JIA LIN

Actor Yoo Ah In, who has been indicted on charges of drug use, is bolstering his legal defense team and has applied for a postponement of the first t…

STUDIO CHOOM is back with yet another flawless dance performance video! In this 'Be Original' video featuring Stray Kids' latest tit…

In the field of materials research, machine learning (ML) techniques have emerged as indispensable tools. However, the opaqueness in decision making by models can compromise the trustworthiness of results, underscoring the crucial need for model interpretability. Explainable machine learning (XML) strives to augment researchers’ comprehension of material properties and performance. Yet, reliance on high-quality datasets and scarcity of prior knowledge pose challenges for XML research, particularly when dealing with smaller datasets. In this study, using spinel as a representative example, we successfully addressed the data challenges in XML through a cationic perturbation strategy. We demonstrate an effective approach for handling information scarcity in small datasets, thus offering a feasible method for material research and broadening the scope of XML applications in materials science. Furthermore, our investigation successfully uncovered potential causal relationships underlying material properties and validated their consistency with physical cognition. These causal relationships can serve as experimental guides, facilitating the design and optimization of new materials. Consequently, this research holds significant scientific merit in advancing XML in the realm of materials science, while providing profound insights into material properties and fostering the development of reliable ML-based materials research.

Abstract Actin is a key component of the cytoskeleton of eukaryotic cells and is involved in numerous cellular functions.

An overview of solvent replacement strategies shows that there is great progress in green chemistry for replacing hazardous di-polar aprotic solvents, such as N,N-dimethylformamide (DMF), 1-methyl-2-pyrrolidinone (NMP), and 1,4-dioxane (DI), used in processing active industrial ingredients (APIs). In synthetic chemistry, alcohols, carbonates, ethers, eucalyptol, glycols, furans, ketones, cycloalkanones, lactones, pyrrolidinone or solvent mixtures, 2-methyl tetrahydrofuran in methanol, HCl in cyclopentyl methyl ether, or trifluoroacetic acid in propylene carbonate or surfactant water (no organic solvents) are suggested replacement solvents. For the replacement of dichloromethane (DCM) used in chromatography, ethyl acetate ethanol or 2-propanol in heptanes, with or without acetic acid or ammonium hydroxide additives, are suggested, along with methanol acetic acid in ethyl acetate or methyl tert-butyl ether, ethyl acetate in ethanol in cyclohexane, CO2-ethyl acetate, CO2-methanol, CO2-acetone, and CO2-isopropanol. Supercritical CO2 (scCO2) can be used to replace many organic solvents used in processing materials from natural sources. Vegetable, drupe, legume, and seed oils used as co-extractants (mixed with substrate before extraction) can be used to replace the typical organic co-solvents (ethanol, acetone) used in scCO2 extraction. Mixed solvents consisting of a hydrogen bond donor (HBD) solvent and a hydrogen bond acceptor (HBA) are not addressed in GSK or CHEM21 solvent replacement guides. Published data for 100 water-soluble and water-insoluble APIs in mono-solvents show polarity ranges appropriate for the processing of APIs with mixed solvents. When water is used, possible HBA candidate solvents are acetone, acetic acid, acetonitrile, ethanol, methanol, 2-methyl tetrahydrofuran, 2,2,5,5-tetramethyloxolane, dimethylisosorbide, Cyrene, Cygnet 0.0, or diformylxylose. When alcohol is used, possible HBA candidates are cyclopentanone, esters, lactone, eucalytol, MeSesamol, or diformylxylose. HBA—HBA mixed solvents, such as Cyrene—Cygnet 0.0, could provide interesting new combinations. Solubility parameters, Reichardt polarity, Kamlet—Taft parameters, and linear solvation energy relationships provide practical ways for identifying mixed solvents applicable to API systems.

Haixue Jia Jia Lin Dianyu Wang Xue Lv Qian Wang Zhongyan Wang

In this study, we investigated the concentration of airborne influenza virus in daycare centers and influencing factors, such as common cold prevalence, air pollutants, and meteorological factors. A total of 209 air samples were collected from daycare centers in Kaohsiung and the influenza virus was analyzed using real-time quantitative polymerase chain reaction. Air pollutants and metrological factors were measured using real-time monitoring equipment. Winter had the highest positive rates of airborne influenza virus and the highest prevalence of the common cold, followed by summer and autumn. The concentration of CO was significantly positively correlated with airborne influenza virus. Daycare center A, with natural ventilation and air condition systems, had a higher concentration of airborne influenza A virus, airborne fungi, and airborne bacteria, as well as a higher prevalence of the common cold, than daycare center B, with a mechanical ventilation system and air purifiers, while the concentrations of CO2, CO, and UFPs in daycare center A were lower than those in daycare center B. We successfully detected airborne influenza virus in daycare centers, demonstrating that aerosol sampling for influenza can provide novel epidemiological insights and inform the management of influenza in daycare centers.

Jia Lin Guoqing Yue Jun Chen Kang Xiao Qing'e Ji First published:

Duke-NUS Medical School, Singapore 169857, Singapore Department of Radiology, Sengkang General Hospital, Singapore 544886, Singapore

Background Fopius arisanus (Sonan) is an important egg–pupal endoparasitic wasp of Bactrocera dorsalis (Hendel). As traditional method of