Adaptive Gait Training of a Lower Limb Rehabilitation Robot Based on Human-Robot Interaction Force Measurement.

The existing fixed gait lower limb rehabilitation robots perform a predetermined walking trajectory for patients, ignoring their residual muscle strength. To enhance patient participation and safety in training, this paper aims to develop a lower limb rehabilitation robot with adaptive gait training capability relying on human-robot interaction force measurement. Firstly, a novel lower limb rehabilitation robot system with several active and passive driven joints is developed, and 2 face-to-face mounted cantilever beam force sensors are employed to measure the human-robot interaction forces. Secondly, a dynamic model of the rehabilitation training robot is constructed to estimate the driven forces of the human lower leg in a completely passive state. Thereafter, based on the theoretical moment from the dynamics and the actual joint interaction force collected by the sensors, an adaptive gait adjustment method is proposed to achieve the goal of adapting to the wearer's movement intention. Finally, interactive experiments are carried out to validate the effectiveness of the developed rehabilitation training robot system. The proposed rehabilitation training robot system with adaptive gaits offers great potential for future high-quality rehabilitation training, e.g., improving participation and safety.

Real-time Trajectory Planning and Tracking Control of Bionic Underwater Robot in Dynamic Environment.

In this article, we study the trajectory planning and tracking control of a bionic underwater robot under multiple dynamic obstacles. We first introduce the design of the bionic leopard cabinet underwater robot developed in our lab. Then, we model the trajectory planning problem of the bionic underwater robot by combining its dynamics and physical constraints. Furthermore, we conduct global trajectory planning for bionic underwater robots based on the temporal-spatial Bezier curves. In addition, based on the improved proximal policy optimization, local dynamic obstacle avoidance trajectory replanning is carried out. In addition, we design the fuzzy proportional-integral-derivative controller for tracking control of the planned trajectory. Finally, the effectiveness of the real-time trajectory planning and tracking control method is verified by comparative simulation in dynamic environment and semiphysical simulation of UWSim. Among them, the real-time trajectory planning method has advantages in trajectory length, trajectory smoothness, and planning time. The error of trajectory tracking control method is controlled around 0.2 m.

Study on the clinical efficacy and safety of baloxavir marboxil tablets in the treatment of influenza A.

Objective: To evaluate the clinical efficacy and safety of baloxavir marboxil tablets in the treatment of influenza A. Methods: According to a random sequence generated by computer software, 200 patients with confirmed influenza A were divided into a study group and a control group with 100 cases in each group. Group allocation was concealed using sealed envelopes. The study group was treated with oral administration of baloxavir marboxil tablets, 40 mg once. The control group was given oral oseltamivir capsules, 75 mg twice a day, for five consecutive days. The therapeutic effects, symptom disappearance time and adverse drug reactions of the two groups after 5 days of treatment were compared. Results: There was no significant difference in the total effective rate between the two groups (99% vs. 98%, p > 0.05). There was no significant difference in fever subsidence time (1.54 ± 0.66 d vs. 1.67 ± 0.71 d, p > 0.05), cough improvement time (2.26 ± 0.91 d vs. 2.30 ± 0.90 d, p > 0.05) and sore throat improvement time (2.06 ± 0.86 d vs. 2.09 ± 0.83 d, p > 0.05) between the two groups. There was no significant difference in the incidence of adverse drug reactions between the two groups (8% vs. 13%, p > 0.05). Conclusion: Baloxavir marboxil tablets can be effectively used in the treatment of patients with influenza A and have a similar efficacy and safety profile as oseltamivir capsules.

Discovery of novel diaryl substituted isoquinolin-1(2H)-one derivatives as hypoxia-inducible factor-1 signaling inhibitors for the treatment of rheumatoid arthritis.

