Discrimination of Beers by Cyclic Voltammetry Using a Single Carbon Screen-printed Electrode

A fast, simple and costless methodology without sample pre-treatment is proposed for the discrimination of beers. It is based on cyclic voltammetry (CV) using commercial carbon screen-printed electrodes (SPCE) and includes a correction of the signals measured with different SPCE units. Data are submitted to partial least squares discriminant analysis (PLS−DA) and support vector machine discriminant analysis (SVM−DA), which allow a reasonable classification of the beers. Also, CV data from beers can be used to predict their alcoholic degree by partial least squares (PLS) and artificial neural networks (ANN). In general, non-linear methods provide better results than linear ones.     

Synthesis and Investigation of Tetrahydro-β-carboline Derivatives as Inhibitors of Plant Pathogenic Fungi

A series of tetrahydro-ß-carbolines substituted with an alkyl or acyl side chain was synthesized and screened for its antifungal activity against plant pathogenic fungi (Bipolaris oryzae, Curvularia lunata, Fusarium semitectum, and Fusarium fujikuroi). The structure activity relationship revealed that the substituent at the piperidine nitrogen plays an important role for increasing antifungal activities. In this series, 2-octyl-2,3,4,9-tetrahydro-1H-pyrido[3,4-b]indole (3g) displayed potent antifungal activities with a minimum inhibitory concentration of 0.1 μg/mL, including good inhibitory activity to the radial growth of fungus at a concentration of 100 μg/mL compared to amphotericin B.     

基于人工神经网络研究芳胺喹唑啉衍生物的抗胃癌活性

基于分子电性距离矢量(M_t)表征21个芳胺喹唑啉衍生物的分子结构,并与其对人胃癌细胞的抗癌活性(pI)关联。通过最佳变量子集回归方法建立上述化合物抗胃癌活性的三参数(M_(15)、M_(18)、M_(82))定量构效关系模型。其交叉验证系数(R_(cv)~2)、非交叉验证系数(R~2)依次为0.386和0.737。通过R、R_(cv)~2、V_(IF)等检验,模型具有较好的相关性、稳健性和预测能力。结果显示,-CH_2-、-CH、-O-、-S-和-NH-等分子结构单元直接影响这些化合物的抗胃癌活性。将M_(15)、M_(18)、M_(82)作为人工神经网络的输入层结点,采用3∶3∶1的网络结构,利用BP算法获得了令人满意的pI模型,其R~2和标准偏差S_D分别为0.992和0.080,表明pI与三参数呈现优异的非线性关系。     

Prediction of an outcome using NETwork Clusters (NET-C)

Birth weight is a key consequence of environmental exposures and metabolic alterations and can influence lifelong health. While a number of methods have been used to examine associations of trace element (including essential nutrients and toxic metals) concentrations or metabolite concentrations with a health outcome, birth weight, studies evaluating how the coexistence of these factors impacts birth weight are extremely limited. Here, we present a novel algorithm NETwork Clusters (NET-C), to improve the prediction of outcome by considering the interactions of features in the network and then apply this method to predict birth weight by jointly modelling trace element and cord blood metabolite data. Specifically, by using trace element and/or metabolite subnetworks as groups, we apply group lasso to estimate birth weight. We conducted statistical simulation studies to examine how both sample size and correlations between grouped features and the outcome affect prediction performance. We showed that in terms of prediction error, our proposed method outperformed other methods such as (a) group lasso with groups defined by hierarchical clustering, (b) random forest regression and (c) neural networks. We applied our method to data ascertained as part of the New Hampshire Birth Cohort Study on trace elements, metabolites and birth outcomes, adjusting for other covariates such as maternal body mass index (BMI) and enrollment age. Our proposed method can be applied to a variety of similarly structured high-dimensional datasets to predict health outcomes.     

MD-UNET: Multi-input dilated U-shape neural network for segmentation of bladder cancer

Accurate segmentation of the tumour area is crucial for the treatment and prognosis of patients with bladder cancer. However, the complex information from the MRI image poses an important challenge for us to accurately segment the lesion, for example, the high distinction among people, size of bladder variation and noise interference. Based on the above issues, we propose an MD-Unet network structure, which uses multi-scale images as the input of the network, and combines max-pooling with dilated convolution to increase the receptive field of the convolutional network. The results show that the proposed network can obtain higher precision than the existing models for the bladder cancer dataset. The MD-Unet can achieve state-of-art performance compared with other methods.     

