Multiple Observer Adaptive Fusion for Uncertainty Estimation and Its Application to Wheel Velocity Systems.

Uncertainty estimation in real-world scenarios is challenged by complexities arising from peaking phenomena and measurement noises. This article introduces a novel scheme for practical uncertainty estimation to mitigate peaking dynamics and enhance overall dynamic behavior. A fusion estimation framework for lumped uncertainties using multiple extended state observers (ESOs) is constructed, and the low-frequency adaptive parameter learning technique is employed to approximate the optimal fusion. The adaptive fusion estimation not only attenuates transient peaks in uncertainty estimation but also attains fast convergence and high accuracy under the high-gain scheduling of ESOs. Furthermore, the robustness of uncertainty estimation against measurement noises is enhanced by cascading filters in the proposed adaptive fusion framework for multiple ESOs. Extensive theoretical analyses are executed to verify practical applicability in peak and noise rejection. Finally, simulations and experiments on the wheel velocity system of a mobile robot are conducted to test the validity and feasibility.

Adaptive Neural Cooperative Control of Multirobot Systems With Input Quantization.

This article develops the adaptive neural cooperative control scheme for a group of mobile robots with a limited sensing range in presence of input quantization by a dynamic surface control technique. First, to make the controller design feasible, the original robotic system is transformed into a new fully actuated system using a transverse function. Then, taking into consideration the effects of a hysteresis quantizer, an adaptive neural cooperative controller is developed based on the universal approximation property of the radial basis function neural networks and the connectivity preservation strategy. Furthermore, the proposed control scheme can guarantee that all closed-loop signals are semi-globally uniformly ultimately bounded. Meanwhile, desired constraints are not breached and tracking errors are within the predefined domains. Finally, several simulation results are carried out to testify the feasibility and efficiency of the theoretical findings revealed in this article.

Cu-Catalyzed Divergent Transformations of Allenylethylene Carbonates with Diboron Reagents.

Divergent transformations of allenylethylene carbonates with diboron reagents catalyzed by copper are disclosed. By using CuCl/IPr·HCl as the catalyst, the allenylethylene carbonates react with B2hex2 to afford 2,4-dien-1-ols as the product in the presence of Cs2CO3 as the base, iPrOH as the additive, and 1,4-dioxane as the solvent. And they react with B2pin2 to form boronic half acids in the presence of NaOtBu as the base, water as the additive, and THF as the solvent. The reactions afford corresponding products in good stereoselectivities and yields, and further derivatizations of boronic half acids and study of the mechanism are also demonstrated.

Trajectory Tracking Control of Human Support Robots via Adaptive Sliding-Mode Approach.

In this article, the tracking problem of the adaptive sliding-mode control (SMC) design for human support robots based on a disturbance observer is investigated. First, a finite-time controller using nonsingular fast terminal SMC is proposed. Then, a robust disturbance observer is developed to estimate system uncertainties and disturbances. Simultaneously, to deal with the unknown bounded disturbance observer error, an adaptive control technology is developed. Furthermore, the proposed controller is synthesized to ensure that the tracking errors can be stabilized in finite time. Finally, simulations are performed to demonstrate that human support robots employing the proposed controller can converge to the desired trajectory.

Sliding Mode Fuzzy Control of Stochastic Nonlinear Systems Under Cyber-Attacks.

In this article, the problem of integral sliding mode control (ISMC) for a class of nonlinear systems with stochastic characteristics under cyber-attack is investigated. The control system and the cyber-attack are modeled as an Itô-type stochastic differential equation. The stochastic nonlinear systems are approached by the Takagi-Sugeno fuzzy model. A dynamic ISMC scheme is applied and the states and control input are analyzed within a universal dynamic model. It is demonstrated that trajectory of the system can be confined to the integral sliding surface within finite time, and the stability of closed-loop system under cyber-attack will be guaranteed by using a set of linear matrix inequalities. Following a standard procedure of universal fuzzy ISMC, it is shown that all signals in the closed-loop system will be guaranteed bounded, and the states are asymptotic stochastic stable if some conditions are met. An inverted pendulum is applied to show the effectiveness of our control scheme.

Cognitive reappraisal and empathy chain-mediate the association between relative deprivation and prosocial behavior in adolescents.