Since synovial hypoxic microenvironment significantly promotes the pathological progress of rheumatoid arthritis (RA), hypoxia-inducible factor 1 (HIF-1) has been emerged as a promising target for the development of novel therapeutic agents for RA treatment. In this study, we designed and synthesized a series of diaryl substituted isoquinolin-1(2H)-one derivatives as HIF-1 signaling inhibitors using scaffold-hopping strategy. By modifying the substituents on N-atom and 6-position of isoquinolin-1-one, we discovered compound 17q with the most potent activities against HIF-1 (IC50 = 0.55 µM) in a hypoxia-reactive element (HRE) luciferase reporter assay. Further pharmacological studies revealed that 17q concentration-dependently blocked hypoxia-induced HIF-1α protein accumulation, reduced inflammation response, inhibited cellular invasiveness and promoted VHL-dependent HIF-1α degradation in human RA synovial cell line. Moreover, 17q improved the pathological injury of ankle joints, decreased angiogenesis and attenuated inflammation response in the adjuvant-induced arthritis (AIA) rat model, indicating the promising therapeutic potential of compound 17q as an effective HIF-1 inhibitor for RA therapy.

A Fish-like Binocular Vision System for Underwater Perception of Robotic Fish.

Biological fish exhibit a remarkably broad-spectrum visual perception capability. Inspired by the eye arrangement of biological fish, we design a fish-like binocular vision system, thereby endowing underwater bionic robots with an exceptionally broad visual perception capacity. Firstly, based on the design principles of binocular visual field overlap and tangency to streamlined shapes, a fish-like vision system is developed for underwater robots, enabling wide-field underwater perception without a waterproof cover. Secondly, addressing the significant distortion and parallax of the vision system, a visual field stitching algorithm is proposed to merge the binocular fields of view and obtain a complete perception image. Thirdly, an orientation alignment method is proposed that draws scales for yaw and pitch angles in the stitched images to provide a reference for the orientation of objects of interest within the field of view. Finally, underwater experiments evaluate the perception capabilities of the fish-like vision system, confirming the effectiveness of the visual field stitching algorithm and the orientation alignment method. The results show that the constructed vision system, when used underwater, achieves a horizontal field of view of 306.56°. The conducted work advances the visual perception capabilities of underwater robots and presents a novel approach to and insight for fish-inspired visual systems.

GCY-20 signaling controls suppression of Caenorhabditis elegans egg laying by moderate cold.

Organisms sensing environmental cues and internal states and integrating the sensory information to control fecundity are essential for survival and proliferation. The present study finds that a moderate cold temperature of 11°C reduces egg laying in Caenorhabditis elegans. ASEL and AWC neurons sense the cold via GCY-20 signaling and act antagonistically on egg laying through the ASEL and AWC/AIA/HSN circuits. Upon cold stimulation, ASEL and AWC release glutamate to activate and inhibit AIA interneurons by acting on highly and lowly sensitive ionotropic GLR-2 and GLC-3 receptors, respectively. AIA inhibits HSN motor neuron activity via acetylcholinergic ACR-14 receptor signaling and suppresses egg laying. Thus, ASEL and AWC initiate and reduce the cold suppression of egg laying. ASEL's action on AIA and egg laying dominates AWC's action. The biased opposite actions of these neurons on egg laying provide animals with a precise adaptation of reproductive behavior to environmental temperatures.

Molecular and circuit mechanisms underlying avoidance of rapid cooling stimuli in C. elegans.

The mechanisms by which animals respond to rapid changes in temperature are largely unknown. Here, we found that polymodal ASH sensory neurons mediate rapid cooling-evoked avoidance behavior within the physiological temperature range in C. elegans. ASH employs multiple parallel circuits that consist of stimulatory circuits (AIZ, RIA, AVA) and disinhibitory circuits (AIB, RIM) to respond to rapid cooling. In the stimulatory circuit, AIZ, which is activated by ASH, releases glutamate to act on both GLR-3 and GLR-6 receptors in RIA neurons to promote reversal, and ASH also directly or indirectly stimulates AVA to promote reversal. In the disinhibitory circuit, AIB is stimulated by ASH through the GLR-1 receptor, releasing glutamate to act on AVR-14 to suppress RIM activity. RIM, an inter/motor neuron, inhibits rapid cooling-evoked reversal, and the loop activities thus equally stimulate reversal. Our findings elucidate the molecular and circuit mechanisms underlying the acute temperature stimuli-evoked avoidance behavior.

