Organofluorine Hydrazone Derivatives as Multifunctional Anti-Alzheimer's Agents with CK2 Inhibitory and Antioxidant Features

A set of novel hydrazone derivatives were synthesized and analyzed for their biological activities. The compounds were tested for their inhibitory effect on the phosphorylating activity of the protein kinase CK2, and their antioxidant activity was also determined in three commonly used assays. The hydrazones were evaluated for their radical scavenging against the DPPH, ABTS and peroxyl radicals. Several compounds have been identified as good antioxidants as well as potent protein kinase CK2 inhibitors. Most hydrazones containing a 4-N(CH₃)₂ residue or perfluorinated phenyl rings showed high activity in the radical-scavenging assays and possess nanomolar IC₅₀ values in the kinase assays.     

Neuroprotective and Anti-Inflammatory Effects of Evernic Acid in an MPTP-Induced Parkinson’s Disease Model

Oxidative stress, mitochondrial dysfunction, and neuroinflammation are strongly associated with the pathogenesis of Parkinson’s disease (PD), which suggests that anti-oxidative and anti-inflammatory compounds might provide an alternative treatment for PD. Here, we evaluated the neuroprotective effects of evernic aid (EA), which was screened from a lichen library provided by the Korean Lichen Research Institute at Sunchon National University. EA is a secondary metabolite generated by lichens, including Ramalina, Evernia, and Hypogymnia, and several studies have described its anticancer, antifungal, and antimicrobial effects. However, the neuroprotective effects of EA have not been studied. We found that EA protected primary cultured neurons against 1-methyl-4-phenylpyridium (MPP⁺)-induced cell death, mitochondrial dysfunction, and oxidative stress, and effectively reduced MPP⁺-induced astroglial activation by inhibiting the NF-κB pathway. In vivo, EA ameliorated MPTP-induced motor dysfunction, dopaminergic neuronal loss, and neuroinflammation in the nigrostriatal pathway in C57BL/6 mice. Taken together, our findings demonstrate that EA has neuroprotective and anti-inflammatory effects in PD models and suggest that EA is a potential therapeutic candidate for PD.     

Effect of formic-acid-to-acetic-acid ratio on the structure and spinnability of aqueous aluminium sol of alumina fibre

The structure and spinnability of sol are essential for obtaining high-performance continuous alumina-based fibres. Herein, the surface electrostatic potential analysis and conventional experimental methods (X-ray diffraction, Fourier-transform infrared spectroscopy, ²⁷Al nuclear magnetic resonance solution spectra and rheological methods) were combined to investigate and discuss the relationship between the structure and spinnability of aluminium carboxylate sols with different formic-acid-to-acetic-acid molar ratios. The results showed that the aluminium carboxylate sol structure changed with the formic-acid-to-acetic-acid molar ratio and greatly affected the sol's spinnability. The tetramer (AlO6 oligomer) products comprising two effective active carboxylate groups in the sol increased with the formic acid content, which helped form linear long-chain polymers. However, excess formic acid content led to a large increase of AlO6 monomer products that were unable to form linear long-chain polymers, which decreased the sol's spinnability. When the formic-acid-to-acetic-acid molar ratio was 1.15:0.85, the sol containing more AlO6 oligomer products had the best spinnability. Furthermore, α-Al₂O₃ ceramic fibres with good morphology, small grains and uniform diameter distribution were obtained after calcination at 1200 °C.     

Features of crystal structures and thermo-mechanical properties of weberites RE3NbO7 (RE=La,Nd, Sm,Eu, Gd) ceramics

The features of crystal structures, thermo-mechanical properties and their dominant mechanisms of weberites RE₃NbO₇ were studied as high-temperature oxides. We concentrated on connections between structures and thermo-mechanical properties, the influences of bond lengths, lattice distortion degrees and microstructures on these properties were estimated. The shortening of bond length and increment of bonding strength would lead to the increase of mechanical properties. The Vickers hardness (4.5-7.8 GPa) and toughness (0.5-1.6 MPa·m^1/2) of weberites RE₃NbO₇ are enhanced by grain refinement and increment of bond strength, while crystal structures, bond lengths, and lattice distortion degrees influenced their Young's modulus (100-170 GPa). Nano-indentation was applied to test the influence of microstructures on modulus and hardness. The dominant mechanisms for mechanical properties and thermal conductivity were proposed, which was conducive to properties tailoring and engineering applications of weberites RE₃NbO₇ oxides.     

