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We synthesized a series of Ru@IL/AC catalysts using the incipient wetness impregnation technique associated with five kinds of ionic liquids, aiming to explore an efficient nonmercuric catalyst for the acetylene hydrochlorination reaction. Over the optimal 1%Ru@15%TPPB/AC catalyst, the acetylene conversion was maintained at 99.7% at 48 h (T = 170 °C, GHSVC2H2 = 360 h–1, and V HCl/V C2H2 = 1.15). Additionally, with lower Ru loading (0.2%Ru@15%TPPB/AC), the acetylene conversion still remained at 99.3% within 400 h. Characterized by CO pulse chemisorption, TEM, XPS, TGA, among other methods, it is indicated that TPPB IL could effectively improve the dispersion of Ru species, suppress the reduction of active Ru species, and inhibit the coke deposition during the acetylene hydrochlorination reaction. The interactive mechanism between TPPB and the reactants and the product was investigated to disclose the effect of TPPB IL on the catalytic performance of Ru-based catalyst, in combination with DFT calculations. The enhanced activity and long-term stability of Ru@IL/AC suggest the promising industrial application as the nonmercuric catalyst for acetylene hydrochlorination.

BackgroundThe COVID-19 epidemic originated in Wuhan City of Hubei Province in December 2019 and has spread throughout China. Understanding the fast evolving epidemiology and transmission dynamics of the outbreak beyond Hubei would provide timely information to guide intervention policy. MethodsWe collected individual information on 8,579 laboratory-confirmed cases from official publically sources reported outside Hubei in mainland China, as of February 17, 2020. We estimated the temporal variation of the demographic characteristics of cases and key time-to-event intervals. We used a Bayesian approach to estimate the dynamics of the net reproduction number (Rt) at the provincial level.

It is very significant to explore the intrinsic differences in breast cancer subtypes. These intrinsic differences are closely related to clinical diagnosis and designation of treatment plans. With the accumulation of biological and medicine datasets, there are many different omics data that can be viewed in different aspects. Combining these multiple omics data can improve the accuracy of prediction. Meanwhile; there are also many different databases available for us to download different types of omics data. In this article, we use estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) to define breast cancer subtypes and classify any two breast cancer subtypes using SMO-MKL algorithm. We collected mRNA data, methylation data and copy number variation (CNV) data from TCGA to classify breast cancer subtypes. Multiple Kernel Learning (MKL) is employed to use these omics data distinctly. The result of using three omics data with multiple kernels is better than that of using single omics data with multiple kernels. Furthermore; these significant genes and pathways discovered in the feature selection process are also analyzed. In experiments; the proposed method outperforms other state-of-the-art methods and has abundant biological interpretations.

To obtain detailed information on the pyrolysis characteristics, a thermogravimetric study on the pyrolysis of 14 typical medical waste compositions was carried out in thermogravimetric analysis (TGA) equipment using dynamic techniques in a stream of N2. An index representing pyrolysis reactivity of waste was presented. Kinetic parameters were obtained by Coats-Redfern method and used to model the TG curve. The results showed that: (a) Plastic, protein, cellulosic material, synthetic fibre, and rubber entered pyrolysis process in succession. (b) There was one decomposition stage in the pyrolysis of one-off medical glove, operating glove, cellulosic waste, absorbable catgut suture and adhesive plaster, while other components had two obvious weight loss stages. (c) The obtained apparent activation energy for second stage pyrolysis was comparably higher than that for first stage. (d) Each stage was controlled by only one kinetic mechanism, in which kinetic parameters were constant. (e) The degradation kinetics of medical waste may be affected by special physical and chemical treatment in the product manufacturing process. (f) Among 13 waste samples, the pyrolysis index of cellulosic matter was the highest, which indicated cellulosic matter had strong pyrolysis reactivity. (g) With increasing heating rate, TG curve and DTG peak shifted to high temperatures and main reaction interval of the sample became longer.

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The solubility of thiabendazole (TBZ) in 12 organic solvents (methanol, ethanol, n-propanol, isopropanol, nbutanol, isobutanol, acetone, butanone, methyl acetate, ethyl acetate, n-butyl acetate, and acetonitrile) was determined by the gravimetric method from 283.15 to 323.15 K. The solubility in all selected solvents increases with increasing temperature and in acetonitrile changes much greater than that in other solvents as temperature increases. The solubility of TBZ is higher in alcohol and ketone solvents than those in esters and nitriles. Besides, the solubility is mainly related to the solvent polarity and cohesive energy density in nonalcohols and influenced by the complicated combination of many factors in alcohols. Further, the Apelblat model, λh model, and NRTL model were used to correlate the solubility of TBZ. The relative deviation is less than 2.23% with the modified Apelblat equation, which shows better fitting performance compared with other two models.

Measuring conditional relatedness between a pair of genes is a fundamental technique and still a significant challenge in computational biology. Such relatedness can be assessed by gene expression similarities while suffering high false discovery rates. Meanwhile, other types of features, e.g., prior-knowledge based similarities, is only viable for measuring global relatedness. In this paper, we propose a novel machine learning model, named Multi-Features Relatedness (MFR), for accurately measuring conditional relatedness between a pair of genes by incorporating expression similarities with prior-knowledge based similarities in an assessment criterion. MFR is used to predict gene-gene interactions extracted from the COXPRESdb, KEGG, HPRD, and TRRUST databases by the 10-fold cross validation and test verification, and to identify gene-gene interactions collected from the GeneFriends and DIP databases for further verification. The results show that MFR achieves the highest area under curve (AUC) values for identifying gene-gene interactions in the development, test, and DIP datasets. Specifically, it obtains an improvement of 1.1% on average of precision for detecting gene pairs with both high expression similarities and high prior-knowledge based similarities in all datasets, comparing to other linear models and coexpression analysis methods. Regarding cancer gene networks construction and gene function prediction, MFR also obtains the results with more biological significances and higher average prediction accuracy, than other compared models and methods. A website of the MFR model and relevant datasets can be accessed from http://bmbl.sdstate.edu/MFR.