Spatial Domain Image Fusion with Particle Swarm Optimization and Lightweight AlexNet for Robotic Fish Sensor Fault Diagnosis.

Safety and reliability are vital for robotic fish, which can be improved through fault diagnosis. In this study, a method for diagnosing sensor faults is proposed, which involves using Gramian angular field fusion with particle swarm optimization and lightweight AlexNet. Initially, one-dimensional time series sensor signals are converted into two-dimensional images using the Gramian angular field method with sliding window augmentation. Next, weighted fusion methods are employed to combine Gramian angular summation field images and Gramian angular difference field images, allowing for the full utilization of image information. Subsequently, a lightweight AlexNet is developed to extract features and classify fused images for fault diagnosis with fewer parameters and a shorter running time. To improve diagnosis accuracy, the particle swarm optimization algorithm is used to optimize the weighted fusion coefficient. The results indicate that the proposed method achieves a fault diagnosis accuracy of 99.72% when the weighted fusion coefficient is 0.276. These findings demonstrate the effectiveness of the proposed method for diagnosing depth sensor faults in robotic fish.

HybrUR: A Hybrid Physical-Neural Solution for Unsupervised Underwater Image Restoration.

Robust vision restoration of underwater images remains a challenge. Owing to the lack of well-matched underwater and in-air images, unsupervised methods based on the cyclic generative adversarial framework have been widely investigated in recent years. However, when using an end-to-end unsupervised approach with only unpaired image data, mode collapse could occur, and the color correction of the restored images is usually poor. In this paper, we propose a data- and physics-driven unsupervised architecture to perform underwater image restoration from unpaired underwater and in-air images. For effective color correction and quality enhancement, an underwater image degeneration model must be explicitly constructed based on the optically unambiguous physics law. Thus, we employ the Jaffe-McGlamery degeneration theory to design a generator and use neural networks to model the process of underwater visual degeneration. Furthermore, we impose physical constraints on the scene depth and degeneration factors for backscattering estimation to avoid the vanishing gradient problem during the training of the hybrid physical-neural model. Experimental results show that the proposed method can be used to perform high-quality restoration of unconstrained underwater images without supervision. On multiple benchmarks, the proposed method outperforms several state-of-the-art supervised and unsupervised approaches. We demonstrate that our method yields encouraging results in real-world applications.

Efficacy and safety of Nirmatrelvir/Ritonavir for treating the Omicron variant of COVID-19.

Objective: To evaluate the efficacy and safety of Nirmatrelvir/Ritonavir in the treatment of the Omicron variant of coronavirus disease 2019 (COVID-19). Methods: Data from 58 patients who were infected with the Omicron variant of COVID-19 were retrospectively collected. The patients were divided into two groups according to the treatment regimen they received. Patients in both groups were given Lianhua Qingwen capsules orally, three times/day, 6 g/time. The study group was given Nirmatrelvir 300 mg/Ritonavir 100 mg orally, q12h, for 5 days, and the control group was not given any antiviral drugs. The two groups were compared in terms of the change in computed tomography (CT) values of COVID-19 nucleic acid, the negative conversion time of COVID-19 RNA, hospitalization time, adverse drug reactions and COVID-19 nucleic acid re-positive tests. Results: The time to increase the CT values in the study group was faster than that in the control group, and the CT values in the study group were significantly larger than in the control group on days four and seven (p < 0.05); The negative conversion time in the study group was shorter than the control group (Z = -2.424, p = 0.015), and the hospitalization time was also shorter (Z = -2.603, p = 0.009). There were no statistically significant adverse drug reactions during hospitalization in both groups (χ2 = 2.747, p = 0.097). None of the study group tested re-positive for SARS-CoV-2 nucleic acid after discharge. Conclusion: The efficacy of Nirmatrelvir/Ritonavir in the treatment of the Omicron variant of COVID-19 was positive and had good tolerance in patients.

Multi-site programmable functionalization of alkenes via controllable alkene isomerization.

