spherical wrist structure presents challenges in solving inverse kinematics, as it cannot decouple joints using the Pieper criterion, unlike spherical wrist structures. To address this issue, this paper presents a closed-form analytical method for solving the inverse kinematics of seven-degree-of-freedom aspheric wrist space manipulators. The method begins by identifying the redundant joint through comparing the volumes of the workspace with different joints fixed. The redundant joint angle is then treated as a parametric joint angle, enabling the derivation of closed-form expressions for the non-parametric joint angles using screw theory. The optimal solution branch is identified through a comparative analysis of various self-motion manifold branches. Additionally, a hybrid approach, combining analytical and numerical methods, is proposed to optimize the parametric joint angle for a trajectory tracking task. Simulation results confirm the effectiveness of the proposed method.

Background: Recent research has shown that lysosomes play a critical role in the onset and progression of malignancy by regulating tumor cell death through several mechanisms. Nevertheless, the involvement of lysosome-associated genes (LSAGs) in lung adenocarcinoma (LUAD) is still not well understood. Methods: LSAGs were identified in malignant lung epithelial cells, as well as biologically and functionally annotated by the comprehensive integration of single-cell and bulk RNA-sequencing data. Prognostic characterization of LSAGs was established, of which the accuracy and reliability were assessed by one-way Cox and LASSO regression. Correlations between LSAG properties and immune cell infiltration, chemotherapy, and immunotherapy were analyzed by integrated omics data. Finally, we characterized the expression of three LSAGs (KCNE1, NPC2, and SFTPD) in malignant lung epithelium and assessed their impact on tumor malignancy related phenotypes. Results: We identified 18 LSAGs associated with prognosis, of which 3 LSAGs were used to construct prognostic models. High-risk patients had worse survival and the model predicted it better than other clinical indicators. Based on the functional enrichment analyses, LSAGs were associated with binding and molecular activity functions, inhibition of DNA damage repair and tumor growth, IL7 signaling pathway, and glycolysis. M0 macrophages and M1 macrophages were substantially enriched in high-risk patients. Conversely, there was a considerable enrichment of resting dendritic cells and M2 macrophages in patients at low risk. We also found that risk scores predicted the outcome of immunotherapy. In vitro, we found that KCNE1, NPC2, and SFTPD were lowly expressed in malignant epithelial cells and patients with low expression of KCNE1, NPC2, and SFTPD had a higher percentage of M2 macrophage infiltration. Overexpression of KCNE1, NPC2, and SFTPD suppressed the proliferation and invasion of malignant cells, and M0 macrophages remarkably reduced M2 macrophage polarization and cellular secretion of pro-tumor cytokines. Conclusions: We used three LASGs—KCNE1, NPC2, and SFTPD—to develop and validate a predictive signature for LUAD patients. Furthermore, we found that low expression of KCNE1, NPC2, and SFTPD promotes lung cancer cell proliferation and invasion and M2 macrophage polarization. Our study may provide fresh perspectives for customized immunotherapy.

Human–robot (HR) collaboration (HRC) is an emerging research field because of the complementary advantages of humans and robots. An HRC framework for robotic assembly based on impedance control is proposed in this paper. In the HRC framework, the human is the decision maker, the robot acts as the executor, while the assembly environment provides constraints. The robot is the main executor to perform the assembly action, which has the position control, drag and drop, positive impedance control, and negative impedance control modes. To reveal the characteristics of the HRC framework, the switch condition map of different control modes and the stability analysis of the HR coupled system are discussed. In the end, HRC assembly experiments are conducted, where the HRC assembly task can be accomplished when the assembling tolerance is 0.08 mm or with the interference fit. Experiments show that the HRC assembly has the complementary advantages of humans and robots and is efficient in finishing complex assembly tasks.

The fermentation process of traditional Baijiu Daqu involves extremely complex changes in microbial communities, and the microbial communities work collaboratively to determine the quality of Daqu. Analyzing the composition and functions of microbial community during Daqu fermentation is the key to revealing its fermentation mechanism, putting forward a rational control strategy and stabilizing the quality of Daqu. Therefore, microbial communities and functions have always been the focus of Daqu research. In this paper, the characteristics of microbial community structure in different types of Baijiu Daqu are summarized. The microbial sources involved in Daqu fermentation, the succession of microbial community and its driving factors, and the changes of microbial community during the storage period of Daqu are reviewed. The functions of microorganisms involved in Daqu fermentation are summarized. Finally, future research prospects are discussed. This review is expected to provide a reference for further research on the fermentation mechanism of Daqu.

