Integrating Neural Radiance Fields End-to-End for Cognitive Visuomotor Navigation.

We propose an end-to-end visuomotor navigation framework that leverages Neural Radiance Fields (NeRF) for spatial cognition. To the best of our knowledge, this is the first effort to integrate such implicit spatial representation with embodied policy end-to-end for cognitive decision-making. Consequently, our system does not necessitate modularized designs nor transformations into explicit scene representations for downstream control. The NeRF-based memory is constructed online during navigation, without relying on any environmental priors. To enhance the extraction of decision-critical historical insights from the rigid and implicit structure of NeRF, we introduce a spatial information extraction mechanism named Structural Radiance Attention (SRA). SRA empowers the agent to grasp complex scene structures and task objectives, thus paving the way for the development of intelligent behavioral patterns. Our comprehensive testing in image-goal navigation tasks demonstrates that our approach significantly outperforms existing navigation models. We demonstrate that SRA markedly improves the agent's understanding of both the scene and the task by retrieving historical information stored in NeRF memory. The agent also learns exploratory awareness from our pipeline to better adapt to low signal-to-noise memory signals in unknown scenes. We deploy our navigation system on a mobile robot in real-world scenarios, where it exhibits evident cognitive capabilities while ensuring real-time performance.

Whole-Genome Analyses Reveal the Distinct Taxonomic Status of the Hainan Population of Endangered Rucervus eldii and Its Conservation Implications.

Eld's deer Rucervus eldii (McClelland, 1842) is an ungulate that lives in tropical lowland forests in several countries of Indochina and Hainan Island of China. Its remaining population is small and scattered, and the species is listed as an Endangered species on the IUCN Red List. The debate over the taxonomic status of the Hainan population has persisted for over a century-as an island-endemic subspecies R. e. hainanus, or an insular population of the subspecies R. e. siamensis, would have significant conservation implications. And, given the Hainan population had experienced both population bottleneck and multiple translocations in the past, conservation genomics would be a powerful tool to evaluate the genetic impacts of these events. In this study, we used conservation genomics assessment to study population differentiation and genetic diversity of R. e. siamensis in Cambodia and three Eld's deer subpopulations on Hainan Island. Based on the unique genetic profile and demographic analysis, this study corroborated previous studies using genetic markers that the Hainan Eld's deer warrants the taxonomic status of a distinct subspecies. The Hainan population exhibits a reduction in genetic diversity and an increase in the level of inbreeding when compared to the population of Cambodia. The signs of purifying selection were found against homozygous loss-of-function mutations to decrease the deleterious burden in the Hainan population. However, there was an accumulation of more deleterious missense mutations. Furthermore, significant differences in genetic diversity and level of inbreeding found among the three Hainan subpopulations indicated population isolation and suboptimal translocation strategies, which calls for urgent, coordinated, and science-based genetic management to ensure the long-term viability of the endemic subspecies hainanus. This study provides guidance for the conservation and management of Eld's deer.

Visuomotor Navigation for Embodied Robots With Spatial Memory and Semantic Reasoning Cognition.

The fundamental prerequisite for embodied agents to make intelligent decisions lies in autonomous cognition. Typically, agents optimize decision-making by leveraging extensive spatiotemporal information from episodic memory. Concurrently, they utilize long-term experience for task reasoning and foster conscious behavioral tendencies. However, due to the significant disparities in the heterogeneous modalities of these two cognitive abilities, existing literature falls short in designing effective coupling mechanisms, thus failing to endow robots with comprehensive intelligence. This article introduces a navigation framework, the hierarchical topology-semantic cognitive navigation (HTSCN), which seamlessly integrates both memory and reasoning abilities within a singular end-to-end system. Specifically, we represent memory and reasoning abilities with a topological map and a semantic relation graph, respectively, within a unified dual-layer graph structure. Additionally, we incorporate a neural-based cognition extraction process to capture cross-modal relationships between hierarchical graphs. HTSCN forges a link between two different cognitive modalities, thus further enhancing decision-making performance and the overall level of intelligence. Experimental results demonstrate that in comparison to existing cognitive structures, HTSCN significantly enhances the performance and path efficiency of image-goal navigation. Visualization and interpretability experiments further corroborate the promoting role of memory, reasoning, as well as their online learned relationships, on intelligent behavioral patterns. Furthermore, we deploy HTSCN in real-world scenarios to further verify its feasibility and adaptability.

