Explore the vast range of titles available.

The object of the study is lignite. Analytical testing techniques, such as elemental analysis, 13C nuclear magnetic resonance (13C NMR) spectroscopy, Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and high-resolution transmission electron microscopy (HRTEM), were used to acquire information on the structural parameters of lignite. The aromaticity of Xianfeng lignite is 43.57%, and the aromatic carbon structure is mainly naphthalene and anthracene/phenanthrene. The aliphatic carbon structure is dominated by cycloalkanes, alkyl side chains, and hydrogenated aromatics. Oxygen is mainly present in ether oxygen, carboxyl, and carbonyl groups. Nitrogen is mainly in the form of pyrrole nitrogen and quaternary nitrogen. Sulfur is mainly thiophene sulfur. According to the analysis results, the molecular structure model of XF lignite was constructed. The molecular formula is C184H172O39N6S2. The 2D structure was converted to a 3D structure using computer simulation software and optimized. The optimized model has a remarkable stereoconfiguration, and the aromatic lamellae are irregularly arranged in space. The aromatic rings were mainly connected by methylene, hypomethylene, methoxy, and aliphatic rings. In addition, the simulated 13C NMR spectra are in good agreement with the experimental spectra. This shows the rationality of the 3D chemical structure model.

The spatial structure of organic compounds (3D molecular structure) acts as an important tool in teaching organic chemistry. This study designed 16 3D molecular structures of hydrocarbons and chemical reaction simulations, using these simulations to design teaching plans for alkenes; guide teachers on how to use the teaching plans; and develop tests to assess students’ chemistry competency. The research aimed to evaluate the impact of using 3D molecular structure simulations on students’ development of chemistry competency. The methodology was conducted on 630 Vietnamese students divided into two groups: an experimental group and a control group. The results of this study have demonstrated the benefits of 3D molecular structure simulation, including enhancement of students’ chemistry competency, promotion of transfer and application of spatial chemistry content to higher-order concepts, and expansion of the planning of the nature and mechanism of chemical reactions. As a result, chemistry teachers can use 3D molecular structure simulation to teach topics such as atomic radius, bond length, and bond angle. This research makes an important contribution to the application of technology in teaching and developing chemistry competence for Vietnamese high school students.

ORF6 is responsible for suppressing the immune response of cells infected by the SARS-CoV-2 virus. It is also the most toxic protein of SARS-CoV-2, and its actions are associated with the viral pathogenicity. Here, we study in silico and in vitro the structure of the protein, its interaction with RAE1 and the mechanism of action behind its high toxicity. We show both computationally and experimentally that SARS-CoV-2 ORF6, embedded in the cytoplasmic membranes, binds to RAE1 and sequesters it in the cytoplasm, thus depleting its availability in the nucleus and impairing nucleocytoplasmic mRNA transport. This negatively affects the cellular genome stability by compromising the cell cycle progression into the S-phase and by promoting the accumulation of RNA–DNA hybrids. Understanding the multiple ways in which ORF6 affects DNA replication may also have important implications for elucidating the pathogenicity of SARS-CoV-2 and developing therapeutic strategies to mitigate its deleterious effects on host cells.

To investigate the molecular structure characteristics and chemical reaction mechanisms of gas coal from the Hong II coal mine of the Ningxia Hongdunzi Coal Industry, this study explores its elemental composition, structural features, and methods for constructing and optimizing molecular models. The basic properties of the coal were determined through proximate and elemental analyses. The carbon structure was characterized using 13C-NMR nuclear magnetic resonance, the N and S chemical states were analyzed with XPS, and the distribution of hydroxyl, aliphatic hydrocarbons, aromatic rings, and oxygen-containing functional groups was characterized by FT-IR. Based on the analysis results, a molecular structure model of Hongdunzi gas coal was constructed with the molecular formula C204H117O17NS, and the calculated results of the model showed high consistency with the experimental spectra of 13C-NMR. The macromolecular model of gas coal was constructed using the Materials Studio 2020 software, and its structure was optimized through geometric optimization and dynamic simulations. After optimization, the total energy of the model was significantly reduced from 8525.12 kcal·mol−1 to 3966.16 kcal·mol−1, highlighting the enhanced stability of the coal molecular structure. This optimization indicates that torsional energy plays a dominant role in molecular stability, while van der Waals forces and electrostatic interactions were significantly improved during the optimization process.

Dysbiosis of the human skin microbiome has long been associated with changes to the pH of the skin, dermal immune function and chronic skin conditions. Dermatological issues have been noted as the most prevalent medical presentation in the microgravity environment of space. The change in gravitational forces has been implicated in human immuno-suppression, also impacted by changes in the gastrointestinal-skin axis and its impact on Vitamin D metabolism, altered microbial gene expression in resident flora (leading changes in biofilm formation) and increased virulence factors in potential pathogens. There are also other stressors to the skin microbiome unique to space travel, including increased exposure to radiation, prolonged periods of dry washing technique, air quality and changes in microbe replication and growth parameters. Optimal microbiome health leads to enhanced skin barrier manufacture and maintenance, along with improved skin immune function and healing. In a microgravity environment expected to be experienced during long space flights, disruptions to the skin microbiome, coupled with increased virulence of pathological viruses and bacteria has implications for holistic skin health, astronaut cognitive function and mental health, and is coupled with slowed rates of wound healing. Scenario management for holistic skin health and restoration of microbiome homeostasis on long space flights require consideration.