Abstract Mechanical pressure overload and other stimuli often contribute to heart hypertrophy, a significant factor in the induction of heart failure. The UDP-glucose ceramide glycosyltransferase (UGCG) enzyme plays a crucial role in the metabolism of sphingolipids through the production of glucosylceramide. However, its role in heart hypertrophy remains unknown. In this study, UGCG was induced in response to pressure overload in vivo and phenylephrine stimulation in vitro. Additionally, UGCG downregulation ameliorated cardiomyocyte hypertrophy, improved cardiomyocyte mitochondrial oxidative stress, and reduced the ERK signaling pathway. Conversely, UGCG overexpression in cardiomyocytes promoted heart hypertrophy development, aggravated mitochondrial oxidative stress, and stimulated ERK signaling. Furthermore, the interaction between beta-1,4-galactosyltransferase 5 (B4GalT5), which catalyses the synthesis of lactosylceramide, and UGCG was identified, which also functions as a synergistic molecule of UGCG. Notably, limiting the expression of B4GalT5 impaired the capacity of UGCG to promote myocardial hypertrophy, suggesting that B4GalT5 acts as an intermediary for UGCG. Overall, this study highlights the potential of UGCG as a modulator of heart hypertrophy, rendering it a potential target for combating heart hypertrophy.

Abstract The serine/threonine kinase AKT is a central node in cell signaling. While aberrant AKT activation underlies the development of a variety of human diseases, how different patterns of AKT-dependent phosphorylation dictate downstream signaling and phenotypic outcomes remains largely enigmatic. Herein, we perform a systems-level analysis that integrates methodological advances in optogenetics, mass spectrometry-based phosphoproteomics, and bioinformatics to elucidate how different intensity, duration, and pattern of Akt1 stimulation lead to distinct temporal phosphorylation profiles in vascular endothelial cells. Through the analysis of ~35,000 phosphorylation sites across multiple conditions precisely controlled by light stimulation, we identify a series of signaling circuits activated downstream of Akt1 and interrogate how Akt1 signaling integrates with growth factor signaling in endothelial cells. Furthermore, our results categorize kinase substrates that are preferably activated by oscillating, transient, and sustained Akt1 signals. We validate a list of phosphorylation sites that covaried with Akt1 phosphorylation across experimental conditions as potential Akt1 substrates. Our resulting dataset provides a rich resource for future studies on AKT signaling and dynamics.

Power transformers are critical equipment responsible for the transmission and transformation of electrical energy, and they are an important component of power system. Under the combined effects of electric field, thermal field, and mechanical stress, insulation degradation is prone to occur in oil-paper insulation structures. Among them, the “liquid-solid” interface formed by oil and oil-immersed pressboard is the weak point in transformer insulation, and extensive operational experience has shown that creeping flashover of insulating pressboard is a common fault in power transformers. Creeping flashover of insulating pressboard can rapidly destroy the insulation structure, leading to irreversible damage to the transformer and posing a serious risk of transformer accidents. In recent years, many scholars have conducted in-depth research on the characteristics and mechanisms of creeping flashover in oil-paper insulation structures using various experimental methods. They have obtained significant findings that have important theoretical and practical implications for optimizing the structure of power transformers and improving their reliability. This paper first introduces the characteristics of the electromagnetic environment inside the power transformer and the common forms of creeping flashover accidents in engineering. Then, classified according to the uneven degree of electric field and creepage distance of test electrodes used in relevant research, and the test methods and flashover characteristics of small size (

Abstract With the rapid development of sensor technology and artificial intelligence, the video gesture recognition technology under the background of big data makes human‐computer interaction more natural and flexible, bringing richer interactive experience to teaching, on‐board control, electronic games, etc. In order to perform robust recognition under the conditions of illumination change, background clutter, rapid movement, partial occlusion, an algorithm based on multi‐level feature fusion of two‐stream convolutional neural network is proposed, which includes three main steps. Firstly, the Kinect sensor obtains RGB‐D images to establish a gesture database. At the same time, data enhancement is performed on training and test sets. Then, a model of multi‐level feature fusion of two‐stream convolutional neural network is established and trained. Experiments result show that the proposed network model can robustly track and recognize gestures, and compared with the single‐channel model, the average detection accuracy is improved by 1.08%, and mean average precision (mAP) is improved by 3.56%. The average recognition rate of gestures under occlusion and different light intensity was 93.98%. Finally, in the ASL dataset, LaRED dataset, and 1‐miohand dataset, recognition accuracy shows satisfactory performances compared to the other method.

Collision detection is very important for robot motion planning. The existing accurate collision detection algorithms regard the evaluation of each node as a discrete event, ignoring the correlation between nodes, resulting in low efficiency. In this paper, we propose a novel approach that transforms collision detection into a binary classification problem. In particular, the proposed method searches the k-nearest neighbor (KNN) of the new node and estimates its collision probability by the prior node. We perform the hierarchical navigable small world (HNSW) method to query the nearest neighbor data and store the detected nodes to build the database incrementally. In addition, this research develops a KNN query technique tailored for linear data, incorporating threshold segmentation to facilitate collision detection along continuous paths. Moreover, it refines the distance function of the collision classifier to enhance the precision of probability estimations. Simulation results demonstrate the effectiveness of the proposed method.

