Computational Investigation Identified Potential Chemical Scaffolds for Heparanase as Anticancer Therapeutics

Heparanase (Hpse) is an endo-β-D-glucuronidase capable of cleaving heparan sulfate side chains. Its upregulated expression is implicated in tumor growth, metastasis and angiogenesis, thus making it an attractive target in cancer therapeutics. Currently, a few small molecule inhibitors have been reported to inhibit Hpse, with promising oral administration and pharmacokinetic (PK) properties. In the present study, a ligand-based pharmacophore model was generated from a dataset of well-known active small molecule Hpse inhibitors which were observed to display favorable PK properties. The compounds from the InterBioScreen database of natural (69,034) and synthetic (195,469) molecules were first filtered for their drug-likeness and the pharmacophore model was used to screen the drug-like database. The compounds acquired from screening were subjected to molecular docking with Heparanase, where two molecules used in pharmacophore generation were used as reference. From the docking analysis, 33 compounds displayed higher docking scores than the reference and favorable interactions with the catalytic residues. Complex interactions were further evaluated by molecular dynamics simulations to assess their stability over a period of 50 ns. Furthermore, the binding free energies of the 33 compounds revealed 2 natural and 2 synthetic compounds, with better binding affinities than reference molecules, and were, therefore, deemed as hits. The hit compounds presented from this in silico investigation could act as potent Heparanase inhibitors and further serve as lead scaffolds to develop compounds targeting Heparanase upregulation in cancer.     

Thermal desorption and pyrolysis direct analysis in real time mass spectrometry of Nafion membrane

Perfluorosulfonic acid ionomer membranes have been widely used as proton conducting membranes in various electrochemical processes such as polymer electrolyte fuel cells and water electrolysis. While their thermal stability has been studied by thermogravimetry and analysis of low molecular weight products, their decomposition mechanism is little understood. In this study a newly developed methodology of thermal desorption and pyrolysis in combination with direct analysis in real time mass spectrometry is applied for Nafion membrane. An ambient ionization source and a high-resolution time-of-flight mass spectrometer enabled unambiguous assignment of gaseous products. Thermal decomposition is initiated by side chain detachment above 350°C, which leaves carbonyls on the main chain at the locations of the side chains. Perfluoroalkanes are released above 400°C by main chain scission and their further decomposition products dominate above 500 °C. DFT calculation of reaction energies and barrier heights of model compounds support proposed decomposition reactions.     

An integrated dimensionality reduction and surrogate optimization approach for plant-wide chemical process operation

With liquefied natural gas becoming increasingly prevalent as a flexible source of energy, the design and optimization of industrial refrigeration cycles becomes even more important. In this article, we propose an integrated surrogate modeling and optimization framework to model and optimize the complex CryoMan Cascade refrigeration cycle. Dimensionality reduction techniques are used to reduce the large number of process decision variables which are subsequently supplied to an array of Gaussian processes, modeling both the process objective as well as feasibility constraints. Through iterative resampling of the rigorous model, this data-driven surrogate is continually refined and subsequently optimized. This approach was not only able to improve on the results of directly optimizing the process flow sheet but also located the set of optimal operating conditions in only 2 h as opposed to the original 3 weeks, facilitating its use in the operational optimization and enhanced process design of large-scale industrial chemical systems.     

In Silico Investigation of Potential Applications of Gamma Carbonic Anhydrases as Catalysts of CO2 Biomineralization Processes: A Visit to the Thermophilic Bacteria Persephonella hydrogeniphila,Persephonella marina,Thermosulfidibacter takaii,and Thermus thermophilus

Carbonic anhydrases (CAs) have been identified as ideal catalysts for CO₂ sequestration. Here, we report the sequence and structural analyses as well as the molecular dynamics (MD) simulations of four γ-CAs from thermophilic bacteria. Three of these, Persephonella marina, Persephonella hydrogeniphila, and Thermosulfidibacter takaii originate from hydrothermal vents and one, Thermus thermophilus HB8, from hot springs. Protein sequences were retrieved and aligned with previously characterized γ-CAs, revealing differences in the catalytic pocket residues. Further analysis of the structures following homology modeling revealed a hydrophobic patch in the catalytic pocket, presumed important for CO₂ binding. Monitoring of proton shuttling residue His69 (P. marina γ-CA numbering) during MD simulations of P. hydrogeniphila and P. marina’s γ-CAs (γ-PhCA and γ-PmCA), showed a different behavior to that observed in the γ-CA of Escherichia coli, which periodically coordinates Zn²⁺. This work also involved the search for hotspot residues that contribute to interface stability. Some of these residues were further identified as key in protein communication via betweenness centrality metric of dynamic residue network analysis. T. takaii’s γ-CA showed marginally lower thermostability compared to the other three γ-CA proteins with an increase in conformations visited at high temperatures being observed. Hydrogen bond analysis revealed important interactions, some unique and others common in all γ-CAs, which contribute to interface formation and thermostability. The seemingly thermostable γ-CA from T. thermophilus strangely showed increased unsynchronized residue motions at 423 K. γ-PhCA and γ-PmCA were, however, preliminarily considered suitable as prospective thermostable CO₂ sequestration agents.     

