Simultaneous fractionation of multiple classes of polyphenols from honeybush tea using solid-phase extraction

The structural diversity of polyphenols and the inherent limitations of current extraction techniques pose a challenge to extract polyphenols using a simple and green method. Hence, in this study, a method was developed to simultaneously fractionate multiple classes of polyphenols by only varying ethanol-water solutions. Honeybush tea, which is rich in polyphenols, was selected as a model for this study. Solvent extraction followed by solid-phase extraction (SPE) was developed to obtain a polyphenol-rich fraction from six honeybush samples. Based on a gradient elution programme (10%, 30%, 50%, 70% and 90% (v/v) ethanol-water solution) of SPE, the Strata X cartridge showed a better recovery of most targeted polyphenols under 0.9 mL of the drying volume and 1 mL min⁻¹ of the dispensing speed. The elution programme for fractionating most polyphenols was as follows: single elution with 50% ethanol, followed by twice elution with 70% ethanol. The antioxidant capacity was used to analyse the differences among the polyphenol-rich fractions from six honeybush samples. Principal component analysis (PCA) revealed that unfermented C. genistoides (GG) has the greatest antioxidant capacity among the honeybush species studied. Additionally, mangiferin, isomangiferin and vicenin-2 were the main contributors to the antioxidant capacity in six honeybush fractions according to the correlation study.     

Material encapsulation in poly(methyl methacrylate) shell: A review

Material encapsulation is a relatively new technique for coating a micro/nanosize particle or droplet with polymeric or inorganic shell. Encapsulation technology has many applications in various fields including drug delivery, cosmetic, agriculture, thermal energy storage, textile, and self-healing polymers. Poly(methyl methacrylate) (PMMA) is widely used as shell material in encapsulation due to its high chemical stability, biocompatibility, nontoxicity, and good mechanical properties. The main approach for micro/nanoencapsulation of materials using PMMA as shell comprises emulsion-based techniques such as emulsion polymerization and solvent evaporation from oil-in-water emulsion. In the present review, we first focus on the encapsulation techniques of liquid materials with PMMA shell by analyzing the effective processing parameters influencing the preparation of PMMA micro/nanocapsules. We then describe the morphology of PMMA capsules in emulsion systems according to thermodynamic relations. The techniques to investigation of mechanical properties of capsule shell and the release mechanisms of core material from PMMA capsules were also investigated. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 48039.     

自然循环窄矩形通道内过冷沸腾两相摩擦阻力特性

采用去离子水作为实验工质，在低压低流速自然循环工况下开展了单面加热可视化窄矩形通道内的过冷沸腾摩擦阻力特性实验研究。实验中测量了实验段内的压降数据，并通过高速摄影仪拍摄了窄矩形通道内的气液两相图像，提出了过冷沸腾条件下的两相摩擦压降的剥离计算方法。基于本实验中获得摩擦压降数据，对分别基于均相流模型和分液相模型的经典两相摩擦压降计算关系式进行了评估，实验结果表明：采用不同等效黏度计算方法的均相流模型计算结果比实验值明显偏小；而分相流模型中，Sun and Mishiba关系式和Tran关系式均能够较好地预测摩擦阻力，计算值与实验值的平均相对偏差在±15%以内。结合实验数据，以分相流模型方法为基础，考虑全液相Reynolds数、Martinelli参数和Laplace数的影响，获得了计算分液相折算系数的经验关系式，与实验数据符合较好, 平均相对误差在10%范围内。     

A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

Clinical narratives such as progress summaries, lab reports, surgical reports, and other narrative texts contain key biomarkers about a patient's health. Evidence-based preventive medicine needs accurate semantic and sentiment analysis to extract and classify medical features as the input to appropriate machine learning classifiers. However, the traditional approach of using single classifiers is limited by the need for dimensionality reduction techniques, statistical feature correlation, a faster learning rate, and the lack of consideration of the semantic relations among features. Hence, extracting semantic and sentiment-based features from clinical text and combining multiple classifiers to create an ensemble intelligent system overcomes many limitations and provides a more robust prediction outcome. The selection of an appropriate approach and its interparameter dependency becomes key for the success of the ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework for prediction of venous thromboembolism (VTE) diagnosis consisting of the following components: a VTE ontology, semantic extraction and sentiment assessment of risk factor framework, and an ensemble classifier. Therefore, a component-based analysis approach was adopted for evaluation using a data set of 250 clinical narratives where knowledge and ensemble achieved the following results with and without semantic extraction and sentiment assessment of risk factor, respectively: a precision of 81.8% and 62.9%, a recall of 81.8% and 57.6%, an F measure of 81.8% and 53.8%, and a receiving operating characteristic of 80.1% and 58.5% in identifying cases of VTE.     

