A Chinese Medicine Formula “Xian-Jia-Tang” for Treating Bladder Outlet Obstruction by Improving Urodynamics and Inhibiting Oxidative Stress through Potassium Channels

The aim of this study is to investigate efficacy of a traditional Chinese medicine formula (named Xian-Jia-Tang, XJT) on bladder outlet obstruction (BOO) in rats and explore its mechanisms. Total 80 BOO model rats were established and randomly divided into 4 groups: physiological saline, XJT, Cesium Chloride (CC), and XJT and CC groups. Meanwhile, 12 rats were used as normal control. Bladder weight and urodynamics were measured. Oxidative stress level and mRNA expressions of potassium channels gene were detected in detrusor. The mRNA and protein levels of hypoxia inducible factor-α (HIF-α) in detrusor were detected by RT-PCR and Western blot. BOO model rats showed significantly higher bladder weight and abnormal urodynamics. XJT significantly improved the abnormal urodynamics and inhibited the oxidative stress and changes of mRNA levels of potassium channels genes in detrusor of BOO model rats. Moreover, KATP and SK2/3 mRNA were overexpressed in BOO model rats treated by XJT. Besides, the significantly increased levels of HIF-α mRNA and protein were also inhibited by XJT. However, these inhibition effects of XJT were weakened by CC. XJT could effectively improve the urodynamics and inhibit the oxidative stress caused by hypoxia through suppressing the role of potassium channels in BOO model rats

The role of tumor-associated macrophages in glioma cohort: through both traditional RNA sequencing and single cell RNA sequencing

Gliomas are the leading cause in more than 50% of malignant brain tumor cases. Prognoses, recurrences, and mortality are usually poor for gliomas that have malignant features. In gliomas, there are four grades, with grade IV gliomas known as glioblastomas (GBM). Currently, the primary methods employed for glioma treatment include surgical removal, followed by chemotherapy after the operation, and targeted therapy. However, the outcomes of these treatments are unsatisfactory. Gliomas have a high number of tumor-associated macrophages (TAM), which consist of brain microglia and macrophages, making them the predominant cell group in the tumor microenvironment (TME). The glioma cohort was analyzed using single-cell RNA sequencing to quantify the genes related to TAMs in this study. Furthermore, the ssGSEA analysis was utilized to assess the TAM-associated score in the glioma group. In the glioma cohort, we have successfully developed a prognostic model consisting of 12 genes, which is derived from the TAM-associated genes. The glioma cohort demonstrated the predictive significance of the TAM-based risk model through survival analysis and time-dependent ROC curve. Furthermore, the correlation analysis revealed the significance of the TAM-based risk model in the application of immunotherapy for individuals diagnosed with GBM. Ultimately, the additional examination unveiled the prognostic significance of PTX3 in the glioma group, establishing it as the utmost valuable prognostic indicator in patients with GBM. The PCR assay revealed the PTX3 is significantly up-regulated in GBM cohort. Additionally, the assessment of cell growth further confirms the involvement of PTX3 in the GBM group. The analysis of cell proliferation showed that the increased expression of PTX3 enhanced the ability of glioma cells to proliferate. The prognosis of glioblastomas and glioma is influenced by the proliferation of tumor-associated macrophages

Intelligent Design of Product Forms Based on Design Cognitive Dynamics and a Cobweb Structure

