NaoXinTong Inhibits the Development of Diabetic Retinopathy in d

Buchang NaoXinTong capsule (NXT) is a Chinese Materia Medica standardized product extracted from 16 Chinese traditional medical herbs and widely used for treatment of patients with cerebrovascular and cardiovascular diseases in China. Formation of microaneurysms plays an important role in the development of diabetic retinopathy. In this study, we investigated if  NXT can protect diabetic mice against the development of diabetic retinopathy. The db/db mice (~6 weeks old), a diabetic animal model, were divided into two groups and fed normal chow or plus NXT for 14 weeks. During the treatment, fasting blood glucose levels were monthly determined. After treatment, retinas were collected to determine retinal thickness, accumulation of carbohydrate macromolecules, and caspase-3 (CAS-3) expression. Our results demonstrate that administration of NXT decreased fasting blood glucose levels. Associated with the decreased glucose levels, NXT blocked the diabetes-induced shrink of multiple layers, such as photoreceptor layer and outer nuclear/plexiform layers, in the retina. NXT also inhibited the diabetes-induced expression of CAS-3 protein and mRNA, MMP-2/9 and TNFα mRNA, accumulation of carbohydrate macromolecules, and formation of acellular capillaries in the retina. Taken together, our study shows that NXT can inhibit the development of diabetic retinopathy and suggests a new potential application of NXT in clinic

Multi-Level Variational Spectroscopy using a Programmable Quantum Simulator

Energy spectroscopy is a powerful tool with diverse applications across various disciplines. The advent of programmable digital quantum simulators opens new possibilities for conducting spectroscopy on various models using a single device. Variational quantum-classical algorithms have emerged as a promising approach for achieving such tasks on near-term quantum simulators, despite facing significant quantum and classical resource overheads. Here, we experimentally demonstrate multi-level variational spectroscopy for fundamental many-body Hamiltonians using a superconducting programmable digital quantum simulator. By exploiting symmetries, we effectively reduce circuit depth and optimization parameters allowing us to go beyond the ground state. Combined with the subspace search method, we achieve full spectroscopy for a 4-qubit Heisenberg spin chain, yielding an average deviation of 0.13 between experimental and theoretical energies, assuming unity coupling strength. Our method, when extended to 8-qubit Heisenberg and transverse-field Ising Hamiltonians, successfully determines the three lowest energy levels. In achieving the above, we introduce a circuit-agnostic waveform compilation method that enhances the robustness of our simulator against signal crosstalk. Our study highlights symmetry-assisted resource efficiency in variational quantum algorithms and lays the foundation for practical spectroscopy on near-term quantum simulators, with potential applications in quantum chemistry and condensed matter physics

COMBINING SATELLITE AND GIS DATA TO ANALYZE CHANGES IN TROPICAL FORESTS ON CENTRAL HAINAN ISLAND IN RESPONSE TO THE NATIONAL LOGGING BAN AND ECONOMIC DEVELOPMENT

Changes in natural forest cover in tropical areas have attracted international attention. Rubber and pulp plantations threaten Hainan Island's natural tropical forests. Remote sensing provides a crucial tool for understanding how forests change in response to forest protection strategies and economic development. China's government has adopted protective measures designed to balance forest protection and economic development on Hainan; however, the effect of both management and economic development on natural tropical forest remains unclear. To identify changes in forest types, object-oriented decision-tree identification techniques were developed using Landsat TM images to identify causes of forest change. GIS techniques allowed analysis of the forest's spatial shift using elevation, slope, transportation corridors, natural reserves, and farmlands linked to three different periods of forestry policy and economic development from 1988 to 2008. The analysis shows: (1) Total tropical forest area increased from 1988 to 2008, while natural tropical forest area increased slightly from 1988 to 1998, but decreased significantly from 1998 to 2008, despite implementation of the Natural Forest Protection Project. Meanwhile, economic forests, mainly rubber and pulp plantations, expanded from 1988 to 2008. (2) Spatial changes occurred. Natural tropical forest shifted from the lower piedmont to higher mountaintops, and economic forests shifted to higher elevations under the complex effects of multiple factors. (3) The observed changes in forest cover could be related to protective measures and economic development, with economic development seemingly having the strongest influence on the condition of the forests. Elevation, slope, transportation corridors, and farmlands also affected the shift of tropical forests

A New Deep Learning Algorithm for SAR Scene Classification Based on Spatial Statistical Modeling and Features Re-Calibration

Synthetic Aperture Radar (SAR) scene classification is challenging but widely applied, in which deep learning can play a pivotal role because of its hierarchical feature learning ability. In the paper, we propose a new scene classification framework, named Feature Recalibration Network with Multi-scale Spatial Features (FRN-MSF), to achieve high accuracy in SAR-based scene classification. First, a Multi-Scale Omnidirectional Gaussian Derivative Filter (MSOGDF) is constructed. Then, Multi-scale Spatial Features (MSF) of SAR scenes are generated by weighting MSOGDF, a Gray Level Gradient Co-occurrence Matrix (GLGCM) and Gabor transformation. These features were processed by the Feature Recalibration Network (FRN) to learn high-level features. In the network, the Depthwise Separable Convolution (DSC), Squeeze-and-Excitation (SE) Block and Convolution Neural Network (CNN) are integrated. Finally, these learned features will be classified by the Softmax function. Eleven types of SAR scenes obtained from four systems combining different bands and resolutions were trained and tested, and a mean accuracy of 98.18% was obtained. To validate the generality of FRN-MSF, five types of SAR scenes sampled from two additional large-scale Gaofen-3 and TerraSAR-X images were evaluated for classification. The mean accuracy of the five types reached 94.56%; while the mean accuracy for the same five types of the former tested 11 types of scene was 96%. The high accuracy indicates that the FRN-MSF is promising for SAR scene classification without losing generality