Extended nnU-Net for Brain Metastasis Detection and Segmentation in Contrast-Enhanced Magnetic Resonance Imaging With a Large Multi-Institutional Data Set.

PURPOSE: The purpose of this study was to investigate an extended self-adapting nnU-Net framework for detecting and segmenting brain metastases (BM) on magnetic resonance imaging (MRI). METHODS AND MATERIALS: Six different nnU-Net systems with adaptive data sampling, adaptive Dice loss, or different patch/batch sizes were trained and tested for detecting and segmenting intraparenchymal BM with a size ≥2 mm on 3 Dimensional (3D) post-Gd T1-weighted MRI volumes using 2092 patients from 7 institutions (1712, 195, and 185 patients for training, validation, and testing, respectively). Gross tumor volumes of BM delineated by physicians for stereotactic radiosurgery were collected retrospectively and curated at each institute. Additional centralized data curation was carried out to create gross tumor volumes of uncontoured BM by 2 radiologists to improve the accuracy of ground truth. The training data set was augmented with synthetic BMs of 1025 MRI volumes using a 3D generative pipeline. BM detection was evaluated by lesion-level sensitivity and false-positive (FP) rate. BM segmentation was assessed by lesion-level Dice similarity coefficient, 95-percentile Hausdorff distance, and average Hausdorff distance (HD). The performances were assessed across different BM sizes. Additional testing was performed using a second data set of 206 patients. RESULTS: Of the 6 nnU-Net systems, the nnU-Net with adaptive Dice loss achieved the best detection and segmentation performance on the first testing data set. At an FP rate of 0.65 ± 1.17, overall sensitivity was 0.904 for all sizes of BM, 0.966 for BM ≥0.1 cm3, and 0.824 for BM <0.1 cm3. Mean values of Dice similarity coefficient, 95-percentile Hausdorff distance, and average HD of all detected BMs were 0.758, 1.45, and 0.23 mm, respectively. Performances on the second testing data set achieved a sensitivity of 0.907 at an FP rate of 0.57 ± 0.85 for all BM sizes, and an average HD of 0.33 mm for all detected BM. CONCLUSIONS: Our proposed extension of the self-configuring nnU-Net framework substantially improved small BM detection sensitivity while maintaining a controlled FP rate. Clinical utility of the extended nnU-Net model for assisting early BM detection and stereotactic radiosurgery planning will be investigated.

Exploring the body surface temperature of the lumbosacral region and relevant back-shu points in patients with lumbar disc herniation induced low back pain based on infrared thermography. / åŸºäºŽçº¢å¤–çƒ­æˆåƒæŠ€æœ¯æŽ¢è®¨è °æ¤Žé—´ç›˜çªå‡ºç—‡è °ç—›æ‚£è€ è °éª¶éƒ¨åŠç›¸å ³èƒŒä¿žç©´è¡¨çš®æ¸©åº¦.

OBJECTIVES: To observe the body surface temperature of the lumbosacral region and relevant back-shu points in patients with lumbar disc herniation (LDH) induced low back pain utilizing infrared thermography, and to explore the functional attribute changes of acupoints under pathological conditions. METHODS: A total of 50 patients with LDH induced low back pain were included as the observation group, and 45 healthy subjects were included as the control group. Using infrared thermography, the body surface temperature of the lumbosacral region and bilateral Sanjiaoshu (BL 22), Shenshu (BL 23), Qihaishu (BL 24), Dachangshu (BL 25), Guanyuanshu (BL 26), Xiaochangshu (BL 27), and Pangguangshu (BL 28) was measured in both groups. The temperature difference values between the bilateral lumbosacral regions and back-shu points of the two groups were calculated. Additionally, the body surface temperature of the affected and healthy sides of the lumbosacral region and relevant back-shu points was compared in the observation group. RESULTS: Compared with the control group, the body surface temperature of the lumbosacral region and the bilateral temperature difference values of the lumbosacral regions were increased in the observation group (P<0.001). The body surface temperature difference values of bilateral Shenshu (BL 23), Qihaishu (BL 24), Dachangshu (BL 25), Guanyuanshu (BL 26) and Xiaochangshu (BL 27) in the observation group were higher than those in the control group (P<0.05, P<0.01, P<0.001). In the observation group, the body surface temperature of the affected side of the lumbosacral region as well as Shenshu (BL 23) and Dachangshu (BL 25) was elevated compared with that of healthy side (P<0.001). CONCLUSIONS: The patients with LDH induced low back pain have imbalanced and asymmetrical distribution of body surface temperature in the lumbosacral region and related back-shu points, Shenshu (BL 23) and Dachangshu (BL 25) have the relative specificity.

