Synthesis and Biological Evaluation of Novel Fusidic Acid Derivatives as Two-in-One Agent with Potent Antibacterial and Anti-Inflammatory Activity

Fusidic acid (FA), a narrow-spectrum antibiotics, is highly sensitive to various Gram-positive cocci associated with skin infections. It has outstanding antibacterial effects against certain Gram-positive bacteria whilst no cross-resistance with other antibiotics. Two series of FA derivatives were synthesized and their antibacterial activities were tested. A new aromatic side-chain analog, FA-15 exhibited good antibacterial activity with MIC values in the range of 0.781-1.563 µM against three strains of Staphylococcus spp. Furthermore, through the assessment by the kinetic assay, similar characteristics of bacteriostasis by FA and its aromatic derivatives were observed. In addition, anti-inflammatory activities of FA and its aromatic derivatives were evaluated by using a 12-O-tetradecanoylphorbol-13-acetate (TPA) induced mouse ear edema model. The results also indicated that FA and its aromatic derivatives effectively reduced TPA-induced ear edema in a dose-dependent manner. Following, multiform computerized simulation, including homology modeling, molecular docking, molecular dynamic simulation and QSAR was conducted to clarify the mechanism and regularity of activities. Overall, the present work gave vital clues about structural modifications and has profound significance in deeply scouting for bioactive potentials of FA and its derivatives

Experimental Study on Tensile Properties of a Novel Porous Metal Fiber/Powder Sintered Composite Sheet

A novel porous metal fiber/powder sintered composite sheet (PMFPSCS) is developed by sintering a mixture of a porous metal fiber sintered sheet (PMFSS) and copper powders with particles of a spherical shape. The characteristics of the PMFPSCS including its microstructure, sintering density and porosity are investigated. A uniaxial tensile test is carried out to study the tensile behaviors of the PMFPSCS. The deformation and failure mechanisms of the PMFSCS are discussed. Experimental results show that the PMFPSCS successively experiences an elastic stage, hardening stage, and fracture stage under tension. The tensile strength of the PMFPSCS is determined by a reticulated skeleton of fibers and reinforcement of copper powders. With the porosity of the PMFSS increasing, the tensile strength of the PMFPSCS decreases, whereas the reinforcement of copper powders increases. At the elastic stage, the structural elastic deformation is dominant, and at the hardening stage, the plastic deformation is composed of the structural deformation and the copper fibers’ plastic deformation. The fracture of the PMFPSCS is mainly caused by the breaking of sintering joints

Stabilization and enhanced energy gap by Mg doping in ε-phase Ga2O3 thin films

Mg-doped Ga2O3 thin films with different doping concentrations were deposited on sapphire substrates using laser molecular beam epitaxy (L-MBE) technique. X-ray diffraction (XRD), x-ray photoelectron spectroscopy (XPS) and ultraviolet-visible (UV-vis) absorption spectrum were used to characterize the crystal structure and optical properties of the as-grown films. Compared to pure Ga2O3 thin film, the Mg-doped thin films have transformed from the most stable β-phase into ε-phase. The absorption edge shifted to about 205 nm and the optical bandgap increased to ∼ 6 eV. These properties reveal that Mg-doped Ga2O3 films may have potential applications in the field of deep ultraviolet optoelectronic devices, such as deep ultraviolet photodetectors, short wavelength light emitting devices and so on

Experimental investigation on the dynamic performance of a hybrid PEM fuel cell/battery system for lightweight electric vehicle application

A hybrid system combining a 2 kW air-blowing proton exchange membrane fuel cell (PEMFC) stack and a lead-acid battery pack is developed for a lightweight cruising vehicle. The dynamic performances of this PEMFC system with and without the assistance of the batteries are systematically investigated in a series of laboratory and road tests. The stack current and voltage have timely dynamic responses to the load variations. Particularly, the current overshoot and voltage undershoot both happen during the step-up load tests. These phenomena are closely related to the charge double-layer effect and the mass transfer mechanisms such as the water and gas transport and distribution in the fuel cell. When the external load is beyond the range of the fuel cell system, the battery immediately participates in power output with a higher transient discharging current especially in the accelerating and climbing processes. The DC-DC converter exhibits a satisfying performance in adaptive modulation. It helps rectify the voltage output in a rigid manner and prevent the fuel cell system from being overloaded. The dynamic responses of other operating parameters such as the anodic operating pressure and the inlet and outlet temperatures are also investigated. The results show that such a hybrid system is able to dynamically satisfy the vehicular power demand.Proton exchange membrane fuel cell Hybrid power system Fuel cell electric vehicle Dynamic performance Transient response