Direct and selective functionalization of hydrocarbon chains is a fundamental problem in synthetic chemistry. Conventional functionalization of C=C double bonds and C(sp3)-H bonds provides some solutions, but site diversity remains an issue. The merging of alkene isomerization with (oxidative) functionalization provides an ideal method for remote functionalization, which would provide more opportunities for site diversity. However, the reported functionalized sites are still limited and focus on a specific terminal position and internal site; new site-selective functionalization, including multi-functionalization, remains a largely unmet challenge. Here we describe a palladium-catalysed aerobic oxidative method for the multi-site programmable functionalization, involving the C=C double bond and multiple C(sp3)-H bonds, of terminal olefins via a strategy that controls the reaction sequence between alkene isomerization and oxidative functionalization. Specifically, 1-acetoxylation (anti-Markovnikov), 2-acetoxylation, 1,2-diacetoxylation and 1,2,3-triacetoxylation have been realized, accompanied by controllable remote alkenylation. This method enables available terminal olefins from petrochemical feedstocks to be readily converted into unsaturated alcohols and polyalcohols and particularly into different monosaccharides and C-glycosides.

Design and Analysis of a Bionic Gliding Robotic Dolphin.

In this paper, we focus on the design and analysis of a bionic gliding robotic dolphin. Inspired by natural dolphins, a novel bionic gliding robotic dolphin is developed. Different from the existing ones, the gliding robotic dolphin developed in this work is specially introduced with a yaw joint to connect its three oscillating joints to improve maneuverability in both dolphin-like swimming and gliding motion. Consequently, the gliding robotic dolphin can realize several flexible motion patterns under the coordination of its flippers, yaw joint, oscillating joints, and buoyancy-driven modular. Thereafter, relying on the Newton-Euler method, a hybrid-driven dynamic model is constructed to further analyze the propulsive performance in both dolphin-like swimming and gliding motions. Finally, various simulations and experiments, including forward swimming, gliding, and turning in both dolphin-like swimming and gliding modes, are carried out to validate the effectiveness of the developed gliding robotic dolphin.

A Survey on Reinforcement Learning Methods in Bionic Underwater Robots.

Bionic robots possess inherent advantages for underwater operations, and research on motion control and intelligent decision making has expanded their application scope. In recent years, the application of reinforcement learning algorithms in the field of bionic underwater robots has gained considerable attention, and continues to grow. In this paper, we present a comprehensive survey of the accomplishments of reinforcement learning algorithms in the field of bionic underwater robots. Firstly, we classify existing reinforcement learning methods and introduce control tasks and decision making tasks based on the composition of bionic underwater robots. We further discuss the advantages and challenges of reinforcement learning for bionic robots in underwater environments. Secondly, we review the establishment of existing reinforcement learning algorithms for bionic underwater robots from different task perspectives. Thirdly, we explore the existing training and deployment solutions of reinforcement learning algorithms for bionic underwater robots, focusing on the challenges posed by complex underwater environments and underactuated bionic robots. Finally, the limitations and future development directions of reinforcement learning in the field of bionic underwater robots are discussed. This survey provides a foundation for exploring reinforcement learning control and decision making methods for bionic underwater robots, and provides insights for future research.

Disexcitation in the ASH/RIM/ADL negative feedback circuit fine-tunes hyperosmotic sensation and avoidance in Caenorhabditis elegans.

Sensations, especially nociception, are tightly controlled and regulated by the central and peripheral nervous systems. Osmotic sensation and related physiological and behavioral reactions are essential for animal well-being and survival. In this study, we find that interaction between secondary nociceptive ADL and primary nociceptive ASH neurons upregulates Caenorhabditis elegans avoidance of the mild and medium hyperosmolality of 0.41 and 0.88 Osm but does not affect avoidance of high osmolality of 1.37 and 2.29 Osm. The interaction between ASH and ADL is actualized through a negative feedback circuit consisting of ASH, ADL, and RIM interneurons. In this circuit, hyperosmolality-sensitive ADL augments the ASH hyperosmotic response and animal hyperosmotic avoidance; RIM inhibits ADL and is excited by ASH; thus, ASH exciting RIM reduces ADL augmenting ASH. The neuronal signal integration modality in the circuit is disexcitation. In addition, ASH promotes hyperosmotic avoidance through ASH/RIC/AIY feedforward circuit. Finally, we find that in addition to ASH and ADL, multiple sensory neurons are involved in hyperosmotic sensation and avoidance behavior.