Nonlinear Observer-Based Visual Servoing and Vibration Control of Flexible Robotic Manipulators With a Fixed Camera.

This article studies the visual servoing and vibration suppression control for flexible manipulators when the system states are unmeasurable and only the image feedback is available. The dynamic equations of flexible manipulators are decomposed into the slow and fast subsystems based on the singular perturbation theory. The nonlinear observers based on the state transformation using the Lie derivatives are proposed to estimate the unmeasurable system states and unknown camera intrinsic parameters at the same time. Then, the image-based controllers utilizing the estimated states are, respectively, designed in the slow and fast subsystems to regulate the image positions of feature points and suppress the vibration of flexible manipulators simultaneously. In the proposed approach, only the visual feedback is required to generate the control input for flexible manipulators, which simplifies the controller implementation. The stability of the proposed control scheme is proved based on the Lyapunov theory. Finally, experimental results on a flexible single-link manipulator are provided to demonstrate the effectiveness of the proposed control approach.

A Tool to Integrate Electrophysiological Mapping for Cardiac Radioablation of Ventricular Tachycardia.

Purpose: Cardiac radioablation is an emerging therapy for recurrent ventricular tachycardia. Electrophysiology (EP) data, including electroanatomic maps (EAM) and electrocardiographic imaging (ECGI), provide crucial information for defining the arrhythmogenic target volume. The absence of standardized workflows and software tools to integrate the EP maps into a radiation planning system limits their use. This study developed a comprehensive software tool to enable efficient utilization of the mapping for cardiac radioablation treatment planning. Methods and Materials: The tool, HeaRTmap, is a Python-scripted plug-in module on the open-source 3D Slicer software platform. HeaRTmap is able to import EAM and ECGI data and visualize the maps in 3D Slicer. The EAM is translated into a 3D space by registration with cardiac magnetic resonance images (MRI) or computed tomography (CT). After the scar area is outlined on the mapping surface, the tool extracts and extends the annotated patch into a closed surface and converts it into a structure set associated with the anatomic images. The tool then exports the structure set and the images as The Digital Imaging and Communications in Medicine Standard in Radiotherapy for a radiation treatment planning system to import. Overlapping the scar structure on simulation CT, a transmural target volume is delineated for treatment planning. Results: The tool has been used to transfer Ensite NavX EAM data into the Varian Eclipse treatment planning system in radioablation on 2 patients with ventricular tachycardia. The ECGI data from CardioInsight was retrospectively evaluated using the tool to derive the target volume for a patient with left ventricular assist device, showing volumetric matching with the clinically used target with a Dice coefficient of 0.71. Conclusions: HeaRTmap smoothly fuses EP information from different mapping systems with simulation CT for accurate definition of radiation target volume. The efficient integration of EP data into treatment planning potentially facilitates the study and adoption of the technique.

A taxonomic revision of the nominotypical subgenus Tiphia Fabricius, 1775(Hymenoptera: Tiphiidae: Tiphiinae) from China, with three new species and a key to the Chinese species.