Characteristics of tiger nut milk: effects of milling

A standard method for the extraction of tiger nut milk has been introduced. It has been shown that, although milling duration improves the yield of tiger nut milk solids and its nutrient composition, there is a quantifiable loss of nutrient in the pressing residue during milk extraction. Milling duration improved the colloidal stability of the milk against creaming during 16 h of storage. A higher milling intensity resulted in the aggregation of biological polymers which resulted in colloidal destabilisation. Milling improved the lightness and stability and reduced browning rate of the tiger nut milk during storage. This report is important for the production of tiger nut milk of consistent and comparable characteristics. Milling has been introduced as a processing method for the qualitative and quantitative modulation of the properties of tiger nut milk. It is recommended to develop further strategies to improve the colloidal stability of tiger nut milk as a beverage.     

Development of green extraction process to produce antioxidant-rich extracts from purple coneflower

The present study was conducted to develop subcritical water extraction (SWE) of Echinacea purpurea flowers. The influence of temperature and extraction time on quality of extracts considering total phenols content, total flavonoids content, antioxidant capacity and extraction yield, was determined. Optimized extraction parameters for maximised investigated responses were as follows: 147.56 °C and 8.43 min. The experimental values agreed with the values predicted, thus indicating the adequacy of central composite experimental design for modelling the SWE of bioactive compounds from E. purpurea. Results of the study also highlighted the potential application of E. purpurea subcritical water extracts as a source of valuable bioactive compounds.     

Precise Extraction of Charge Carrier Mobility for Organic Transistors

Unreliable mobility values, and particularly greatly overestimated values and severely distorted temperature dependences, have recently hampered the development of the organic transistor field. Given that organic field‐effect transistors (OFETs) have been routinely used to evaluate mobility, precise parameter extraction using the electrical properties of OFETs is thus of primary importance. This review examines the origins of the various mobilities that must be determined for OFET applications, the relevant extraction methods, and the data selection limitations, which help in avoiding conceptual errors during mobility extraction. For increased precision, the review also discusses device fabrication considerations, calibration of both the specific gate‐dielectric capacitance and the threshold voltage, the contact effects, and the bias and temperature dependences, which must actually be handled with great care but have mostly been overlooked to date. This review serves as a systematic overview of the OFET mobility extraction process to ensure high precision and will also aid in improving future research.     

生物预处理技术在天然产物提取中的应用研究进展

生物预处理技术运用于天然产物提取是借助微生物或生物酶的作用，在适当条件下对天然资源进行处理，破坏植物原料的细胞壁，促进天然产物释放到提取介质中，以提高分离效率，有助于保证天然产物活性。生物预处理技术按形式可以分为微生物发酵技术和生物酶处理技术。针对2种生物预处理技术的作用机制和应用进行综述，旨在为天然产物开发利用提供参考。     

Unusual Two-Step Assembly of a Minimalistic Dipeptide-Based Functional Hypergelator

Self-assembled peptide hydrogels represent the realization of peptide nanotechnology into biomedical products. There is a continuous quest to identify the simplest building blocks and optimize their critical gelation concentration (CGC). Herein, a minimalistic, de novo dipeptide, Fmoc-Lys(Fmoc)-Asp, as an hydrogelator with the lowest CGC ever reported, almost fourfold lower as compared to that of a large hexadecapeptide previously described, is reported. The dipeptide self-assembles through an unusual and unprecedented two-step process as elucidated by solid-state NMR and molecular dynamics simulation. The hydrogel is cytocompatible and supports 2D/3D cell growth. Conductive composite gels composed of Fmoc-Lys(Fmoc)-Asp and a conductive polymer exhibit excellent DNA binding. Fmoc-Lys(Fmoc)-Asp exhibits the lowest CGC and highest mechanical properties when compared to a library of dipeptide analogues, thus validating the uniqueness of the molecular design which confers useful properties for various potential applications.     

Application of higher order spectral features and support vector machines for bearing faults classification

Condition monitoring and fault diagnosis of rolling element bearings timely and accurately are very important to ensure the reliability of rotating machinery. This paper presents a novel pattern classification approach for bearings diagnostics, which combines the higher order spectra analysis features and support vector machine classifier. The use of non-linear features motivated by the higher order spectra has been reported to be a promising approach to analyze the non-linear and non-Gaussian characteristics of the mechanical vibration signals. The vibration bi-spectrum (third order spectrum) patterns are extracted as the feature vectors presenting different bearing faults. The extracted bi-spectrum features are subjected to principal component analysis for dimensionality reduction. These principal components were fed to support vector machine to distinguish four kinds of bearing faults covering different levels of severity for each fault type, which were measured in the experimental test bench running under different working conditions. In order to find the optimal parameters for the multi-class support vector machine model, a grid-search method in combination with 10-fold cross-validation has been used. Based on the correct classification of bearing patterns in the test set, in each fold the performance measures are computed. The average of these performance measures is computed to report the overall performance of the support vector machine classifier. In addition, in fault detection problems, the performance of a detection algorithm usually depends on the trade-off between robustness and sensitivity. The sensitivity and robustness of the proposed method are explored by running a series of experiments. A receiver operating characteristic (ROC) curve made the results more convincing. The results indicated that the proposed method can reliably identify different fault patterns of rolling element bearings based on vibration signals.