Development and 3-D Path-Following Control of an Agile Robotic Manta With Flexible Pectoral Fins.

The broad and powerful pectoral fins of manta rays are crucial to their efficient and maneuverable swimming. However, very little is currently known about the pectoral-fin-driven 3-D locomotion of manta-inspired robots. This study is focused on the development and 3-D path-following control of an agile robotic manta. First, a novel robotic manta with 3-D mobility is constructed, of which the distinctive pectoral fins provide the only propulsion. Specifically, the unique pitching mechanism is detailed in which the time-coupled coordination movement of the pectoral fins is applied. Second, based on a 6-axis force measuring platform, the propulsion characteristics of the flexible pectoral fins are analyzed. Then, the force-data-driven 3-D dynamic model is further established. Third, a control scheme combined with a line-of-sight (LOS) guidance system and a sliding-mode fuzzy controller is conceived, addressing the 3-D path-following task. Finally, various simulated and aquatic experiments are conducted, demonstrating the superior performance of our prototype and the effectiveness of the proposed path-following scheme. This study will hopefully generate fresh insights into the updated design and control of agile bioinspired robots performing underwater tasks in dynamic environments.

Platform development and gliding optimization of a robotic flying fish with morphing pectoral fins.

The aquatic-aerial robot with the free interface crossing can enhance adaptability in complex aquatic environments. However, its design is extremely challenging for the striking discrepancies in propulsion principles. The flying fish in nature exhibits remarkable multi-modal cross-domain locomotion capability, such as high-maneuvers swimming, agile water-air crossing, and long-distance gliding, providing extensive inspiration. In this paper, we present a unique aquatic-aerial robotic flying fish with powerful propulsion and a pair of morphing wing-like pectoral fins to realize cross-domain motion. Furthermore, to explore the gliding mechanism of flying fish, a dynamic model with a morphing structure of pectoral fins is established, and a double deep Q-network-based control strategy is proposed to optimize the gliding distance. Finally, experiments were conducted to analyze the locomotion of the robotic flying fish. The results suggest that the robotic flying fish can successfully perform the 'fish leaping and wing spreading' cross-domain locomotion with an exiting speed of 1.55 m s-1(5.9 body lengths per second, BL/s) and a crossing time of 0.233 s indicating its great potential in cross-domain. Simulation results have validated the effectiveness of the proposed control strategy and indicated that the dynamical adjustment of morphing pectoral fins contributes to improving the gliding distance. The maximum gliding distance has increased by 7.2%. This study will offer some significant insights into the system design and performance optimization of aquatic-aerial robots.

Calibration Model Optimization for Strain Metrology of Equal Strength Beams Using Deflection Measurements.

Strain sensors, especially fiber Bragg grating (FBG) sensors, are of great importance in structural health monitoring, mechanical property analysis, and so on. Their metrological accuracy is typically evaluated by equal strength beams. The traditional strain calibration model using the equal strength beams was built based on an approximation method by small deformation theory. However, its measurement accuracy would be decreased while the beams are under the large deformation condition or under high temperature environments. For this reason, an optimized strain calibration model is developed for equal strength beams based on the deflection method. By combining the structural parameters of a specific equal strength beam and finite element analysis method, a correction coefficient is introduced into the traditional model, and an accurate application-oriented optimization formula is obtained for specific projects. The determination method of optimal deflection measurement position is also presented to further improve the strain calibration accuracy by error analysis of the deflection measurement system. Strain calibration experiments of the equal strength beam were carried out, and the error introduced by the calibration device can be reduced from 10 µÎµ to less than 1 µÎµ. Experimental results show that the optimized strain calibration model and the optimum deflection measurement position can be employed successfully under large deformation conditions, and the deformation measurement accuracy is improved greatly. This study is helpful to effectively establish metrological traceability for strain sensors and furthermore improve the measurement accuracy of strain sensors in practical engineering scenarious.

Decoupled Metric Network for Single-Stage Few-Shot Object Detection.