In this study, twenty-one species of the nominotypical subgenus Tiphia Fabricius from China are described and illustrated in detail, including three new species and ten new records as follows: Tiphia (Tiphia) flavobrunnea sp. nov., T. (T.) longistria sp. nov., T. (T.) mediocarinata sp. nov., T. (T.) aurata Allen, 1975, T. (T.) borneana Cameron, 1907, T. (T.) dayi Krombein, 1982, T. (T.) dutti Allen, 1975, T. (T.) incisa Cameron, 1897, T. (T.) lawrencei Allen, 1975, T. (T.) pulchaukiae Allen, 1975, T. (T.) s-secunda Allen, 1975, T. (T.) toreuta Boni Bartalucci, 2010 and T. (T.) weismani Krombein, 1982. In addition, the females of four species are firstly described, namely T. (T.) chihpenchia Tsuneki, 1986a, T. (T.) cilicincta Allen & Jaynes, 1930, T. (T.) mongolica Tsuneki, 1986b, and T. (T.) wuyiana Chen & Yang, 2003. And the males of four following species are also firstly described: T. (T.) choui Chen & Yang, 1991, T. (T.) nervidirecta Allen & Jaynes, 1930, T. (T.) pigmentata Allen & Jaynes 1930, and T. (T.) taiwana Ishikawa, 1967. Based on our collections and relevant references, a key to the Chinese species of the subgenus Tiphia is firstly compiled.

Automatic segmentation of vestibular schwannomas from T1-weighted MRI with a deep neural network.

BACKGROUND: Long-term follow-up using volumetric measurement could significantly assist in the management of vestibular schwannomas (VS). Manual segmentation of VS from MRI for treatment planning and follow-up assessment is labor-intensive and time-consuming. This study aims to develop a deep learning technique to fully automatically segment VS from MRI. METHODS: This study retrospectively analyzed MRI data of 737 patients who received gamma knife radiosurgery for VS. Treatment planning T1-weighted isotropic MR and manually contoured gross tumor volumes (GTV) were used for model development. A 3D convolutional neural network (CNN) was built on ResNet blocks. Spatial attenuation and deep supervision modules were integrated in each decoder level to enhance the training for the small tumor volume on brain MRI. The model was trained and tested on 587 and 150 patient data, respectively, from this institution (n = 495) and a publicly available dataset (n = 242). The model performance were assessed by the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), average symmetric surface (ASSD) and relative absolute volume difference (RAVD) of the model segmentation results against the GTVs. RESULTS: Measured on combined testing data from two institutions, the proposed method achieved mean DSC of 0.91 ± 0.08, ASSD of 0.3 ± 0.4 mm, HD95 of 1.3 ± 1.6 mm, and RAVD of 0.09 ± 0.15. The DSCs were 0.91 ± 0.09 and 0.92 ± 0.06 on 100 testing patients of this institution and 50 of the public data, respectively. CONCLUSIONS: A CNN model was developed for fully automated segmentation of VS on T1-Weighted isotropic MRI. The model achieved good performance compared with physician clinical delineations on a sizeable dataset from two institutions. The proposed method potentially facilitates clinical workflow of radiosurgery for VS patient management.

Design, Control, and Experiments of a Novel Robotic Uterine Manipulator With the Motorized 3-DoF Manipulation Rod.

To address the issue of declining performance over time with manual uterine manipulation during minimally invasive gynecologic surgery, we propose a novel uterine manipulation robot that consists of a 3-DoF remote center of motion (RCM) mechanism and a 3-DoF manipulation rod. This allows for tireless, stable, and safer manipulation in place of a human assistant. For the RCM mechanism, we propose a single-motor bilinear-guided mechanism that can achieve a wide range of pitch motion (-50  âˆ¼  34 degrees) while maintaining a compact structure. This novel uterine manipulation robot is equipped with a manipulation rod that has a tip diameter of only 6 mm, allowing it to accommodate almost any patient's cervix. The 30-degree distal pitch motion and ±45-degree distal roll motion of the instrument further improve uterine visualization. Additionally, the tip of the rod can be opened into a T-shape to minimize damage to the uterus. Laboratory experiments have shown the mechanical RCM accuracy of 0.373 mm and the maximum load of the distal pitch joint of 500 g. Feasibility has been demonstrated through ex-vivo and cadaver tests, as well as clinical trials.

[The specificity of body surface temperature of relevant back-shu points in patients with chronic persistent asthma based on infrared thermal imaging technology].