The aviation assembly domain, which is a complex system, involves the multi-dimensional information of parts, processes, tools, plants and operation projects. In order to assist the knowledge management from natural language text in the aircraft manufacturing process, this paper proposes the corresponding ontology scheme and the joint knowledge extraction model, which is necessary for construct the knowledge graph in the aviation assembly domain. The model is able to automated end-to-end construct knowledge graph. The proposed model, which is based on reinforcement learning approach and a novel labeling scheme, takes the constraint relationships between entities and relations as an important identification basis. The model does not rely on manual feature setting, while it greatly reduces the training cost. The proposed joint knowledge extraction model was testified from the practical scenarios of the general assembly and component assembly. The experimental results showed that the proposed model showed an excellent performance in the aviation assembly domain, with the F1-score of 89.71% for entities, the F1-score of 91.27% for relations, and the overall average F1-score of 82.41%. Based on the superior performance of the model, the knowledge graph of the general assembly and component assembly, which included 1, 308 pairs of triples composed of five kinds of entities and three kinds of relations, was further constructed in the aviation assembly domain.

Excitons dominate the photonic and optoelectronic properties of a material. Although significant advancements exist in understanding various types of excitons, progress on excitons that are indirect in both real- and momentum-spaces is still limited. Here, we demonstrate the real- and momentum-indirect neutral and charged excitons (including their phonon replicas) in a multi-valley semiconductor of bilayer MoS2, by performing electric-field/doping-density dependent photoluminescence. Together with first-principles calculations, we uncover that the observed real- and momentum-indirect exciton involves electron/hole from K/Γ valley, solving the longstanding controversy of its momentum origin. Remarkably, the binding energy of real- and momentum-indirect charged exciton is extremely large (i.e., ~59 meV), more than twice that of real- and momentum-direct charged exciton (i.e., ~24 meV). The giant binding energy, along with the electrical tunability and long lifetime, endows real- and momentum-indirect excitons an emerging platform to study many-body physics and to illuminate developments in photonics and optoelectronics.

Conservation tillage has been promoted as an effective practice to preserve soil health and enhance agroecosystem services. Changes in tillage intensity have a profound impact on soil nitrogen cycling, yet their influence on nitrate losses at large spatiotemporal scales remains uncertain. This study examined the effects of tillage intensity on soil nitrate losses in the US Midwest from 1979–2018 using field data synthesis and process-based agroecosystem modeling approaches. Our results revealed that no-tillage (NT) or reduced tillage intensity (RTI) decreased nitrate runoff but increased nitrate leaching compared to conventional tillage. These trade-offs were largely caused by altered water fluxes, which elevated total nitrate losses. The structural equation model suggested that precipitation had more pronounced effects on nitrate leaching and runoff than soil properties (i.e. texture, pH, and bulk density). Reduction in nitrate runoff under NT or RTI was negatively correlated with precipitation, and the increased nitrate leaching was positively associated with soil bulk density. We further explored the combined effects of NT or RTI and winter cover crops and found that incorporating winter cover crops into NT systems effectively reduced nitrate runoff but did not significantly affect nitrate leaching. Our findings underscore the precautions of implementing NT or RTI to promote sustainable agriculture under changing climate conditions. This study provides valuable insights into the complex relationship between tillage intensity and nitrate loss pathways, contributing to informed decision-making in climate-smart agriculture.

In order to improve the operation performance and environmental adaptability of the manipulator, it is essential to control the position and force of the manipulator simultaneously. This paper proposed a hybrid control method of auto-dynamic bit based on bit control to realize flexible grasping of manipulator in unknown environment. The constraint conditions and control types of the hybrid force/bit control are analyzed firstly, and the dynamic model when contacting with the environment is established. Then, an improved position controller based on conventional fuzzy-PID control strategy is introduced to realize the hybrid force/bit control based on the position control by combining constraint estimation method and impedance force control. Finally, the control performance of the force/bit hybrid controller is verified by simulation experiments. The results show that the designed control strategy has relatively ideal force control precision and higher trajectory tracking ability, and the manipulator model has better compliance.

The additional moisture can be easily introduced into dielectric materials with the nano-doping, which could have adverse impacts on the insulating properties. Herein, samples with different moisture absorption are tested using Terahertz (THz) waves. The relaxation resonance integrated polarization model is developed to illustrate the water absorption effect by the nano-filler on the THz spectrum, which is related to the hydrogen bond (HB) state of water molecular clusters. Detection of moisture content is realized by the polarized resonance intensity in the proposed model. The different HB states of free water and bound water are effectively distinguished using the Debye parameters in the model, and the formation process of water involved in the HB network is analyzed. Meanwhile, the proposed model is verified by molecular simulation. This work provides an application-worthy method to distinguish nuances in moisture contents and distribution in nano-dielectrics, with advantages of non-destructiveness, rapidity and 2D visualization.