Dominance evaluation of structural factors in a passive air-breathing direct methanol fuel cell based on orthogonal array analysis

This study comprehensively investigates the dominance of various structural factors in a passive air-breathing DMFC by means of orthogonal array analysis (OAA). Two membrane types, two assembly patterns of the diffusion layer and two open ratios of the current collector are prepared. Three target variables are selected as the performance indexes including the maximum power density (MPD), limiting current density (LCD) and open circuit voltage (OCV). The range analysis (RA) method and effect curves (ECs) are used to characterize the OAA data. The RA results demonstrate that the current collector and diffusion layer combine to dominate the values of MPD and LCD in a wide range of methanol concentrations from 0.5 to 8 M. The dominant structural factors related to the value of OCV at different methanol concentrations are also explored. In addition, the effect curves show that a medium methanol concentration like 2 M generally promotes higher values of MPD and LCD, while a relatively lower methanol concentration like 0.5 M benefits a higher value of OCV than others in a general statistical sense.Direct methanol fuel cell Passive Air-breathing Dominance Structural factor Orthogonal array analysis

A Thermoplastic Multilayered Carbon-Fabric/Polycarbonate Laminate Prepared by a Two-Step Hot-Press Technique

Carbon fiber (CF) reinforced thermoplastic composites have gradually become increasingly popular in composite production owing to their lower hazard level, good structural flexibility and recyclability. In this work, a multilayered carbon–fabric/polycarbonate laminate (multi-CFPL) was fabricated by a two-step hot-press process, mainly based on the thermoplastic properties of its polycarbonate (PC) matrix. Different from the conventional one-step method, the two-step hot-press process was composed of two separate procedures. First, a unit-hot-press operation was introduced to prepare a single-layered carbon–fabric/PC laminate (simplified as unit-CFPL). Subsequently, a laminating-hot-press was employed to compress several as-prepared unit-CFPLs bonded together. This combined process aims to reduce the hot-press temperature and pressure, as well as facilitate the structure designability of this new composite. Several mechanical investigations were conducted to analyze the effect of the hot-press parameters and unit-CFPL numbers on the performance of this multi-CFPL material, including flexural, uniaxial tensile and impact tests. The results reveal that the multi-CFPL exhibits a good stability of flexural and tensile properties in terms of strength and modulus. Furthermore, during impact tests, the multi-CFPL presents an accelerated growth of peak force and energy absorption capability with increasing unit-CFPL layers

A few-shot learning method for tobacco abnormality identification.

Tobacco is a valuable crop, but its disease identification is rarely involved in existing works. In this work, we use few-shot learning (FSL) to identify abnormalities in tobacco. FSL is a solution for the data deficiency that has been an obstacle to using deep learning. However, weak feature representation caused by limited data is still a challenging issue in FSL. The weak feature representation leads to weak generalization and troubles in cross-domain. In this work, we propose a feature representation enhancement network (FREN) that enhances the feature representation through instance embedding and task adaptation. For instance embedding, global max pooling, and global average pooling are used together for adding more features, and Gaussian-like calibration is used for normalizing the feature distribution. For task adaptation, self-attention is adopted for task contextualization. Given the absence of publicly available data on tobacco, we created a tobacco leaf abnormality dataset (TLA), which includes 16 categories, two settings, and 1,430 images in total. In experiments, we use PlantVillage, which is the benchmark dataset for plant disease identification, to validate the superiority of FREN first. Subsequently, we use the proposed method and TLA to analyze and discuss the abnormality identification of tobacco. For the multi-symptom diseases that always have low accuracy, we propose a solution by dividing the samples into subcategories created by symptom. For the 10 categories of tomato in PlantVillage, the accuracy achieves 66.04% in 5-way, 1-shot tasks. For the two settings of the tobacco leaf abnormality dataset, the accuracies were achieved at 45.5% and 56.5%. By using the multisymptom solution, the best accuracy can be lifted to 60.7% in 16-way, 1-shot tasks and achieved at 81.8% in 16-way, 10-shot tasks. The results show that our method improves the performance greatly by enhancing feature representation, especially for tasks that contain categories with high similarity. The desensitization of data when crossing domains also validates that the FREN has a strong generalization ability