OBJECTIVE: To detect the body surface temperature of the relevant back-shu points in patients with chronic persistent asthma by infrared thermal imaging technology, and observe the specific changes of the body surface temperature of the relevant back-shu points under the condition of lung disease. METHODS: Forty-five patients with chronic persistent asthma (observation group) and 45 healthy subjects (control group) were selected. The body surface temperature of bilateral Feishu (BL 13), Geshu (BL 17), Pishu (BL 20) and Shenshu (BL 23) were measured by BK-MT02A medical infrared thermography. RESULTS: The body surface temperature of bilateral Feishu (BL 13), Geshu (BL 17), Pishu (BL 20) and Shenshu (BL 23) in the observation group was higher than that in the control group (P<0.01, P<0.05). The body surface temperature of bilateral Feishu (BL 13) and Geshu (BL 17) was higher than that of ipsilateral Pishu (BL 20) and Shenshu (BL 23) in the two groups (P<0.01, P<0.05). There was no statistically significant difference in body surface temperature between ipsilateral Feishu (BL 13) and Geshu (BL 17), between ipsilateral Pishu (BL 20) and Shenshu (BL 23) (P>0.05). CONCLUSION: The pathological increase of body surface temperature of Feishu (BL 13), Geshu (BL 17), Pishu (BL 20) and Shenshu (BL 23) in patients with chronic persistent asthma indicates that above acupoints have specificity in reflecting lung diseases. The Feishu (BL 13) and Geshu (BL 17), which have significantly increased body surface temperature, not only provide objective basis for the pathological pathogenesis of "deficiency in origin and excess in symptom" in patients with chronic persistent asthma, but also reflect the different expressions of different acupoints on the same meridian for the lung diseases.

Microfluidic Approaches for Microactuators: From Fabrication, Actuation, to Functionalization.

Microactuators can autonomously convert external energy into specific mechanical motions. With the feature sizes varying from the micrometer to millimeter scale, microactuators offer many operation and control possibilities for miniaturized devices. In recent years, advanced microfluidic techniques have revolutionized the fabrication, actuation, and functionalization of microactuators. Microfluidics can not only facilitate fabrication with continuously changing materials but also deliver various signals to stimulate the microactuators as desired, and consequently improve microfluidic chips with multiple functions. Herein, this cross-field that systematically correlates microactuator properties and microfluidic functions is comprehensively reviewed. The fabrication strategies are classified into two types according to the flow state of the microfluids: stop-flow and continuous-flow prototyping. The working mechanism of microactuators in microfluidic chips is discussed in detail. Finally, the applications of microactuator-enriched functional chips, which include tunable imaging devices, micromanipulation tools, micromotors, and microsensors, are summarized. The existing challenges and future perspectives are also discussed. It is believed that with the rapid progress of this cutting-edge field, intelligent microsystems may realize high-throughput manipulation, characterization, and analysis of tiny objects and find broad applications in various fields, such as tissue engineering, micro/nanorobotics, and analytical devices.

Efficient 3D Deep LiDAR Odometry.

An efficient 3D point cloud learning architecture, named EfficientLO-Net, for LiDAR odometry is first proposed in this article. In this architecture, the projection-aware representation of the 3D point cloud is proposed to organize the raw 3D point cloud into an ordered data form to achieve efficiency. The Pyramid, Warping, and Cost volume (PWC) structure for the LiDAR odometry task is built to estimate and refine the pose in a coarse-to-fine approach. A projection-aware attentive cost volume is built to directly associate two discrete point clouds and obtain embedding motion patterns. Then, a trainable embedding mask is proposed to weigh the local motion patterns to regress the overall pose and filter outlier points. The trainable pose warp-refinement module is iteratively used with embedding mask optimized hierarchically to make the pose estimation more robust for outliers. The entire architecture is holistically optimized end-to-end to achieve adaptive learning of cost volume and mask, and all operations involving point cloud sampling and grouping are accelerated by projection-aware 3D feature learning methods. The superior performance and effectiveness of our LiDAR odometry architecture are demonstrated on KITTI, M2DGR, and Argoverse datasets. Our method outperforms all recent learning-based methods and even the geometry-based approach, LOAM with mapping optimization, on most sequences of KITTI odometry dataset. We open sourced our codes at: https://github.com/IRMVLab/EfficientLO-Net.

Role of Ezrin in Asthma-Related Airway Inflammation and Remodeling.