Objective: Engeletin is a potent natural compound with antioxidant and anti-inflammatory properties. However, its role in cardiac remodeling remains unclear. Herein, the aim of the present study was to explore the effects of engeletin on cardiac structural and electrical remodeling and its underlying mechanism. Methods: and results: A cardiac remodeling mice model using isoproterenol (ISO)-induced myocardial fibrosis was constructed and divided into the following four groups: control group; engeletin group; ISO group; engeletin + ISO group. Our results demonstrated that engeletin alleviated ISO-induced myocardial fibrosis and dysfunction. Moreover, engeletin significantly prolonged the QT and corrected QT (QTc) intervals, effective refractory period (ERP), and action potential duration (APD), and enhanced connexin protein 43 (Cx43) and ion channel expressions, thereby decreasing ventricular fibrillation (VF) susceptibility. Additionally, dihydroethidium staining illustrated that engeletin decreased reactive oxygen species (ROS) production. Of note, engeletin also increased the levels of superoxide dismutase and glutathione and decreased the activity of malondialdehyde and L-Glutathione oxidized. Moreover, engeletin significantly increased the expression of nuclear factor erythroid 2-related factor 2 (Nrf2) and heme oxygenase-1 (HO-1). Furthermore, in vitro administration of an Nrf2 inhibitor abolished the anti-oxidant properties of engeletin. Conclusion: Engeletin ameliorated cardiac structural and electrical remodeling, ion channel remodeling, and oxidative stress induced by ISO in mice, thereby reducing VF susceptibility. These effects may be attributed to the anti-oxidant properties of engeletin associated with the Nrf2/HO-1 pathway.

With the diversity of manipulator grasping methods and the complexity of the unstructured environment, the grasping planning of the target object is very complicated. However, the external factor for the manipulator grasping is primarily the external shape of the target object. Therefore, it is of great significance to establish a grasping system based on target shape analysis. This paper proposes a method for determining the grasping posture of manipulator based on shape analysis and force closure. The irregular or complex objects are reduced to a combination of some basic shapes. The 3D data points of the object are split into blocks and each part is fitted to sphere, cylinder or rectangle by a best-fit algorithm. This allows the grasping posture of the manipulator to be determined quickly. The grasping characteristics of the object are analyzed and the grasping surface is described qualitatively by means of a superellipse. The grasping of objects is simplified according to the stable grasping condition of force closure. The force spiral space for grasping defines the grasping quality of the manipulator. The best grasping posture is obtained by evaluating the indicators. This method eliminates complex training processes, reduces the complexity of robotic grasping and highly universal.

Florpyrauxifen-benzyl is an herbicide that has been developed in recent years. Its degradation mode in paddy soil environments is not clear. In this study, the degradation dynamics in paddy soil and water were studied by ultrahigh-performance liquid chromatography. Microbial degradation was the main degradation pathway. Using third-generation high-throughput sequencing technology, the changes in the soil bacterial community structure were studied. After 30 days of application, compared with the control group (F0), the abundance of Sphingomonas, Lysobacter, and Flavisolibacter in the recommended and repeated application groups (F1, F5 and F10) increased significantly, and uncultured bacterium and Terrimonas decreased significantly. Compared with the F0 and F1 groups, the species diversity of the F0 and F1 groups showed a significant increase over time. The species diversity of the F5 and F10 groups decreased significantly on Days 5 and 15. On Day 30, the recovery even exceeded that of the control group. Luteimonas and five other genera were positively correlated with herbicide residues, and Pseudolabrys and two other genera were negatively correlated. Repeated application showed a significant effect on the structure of the soil bacterial community, mainly showing a trend of a significant decrease in the initial stage and gradual recovery in the later stage. The results will guide the safe and rational use of florpyrauxifen-benzyl and provide a scientific basis for florpyrauxifen-benzyl dynamic supervision of environmental pollution and protection of black soil in Northeast China.

Abstract Background The corpus callosum (CC) deficits have been well documented in chronic schizophrenia. However, the long-term impacts of antipsychotic monotherapies on callosal anatomy remain unclear. This cross-sectional study sought to explore micro- and macro-structural characteristics of the CC in never-treated patients and those with long-term mono-antipsychotic treatment. Methods The study included 23 clozapine-treated schizophrenia patients (CT-SCZ), 19 risperidone-treated schizophrenia patients (RT-SCZ), 23 never-treated schizophrenia patients (NT-SCZ), and 35 healthy controls (HCs). High resolution structural images and diffusion tensor imaging (DTI) data for each participant were obtained via a 3.0 T MR scanner. FreeSurfer was used to examine the volumes and fractional anisotropy (FA) values of the CC for each participant. Results There were significant deficits in the total and sub-regional CC volume and white matter integrity in NT-SCZ in comparison with healthy subjects. Compared with NT-SCZ, both CT-SCZ and RT-SCZ showed significantly increased FA values in the anterior CC region, while only RT-SCZ showed significantly increased volume in the mid-anterior CC region. Moreover, the volume of the mid-anterior CC region was significantly smaller in CT-SCZ compared to HCs. No correlations of clinical symptoms with callosal metrics were observed in schizophrenia patients. Conclusions Our findings provide insight into micro- and macro-structural characteristics of the CC in chronic schizophrenia patients with or without antipsychotics. These results suggest that the pathology itself is responsible for cerebral abnormalities in schizophrenia and that chronic exposure to antipsychotics may have an impact on white matter structure of schizophrenia patients, especially in those with risperidone treatment.