Background: Relative deprivation is one of the factors that influences the development of personality and behavior. However, it is still unclear whether and how relative deprivation decreases the prosocial behavior in adolescents. This study aimed to examine the association between relative deprivation and adolescent prosocial behavior and the role of emotion regulation strategies and empathy in modifying this association. Methods: The present study included 609 secondary school students (M = 15.42 years, SD = 0.653) in Fujian Province, China. All participants completed the Relative Deprivation Questionnaire, Emotion Regulation Scale, the Basic Empathy Scale, and Prosocial Behavior Scale. The collected data were analyzed using SPSS 25.0 and Mplus 7.4. Results: Relative deprivation was negatively correlated with cognitive reappraisal, but positively correlated with expressive suppression. Cognitive reappraisal was positively correlated with empathy and prosocial behavior, but expressive suppression was not. Empathy was positively correlated with prosocial behavior. Relative deprivation decreased prosocial behavior through (a) cognitive reappraisal, (b) empathy, and (c) chain mediation of cognitive reappraisal and empathy. No significant mediating effect of expressive suppression was found. Conclusion: The results indicate that relative deprivation decreases adolescent prosocial behavior, and that cognitive reappraisal and empathy are the potential psychological mechanisms that affect the association between relative deprivation and adolescent prosocial behavior.

IgG Fc-Binding Peptide-Conjugated Pan-CoV Fusion Inhibitor Exhibits Extended In Vivo Half-Life and Synergistic Antiviral Effect When Combined with Neutralizing Antibodies.

The peptide-based pan-coronavirus fusion inhibitor EK1 is in phase III clinical trials, and it has, thus far, shown good clinical application prospects against SARS-CoV-2 and its variants. To further improve its in vivo long-acting property, we herein developed an Fc-binding strategy by conjugating EK1 with human immunoglobulin G Fc-binding peptide (IBP), which can exploit the long half-life advantage of IgG in vivo. The newly engineered peptide IBP-EK1 showed potent and broad-spectrum inhibitory activity against SARS-CoV-2 and its variants, including various Omicron sublineages and other human coronaviruses (HCoVs) with low cytotoxicity. In mouse models, IBP-EK1 possessed potent prophylactic and therapeutic efficacy against lethal HCoV-OC43 challenge, and it showed good safety profile and low immunogenicity. More importantly, IBP-EK1 exhibited a significantly extended in vivo half-life in rhesus monkeys of up to 37.7 h, which is about 20-fold longer than that reported for EK1. Strikingly, IBP-EK1 displayed strong in vitro or ex vivo synergistic anti-HCoV effect when combined with monoclonal neutralizing antibodies, including REGN10933 or S309, suggesting that IBP-conjugated EK1 can be further developed as a long-acting, broad-spectrum anti-HCoV agent, either alone or in combination with neutralizing antibodies, to combat the current COVID-19 pandemic or future outbreaks caused by emerging and re-emerging highly pathogenic HCoVs.

Long-Term Tracking of Evasive Urban Target Based on Intention Inference and Deep Reinforcement Learning.

Unmanned aerial vehicles (UAVs) have been widely used in urban target-tracking tasks, where long-term tracking of evasive targets is of great significance for public safety. However, the tracked targets are easily lost due to the evasive behavior of the targets and the unstructured characteristics of the urban environment. To address this issue, this article proposes a hybrid target-tracking approach based on target intention inference and deep reinforcement learning (DRL). First, a target intention inference model based on convolution neural networks (CNNs) is built to infer target intentions by fusing urban environment information and observed target trajectory. Then, the prediction of the target trajectory can be inspired by the inferred target intentions, which can further provide effective guidance to the target search process. In order to fully explore the policy space, the target search policy is developed under a DRL framework, where the search policy is modeled as a deep neural network (DNN) and trained by interacting with the task environment. The simulation results show that the inference of the target intentions can effectively guide the UAV to search for the target and significantly improve the target-tracking performance. Meanwhile, the generalization results indicate that the proposed DRL-based search policy has high robustness to the uncertainty of the target behavior.

Prediction of knee joint pain in Tai Chi practitioners: a cross-sectional machine learning approach.