基于测绘地理信息技术的涉地税源风险识别模式应用研究

为解决沧州市涉地税源管理手段单一等问题,文中提出以测绘地理信息技术为支撑,通过对多源数据组织管理,建立风险分析指标,采用多种GIS空间分析方法构建了涉地风险税源识别模式并在沧州进行应用。结果表明:该模式能有效识别多种涉地风险,为涉地税源管理提供了支撑。     

地表径流速度对城市内涝影响规律

为分析地表径流速度对城市内涝的影响,采用一维地下排水管网与二维城区地形的动态耦合模型,选取大连市某排水区块作为研究区域,设置4种地表径流速度10种设计降雨场景,模拟分析在不同重现期设计降雨及不同地表径流速度下研究区的内涝积水特性。结果表明:随着地表径流速度降低,管道排水压力变小,管道排水达标率最高可提升48.05%,且降雨重现期越短,地表径流流速对管道排水压力的削减效果越明显;地表径流流速对检查井溢流量影响显著,随着地表径流速度降低,检查井溢流量峰值最高可减小2 750 m~3,峰现时间最长可滞后56 min,同时随着降雨重现期增长,地表径流流速对检查井溢流量的削减效果减弱;研究区低、高风险区淹没面积随地表径流速度降低,最高可分别减小1.64万、8.37万m~2,但中风险区淹没面积变化反复。     

Optimization of PEMFC system operating conditions based on neural network and PSO to achieve the best system performance

PEMFC system is a complex new clean power system. Based on MATLAB/Simulink, this paper develops a system-level dynamic model of PEMFC, including the gas supply system, hydrogen supply system, hydrothermal management system, and electric stack. The neural network fits the electric stack model to the simulation data. The effects of different operating conditions on the PEMFC stack power and system efficiency are analyzed. Combining the power of the reactor and the system efficiency to define the integrated performance index, the particle swarm optimization (PSO) algorithm is introduced to optimize the power density and system efficiency of the PEMFC with multiple objectives. The final optimal operating point increases the power density and system efficiency by 1.33% and 12.8%, respectively, which maximizes the output performance and reduces the parasitic power.     

关于COVID-19、膜脂质与公众健康（网络首发、推荐阅读）

冠状病毒具有一层脂质膜。虽然复制需要劫持宿主的RNA工具来合成病毒体蛋白，但必须将其包裹在脂质膜中，促其萌生以扩展感染。最近研究表明，某些必需脂肪酸可以抑制其复制活性。脂质膜通常被认为是水溶物的脂肪屏障，但它对细胞和亚细胞的功能是高度有序和组分特异性的，其对病毒外壳可能也有最佳的特异性。虽然复制中DNA、RNA和蛋白质组成不受饮食影响，但脂质膜受其影响。此外，自1960年代以来，人们就知道男性对这些必需脂肪酸和膜完整性不足的敏感性高于女性。有证据表明，花生四烯酸和二十二碳六烯酸具有抗病毒、免疫、抗炎、控制血压和消退素活性，因此，迫切需要考察它们在Covid-19预防和治疗中的地位，也需重新评估现行的膳食指导。当前，有关脑、神经、血管和免疫系统等富含膜系统对脂质需求还尚未被详细认识。毫无疑问，这些脂质在几百万年来塑造人类基因组方面具有重要意义，因此，如果这些膜脂质失衡将会使人类面临机体紊乱和感染风险，且男性比女性风险更大。     

Data requirements for improving the Quantitative Risk Assessment of liquid hydrogen storage systems

Quantitative Risk Assessment (QRA) supports the development of risk-informed safety codes and standards which are employed to enable the safe deployment of hydrogen technologies essential to decarbonize the transportation sector. System reliability data is a necessary input for rigorous QRA. The lack of reliability data for bulk liquid hydrogen (LH₂) storage systems located on site at fueling stations limits the use of QRAs. In turn, this hinders the ability to develop the necessary safety codes and standards that enable worldwide deployment of these stations. Through a QRA-based analysis of a LH₂ storage system, this work focuses on identifying relevant scenario and probability data currently available and ascertaining future data collection requirements regarding risks specific to liquid hydrogen releases. The work developed consists of the analysis of a general bulk LH₂ storage system design located at a hydrogen fueling station. Failure Mode and Effect Analysis (FMEA) and traditional QRA modeling tools such as Event Sequence Diagrams (ESD) and Fault Tree Analysis (FTA) are employed to identify, rank, and model risk scenarios related to the release of LH₂. Based on this analysis, scenario and reliability data needs to add LH₂-related components to QRA are identified with the purpose of improving the future safety and risk assessment of these systems.     

Optimization of flow in additively manufactured porous columns with graded permeability

Chemical engineering systems often involve a functional porous medium, such as in catalyzed reactive flows, fluid purifiers, and chromatographic separations. Ideally, the flow rates throughout the porous medium are uniform, and all portions of the medium contribute efficiently to its function. The permeability is a property of a porous medium that depends on pore geometry and relates flow rate to pressure drop. Additive manufacturing techniques raise the possibilities that permeability can be arbitrarily specified in three dimensions, and that a broader range of permeabilities can be achieved than by traditional manufacturing methods. Using numerical optimization methods, we show that designs with spatially varying permeability can achieve greater flow uniformity than designs with uniform permeability. We consider geometries involving hemispherical regions that distribute flow, as in many glass chromatography columns. By several measures, significant improvements in flow uniformity can be obtained by modifying permeability only near the inlet and outlet.