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The black carabid beetle Calosoma maximoviczi is a successful predator that serves as both a beneficial insect and a severe threat to economic herbivores. Its hunting technique relies heavily on olfaction, but the underlying mechanism has not been studied. Here, we report the electrophysiological, ecological and molecular traits of bioactive components identified from a comprehensive panel of natural odorants in the beetle-prey-plant system. The aim of this work was to investigate olfactory perceptions and their influence on the behaviours of C. maximoviczi.Among the 200 identified volatiles, 18 were concentrated in beetle and prey samples, and 14 were concentrated in plants. Insect feeding damage to plants led to a shift in the emission fingerprint. Twelve volatiles were selected using successive electrophysiological tests. Field trials showed that significant sex differences existed when trapping with a single chemical or chemical mixture. Expression profiles indicated that sex-biased catches were related to the expression of 15 annotated CmaxOBPs and 40 CmaxORs across 12 chemosensory organs. In silico evaluations were conducted with 16 CmaxORs using modelling and docking. Better recognition was predicted for the pairs CmaxOR5-(Z)-3-hexenyl acetate, CmaxOR6-β-caryophyllene, CmaxOR18-(E)-β-ocimene and CmaxOR18-tetradecane, with higher binding affinity and a suitable binding pocket. Lastly, 168Y in CmaxOR6 and 142Y in CmaxOR18 were predicted as key amino acid residues for binding β-caryophyllene and tetradecane, respectively.This work provides an example pipeline for de novo investigation in C. maximoviczi baits and the underlying olfactory perceptions. The results will benefit the future development of trapping-based integrated pest management strategies and the deorphanization of odorant receptors in ground beetles. © 2022 Society of Chemical Industry.

In this study, the chitosan‐based release microspheres were prepared by spray drying method. Chitosan was used as the carrier material, and Panax notoginseng extract, Codonopsis extract, and Atractylodes extract (the mass ratio was 2:7:5) were active substance. The spray drying preparation process of microsphere was optimized by single factor experiment and L9 (34) orthogonal design. Drug loading (DL), particle size, and sustained release performance of microspheres were investigated. The mass fraction of chitosan was 1.5%, the mass ratio of drug to chitosan was 1:3, the inlet air temperature was 130°C, and the injection rate was 400 ml/hr. The chitosan‐based microspheres prepared under the above conditions had a smooth surface, and the DL was 23.87 ± 0.93%; the average particle diameter was 10.27 ± 1.05 μm, and the encapsulation efficiency (EE) of the microspheres was 91.28 ± 1.04%. The preparation process of chitosan‐based drug microsphere prepared by spray drying method was simple and stable. The prepared microspheres in this paper showed a sustained release effect in vitro., To study the characteristics and the drug release of the chitosan‐based microspheres in vitro. The gastric mucosal protective drug microspheres were used chitosan as the carrier material and prepared with Panax notoginseng extract, Codonopsis extract, and Atractylodes extract by spray drying. The preparation process of chitosan‐based gastric mucosa protective drug microsphere was simple and stable.

Processable molecular-sieving membranes are important materials for realizing energy-efficient precombustion CO2 capture during industrial-scale hydrogen production. However, the promising design of mixed matrix membranes (MMMs) that aims to integrate the molecular-sieving properties of nanoporous architectures with industrial processable polymers still faces performance and fabrication issues due to the formation of segregated nanofiller domains in their polymer matrices. Here, an unconventional nanocomposite membrane design is proposed using soluble organic macrocyclic cavitands (OMCs) with tunable open cavity sizes that not only mitigate the formation the discrete nanofiller phases but also deliver distinct molecular-sieving separations. The versatile organic-solvent solubility coupled with highly interactive functionalities of OMCs allows them to obtain molecularly homogeneous mixing with matrix polymers and form only one integral continuous phase crucial to the robust processability of polymers. A series of polybenzimidazole-based molecularly mixed composite membranes (MMCMs) are fabricated via the incorporation of a soluble and thermally stable OMC choice, sulfocalixarenes, with various cavity sizes. These membranes achieve outstanding high-temperature mixed-gas H2 /CO2 separation performances comparable with several state-of-the-art molecular-sieving membranes owing to effective size-sieving gas passages through the open or partially-intruded supramolecular cavities. The broadly tunable structures and functionalities of OMCs would make their MMCMs attractive for other energy-intensive molecular separations.

