Development and Validation of a HPLC-UV Method with Pre-column Derivatization for Determination of Cinnabar in Jufang Zhibao Pills

In this work, a reliable and accurate high-performance liquid chromatography method with pre-column derivatization was established and validated for determination of cinnabar in Jufang Zhibao pills. Scanning electron microscope (SEM) image was used to identify the types of cinnabar crude drug in Jufang Zhibao pills. The chromatography separation was performed on a Welch XB-C18 column (250 mm × 4.6 mm, 5 μm). The mobile phase consists of water spiked with 0.022 mmol/L sodium diethyldithiocarbamate (A, pH adjusted to 8–9 by ammonia water) and methanol (B, 80:20, v/v) at flow rate of 1.0 ml/min with the detected wavelength was 272 nm. The oven temperature was set at 35°C. The calibration for cinnabar content has good linearity (R2 =0.9999) over the range of 2.43–300 μg/ml and the average recovery was less then 1.90%. The limits of detection and quantification were 0.1127 μg and 0.2065 μg/ml. The results indicated that the proposed method has advantages of high accuracy, good repeatability and stability and can be successfully used for determination of cinnabar in Jufang Zhibao pills. It provides a basis for drug manufacture quality control and proves the feasibility of the pre-column derivatization method during the determination of cinnabar in Jufang Zhibao pills

AGG-Net: Attention Guided Gated-convolutional Network for Depth Image Completion

Recently, stereo vision based on lightweight RGBD cameras has been widely used in various fields. However, limited by the imaging principles, the commonly used RGB-D cameras based on TOF, structured light, or binocular vision acquire some invalid data inevitably, such as weak reflection, boundary shadows, and artifacts, which may bring adverse impacts to the follow-up work. In this paper, we propose a new model for depth image completion based on the Attention Guided Gated-convolutional Network (AGG-Net), through which more accurate and reliable depth images can be obtained from the raw depth maps and the corresponding RGB images. Our model employs a UNet-like architecture which consists of two parallel branches of depth and color features. In the encoding stage, an Attention Guided Gated-Convolution (AG-GConv) module is proposed to realize the fusion of depth and color features at different scales, which can effectively reduce the negative impacts of invalid depth data on the reconstruction. In the decoding stage, an Attention Guided Skip Connection (AG-SC) module is presented to avoid introducing too many depth-irrelevant features to the reconstruction. The experimental results demonstrate that our method outperforms the state-of-the-art methods on the popular benchmarks NYU-Depth V2, DIML, and SUN RGB-D.Comment: 9 pages, 7 figures, ICCV202

Ultrafast Relaxation Dynamics of Photoexcited Dirac Fermion in The Three Dimensional Dirac Semimetal Cadmium Arsenide

Three dimensional (3D) Dirac semimetals which can be seen as 3D analogues of graphene have attracted enormous interests in research recently. In order to apply these ultrahigh-mobility materials in future electronic/optoelectronic devices, it is crucial to understand the relaxation dynamics of photoexcited carriers and their coupling with lattice. In this work, we report ultrafast transient reflection measurements of the photoexcited carrier dynamics in cadmium arsenide (Cd3As2), which is one of the most stable Dirac semimetals that have been confirmed experimentally. By using low energy probe photon of 0.3 eV, we probed the dynamics of the photoexcited carriers that are Dirac-Fermi-like approaching the Dirac point. We systematically studied the transient reflection on bulk and nanoplate samples that have different doping intensities by tuning the probe wavelength, pump power and lattice temperature, and find that the dynamical evolution of carrier distributions can be retrieved qualitatively by using a two-temperature model. This result is very similar to that of graphene, but the carrier cooling through the optical phonon couplings is slower and lasts over larger electron temperature range because the optical phonon energies in Cd3As2 are much lower than those in graphene

PNet—A Deep Learning Based Photometry and Astrometry Bayesian Framework

Time-domain astronomy has emerged as a vibrant research field in recent years, focusing on celestial objects that exhibit variable magnitudes or positions. Given the urgency of conducting follow-up observations for such objects, the development of an algorithm capable of detecting them and determining their magnitudes and positions has become imperative. Leveraging the advancements in deep neural networks, we present PNet, an end-to-end framework designed not only to detect celestial objects and extract their magnitudes and positions, but also to estimate the photometric uncertainty. PNet comprises two essential steps. First, it detects stars and retrieves their positions, magnitudes, and calibrated magnitudes. Subsequently, in the second phase, PNet estimates the uncertainty associated with the photometry results, serving as a valuable reference for the light-curve classification algorithm. Our algorithm has been tested using both simulated and real observation data, demonstrating the ability of PNet to deliver consistent and reliable outcomes. Integration of PNet into data-processing pipelines for time-domain astronomy holds significant potential for enhancing response speed and improving the detection capabilities for celestial objects with variable positions and magnitudes

