Reactive Hydrogel Patch for SERS Detection of Environmental Formaldehyde.

The tobacco epidemic is a public health threat that has taken a heavy toll of lives around the globe each year. As one of the poison species from smoking, formaldehyde (FA) affects both the smokers and nearby persons who are exposed to second-hand smoke. Therefore, on-site tracking of FA exposure could evaluate the public environmental safety and mitigate the potential hazards. Herein, we first prepare SiO2-shelled AuAg alloy nanoparticles (AuAg@SiO2) and then embed AuAg@SiO2 within agarose hydrogel to construct the three-dimensional (3D) surface-enhanced Raman scattering (SERS) substrate. A reagent of 3-methyl-2-benzothiazolinone hydrazine (MBTH) with reaction activity toward FA is loaded in the 3D substrate to obtain the selective SERS patch (designated as M-hydrogel patch). Based on a marker Raman peak at 1273 cm-1 from the reaction product of MBTH-FA, the M-hydrogel patch is used to realize SERS detection of FA in smoke. A good linear relationship from 5 × 10-4 to 5 mg/m3 and a limit of detection (LOD) of 2.92 × 10-5 mg/m3 could be reached. While for detection of FA in aqueous, a linear range of 1 × 10-7-1 × 10-3 mg/mL with an LOD of 1.46 × 10-8 mg/mL could be achieved. As the real application, the proposed M-hydrogel patch could be placed anywhere indoor to SERS evaluate the spatial distribution of FA in tobacco smoke, which is in connection with the second-hand smoke effect on children and adults. Such M-hydrogel patch-based SERS assay is exerted for on-field detection of FA in water and furniture panels by using a portable Raman system, showing satisfactory selectivity and reproducibility.

Dual-Arm Robot Trajectory Planning Based on Deep Reinforcement Learning under Complex Environment.

In this article, the trajectory planning of the two manipulators of the dual-arm robot is studied to approach the patient in a complex environment with deep reinforcement learning algorithms. The shape of the human body and bed is complex which may lead to the collision between the human and the robot. Because the sparse reward the robot obtains from the environment may not support the robot to accomplish the task, a neural network is trained to control the manipulators of the robot to prepare to hold the patient up by using a proximal policy optimization algorithm with a continuous reward function. Firstly, considering the realistic scene, the 3D simulation environment is built to conduct the research. Secondly, inspired by the idea of the artificial potential field, a new reward and punishment function was proposed to help the robot obtain enough rewards to explore the environment. The function is consisting of four parts which include the reward guidance function, collision detection, obstacle avoidance function, and time function. Where the reward guidance function is used to guide the robot to approach the targets to hold the patient, the collision detection and obstacle avoidance function are complementary to each other and are used to avoid obstacles, and the time function is used to reduce the number of training episode. Finally, after the robot is trained to reach the targets, the training results are analyzed. Compared with the DDPG algorithm, the PPO algorithm reduces about 4 million steps for training to converge. Moreover, compared with the other reward and punishment functions, the function used in this paper will obtain many more rewards at the same training time. Apart from that, it will take much less time to converge, and the episode length will be shorter; so, the advantage of the algorithm used in this paper is verified.

Astragalus membranaceus up-regulate Cosmc expression and reverse IgA dys-glycosylation in IgA nephropathy.

BACKGROUND: Decreased Core I ß3-Gal-T-specific molecular chaperone (Cosmc) expression induced IgA1 aberrant glycosylation is the main characteristic of IgA nephropathy (IgAN). This study tried to elucidate the effect of Astragalus membranaceus on Cosmc expression and IgA O-glycosylation of peripheral B lymphocytes in IgAN patients. METHODS: Peripheral B lymphocytes of 21 IgAN patients and 10 normal controls were isolated and cultured with or without lipopolysaccharide (LPS) and Astragalus membranaceus injection (AMI). Cosmc mRNA and protein expression levels were measured by real-time RT-PCR and Western blot. IgA1 and glycosylation level were determined by enzyme-linked immunosorbent assay (ELISA) and VV lectin-binding method. RESULTS: Cosmc mRNA expression and IgA1 O-glycosylation level in IgAN patients was significantly lower than normal controls at baseline. Treatment of LPS could obviously inhibit Cosmc expression and increase the IgA1 secretion in peripheral B lymphocytes of IgAN patients, which resulted in a significantly increase in IgA1 aberrant glycosylation level. Addition of AMI could remarkably up regulated Cosmc expression, decrease IgA1 secretion, and reverse glycosylation level in a dose related manner. CONCLUSION: AMI can up-regulate Cosmc expression of peripheral B lymphocytes and reverse IgA1 aberrant O-glycosylation level, which might be the underlying mechanism of AMI therapy in treating IgAN. TRIAL REGISTRATION: TCTR20140515001 (Registration Date: 2014-05-15).