OBJECTIVE: To build a supervised machine learning-based classifier, which can accurately predict whether Tai Chi practitioners may experience knee pain after years of exercise. DESIGN: A prospective approach was used. Data were collected using face-to-face through a self-designed questionnaire. SETTING: Single centre in Shanghai, China. PARTICIPANTS: A total of 1750 Tai Chi practitioners with a course of Tai Chi exercise over 5 years were randomly selected. MEASURES: All participants were measured by a questionnaire survey including personal information, Tai Chi exercise pattern and Irrgang Knee Outcome Survey Activities of Daily Living Scale. The validity of the questionnaire was analysed by logical analysis and test, and the reliability of this questionnaire was mainly tested by a re-test method. Dataset 1 was established by whether the participant had knee pain, and dataset 2 by whether the participant's knee pain affected daily living function. Then both datasets were randomly assigned to a training and validating dataset and a test dataset in a ratio of 7:3. Six machine learning algorithms were selected and trained by our dataset. The area under the receiver operating characteristic curve was used to evaluate the performance of the trained models, which determined the best prediction model. RESULTS: A total of 1703 practitioners completed the questionnaire and 47 were eliminated for lack of information. The total reliability of the scale is 0.94 and the KMO (Kaiser-Meyer-Olkin measure of sampling adequacy) value of the scale validity was 0.949 (>0.7). The CatBoost algorithm-based machine-learning model achieved the best predictive performance in distinguishing practitioners with different degrees of knee pain after Tai Chi practice. 'Having knee pain before Tai Chi practice', 'knee joint warm-up' and 'duration of each exercise' are the top three factors associated with pain after Tai Chi exercise in the model. 'Having knee pain before Tai Chi practice', 'Having Instructor' and 'Duration of each exercise' were most relevant to whether pain interfered with daily life in the model. CONCLUSION: CatBoost-based machine learning classifier accurately predicts knee pain symptoms after practicing Tai Chi. This study provides an essential reference for practicing Tai Chi scientifically to avoid knee pain.

Antibiotic-Induced Dysbiosis of the Gut Microbiota Impairs Gene Expression in Gut-Liver Axis of Mice.

Antibiotics can be a double-edged sword. The application of broad-spectrum antibiotics leads to the suppression of microorganisms in the human body without selective targeting, including numerous non-pathogenic microorganisms within the gut. As a result, dysbiosis of the gut microbiota can occur. The gut microbiota is a vast and intricate ecosystem that has been connected with various illnesses. Significantly, the gut and liver function in a closely coupled anatomical and physiological relationship referred to as the "gut-liver axis". Consequently, metabolites stemming from the gut microbiota migrate via the portal vein to the liver, thereby influencing gene expression and proper physiological activity within the liver. This study aimed to investigate the dysbiosis of gut microbiota ecology and the disruption of gene expression resulting from oral antibiotics and their subsequent recovery. In the experiment, mice were tube-fed neomycin (0.5 mg/mL) and ampicillin (1 mg/mL) for 21 days (ABX group) to conduct 16s rRNA sequencing. By simultaneously analyzing public datasets PRJDB6615, which utilized the same antibiotics, it was found that nearly 50% of the total microbiota abundance was attributed to the f__Lactobacillaceae family. Additionally, datasets GSE154465 and GSE159761, using the same antibiotics, were used to screen for differentially expressed genes pre-and post-antibiotic treatment. Quantitative real-time PCR was employed to evaluate gene expression levels before and after antibiotic treatment. It was discovered that oral antibiotics significantly disrupted gene expression in the gut and liver, likely due to the dysregulation of the gut microbiota ecology. Fecal microbiota transplantation (FMT) was found to be an effective method for restoring gut microbiota dysbiosis. To further enhance the restoration of gut microbiota and gene expression, an antioxidant, vitamin C, was added to the FMT process to counteract the oxidative effect of antibiotics on microorganisms. The results showed that FMTs with vitamin C were more effective in restoring gut microbiota and gene expression to the level of the fecal transplant donor.

Analgesic Effect of Tuina on Rat Models with Compression of the Dorsal Root Ganglion Pain.