Abstract Background Increasing attention has been paid to the effect of Epimedium on the nervous system, particularly anti-depression function. In the present study, we applied network pharmacology to introduce a testable hypothesis on the multi-target mechanisms of Epicedium against depression. Methods By reconstructing the network of protein–protein interaction and drug–component–target, we predicted the key protein targets of Epicedium for the treatment of depression. Then, through molecular docking, the interaction of the main active components of Epicedium and predicted candidate targets were verified. Results Nineteen active compounds were selected from Epicedium. There were 200 targets associated with Epicedium and 537 targets related to depression. The key targets of Epicedium for treating depression were IL6, VEGFA, AKT1, and EGF. According to gene ontology functional enrichment analysis, 22 items of biological process (BP), 13 items of cell composition (CC) and 9 items of molecular function (MF) were obtained. A total of 56 signaling pathways (P < 0.05) were identified by Kyoto Encyclopedia of Genes and Genomes analysis, mainly involving depression-related pathways such as dopaminergic synapse, TNF signaling pathway, and prolactin signaling pathway. The results of molecular docking showed that the most important activity components, including luteoklin, quercetin and kaempferol, were well combined with the key targets. Conclusions Luteoklin, quercetin, kaempferol and other active compounds in Epicedium can regulate multiple signaling pathways and targets such as IL6, AKT1, and EGF, therefore playing therapeutic roles in depression.

Overhead transmission line is the main method of power transmission. Conductors, insulators, and towers are the primary electrical equipment of overhead transmission lines. Due to overhead transmission lines work in the natural environment, problems such as icing, corona discharge, contamination deposition, and corrosion will arise. As a result, some accidents may occur, which cause enormous economic losses. The above problems can be solved by coating functional coatings with superhydrophobic, semiconductive, anti-corrosion, and other characteristics on electrical equipment, which has the advantages of low cost and high efficiency. Therefore, functional coatings have become a research hotspot in the field of external insulation in recent years. In view of the various problems of different electrical equipment in overhead transmission lines, distinctive solutions need to be adopted, so this review classifies the coatings according to the usage scenarios and functions. In each category, first briefly outlines the causes of the electrical equipment problem, then introduces the mechanism of using this type of functional coating to solve the problem, next summarizes the development and application status of this type of coating, after summarizes the limitations of this coating, and finally provides a summary of the key issues in the research of functional coatings and gives an outlook on potential future research directions. This review intends to provide readers with a comprehensive overview of the performance principles and current application status of functional coatings for overhead transmission lines.

Diquat is used in agricultural contexts to control the growth of broadleaf and grassy weeds in both terrestrial and aquatic areas. Diquat can be readily absorbed by the soil and can remain therein for extended periods of time, altering the local microenvironment. In this study, the Meyerozyma guilliermondii Wyslmt yeast strain, which has the capacity to degrade Diquat, was isolated from soil exposed to long-term Diquat treatment. Over a 7-day incubation period, this strain was able to remove 42.51% of available Diquat (100 mg/L). RNA-Seq was performed to assess changes in gene expression in this yeast strain over the course of Diquat degradation, revealing 63 and 151 upregulated and downregulated genes, respectively. KEGG pathway enrichment analysis revealed these genes to be most highly enriched in the carbohydrate metabolism pathway. Through functional annotation and gene expression analyses, we identified seven genes were predicted to be involved in Diquat biodegradation. Results of qRT-PCR assays indicated that the relative mRNA expression levels of these seven genes were significantly higher relative to the control group. Together these analyses led to the identification of DN676 as a candidate Diquat-degrading gene. When a pET-DN676 vector was expressed in E. coli BL21, this strain was able to remove 12.49% of provided Diquat (100 mg/L) over the course of a 7-day incubation. These results thus confirmed that the DN676 gene can promote Diquat degradation, with these studies having yielded an engineered BL21-pET-DN676 bacterial strain capable of degrading Diquat.

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.

Abstract Motion control of the human hand is the most complex part of the human body. It has always been a challenge for a good balance between the cost, weight, responding speed, grasping force, finger extension, and dexterity of prosthetic hand. To solve these issues, a 3D-printed cable driven humanoid hand based on bidirectional elastomeric passive transmission (BEPT) is designed in this paper. A semi-static model of BEPT is investigated based on energy conservation law to analyze the mechanical properties of BEPT and a dynamical simulation of finger grasping is conducted. For a good imitation of human hand and an excellent grasping performance, specific BEPT is selected according to human finger grasping experiments. The advantage of BEPT based humanoid hand is that a good balance between the price and performance of the humanoid hand is achieved. Experiments proved that the designed prosthetic hand’s single fingertip force can reach 33 N and the fastest fingertip grasping speed realized 0.6 s/180°. It also has a good force compliance effect with only 430g’s weight. It can not only grab fragile objects like raw eggs and paper cup, but also achieve strong grasping force to damage metal cans. This humanoid hand has considerable application prospects in artificial prosthesis, human-computer interaction, and robot operation.

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.