基于乙酰胆碱酯酶和氧化应激研究海胆酮对阿尔茨海默症的作用机制

海胆酮是一种酮式类胡萝卜素，主要从海胆及藻类等海洋生物中提取。本文研究海胆酮对乙酰胆碱酯酶（acetylcholinesterase，AChE）的抑制作用，应用酶动力学、荧光光谱、圆二色光谱和分子对接技术研究海胆酮对AChE的抑制机理，并用淀粉样β蛋白片段25～35（amyloid beta-peptide 25-35，Aβ25-35）诱导大鼠肾上腺嗜铬细胞瘤细胞（PC12细胞）建立阿尔茨海默症（Alzheimer’s disease，AD）模型，研究海胆酮对AD细胞模型氧化应激损伤的作用。结果表明，海胆酮有很强的AChE抑制活性，其半抑制质量浓度为（16.29±0.97）μg/mL，抑制常数Ki为3.82 μg/mL，表现为竞争性抑制；海胆酮可诱导AChE二级结构改变，更容易与AChE活性中心氨基酸Ser200、His440、Trp84和Tyr121结合，阻碍底物碘代硫代乙酰胆碱（acetylthiocholine iodide，ATCI）与酶结合，从而引起酶活力降低。海胆酮能有效抑制Aβ25-35诱导PC12细胞的AChE活力，降低丙二醛含量，增加超氧化物歧化酶、过氧化氢酶和谷胱甘肽过氧化物酶活力，减轻Aβ25-35诱导的PC12细胞氧化应激损伤。本研究基于AChE和氧化应激阐明了海胆酮对AD的潜在作用机制，为海胆酮在功能食品、生物医药等领域的应用提供了数据支持和理论根据。     

Destructive and non-destructive evaluation of concrete for optimum sand to aggregate volume ratio

Aggregates are the biggest contributor to concrete volume and are a crucial parameter in dictating its mechanical properties. As such, a detailed experimental investigation was carried out to evaluate the effect of sand-to-aggregate volume ratio (s/a) on the mechanical properties of concrete utilizing both destructive and non-destructive testing (employing UPV (ultrasonic pulse velocity) measurements). For investigation, standard cylindrical concrete samples were made with different s/a (0.36, 0.40, 0.44, 0.48, 0.52, and 0.56), cement content (340 and 450 kg/m³), water-to-cement ratio (0.45 and 0.50), and maximum aggregate size (12 and 19 mm). The effect of these design parameters on the 7, 14, and 28 d compressive strength, tensile strength, elastic modulus, and UPV of concrete were assessed. The careful analysis demonstrates that aggregate proportions and size need to be optimized for formulating mix designs; optimum ratios of s/a were found to be 0.40 and 0.44 for the maximum aggregate size of 12 and 19 mm, respectively, irrespective of the W/C (water-to-cement) and cement content.     

Resource potential for wind-hydrogen power in Ukraine

The paper provides an assessment of the current wind energy potential in Ukraine, and discusses developmental prospects for wind-hydrogen power generation in the country. Hydrogen utilization is a highly promising option for Ukraine's energy system, environment, and business. In Ukraine, an optimal way towards clean zero-carbon energy production is through the development of the wind-hydrogen sector. In order to make it possible, the energy potential of industrial hydrogen production and use has to be studied thoroughly.Ukraine possesses huge resources for wind energy supply. At the beginning of 2020, the total installed capacity of Ukrainian wind farms was 1.17 GW. Wind power generation in Ukraine has significant advantages in comparison to the use of traditional sources such as thermal and nuclear energy.In this work, an assessment of the wind resource potential in Ukraine is made via the geographical approach suggested by the authors, and according to the «Methodical guidelines for the assessment of average annual power generation by a wind turbine based on the long-term wind speed observation data». The paper analyses the long-term dynamics of average annual wind speed at 40 Ukrainian weather stations that provide valid data. The parameter for the vertical wind profile model is calculated based on the data reanalysis for 10 m and 50 m altitudes. The capacity factor (CF) for modern wind turbine generators is determined. The CF spatial distribution for an average 3 MW wind turbine and the power generation potential for the wind power plants across the territory of Ukraine are mapped.Based on the wind energy potential assessment, the equivalent possible production of water electrolysis-derived green hydrogen is estimated. The potential average annual production of green hydrogen across the territory of Ukraine is mapped.It is concluded that Ukraine can potentially establish wind power plants with a total capacity of 688 GW on its territory. The average annual electricity production of this system is supposed to reach up to 2174 bln kWh. Thus, it can provide an average annual production of 483 billion Nm³ (43 million tons) of green hydrogen by electrolysis. The social efficiency of investments in wind-hydrogen electricity is presented.     

唐家河煤矿工业场地位置选择及开拓方案设计

通过对唐家河煤矿自然地形条件及外部条件分析,结合井下煤层赋存情况,设计提出三个工业场地选址方案和两个开拓布置方案,通过技术、经济比较,选择陈家湾场地立井开拓方式,减少了矿井初期投资及后期运营费。     

Predicting hydrogen adsorption and desorption rates in cylindrical metal hydride beds: Empirical correlations and machine learning

A major limitation in chemisorptive hydrogen storage in metal hydrides is the long time required for the adsorption reaction during charging. This study investigates how the shape and material of the reaction chamber influences the adsorption and desorption rates. Numerical simulations of hydrogen storage in a cylindrical reaction chamber filled with LaNi₅ hydride are conducted for a range of chamber thermal conductivities and aspect ratios. The results show that adsorption and desorption processes are limited by thermal diffusion in the hydride bed and storage chamber. A storage efficiency is proposed based on an ideal isothermal process and used to evaluate the impact of chamber thermal conductivity and aspect ratio on the adsorption and desorption rates. Empirical correlations are proposed for predicting the adsorption and desorption efficiency of cylindrical LaNi₅ hydride beds. Finally, a machine-learning based data model for predicting storage efficiency in metal hydride chambers is presented. Comparison against the empirical correlations highlights that the machine learning-based data model can predict the storage efficiency more accurately.