Ezrin is an actin binding protein connecting the cell membrane and the cytoskeleton, which is crucial to maintaining cell morphology, intercellular adhesion, and cytoskeleton remodeling. Asthma involves dysfunction of inflammatory cells, cytokines, and airway structural cells. Recent studies have shown that ezrin, whose function is affected by extensive phosphorylation and protein interactions, is closely associated with asthma, may be a therapeutic target for asthma treatment. In this review, we summarize studies on ezrin and discuss its role in asthma-related airway inflammation and remodeling.

Synthesis of naphthalene-substituted aromatic esters via Rh(III)-catalyzed C-H bond naphthylation and cascade directing group transformation.

A regioselective Rh(III)-catalyzed C-H bond naphthylation and cascade directing group (DG) transformation has been realized with aryl imidates and oxa bicyclic alkenes. Diverse naphthalene-substituted aromatic esters were synthesized fruitfully. The DG transformation into ester endows the strategy with chances of further C-H bond functionalization and derivatization. Preliminary mechanistic studies demonstrate that the ester carbonyl oxygen possibly originates from the corresponding oxa bicyclic alkene.

Learning of Long-Horizon Sparse-Reward Robotic Manipulator Tasks With Base Controllers.

Deep reinforcement learning (DRL) enables robots to perform some intelligent tasks end-to-end. However, there are still many challenges for long-horizon sparse-reward robotic manipulator tasks. On the one hand, a sparse-reward setting causes exploration inefficient. On the other hand, exploration using physical robots is of high cost and unsafe. In this article, we propose a method of learning long-horizon sparse-reward tasks utilizing one or more existing traditional controllers named base controllers in this article. Built upon deep deterministic policy gradients (DDPGs), our algorithm incorporates the existing base controllers into stages of exploration, value learning, and policy update. Furthermore, we present a straightforward way of synthesizing different base controllers to integrate their strengths. Through experiments ranging from stacking blocks to cups, it is demonstrated that the learned state-based or image-based policies steadily outperform base controllers. Compared to previous works of learning from demonstrations, our method improves sample efficiency by orders of magnitude and improves performance. Overall, our method bears the potential of leveraging existing industrial robot manipulation systems to build more flexible and intelligent controllers.

[Components of drugs in acupoint sticking therapy and its mechanism of intervention on bronchial asthma based on UPLC-Q-TOF-MS combined with network pharmacology and experimental verification].

UPLC-Q-TOF-MS combined with network pharmacology and experimental verification was used to explore the mechanism of acupoint sticking therapy(AST) in the intervention of bronchial asthma(BA). The chemical components of Sinapis Semen, Cory-dalis Rhizoma, Kansui Radix, Asari Radix et Rhizoma, and Zingiberis Rhizoma Recens were retrieved from TCMSP as self-built database. The active components in AST drugs were analyzed by UPLC-Q-TOF-MS, and the targets were screened out in TCMSP and Swiss-TargetPrediction. Targets of BA were collected from GeneCards, and the intersection of active components and targets was obtained by Venny 2.1.0. The potential targets were imported into STRING and DAVID for PPI, GO, and KEGG analyses. The asthma model induced by house dust mite(HDM) was established in mice. The mechanism of AST on asthmatic mice was explored by pulmonary function, Western blot, and flow cytometry. The results indicated that 54 active components were obtained by UPLC-Q-TOF-MS and 162 potential targets were obtained from the intersection. The first 53 targets were selected as key targets. PPI, GO, and KEGG analyses showed that AST presumedly acted on SRC, PIK3 CA, and other targets through active components such as sinoacutine, sinapic acid, dihydrocapsaicin, and 6-gingerol and regulated PI3 K-AKT, ErbB, chemokine, sphingolipid, and other signaling pathways to intervene in the pathological mechanism of BA. AST can improve lung function, down-regulate the expression of PI3 K and p-AKT proteins in lung tissues, enhance the expression of PETN protein, and reduce the level of type Ⅱ innate immune cells(ILC2 s) in lung tissues of asthmatic mice. In conclusion, AST may inhibit ILC2 s by down-regulating the PI3 K-AKT pathway to relieve asthmatic airway inflammation and reduce airway hyperresponsiveness.

Visual Servoing Pushing Control of the Soft Robot with Active Pushing Force Regulation.