This paper describes catalytic consequencesThis paper describes catalytic consequences of Pt/CeO2-Al2O3 catalysts promoted with Ga species for propane dehydrogenation. A series of PtGa/CeO2-Al2O3 catalysts were prepared by a sequential impregnation method. The as-prepared catalysts were characterized employing N2 adsorption-desorption, X-ray diffrtaction, temperature programmed reduction, O2 volumetric chemisorption, H2-O2 titration, and transmission electron microscopy. We have shown that Ga3+ cations are incorporated into the cubic fluorite structure of CeO2, enhancing both lattice oxygen storage capacity and surface oxygen mobility. The enhanced reducibility of CeO2 is indicative of higher capability to eliminate the coke deposition and thus is beneficial to the improvement of catalytic stability. Density functional theory calculations confirm that the addition of Ga is prone to improve propylene desorption and greatly suppress deep dehydrogenation and the following coke formation. The catalytic performance shows a strong dependence on the content of Ga addition. The optimal loading content of Ga is 3 wt %, which results in the maximal propylene selectivity together with the best catalytic stability against coke accumulation. © 2016 American Institute of Chemical Engineers AIChE J, 62: 4365–4376, 2016

The classical traditional Chinese formulation LiuweiDihuang, shown to have clinical efficacy for "nourishing kidney-yin" in traditional Chinese medicine, has been used for thousands of years in China. Little attention, however, has been paid to quality control methods for this formulation. Hence, a rapid and sensitive analytical technique is urgently needed for the evaluation of LiuweiDihuang preparations to assess its quality and pharmacological functionality. In this study, an ultra high performance liquid chromatography dual-wavelength method was developed to simultaneously determine 11 constituents in LiuweiDihuang preparations. This robust approach provided a fast and comprehensive quantitative determination of the major bioactive markers within LiuweiDihuang preparations. To distinguish four dosage forms of LiuweiDihuang preparations, a random forest technique was applied on the spectrometric fingerprint data obtained. This combination approach of chromatographic techniques and data analyses might serve as a rapid and efficient tool to ensure the quality of LiuweiDihuang preparations and other Chinese medicinal formulations and can support quality control and scientific research into the pharmacological potential for these formulations.

Lipophilicity, evaluated by either n-octanol/water partition coefficient or n-octanol/buffer solution distribution coefficient, is of high importance in pharmacology, toxicology, and medicinal chemistry. A quantitative structure– property relationship study was carried out to predict distribution coefficients at pH7.4 (logD7.4) of a large data set consisting of 1130 organic compounds. Partial least squares and support vector machine (SVM) regressions were employed to build prediction models with 30 molecular descriptors selected by genetic algorithm. The obtained results demonstrated that the SVM model is more reliable and has a better prediction performance than the partial least squares model. The square correlation coefficients of fitting, cross validation, and prediction are 0.92, 0.90, and 0.89, respectively. The corresponding root mean square errors are 0.52, 0.59, and 0.56, respectively. The robustness, reliability, and generalization ability of the model were assessed by Y-randomization test and applicability domain. When compared with logD7.4 values calculated by five existing methods from Discovery Studio and ChemAxon, our SVM model shows superiority over them. The results indicated that our model could give a reliable and robust prediction of logD7.4. Copyright © 2015 John Wiley & Sons, Ltd. Supporting information may be found in the online version of this paper

With the rapid development of new -omics measurement methods, there is an increasing interest in studying the correlation structure between two or more data sets. Multivariate methods such as canonical correlation analysis (CCA) have been proposed to analyze the intrinsic correlation relationship by integrating two data sets. However, because of the high dimensionality of data and the relative scarcity of samples, the ordinary CCA is usually faced with variable selection problems and thereby fails to obtain a satisfactory relationship. Here, we explored the potential of sparse CCA (SCCA) to find the correlative components in two sparse views. SCCA aims at finding sparse projection directions to well extract the correlation between two data sets. We applied this method to one simulation data and one real -omics data to illustrate the performance of SCCA. The results from two studies show that SCCA could effectively find the correlated patterns between two data sets, which are of high importance for understanding the relationship between two underlying chemical or biological processes. The corresponding variable subsets selected by sparse weight vectors can assist in a better interpretation of the chemical or biological process. The integrative analysis from two views by SCCA helps in improving the discriminative ability of classification models for various -omics studies. Copyright © 2015 John Wiley & Sons, Ltd.