There is a growing appreciation that a tight relationship exists between cholesterol homeostasis and immunity in leukocytes; however, this relationship has not been deeply explored in the vascular endothelium. Endothelial cells (ECs) rapidly respond to extrinsic signals, such as tissue damage or microbial infection, by upregulating factors to activate and recruit circulating leukocytes to the site of injury and aberrant activation of ECs leads to inflammatory based diseases, such as multiple sclerosis and atherosclerosis. Here, we studied the role of cholesterol and a key transcription regulator of cholesterol homeostasis, SREBP2, in the EC responses to inflammatory stress. Treatment of primary human ECs with pro-inflammatory cytokines upregulated SREBP2 cleavage and cholesterol biosynthetic gene expression within the late phase of the acute inflammatory response. Furthermore, SREBP2 activation was dependent on NF-κB DNA binding and canonical SCAP-SREBP2 processing. Mechanistically, inflammatory activation of SREBP was mediated by a reduction in accessible cholesterol, leading to heightened sterol sensing and downstream SREBP2 cleavage. Detailed analysis of NF-κB inducible genes that may impact sterol sensing resulted in the identification of a novel RELA-inducible target, STARD10, that mediates accessible cholesterol homeostasis in ECs. Thus, this study provides an in-depth characterization of the relationship between cholesterol homeostasis and the acute inflammatory response in EC.

In recent years, solid organic materials such as silicone rubber and epoxy resin have been widely used in electrical equipment due to their excellent insulation properties. However, as a result of manufacturing and design flaws as well as aging issues during operation, the insulating materials in the linked state no longer fit tightly and tiny structural defects (defect size less than 10 mm) develop, such as debonding at the composite interface, pores or cracks within the insulating material, etc. Tiny defects are prone to partial discharges and breakdowns, compromising the safety of high-voltage power equipment, particularly when subjected to strong electric fields. Therefore, it is necessary to carry out non-destructive testing (NDT) for such tiny defects. Such defects are small in size, easily buried in the material, and even some are wrapped in metal, which in turn requires very high detection accuracy, but traditional methods are difficult to achieve, so NDT technologies for tiny defects within insulating materials have become a research hotspot in the field of electric power in recent years. This paper firstly introduces the sources of tiny defects in solid organic insulating materials for electrical equipment. Secondly, the harm caused by structural defects is elaborated. Finally, emerging NDT methods and their advantages and limitations in defect detection are described in detail. The review aims to provide the reader with a comprehensive overview of most of the NDT techniques used in the detection of tiny defects within solid organic insulating materials for electrical equipment and their most salient features.

Soybean continuous cropping (SC) leads to continuous cropping obstacles, and soil-borne fungal diseases occur frequently. Rotation can alleviate continuous cropping obstacles. However, the long-term effects of continuous cropping and rotation on the structure and function of the fungal community in soil are not clear. In this study, five cropping systems, SC, fallow (CK), fallow-soybean (FS), corn–soybean (CS), and wheat–soybean (WS), were implemented in the long-term continuous cropping area of soybean. After 13 years of planting, high-throughput sequencing was used to evaluate the structure and diversity of soil fungal communities and to study the relationship between fungal communities and soil environmental factors. The results showed that the abundance and diversity of fungal flora in SC soil were the highest. There were significant differences in the formation of soil fungal communities between soybean continuous cropping and the other treatments. There were 355 species of endemic fungi in SC soil. There were 231 and 120 endemic species in WS and CS, respectively. The relative abundance of the potential pathogens Lectera, Gibberella, and Fusarium in the SC treatment soil was significantly high, and the abundance of all potential pathogens in CK was significantly the lowest. The abundance of Lectera and Fusarium in CS was significantly the lowest. There was a positive correlation between potential pathogens in the soil. The relative abundance of potential pathogens in the soil was significantly positively correlated with the relative abundance of Ascomycetes and negatively correlated with the relative abundance of Basidiomycetes. Potential pathogenic genera had a significant negative correlation with soil OM, available Mn, K and soil pH and a significant positive correlation with the contents of soil available Cu, Fe, and Zn. In general, the fungal communities of SC, FS, WS, and CS were divided into one group, which was significantly different from CK. WS and CS were more similar in fungal community structure. The CK and CS treatments reduced the relative abundance of soil fungi and potential pathogens. Our study shows that SC and FS lead to selective stress on fungi and pathogenic fungi and lead to the development of fungal community abundance and diversity, while CK and CS can reduce this development, which is conducive to plant health.

Objective To determine the effect of serum bilirubin level on the parameters of quantitative electroencephalogram (qEEG) in neonates and investigate their correlation. Methods Clinical data of the neonatal infants (with a gestational age of 35~42 weeks) admitted to the Department of Neonatology, the Third Xiangya Hospital from January to December 2019 were collected and analyzed. According to the serum level of total serum bilirubin (TSB) in 24 h before qEEG, the children were divided into group A (< 221 μmol/L), group B (221 ~342 μmol/L) and group C (≥342 μmol/L). The changes in qEEG parameters were compared among the 3 groups, and the correlation of the TSB level with the qEEG parameters were analyzed. Results There were 104 neonatal infants enrolled, including 56 in group A, 24 in group B, and 24 in group C. In the frontal region, there were no statistical differences in the total spectral power, spectral edge frequency (SEF), absolute power of frequency bands and relative power of frequency bands in the 3 groups. But the total spectral power in group C was significantly higher than that in the other 2 groups (P < 0.05). In the temporal region and occipital region, SEF differed significantly among the 3 groups (P < 0.05), and the SEF in group C was significantly lower than that in the other 2 groups (P < 0.05). SEFs were negatively correlated with TSB in the frontal region, temporal region, and occipital region (P < 0.05). Conclusion In neonates, serum TSB level is correlated with SEF of qEEG, SEF might become an important indicator to monitor the effect of neonatal hyperbilirubinemia on brain function.