Neuropathic pain is a prevalent condition that affects 6.9%-10% of the population and results from nerve damage due to various etiologies, such as lumbar disc herniation, spinal canal stenosis, and intervertebral foramen stenosis. Although Tuina, a traditional Chinese manual therapy, has shown analgesic effects in clinical practice for the treatment of neuropathic pain, its underlying neurobiological mechanisms remain unclear. Animal models are essential for elucidating the basic principles of Tuina. In this study, we propose a standardized Tuina protocol for rats with compression of the dorsal root ganglion (DRG), which involves inducing DRG compression by inserting a stainless steel rod into the intervertebral foramen, performing Tuina manipulation with specific parameters of location, intensity, and frequency in a controlled environment, and assessing the behavioral and histopathological outcomes of Tuina treatment. This article also discusses the potential clinical implications and limitations of the study and suggests directions for future research on Tuina.

Triterpenoids of Ganoderma lucidum inhibited S180 sarcoma and H22 hepatoma in mice by regulating gut microbiota.

In order to explore effect of natural plant extracts on anti-tumor and prevent tumor development. The study assessed the antitumor effect of triterpenoids of Ganoderma lucidum (TGL) on S180 and H22 tumor bearing mice. A triterpene compound, 2α, 3α, 23-trihydroxy-urs-12-en-28-oic acid, was successfully isolated and purified from G. lucidum. S180 and H22 cells were subcutaneously inoculated in the left axilla of mice to establish a transplantable tumor model. After, the mice were orally treated with TGL and evaluated by tumor inhibition rate, organ index, and the serum index. The Bax and Bcl-2 proteins and gut microbiota was analyzed using western blot and 16S rDNA sequencing respectively. The results showed the tumor inhibition rates of TGL were higher than 40% in H22 and S180 tumor bearing mice. TGL had a protective effect on the spleen and thymus, and improved lipid peroxidation caused by the increased free radicals. TGL downregulated Bcl-2 and upregulated Bax. In particular, TGL treatment improved the reduction of gut microbiota richness and structure.

Tuina Manipulation to Reduce Inflammation and Cartilage Loss in Knee Osteoarthritis Rats.

Clinical trials suggest that Tuina manipulation is effective in treating knee osteoarthritis (KOA), while further studies are required to discover its mechanism. Therefore, the manipulation of animal models of knee osteoarthritis is critical. This protocol provides a standard process for Tuina manipulation on KOA rats and a preliminary exploration of the mechanism of Tuina for KOA. The press and kneading manipulation method (a kind of Tuina manipulation that refers to pressing and kneading the specific area of the body surface) is applied on 5 acupoints around the knee joint of rats. The force and frequency of the manipulation were standardized by finger pressure recordings, and the position of the rat during manipulation is described in detail in the protocol. The effect of manipulation can be measured by pain behavior tests and microscopic findings in synovial and cartilage. KOA rats showed significant improvement in pain behavior. The synovial tissue inflammatory infiltration was reduced in the Tuina group, and the expression of tumor necrosis factor (TNF)-α was significantly lower. Compared to the control group, chondrocyte apoptosis was less in the Tuina group. This study provides a standardized protocol for Tuina manipulation on KOA rats and preliminary proof that the therapeutic effects of Tuina may be related to reducing synovial inflammation and delayed chondrocyte apoptosis.

NT-proBNP in Different Patient Groups of COPD: A Systematic Review and Meta-Analysis.

Purpose: NT-proBNP, a peptide biomarker synthesized and secreted by cardiomyocytes in response to cardiac load, has gained attention in recent years for its potential role in respiratory diseases. Chronic Obstructive Pulmonary Disease (COPD), a chronic and progressive inflammatory condition affecting the respiratory system, is frequently associated with comorbidities involving the cardiovascular system. Consequently, the aim of this systematic review and meta-analysis was to evaluate the variations in NT-proBNP levels across distinct patient groups with COPD and establish a foundation for future investigations into the precise clinical significance of NT-proBNP in COPD. Methods: The search databases for this study were conducted in PubMed, Excerpt Medica database (Embase), Web of Science (WOS), and Cochrane Library databases. Databases were searched for studies on the predictive value of NT-proBNP in adult COPD patients. Results: A total of 29 studies (8534 participants) were included. Patients with stable COPD exhibit elevated levels of NT-proBNP [standardized mean difference(SMD) [95CI%]=0.51 [0.13,0.89]; p=0.0092]. COPD patients with predicted forced expiratory volume in 1 s (FEV1) < 50% exhibit significantly elevated levels of NT-proBNP compared to those with FEV1 ⩾50%[SMD [95CI%]=0.17 [0.05,0.29]; p=0.0058]. NT-proBNP levels were significantly higher in acute exacerbations (AECOPD) compared to patients with stable COPD [SMD [95CI%]=1.18 [0.07,2.29]; p=0.037]. NT-proBNP levels was significantly higher in non-survivors than in survivors of hospitalised AECOPD patients [SMD [95CI%]=1.67 [0.47,2.88]; p=0.0063]. Both COPD patients with pulmonary hypertension(PH) [SMD [95CI%]=0.82 [0.69,0.96]; p<0.0001] and chronic heart failure(CHF) [SMD [95CI%]=1.49 [0.96,2.01]; p<0.0001] showed higher NT-proBNP level. Conclusion: NT-proBNP, a biomarker commonly used in clinical practice to evaluate cardiovascular disease, demonstrates significant variations in different stages of COPD and during the progression of the disease. The fluctuations in NT-proBNP levels could be indicative of the severity of pulmonary hypoxia and inflammation and cardiovascular stress among COPD patients. Therefore, assessing NT-proBNP levels in COPD patients can aid in making informed clinical decisions.