Aloperine attenuates high glucose-induced oxidative injury in Schwann cells via activation of NRF2/HO-1 pathway

Purpose: To determine the involvement of nuclear factor erythroid 2-related factor 2 (NRF2) and heme oxygenase-1 (HO-1) in the action of aloperine on Schwann cell injury caused by high glucose (HG).Methods: Cell viability was determined using MTT assay while the release of lactate dehydrogenase (LDH) was determined by biochemical assay. Apoptosis was assessed using flow cytometry, while the levels of malondialdehyde (MDA) were determined by Annexin V-FIT staining. Glutathione Stransferase (GST), glutathione peroxidase (GPX), and reactive oxygen species (ROS) were determined using enzyme-linked immunosorbent assay.Results: Treatment with HG suppressed RSC96 cell viability and increased LDH release, while aloperine reversed these results (p < 0.05). Apoptosis of RSC96 cells was induced by HG stimulation, but was abolished by aloperine. The levels of ROS, MDA, and GST were enhanced in cells followingtreatment with HG, but was reversed by aloperine (p < 0.05). The decreased level of GPX caused by HG in RSC96 cells was elevated by aloperine. Moreover, aloperine upregulated NRF2 and HO-1 in RSC96 cells treated with HG (p < 0.05).Conclusion: Aloperine attenuates HG-induced oxidative injury in Schwann cells via activation of NRF2/HO-1 pathway, suggesting its potential as a potent drug for the management of diabetic peripheral neuropathy. Keywords: Aloperine, Schwann cells, High glucose, Oxidative stress, NRF2, HO-

Finite time point-stabilization of underwater spherical roving robot

This paper addresses the point stabilization problem for the underwater spherical roving robot (BYSQ-3) in the horizontal plane. The finite-time stable control laws are adopted to steer the robot to the origin fast, accurately and reliably. Firstly, the inner structure and operational principle of the robot is described and the kinematic and dynamic equations are established. Secondly, the diffeomorphism transformation and change of inputs are introduced to decouple the multivariable coupling system into two subsystems. The second subsystem consists of two double integrator systems. The finite-time controller is introduced to ensure part states converge to zero in finite time. Then, the other states are steered to the origin using the same method. Thirdly, the design process has no virtual input and the stability analysis is simple, the controller designed is easy for engineering implementation. The simulation and experiment results are presented to validate the shorter convergence time and better stability character of the controller

Photometric Metallicity Calibration with SDSS and SCUSS and its Application to distant stars in the South Galactic Cap

Based on SDSS g, r and SCUSS (South Galactic Cap of u-band Sky Survey) $u$ photometry, we develop a photometric calibration for estimating the stellar metallicity from $u-g$ and $g-r$ colors by using the SDSS spectra of 32,542 F- and G-type main sequence stars, which cover almost $3700$ deg$^{2}$ in the south Galactic cap. The rms scatter of the photometric metallicity residuals relative to spectrum-based metallicity is $0.14$ dex when $g-r<0.4$, and $0.16$ dex when $g-r>0.4$. Due to the deeper and more accurate magnitude of SCUSS $u$ band, the estimate can be used up to the faint magnitude of $g=21$. This application range of photometric metallicity calibration is wide enough so that it can be used to study metallicity distribution of distant stars. In this study, we select the Sagittarius (Sgr) stream and its neighboring field halo stars in south Galactic cap to study their metallicity distribution. We find that the Sgr stream at the cylindrical Galactocentric coordinate of $R\sim 19$ kpc, $\left| z\right| \sim 14$ kpc exhibits a relative rich metallicity distribution, and the neighboring field halo stars in our studied fields can be modeled by two-Gaussian model, with peaks respectively at [Fe/H]$=-1.9$ and [Fe/H]$=-1.5$.Comment: 8 pages, 7 figures, Accepted for publication in MNRA