Soft robots characterize operational safety due to inherent compliance from their soft mechanism, whereas their mechanism enhances the difficulty in accurate closed-loop control. To explore their applicability in manipulation tasks, in this article, we propose a visual servoing pushing controller considering the effect of contact. The controller is designed to simultaneously complete the positioning and manipulation tasks. To further improve the manipulation performance, an active pushing force regulation method is proposed and integrated into the controller. The proposed control algorithm is validated experimentally. The results show that the controller guarantees the convergence to the image error and meanwhile, it improves the pushing manipulation performance.

[Anti-asthma components and mechanism of Kechuanting acupoint application therapy: based on serum metabolomics and network pharmacology].

This study aims to explore the anti-asthma components and mechanism of Kechuanting acupoint application therapy(KAAT) based on serum metabolomics and network pharmacology. A total of 60 asthma patients who had used low-dose inhaled corticosteroids-formoterol(ICS-formoterol) for a long time were randomized into the western medicine group(low-dose ICS-formoterol) and western medicine+Kechuanting group(KAAT+low-dose ICS-Formoterol), 30 in either group. In addition, 30 healthy people were included as the control(no intervention). The asthma control test(ACT) score, forced expiratory volume in 1 second(FEV1), and peak expiratory flow(PEF) were measured in the western medicine group and western medicine+Kechuanting group before and after treatment. The potential biomarkers of KAAT in the treatment of asthma were screened by gas chromatography-mass spectrometry combined with multivariate analysis, and the related metabolic pathways were further analyzed. UPLC/LTQ-Orbitrap-MS, together with network pharmacology, was employed to construct the component-target-pathway network. Thereby, the effective components and me-chanism of KAAT in the treatment of asthma were clarified. According to the ACT score, FEV1, and PEF, KAAT was effective in the treatment of asthma. A total of 10 endogenous biomarkers of KAAT in the treatment of asthma were screened by serum metabolomics, and the pathways of the metabolism of glycine, serine and threonine, and the metabolism of glyoxylic acid and dicarboxylic acid were obtained. UPLC/LTQ-Orbitrap-MS identified 51 chemical components of KAAT: 24 flavonoids, 11 alkaloids, 8 phenols, 2 diterpenoids, 2 triterpenoids, 2 glycosides, and 2 aldehydes. Network pharmacology analysis suggested that KAAT mainly acted on serum crea-tinine(SRC), matrix metalloproteinase 9(MMP-9), and other target proteins. The treatment was closely related to metabolic pathway, phosphatidylinositol 3-kinase-protein kinase B(PI3 K-Akt), mitogen-activated protein kinase(MAPK), and calcium signaling pathway. Sinapine thiocyanate, corydaline, dihydroberberine, stylopine, leonticine, N-methyl tetrahydroberberine, kaempferide, erio-dictyol, quercetin, catechin, 6-gingerol, 6-shogaol, ingenol, and luteolin may be potential effective compounds of KAAT in the treatment of asthma. This study preliminarily revealed that the effective components and mechanism of KAAT in treatment of asthma based on serum metabolomics and network pharmacology. It lays a theoretical foundation for in-depth study of the mechanism and clinical development and application.

Visual Servoing of Flexible-Link Manipulators by Considering Vibration Suppression Without Deformation Measurements.

Visual servoing and vibration suppression of spatial flexible-link manipulators with a fixed camera setup are addressed in this article. The singular perturbation method is adopted to decouple the dynamic equations of the flexible manipulator; hence, two subsystems that represent the rigid robot motion and flexible-link vibration are obtained, respectively. Then, for the slow subsystem related to the rigid motion, an image-based controller is designed to converge the image errors with the consideration of compensating for the errors of approximating the Jacobian matrix. For the fast subsystem corresponding to the elastic vibration, to eliminate the requirements of measuring the vibration states, an observer is designed to estimate the fast states and then a feedback controller of the fast subsystem is presented to suppress the vibration of the flexible manipulator by using the estimation values. The closed-loop stabilities of the slow and fast subsystem are both proved by employing the Lyapunov theory. Numerical simulations demonstrate the effectiveness of the proposed controller, which shows that the image errors approach zero with the vibration of the flexible manipulator damped out simultaneously.