Summary: Objective: Low back pain (LBP) is the predominant cause of disc degeneration in patients, which brings serious social problems and economic burdens. Increasing evidence has indicated that intervertebral disc degeneration (IDD) is one of the most common causes triggering LBP. Accumulating evidence has shown that circRNAs are involved in the pathological process of IDD. Nevertheless, the circRNA-mediated IDD pathogenesis still remains unknown. This study explored the potential mechanism and functions of circ-FAM169A in NPCs. Methods: Bioinformatics analysis was conducted to identify key circRNA, miRNA and mRNA and predict their potential role in IDD. Dual-luciferase reporter assay, western blot, qRT-PCR, and fluorescence in situ hybridisation (FISH) were used to demonstrate the interaction among circ-FAM169A, miR-583 and Sox9 in NPCs. Results: Herein, we identified circ-FAM169A, which was dramatically up-regulated in degenerative nucleus pulposus (NP) tissues and negatively correlated with expression levels of miR-583. We constructed a circ-FAM169A-miR-583-mRNAs co-expression network and predicted circ-FAM169A-miR-583 pathway predominantly involved in extracellular matrix metabolism and cell apoptosis etc. FISH experiments confirmed circ-FAM169A and miR-583 co-existence in the cytoplasm of NPCs. Luciferase reporter assay illustrated that circ-FAM169A was directly bound to miR-583 and Sox9 was the directly target gene of miR-583. Additionally, miR-583 negatively regulated Sox9 mRNA and protein levels in NPCs. Conclusion: Findings of this study indicated that circ-FAM169A-miR-583 pathway may play a significant role in the regulation of IDD, which will provide novel diagnostic biomarkers and develop effective treatment strategy of IDD diseases. The translational potential of this article: This study suggested that circ-FAM169A-miR-583 pathway may regulate NPCs apoptosis and extracellular matrix synthesis and catabolism by targeting Sox9. It provides a novel therapeutic target and strategy for IVDD diseases.

With the rapid development of sensor technology and artificial intelligence, the video gesture recognition technology under the background of big data makes human–computer interaction more natural and flexible, bringing the richer interactive experience to teaching, on‐board control, electronic games etc. To perform robust recognition under the conditions of illumination change, background clutter, rapid movement, and partial occlusion, an algorithm based on multi‐level feature fusion of two‐stream convolutional neural network is proposed, which includes three main steps. Firstly, the Kinect sensor obtains red–green–blue‐depth (RGB‐D) images to establish a gesture database. At the same time, data enhancement is performed on the training set and test set. Then, a model of multi‐level feature fusion of a two‐stream convolutional neural network is established and trained. Experiments show that the proposed network model can robustly track and recognise gestures under complex backgrounds (such as similar complexion, illumination changes, and occlusion), and compared with the single‐channel model, the average detection accuracy is improved by 1.08%, and mean average precision is improved by 3.56%.

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.

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.

Long non-coding RNAs (LncRNAs) are vital in the treatment of laryngeal squamous cell carcinoma (LSCC). This study estimated the mechanism of lncRNA CCAT1 (CCAT1) in LSCC cells. The expression of CCAT1 in the human laryngeal mucosal epithelial cells (HLCs) and LSCC cells (Hep-2 and TU177) was detected. CCK-8 and Transwell assays were used to evaluate the cell proliferative, migrative, and invasive abilities, respectively. The subcellular localization of CCAT1 was verified by RNA-FISH and cytoplasmic isolation assays. The targeted relationship among CCAT1, miR-218-5p, and BMI1 was verified by dual-luciferase assay. Expressions of miR-218-5p and BMI1 were detected by RT-qPCR. Our results depicted that CCAT1 was highly-expressed in Hep-2 and TU177 cells. Silencing CCAT1 inhibited the proliferation, migration, and invasion of Hep-2 and TU177 cells. Mechanically, CCAT1 regulated the BMI1 expression by competitively binding to miR-218-5p as a competing endogenous RNA (ceRNA), and thus facilitated the growth of Hep-2 and TU177 cells. Downregulation of miR-218-5p or upregulation of BMI1 inhibited the inhibitory effect of silencing CCAT1 on Hep-2 and TU177 cell proliferation, invasion, and migration. In conclusion, our study elicited that lncRNA CCAT1 facilitated the proliferation, migration, and invasion of Hep-2 and TU177 cells by sponging miR-218-5p and regulating the downstream BMI1.

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.

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.