Background Drug-target identification is crucial to discover novel applications for existing drugs and provide more insights about mechanisms of biological actions, such as adverse drug effects (ADEs). Computational methods along with the integration of current big data sources provide a useful framework for drug-target and drug-adverse effect discovery. Results In this article, we propose a method based on the integration of 3D chemical similarity, target and adverse effect data to generate a drug-target-adverse effect predictor along with a simple leveraging system to improve identification of drug-targets and drug-adverse effects. In the first step, we generated a system for multiple drug-target identification based on the application of 3D drug similarity into a large target dataset extracted from the ChEMBL. Next, we developed a target-adverse effect predictor combining targets from ChEMBL with phenotypic information provided by SIDER data source. Both modules were linked to generate a final predictor that establishes hypothesis about new drug-target-adverse effect candidates. Additionally, we showed that leveraging drug-target candidates with phenotypic data is very useful to improve the identification of drug-targets. The integration of phenotypic data into drug-target candidates yielded up to twofold precision improvement. In the opposite direction, leveraging drug-phenotype candidates with target data also yielded a significant enhancement in the performance. Conclusions The modeling described in the current study is simple and efficient and has applications at large scale in drug repurposing and drug safety through the identification of mechanism of action of biological effects.

Las microalgas son microorganismos fotosintéticos con gran potencial para abastecer las demandas energéticas mundiales. Sin embargo, los limitados conocimientos que se tienen de estos organismos, en particular a nivel molecular de los procesos metabólicos, han limitado su uso con estos propósitos. En esta investigación se ha realizado el análisis in silico de la subunidad alfa de la acetil-Coenzima A carboxilasa heteromérica (αACCasa), una enzima clave en la biosíntesis de lípidos de las microalgas Chlorella sp. y Scenedesmus sp. Asimismo, se ha medido la expresión de este gen en ambas especies cultivadas en medios deficientes de nitrógeno. Los resultados indican que la αACCasa muestra conservación estructural y funcional en ambas especies de microalgas y su mayor similitud genética con otras especies de microalgas. Asimismo, se ha mostrado que el nivel de expresión del gen se incrementa significativamente cuando las microalgas son cultivadas en ausencia de nitrógeno, lo cual se relaciona a su vez con una mayor acumulación de lípidos microalgales. En conclusión, el análisis in silico de la αACCasa de Chlorella sp. y Scenedesmus sp. presentan características estructurales, funcionales y evolutivas muy similares con otras especies de microalgas y plantas. Asimismo, el estudio revela que en ambas especies el gen se sobreexpresa cuando las microalgas son sometidas a estrés por deficiencia de nitrógeno, el cual se relaciona significativamente con la acumulación de lípidos totales en estas células.

This chapter contains sections titled: Introduction Literature Review Hierarchical Multi‐Class Classifiers Experimental Results Conclusions References

AbstractA novel sandwich-like heterotrinuclear [Zn(II)2Mn(III)] complex, [Zn(L)Cl2Mn]Cl, has been constructed successfully from a bicompartmental 3-MeOSalphen ligand (H2L), N,N′-bis(3-methoxy-salicylidene)phenylene-1,2-diamine. The chemically obained structure was determined through single crystal X-ray diffraction and spectrochemical (IR and UV-Vis) analyses. The structural studies revealed that characteristic inner N2O2 compartment of H2L is occupied by Zn(II) atom and outer O2O2 compartment is coordinated by Mn(III) atom in the Zn(II)–Mn(III) complex. The Zn(II)–Mn(III) complex can be used as a potential light-emitting material owing to the fluorescent property. In addition, density functional theory (DFT) and time-dependent DFT (TD-DFT) calculations were performed to take insight of experimental electronic spectra. Interaction region indicator (IRI) analyses were calculated to show relative chemical bond changes before and after coordination.

Plastics are key components of almost any technology today. Although their production consumes substantial feedstock resources, plastics are largely disposed of after their service life. In terms of a circular economy 1 – 8 , reuse of post-consumer sorted polymers (‘mechanical recycling’) is hampered by deterioration of materials performance 9 , 10 . Chemical recycling 1 , 11 via depolymerization to monomer offers an alternative that retains high-performance properties. The linear hydrocarbon chains of polyethylene 12 enable crystalline packing and provide excellent materials properties 13 . Their inert nature hinders chemical recycling, however, necessitating temperatures above 600 degrees Celsius and recovering ethylene with a yield of less than 10 per cent 3 , 11 , 14 . Here we show that renewable polycarbonates and polyesters with a low density of in-chain functional groups as break points in a polyethylene chain can be recycled chemically by solvolysis with a recovery rate of more than 96 per cent. At the same time, the break points do not disturb the crystalline polyethylene structure, and the desirable materials properties (like those of high-density polyethylene) are fully retained upon recycling. Processing can be performed by common injection moulding and the materials are well-suited for additive manufacturing, such as 3D printing. Selective removal from model polymer waste streams is possible. In our approach, the initial polymers result from polycondensation of long-chain building blocks, derived by state-of-the-art catalytic schemes from common plant oil feedstocks, or microalgae oils 15 . This allows closed-loop recycling of polyethylene-like materials. Polycarbonates and polyesters with materials properties like those of high-density polyethylene can be recycled chemically by depolymerization to their constituent monomers, re-polymerization yielding material with uncompromised processing and materials properties.