Efficacy and safety of three species of Rhodiola L. in patients with chronic obstructive pulmonary disease: A systematic review and meta-analysis.

Background: Chronic obstructive pulmonary disease (COPD) is characterized by chronic hypoxia, inflammation, oxidative stress, and irreversible airflow limitations. Rhodiola L. is a genus of botanical drugs used in traditional medicine that may influence COPD. Objective: A systematic review of the safety and efficacy of Rhodiola L. in patients with COPD. Material and methods: We searched the PubMed, Embase, Cochrane Library, Web of Science, Scopus, China National Knowledge Infrastructure (CNKI), Chongqing VIP, Wanfang, and SinoMed databases. The search strategy used terms including "COPD" and "Rhodiola." Two independent reviewers conducted the literature screening, data extraction, and risk of bias assessment, with a third reviewer involved to resolve disagreements. Statistical analysis was conducted in Review Manager (version 5.4.1), following the Cochrane Handbook. Results: This review included nine studies, of which two focused on Rhodiola crenulata (Hook.f. and Thomson) H. Ohba (R. crenulata) and two on Rhodiola kirilowii (Regel) Maxim (R. kirilowii); the remaining five focused on Rhodiola wallichiana (Hook.) S.H.Fu (R. wallichiana). Compared with the placebo, patients who received Rhodiola L. presented no more adverse events (p = 0.65) but showed significant improvement in the percentage of forced expiratory volume in 1 s at prediction (FEV1%pred), forced expiratory volume in 1 s (FEV1), the ratio of forced expiratory volume in 1 s on forced vital capacity (FEV1/FVC), saturation of oxygen in arterial blood, partial pressure of oxygen in arterial blood (PaO2), partial pressure of carbon dioxide in arterial blood (PaCO2), systolic pulmonary arterial pressure, diastolic pulmonary arterial pressure, COPD assessment test, efficient rate, C-reactive protein, and N-terminal pro-B-type natriuretic peptide (all p < 0.01). Compared with ambroxol, R. kirilowii provided additional benefits to patients with COPD in FEV1%pred, FEV1, FEV1/FVC, PaO2, PaCO2, 8-iso-prostaglandin F2α, superoxide dismutase, glutathione, malondialdehyde, and total antioxidant capacity (all p < 0.01). Conclusion: Among the Rhodiola L. genus, this review included R. wallichiana, R. crenulata, and R. kirilowii, which might be safe and effective in COPD. Although this study has several limitations, further RCTs are needed. Systematic Review Registration: [https://www.crd.york.ac.uk/PROSPERO/ display_record.php?RecordID=302881], identifier [CRD42022361890].

The role of B3GNT3 as an oncogene in the growth, invasion and migration of esophageal cancer cells.