Improved Aerosol Optical Thickness, Columnar Water Vapor, and Surface Reflectance Retrieval from Combined CASI and SASI Airborne Hyperspectral Sensors

An increasingly common requirement in remote sensing is the integration of hyperspectral data collected simultaneously from different sensors (and fore-optics) operating across different wavelength ranges. Data from one module are often relied on to correct information in the other, such as aerosol optical thickness (AOT) and columnar water vapor (CWV). This paper describes problems associated with this process and recommends an improved strategy for processing remote sensing data, collected from both visible to near-infrared and shortwave infrared modules, to retrieve accurate AOT, CWV, and surface reflectance values. This strategy includes a workflow for radiometric and spatial cross-calibration and a method to retrieve atmospheric parameters and surface reflectance based on a radiative transfer function. This method was tested using data collected with the Compact Airborne Spectrographic Imager (CASI) and SWIR Airborne Spectrographic Imager (SASI) from a site in Huailai County, Hebei Province, China. Various methods for retrieving AOT and CWV specific to this region were assessed. The results showed that retrieving AOT from the remote sensing data required establishing empirical relationships between 465.6 nm/659 nm and 2105 nm, augmented by ground-based reflectance validation data, and minimizing the merit function based on AOT@550 nm optimization. The paper also extends the second-order difference algorithm (SODA) method using Powell’s methods to optimize CWV retrieval. The resulting CWV image has fewer residual surface features compared with the standard methods. The derived remote sensing surface reflectance correlated significantly with the ground spectra of comparable vegetation, cement road and soil targets. Therefore, the method proposed in this paper is reliable enough for integrated atmospheric correction and surface reflectance retrieval from hyperspectral remote sensing data. This study provides a good reference for surface reflectance inversion that lacks synchronized atmospheric parameters

Growth of van der Waals Halide Perovskites within the Interlayer Spacings of Mica

Two-dimensional hybrid organic–inorganic perovskites (2D-HOIPs) have been extensively researched for use in solar cells as well as optoelectronic devices over the past few years. Controllable growth of single-crystalline 2D-HOIP thin films has been regarded as a key component for the development of high-performance end devices. Here, we report a solution-based method for the growth of 2D-HOIPs using muscovite mica as a van der Waals substrate that yields millimeter-scale perovskite flakes. Interestingly, the grown 2D-HOIP flakes lie embedded within the interlayer spacings of muscovite mica. We find that such 2D-HOIP flakes buried in mica demonstrate enhanced photostability in comparison to conventional 2D-HOIP flakes. Such liquid-phase growth in the interlayer spacings of van der Waals substrates opens a new avenue for developing novel material structures for designing optoelectronic devices

Quantitative Profiling of Polar Metabolites in Herbal Medicine Injections for Multivariate Statistical Evaluation Based on Independence Principal Component Analysis

<div><p>Botanical primary metabolites extensively exist in herbal medicine injections (HMIs), but often were ignored to control. With the limitation of bias towards hydrophilic substances, the primary metabolites with strong polarity, such as saccharides, amino acids and organic acids, are usually difficult to detect by the routinely applied reversed-phase chromatographic fingerprint technology. In this study, a proton nuclear magnetic resonance (¹H NMR) profiling method was developed for efficient identification and quantification of small polar molecules, mostly primary metabolites in HMIs. A commonly used medicine, Danhong injection (DHI), was employed as a model. With the developed method, 23 primary metabolites together with 7 polyphenolic acids were simultaneously identified, of which 13 metabolites with fully separated proton signals were quantified and employed for further multivariate quality control assay. The quantitative ¹H NMR method was validated with good linearity, precision, repeatability, stability and accuracy. Based on independence principal component analysis (IPCA), the contents of 13 metabolites were characterized and dimensionally reduced into the first two independence principal components (IPCs). IPC1 and IPC2 were then used to calculate the upper control limits (with 99% confidence ellipsoids) of χ² and Hotelling T² control charts. Through the constructed upper control limits, the proposed method was successfully applied to 36 batches of DHI to examine the out-of control sample with the perturbed levels of succinate, malonate, glucose, fructose, salvianic acid and protocatechuic aldehyde. The integrated strategy has provided a reliable approach to identify and quantify multiple polar metabolites of DHI in one fingerprinting spectrum, and it has also assisted in the establishment of IPCA models for the multivariate statistical evaluation of HMIs.</p></div

High-Performance Polymer Solar Cells Realized by Regulating the Surface Properties of PEDOT:PSS Interlayer from Ionic Liquids

Significant efforts have been dedicated to the interface engineering of organic photovoltaic device, suggesting that the performance and aging of the device are not only dependent on the active layer, but also governed by the interface with electrodes. In this work, controllable interfacial dipole and conductivity have been achieved in ionic liquids (ILs) modified poly­(3,4-ethylenedioxythiophene):poly­(styrenesulfonate) (PEDOT:PSS). We conclude that an appropriate interfacial conductivity is as essential as the suitable work function for an efficient buffer layer. Through forming favorable dipoles for hole transportation and reducing the film resistance by [HOEMIm]­[HSO4] treatment, an averaged performance of 8.64% is obtained for OPVs based on PTB7:PC71BM bulk heterojunction with improved stability. However, the improvement of performance is inconspicuous for OPVs based on PTB7-Th:PC71BM bulk heterojunction due to the incompetent energy level of high concentration ILs-modified PEDOT:PSS. The enhanced in-plane conductivity will reduce shunt resistance, and produce a fake high short-circuit current density (<i>J</i>_sc) with a lower fill factor. We point out that the <i>J</i>_sc can be improved by decreasing series resistance; meanwhile, the accompanying reduced shunt resistance has an unfavorable effect on device performance