Design is a complex, iterative, and innovative process. By traditional methods, it is difficult for designers to have an integral priori design experience to fully explore a wide range of design solutions. Therefore, refined intelligent design has become an important trend in design research. More powerful design thinking is needed in intelligent design process. Combining cognitive dynamics and a cobweb structure, an intelligent design method is proposed to formalize the innovative design process. The excavation of the dynamic mechanism of the product evolution process during product development is necessary to predict next-generation multi-image product forms from a larger design space. First, different design thinking stimulates the information source and is obtained by analyzing the designers’ thinking process when designing and mining the dynamic mechanism behind it. Based on the nonlinear cognitive cobweb process proposed by Francisco and a natural cobweb structure, the product image cognitive cobweb model (PICCM) is constructed. Then, natural cobweb predation behavior is simulated using a stimulus information source to impact the PICCM. This process uses genetic algorithms to obtain numerous offspring forms, and the PICCM’s mechanical properties are the energy loss parameters in the impact information. Furthermore, feasible solutions are selected from intelligent design sketches by the product artificial form evaluation system based on designers’ cognition, and a new product image cognitive cobweb system is reconstructed. Finally, a case study demonstrates the efficiency and feasibility of the proposed approach

Tree Species Classification of Forest Stands Using Multisource Remote Sensing Data

The spatial distribution of forest stands is one of the fundamental properties of forests. Timely and accurately obtained stand distribution can help people better understand, manage, and utilize forests. The development of remote sensing technology has made it possible to map the distribution of tree species in a timely and accurate manner. At present, a large amount of remote sensing data have been accumulated, including high-spatial-resolution images, time-series images, light detection and ranging (LiDAR) data, etc. However, these data have not been fully utilized. To accurately identify the tree species of forest stands, various and complementary data need to be synthesized for classification. A curve matching based method called the fusion of spectral image and point data (FSP) algorithm was developed to fuse high-spatial-resolution images, time-series images, and LiDAR data for forest stand classification. In this method, the multispectral Sentinel-2 image and high-spatial-resolution aerial images were first fused. Then, the fused images were segmented to derive forest stands, which are the basic unit for classification. To extract features from forest stands, the gray histogram of each band was extracted from the aerial images. The average reflectance in each stand was calculated and stacked for the time-series images. The profile curve of forest structure was generated from the LiDAR data. Finally, the features of forest stands were compared with training samples using curve matching methods to derive the tree species. The developed method was tested in a forest farm to classify 11 tree species. The average accuracy of the FSP method for ten performances was between 0.900 and 0.913, and the maximum accuracy was 0.945. The experiments demonstrate that the FSP method is more accurate and stable than traditional machine learning classification methods

Characterization of Photocurrent Generation Dynamics in Polymer Solar Cells Based on ZnO/CdS-Core/Shell Nanoarrays by Intensity Modulated Photocurrent Spectroscopy: Theoretical Modeling

A theoretical model is developed for the dynamic characterization of hybrid polymer-based solar cells (HPSCs) based on vertically aligned ZnO/CdS-core/shell nanorod arrays (ZC-NAs) by intensity modulated photocurrent spectroscopy (IMPS). The model describes the effects of CdS shell formation on charge generation and transport dynamics. Particularly, an analytical expression for the ineffective polymer phase model in nanoarray solar cells is developed and introduced into IMPS model for the first time. The main expectations of the IMPS model are confirmed by the experimental data of the polymer/ZC-NA cells with the CdS shell thickness (<i>L</i>) of 3–8 nm. It is shown that the contributions from CdS absorption (<i>f</i>₁) and polymer absorption (<i>f</i>₂) to charge generation are determined by the core/shell nanoarray structure and the intrinsic polymer property, while the optimal CdS shell thickness (<i>L</i>_opt) depends on the interspacing between ZnO core nanorods and the exciton diffusion length of the polymer. The photocurrent generation is dominantly the competitive results of <i>f</i>₁ and <i>f</i>₂ contributions subjected to the change in <i>L</i>, with the polymer as a dominant absorption material. Fittings of the measured IMPS responses to the IMPS model reveal that the <i>L</i>-dependence of photocurrent generation dominantly originates from <i>f</i>₁, <i>f</i>₂, and the polymer exciton dissociation rate <i>S</i> at the polymer/CdS interface. Moreover, the first-order rate constants for the surface defects to trap and detrap the injected electrons in ZnO core nanorods are found to decrease with CdS shell growth and become saturated at <i>L</i>_opt. Furthermore, it is demonstrated that the effective electron diffusion coefficient <i>D</i>_e in the ZnO nanorods reaches a peak value at <i>L</i>_opt as the result of the largest photogenerated electron density in conduction band. Those results provide a guide to the design of efficient HPSCs based on the core/shell nanoarrays with complementary properties