Retention time shift is one of the most challenging problems during the preprocessing of massive chromatographic datasets. Here, an improved version of the moving window fast Fourier transform cross-correlation algorithm is presented to perform nonlinear and robust alignment of chromatograms by analyzing the shifts matrix generated by moving window procedure. The shifts matrix in retention time can be estimated by fast Fourier transform cross-correlation with a moving window procedure. The refined shift of each scan point can be obtained by calculating the mode of corresponding column of the shifts matrix. This version is simple, but more effective and robust than the previously published moving window fast Fourier transform cross-correlation method. It can handle nonlinear retention time shift robustly if proper window size has been selected. The window size is the only one parameter needed to adjust and optimize. The properties of the proposed method are investigated by comparison with the previous moving window fast Fourier transform cross-correlation and recursive alignment by fast Fourier transform using chromatographic datasets. The pattern recognition results of a gas chromatography mass spectrometry dataset of metabolic syndrome can be improved significantly after preprocessing by this method. Furthermore, the proposed method is available as an open source package at https://github.com/zmzhang/MWFFT2.

An effective and practical method, based on automatic peak detection, Lorentzian fitting, and polynomial fitting, is developed for aligning spectra, which can significantly reduce or eliminate systematic differences between Raman spectrometers. In this work, Lorentzian fitting of the experimental spectra is performed to precisely locate the peak positions. Then the standardization procedure is illustrated on the spectra of a chemical standard measured on primary and secondary instruments, and the spectra can be successfully aligned to each other after shift correction. It is shown that the similarity of pharmaceutical spectra acquired on three Raman spectrometers is then considerably improved after removal of the Raman shift difference. The result shows that the similarity of the spectra between primary and secondary spectrometers increases dramatically from about 0.8 to 0.95 after spectra standardization. This means that the proposed standardization method can effectively reduce or eliminate systematical errors and enhance spectral compatibility across different instruments. Copyright © 2014 John Wiley & Sons, Ltd.

Nine compounds were successfully separated from Salvia plebeia R.Br. using two-step high-speed counter-current chromatography with three elution modes. Elution-extrusion counter-current chromatography was applied in the first step, while classical counter-current chromatography and recycling counter-current chromatography were used in the second step. Three solvent systems, n-hexane/ethyl acetate/ethanol/water (4:6.5:3:7, v/v), methyl tert-butyl ether/ethyl acetate/n-butanol/methanol/water (6:4:1:2:8, v/v) and n-hexane/ethyl acetate/methanol/water (5:5.5:5:5, v/v) were screened and optimized for the two-step separation. The separation yielded nine compounds, including caffeic acid (1), 6-hydroxyluteuolin-7-glucoside (2), 5,7,3',4'-tetrahydroxy-6-methoxyflavanone-7-glucoside (3), nepitrin (4), rosmarinic acid (5), homoplantaginin (6), nepetin (7), hispidulin (8), and 5,6,7,4'-tertrahydroxyflavone (9). To the best of our knowledge, 5,7,3',4'-tetrahydroxy-6-methoxyflavanone-7-glucoside and 5,6,7,4'-tertrahydroxyflavone have been separated from Salvia plebeia R.Br. for the first time. The purities and structures of these compounds were identified by high-performance liquid chromatography, electrospray ionization mass spectrometry, (1)H and (13)C NMR spectroscopy. This study demonstrates that high-speed counter-current chromatography is a useful and flexible tool for the separation of components from a complex sample.

A new calibration transfer method that applies canonical correlation analysis (CCA) to transfer the informative components extracted from a spectral dataset is proposed to reduce the interference of noise, background and non-predicted properties. This method employs the partial least squares method to extract the informative components related to the predicted properties from the raw spectra and then corrects the informative components based on CCA. The performance of this algorithm was tested using three pairs of spectra batches: two pairs of corn spectra and one pair of tri-component solvent spectra. The results showed that this method can significantly reduce prediction errors compared with CCA and piecewise direct standardization. Copyright © 2014 John Wiley & Sons, Ltd.

In order to define an applicability domain for quantitative structure-activity relationship modeling, a combinational strategy of model disturbance and outlier comparison is developed. An indicator named model disturbance index was defined to estimate the prediction error. Moreover, the information of the outliers in the training set was used to filter the unreliable samples in the test set based on "structural similarity". Chromatography retention indices data were used to investigate this approach. The relationship between model disturbance index and prediction error can be found. Also, the comparison between the outlier set and the test set could provide additional information about which unknown samples should be paid more attentions. A novel technique based on model population analysis was used to evaluate the validity of applicability domain. Finally, three commonly used methods, i.e. Leverage, descriptor range-based and model perturbation method, were compared with the proposed approach.