Abstract Background Osteoporosis is a common metabolic bone disease without effective treatment. Bone marrow-derived mesenchymal stem cells (BMSCs) have the potential to differentiate into multiple cell types. Increased adipogenic differentiation or reduced osteogenic differentiation of BMSCs might lead to osteoporosis. Whether static magnetic fields (SMFs) might influence the adipo-osteogenic differentiation balance of BMSCs remains unknown. Methods The effects of SMFs on lineage differentiation of BMSCs and development of osteoporosis were determined by various biochemical (RT-PCR and Western blot), morphological (staining and optical microscopy), and micro-CT assays. Bioinformatics analysis was also used to explore the signaling pathways. Results In this study, we found that SMFs (0.2–0.6 T) inhibited the adipogenic differentiation of BMSCs but promoted their osteoblastic differentiation in an intensity-dependent manner. Whole genomic RNA-seq and bioinformatics analysis revealed that SMF (0.6 T) decreased the PPARγ-mediated gene expression but increased the RUNX2-mediated gene transcription in BMSCs. Moreover, SMFs markedly alleviated bone mass loss induced by either dexamethasone or all-trans retinoic acid in mice. Conclusions Taken together, our results suggested that SMF-based magnetotherapy might serve as an adjunctive therapeutic option for patients with osteoporosis.

Human computer interaction plays an increasingly important role in our life. People need more intelligent, concise and efficient human-computer interaction. It is of great significance to optimize the process of human-computer interaction by using appropriate calculation methods. In order to eliminate the interference data of thumb recognition based on sEMG signal in the process of human-computer interaction, simplify the data processing, and improve the working efficiency of general equipment of sEMG signal. In the process of gesture recognition using sEMG signals generated by thumb, a method of redundant electrode determination based on variance theory is proposed. The redundancy of five groups of action signals is divided into 16 levels and visualized. By comparing the results of thumb motion recognition when different redundant channels are removed, the optimal channel combination in the process of thumb motion recognition is obtained. Finally, two kinds of classifiers suitable for sEMG signal field are selected, and the classification results are compared, and the best method of thumb motion pattern recognition is obtained.

Soil organic carbon (SOC) in croplands is a key property of soil quality for ensuring food security and agricultural sustainability, and also plays a central role in the global carbon (C) budget. When managed sustainably, soils may play a critical role in mitigating climate change by sequestering C and decreasing greenhouse gas emissions into the atmosphere. However, the magnitude and spatio-temporal patterns of global cropland SOC are far from well constrained due to high land surface heterogeneity, complicated mechanisms, and multiple influencing factors. Here, we use a process-based agroecosystem model (DLEM-Ag) in combination with diverse spatially-explicit gridded environmental data to quantify the long-term trend of SOC storage in global cropland area during 1901-2010 and identify the relative impacts of climate change, elevated CO2, nitrogen deposition, land cover change, and land management practices such as nitrogen fertilizer use and irrigation. Model results show that the total SOC and SOC density in the 2000s increased by 125% and 48.8%, respectively, compared to the early 20th century. This SOC increase was primarily attributed to cropland expansion and nitrogen fertilizer use. Factorial analysis suggests that climate change reduced approximately 3.2% (or 2,166 Tg C) of the total SOC over the past 110 years. Our results indicate that croplands have a large potential to sequester C through implementing better land use management practices, which may partially offset SOC loss caused by climate change.

Estimation of absolute temperature distributions is crucial for many thermal processes in the nonlinear distributed parameter systems, such as predicting the curing temperature distribution of the chip, the temperature distribution of the catalytic rod, and so on. In this work, a spatiotemporal model based on the Karhunen-Loève (KL) decomposition, the multilayer perceptron (MLP), and the long short-term memory (LSTM) network, named KL-MLP-LSTM, is developed for estimating temperature distributions with a three-step procedure. Firstly, the infinite-dimensional model is transformed into a finite-dimensional model, where the KL decomposition method is used for dimension reduction and spatial basis functions extraction. Secondly, a novel MLP-LSTM hybrid time series model is constructed to deal with the two inherently coupled nonlinearities. Finally, the spatiotemporal temperature distribution model can be reconstructed through spatiotemporal synthesis. The effectiveness of the proposed model is validated by the data from a snap curing oven thermal process. Satisfactory agreement between the results of the current model and the other well-established model shows that the KL-MLP-LSTM model is reliable for estimating the temperature distributions during the thermal process.

Tool condition monitoring is becoming particularly important in the cutting process. More and more researchers start to integrate the sensors into the tool holder due to its convenient use and wide applicability. However, it is difficult to transmit out the condition data and supply stable power to the sensor-integrated systems through the traditional wired way as the tool holder is under high-speed rotation in milling or drilling process. In this paper, a novel simultaneous wireless power and data transfer system over one pair of coils for sensor-integrated rotating cutter is presented, and the eddy effect caused by the metal handle that impacts the system performance is solved with ferrite shielding. To transmit out the cutting condition data wirelessly, the synchronous detection and envelope detection technology are employed. Meanwhile, the synchronous rectification technology is adopted to improve the performance of the wireless power transfer. Eventually, the experimental platform has been set up and the cutting tests have also been carried out. The results show that the designed new wireless power and data transfer system over one pair of coils is stable and practical to track and transmit out the condition data of the rotating cutter.

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.

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.