PURPOSE: To investigate the role and mechanism of ß1,3-N-acetylglucosaminyltransferase-3 gene (B3GNT3) in esophageal cancer (ESCA). METHODS: The starBase database was used to evaluate the expression of B3GNT3. B3GNT3 function was measured using KYSE-30 and KYSE-410 cells of esophageal squamous cell carcinoma (ESCC) cell lines. The mRNA levels were detected by quantitative real-time polymerase chain reaction (qRT-PCR). Cell counting kit-8, clone formation assay and transwell assay were used to detect the changes of proliferation, invasion and migration. RESULTS: B3GNT3 expression was higher in ESCA tissues than in normal tissues. The overall survival rate of ESCA patients with high B3GNT3 expression was lower than that of ESCA patients with low B3GNT3 expression. In vitro functional experiments showed that the proliferation ability, migration and invasion ability of KYSE-30 and KYSE-410 cells with B3GNT3 interference were lower than those of the control, and the overexpression of B3GNT3 had the opposite effect. After silencing B3GNT3 expression in ESCC cell lines, the growth of both cell lines was inhibited and the invasiveness was decreased. Knockdown of B3GNT3 reduced the growth rate and Ki-67 expression level. CONCLUSIONS: B3GNT3, as an oncogene, may promote the growth, invasion and migration of ESCC cell.

A systematic review and Bayesian meta-analysis of the antibiotic treatment courses in AECOPD.

Background: No consensus exists on the antibiotic treatment course for patients with acute exacerbations of chronic obstructive pulmonary disease (AECOPD). Former studies indicate that shorter courses might have the same efficacy with fewer adverse events, which is inconsistent with guidelines and general practice. Existing evidence allows us to conduct a systematic review and Bayesian analysis on this topic. Methods: Four databases were searched from their inception to January 5, 2023. All statistical estimations were performed using R. "Gemtc" was the core package of analysis. CINeMA was used to assess the grade of confidence of the results. Results: Fourteen studies were included in the Bayesian meta-analysis. No difference in the clinical success rate of antibiotic treatment was observed from a super short course (1-3 days) to a long course (≥10 days). Considering the adverse events, the short course (4-6 days) might be the safest. The majority of results were of high or moderate confidence grade. Conclusion: Short course might cause the fewest adverse events. The clinical efficacy of antibiotics might not depend on the course length. Undeniably, more systematic explorations are warranted to investigate the clinical application of a shorter course of antibiotic treatment.

Fast and Smooth Composite Local Learning-Based Adaptive Control.

Model structure representation and fast estimation of perturbations are two key research aspects in adaptive control. This work proposes a composite local learning adaptive control framework, which possesses fast and flexible approximation to system uncertainties and meanwhile smoothens control inputs. Local learning, which is a nonparametric regression approach, is able to automatically adjust the structure of approximator based on data distribution from the local region, but it is sensitive to the outliers and measurement noises. To tackle this problem, the regression filter technique is employed to attenuate the adverse effect of noises by smoothing the output response and state features. In addition, the stable integral adaptation is integrated into local learning framework to further enhance the system robustness and smoothness of the estimation. Through the online elimination of uncertainties, the nominal control performance is recovered when the plant encounters violent perturbations. Stability analysis and numerical simulations are performed to demonstrate the effectiveness and benefits of the proposed control method. The proposed approach exhibits a promising performance in terms of rapid perturbation elimination and accurate tracking control.

Robotic Manipulation in Dynamic Scenarios via Bounding-Box-Based Hindsight Goal Generation.

By relabeling past experience with heuristic or curriculum goals, state-of-the-art reinforcement learning (RL) algorithms such as hindsight experience replay (HER), hindsight goal generation (HGG), and graph-based HGG (G-HGG) have been able to solve challenging robotic manipulation tasks in multigoal settings with sparse rewards. HGG outperforms HER in challenging tasks in which goals are difficult to explore by learning from a curriculum, in which intermediate goals are selected based on the Euclidean distance to target goals. G-HGG enhances HGG by selecting intermediate goals from a precomputed graph representation of the environment, which enables its applicability in an environment with stationary obstacles. However, G-HGG is not applicable to manipulation tasks with dynamic obstacles, since its graph representation is only valid in static scenarios and fails to provide any correct information to guide the exploration. In this article, we propose bounding-box-based HGG (Bbox-HGG), an extension of G-HGG selecting hindsight goals with the help of image observations of the environment, which makes it applicable to tasks with dynamic obstacles. We evaluate Bbox-HGG on four challenging manipulation tasks, where significant enhancements in both sample efficiency and overall success rate are shown over state-of-the-art algorithms. The videos can be viewed at https://videoviewsite.wixsite.com/bbhgg.