Two-Dimensional Lamellar Mo2C for Electrochemical Hydrogen Production: Insights into the Origin of Hydrogen Evolution Reaction Activity in Acidic and Alkaline Electrolytes

Two-Dimensional Lamellar Mo2C for Electrochemical Hydrogen Production: Insights into the Origin of Hydrogen Evolution Reaction Activity in Acidic and Alkaline Electrolyte

Low-dose imaging denoising with one pair of noisy images

Low-dose imaging techniques have many important applications in diverse fields, from biological engineering to materials science. Samples can be protected from phototoxicity or radiation-induced damage using low-dose illumination. However, imaging under a low-dose condition is dominated by Poisson noise and additive Gaussian noise, which seriously affects the imaging quality, such as signal-to-noise ratio, contrast, and resolution. In this work, we demonstrate a low-dose imaging denoising method that incorporates the noise statistical model into a deep neural network. One pair of noisy images is used instead of clear target labels and the parameters of the network are optimized by the noise statistical model. The proposed method is evaluated using simulation data of the optical microscope, and scanning transmission electron microscope under different low-dose illumination conditions. In order to capture two noisy measurements of the same information in a dynamic process, we built an optical microscope that is capable of capturing a pair of images with independent and identically distributed noises in one shot. A biological dynamic process under low-dose condition imaging is performed and reconstructed with the proposed method. We experimentally demonstrate that the proposed method is effective on an optical microscope, fluorescence microscope, and scanning transmission electron microscope, and show that the reconstructed images are improved in terms of signal-to-noise ratio and spatial resolution. We believe that the proposed method could be applied to a wide range of low-dose imaging systems from biological to material science.Published versionThis work was funded by National Key Research and Development Program of China (2021YFB3602604); National Natural Science Foundation of China (61975205,62075221, 62131011); Fusion Foundation of Research and Education of CAS; University of Chinese Academy of Sciences; Fundamental Research Funds for the Central Universities; Funded Project of Hebei Province Innovation Capability Improvement Plan, China (20540302D)

IPA-3: An Inhibitor of Diadenylate Cyclase of Streptococcus suis with Potent Antimicrobial Activity

Antimicrobial resistance (AMR) poses a huge threat to public health. The development of novel antibiotics is an effective strategy to tackle AMR. Cyclic diadenylate monophosphate (c-di-AMP) has recently been identified as an essential signal molecule for some important bacterial pathogens involved in various bacterial physiological processes, leading to its synthase diadenylate cyclase becoming an attractive antimicrobial drug target. In this study, based on the enzymatic activity of diadenylate cyclase of Streptococcus suis (ssDacA), we established a high-throughput method of screening for ssDacA inhibitors. Primary screening with a compound library containing 1133 compounds identified IPA-3 (2,2&prime;-dihydroxy-1,1&prime;-dinapthyldisulfide) as an ssDacA inhibitor. High-performance liquid chromatography (HPLC) analysis further indicated that IPA-3 could inhibit the production of c-di-AMP by ssDacA in vitro in a dose-dependent manner. Notably, it was demonstrated that IPA-3 could significantly inhibit the growth of several Gram-positive bacteria which harbor an essential diadenylate cyclase but not E. coli, which is devoid of the enzyme, or Streptococcus mutans, in which the diadenylate cyclase is not essential. Additionally, the binding site in ssDacA for IPA-3 was predicted by molecular docking, and contains residues that are relatively conserved in diadenylate cyclase of Gram-positive bacteria. Collectively, our results illustrate the feasibility of ssDacA as an antimicrobial target and consider IPA-3 as a promising starting point for the development of a novel antibacterial