Within the last few years, great efforts have been made to study few-shot learning. Although general object detection is advancing at a rapid pace, few-shot detection remains a very challenging problem. In this work, we propose a novel decoupled metric network (DMNet) for single-stage few-shot object detection. We design a decoupled representation transformation (DRT) and an image-level distance metric learning (IDML) to solve the few-shot detection problem. The DRT can eliminate the adverse effect of handcrafted prior knowledge by predicting objectness and anchor shape. Meanwhile, to alleviate the problem of representation disagreement between classification and location (i.e., translational invariance versus translational variance), the DRT adopts a decoupled manner to generate adaptive representations so that the model is easier to learn from only a few training data. As for a few-shot classification in the detection task, we design an IDML tailored to enhance the generalization ability. This module can perform metric learning for the whole visual feature, so it can be more efficient than traditional DML due to the merit of parallel inference for multiobjects. Based on the DRT and IDML, our DMNet efficiently realizes a novel paradigm for few-shot detection, called single-stage metric detection. Experiments are conducted on the PASCAL VOC dataset and the MS COCO dataset. As a result, our method achieves state-of-the-art performance in few-shot object detection. The codes are available at https://github.com/yrqs/DMNet.

Barrier-Based Adaptive Line-of-Sight 3-D Path-Following System for a Multijoint Robotic Fish With Sideslip Compensation.

This article proposes a novel barrier-based adaptive line-of-sight (ALOS) three-dimensional (3-D) path-following system for an underactuated multijoint robotic fish. The framework of the developed path-following system is established based on a detailed dynamic model, including a barrier-based ALOS guidance strategy, three integrated inner-loop controllers, and a nonlinear disturbance observer (NDOB)-based sideslip angle compensation, which is employed to preserve a reliable tracking under a frequently varying sideslip angle of the robotic fish. First, a barrier-based convergence strategy is proposed to deal with probable along-track error disruption and suppress the error within a manageable range. Meanwhile, an improved adaptive guidance scheme is adopted with an appropriate look-ahead distance. Afterward, a novel NDOB-based sideslip angle compensation is put forward to identify the varying sideslip angle independent of speed estimation. Subsequently, inner-loop controllers are intended for regulation about the controlled references, including a super-twisting sliding-mode control (STSMC)-based speed controller, a global fast terminal sliding-mode control (GFTSMC)-based heading controller, and a GFTSMC-based depth controller. Finally, simulations and experiments with quantitative comparison in 3-D linear and helical path following are presented to verify the effectiveness and robustness of the proposed system. This path-following system provides a solid foundation for future marine autonomous cruising of the underwater multijoint robot.

Reproductive toxicity of enrofloxacin in Caenorhabditis elegans involves oxidative stress-induced cell apoptosis.

Fluoroquinolone antibiotics (FQs) that persist and bioaccumulate in the environment have aroused people's great concern. Here, we studied the adverse effects of FQs in soil animals of Caenorhabditis elegans via food-chronically exposure. The result shows C. elegans exposed to FQs exhibited reproductive toxicity with small-brood size and low-egg hatchability. To study the underlying mechanism, we conduct a deep investigation of enrofloxacin (ENR), one of the most frequently detected FQs, on nematodes which is one of commonly used animal indicator of soil sustainability. The concentration-effect curves simulated by the Hill model showed that the half effect concentrations (EC50) of ENR were (494.3 ± 272.9) µmol/kg and (107.4 ± 30.9) µmol/kg for the brood size and the hatchability, respectively. Differential gene expression between the control and the ENR-exposure group enriched with the oxidative stress and cell apoptosis pathways. The results together with the enzyme activity in oxidative stress and the cell corpses suggested that ENR-induced reproductive toxicity was related to germ cell apoptosis under oxidative stress. The risk quotients of some soil and livestock samples were calculated based on the threshold value of EC10 for the egg hatchability (2.65 µmol/kg). The results indicated that there was possible reproductive toxicity on the nematodes in certain agricultural soils for the FQs. This study suggested that chronic exposure to FQs at certain levels in environment would induce reproductive toxicity to the nematodes and might reduce the soil sustainability, alarming the environment risks of antibiotics abuse.