螺旋CT成像联合血清copeptin、IL-18水平检测在急性脑梗死患者神经功能及预后评估中的应用价值

本文选取74例急性脑梗死(ACI)患者作为研究对象,入院时根据美国国立卫生院卒中量表(NIHSS)评分分为重度组(NIHSS评分>15分,n=21)、中度组(NIHSS评分5~15分,n=24)、轻度组(NIHSS评分<5分,n=29),均接受血清copeptin和IL-18水平检测及螺旋CT成像检查。结果发现,随ACI神经功能缺损加重,血清copeptin、白细胞介素18(IL-18)水平及峰值时间升高,CBV、CBF异常下降。经ROC曲线显示,血清IL-18联合CBF预测ACI患者预后不良的曲线下面积(AUC)最大。由上述结果可见螺旋CT成像参数、血清copeptin、IL-18水平与ACI患者神经功能存在一定相关性,三者联合为临床实现ACI患者个体化治疗和改善预后提供了可能。     

Coating Cellulosic Material with Ag Nanowires to Fabricate Wearable IR-Reflective Device for Personal Thermal Management: The Role of Coating Method and Loading Level

Textiles coated with silver nanowires (AgNWs) are effective at suppressing radiative heat loss without sacrificing breathability. Many reports present the applicability of AgNWs as IR-reflective wearable textiles, where such studies partially evaluate the parameters for practical usage for large-scale production. In this study, the effect of the two industrial coating methods and the loading value of AgNWs on the performance of AgNWs-coated fabric (AgNWs-CF) is reported. The AgNWs were synthesized by the polyol process and applied onto the surface of cotton fabric using either dip- or spray-coating methods with variable loading levels of AgNWs. X-ray diffraction, scanning electron microscopy (SEM), infrared (IR) reflectance, water vapor permeability (WVP), and electrical resistance properties were characterized. The results report the successful synthesis of AgNWs with a 30 μm length. The results also show that the spray coating method has a better performance for reflecting the IR radiation to the body, which increases with a greater loading level of the AgNWs. The antibacterial results show a good inhibition zone for cotton fabric coated by both methods, where the spray-coated fabric has a better performance overall. The results also show the coated fabric with AgNWs maintains the level of fabric breathability similar to control samples. AgNWs-CFs have potential utility for cold weather protective clothing in which heat dissipation is attenuated, along with applications such as wound dressing materials that provide antibacterial protection.     

Protein release from interpenetrating polymer network hydrogels triggered by endogenous biomarkers

A biocompatible drug delivery system with a high-sensitive stimuli-responsive behavior is reported. Calcium alginate hydrogels interpenetrated with polyvinyl alcohol–diboronate polymer network (IPN) effectively respond to the presence of hydrogen peroxide through oxidative degradation of boronate esters. The degradation of the IPN entails the reopening of the original alginate pores, resulting in a 5–9 times increase in release rates of encapsulated proteins with molecular masses ranging from 16.7 to 66 kDa. The release can be triggered by hydrogen peroxide concentrations as low as 50 μM in the bulk solution. Alternatively, hydrogen peroxide can also be generated inside the hydrogels by incorporation of oxidase enzymes in the presence of their substrates, such as lactate, glucose, or hypoxanthine, which can serve as biomarkers of certain physiological disorders.     

Process prediction of Ni–SiC coatings based on RBF-BP model

The preparation of Ni–SiC coatings using magnetic field-assisted jet electrodeposition under various plating settings is described in this study. A RBF-BP composite neural network with 4 × 4 × 4 × 7 × 10 × 1 was used to predict the corrosion resistance of Ni–SiC coatings prepared by employing different plating parameters. The results show that the fitting degree between the expected value and the actual value of the RBF-BP composite neural network is 0.97497. Moreover, the hybrid neural network can accurately predict the corrosion resistance of Ni–SiC coatings prepared under different process parameters. The corrosion weight loss of the coating is the lowest at the current density of 4 A/dm², a jet rate of 3 m/s, a SiC particle concentration of 8 g/L, and at a magnetic field intensity of 0.8 T, demonstrating its corrosion resistance under these conditions. According to the coating characterization analysis, the coating's grain size was significantly refined, and the surface was smoother with a high amount of uniformly sized SiC nanoparticles.     

Preparation and properties of ZnMoO4 anode materials with polymer network gel method

Polymer network gel method combines the advantages of solid-phase method and liquid phase method, triggering acrylamide (AM) radical polymerization in aqueous solution and N, N′- methylene bis acrylamide (MBAM) active double bond cross-linking reaction, forming polymer chains to form a three-dimensional network. The polymer network space formed by the gel is bound and evenly distributed to the ions in the solution, thereby reducing the contact and aggregation of molecules and achieving the purpose of uniform particle size and small particle size. The principle diagram of network gel is shown in Figure. Using cubic zinc acetate and ammonium molybdate tetrahydrate as raw materials, cubic ZnMoO₄ negative electrode materials were prepared with polymer network gel method. The polymer network gel method has various effects on the structure, morphology and electrochemical properties of materials. Besides, the calcination temperature and calcination time were also the key factors to the electrochemical properties of the materials. In this paper, the effects of the ratio of monomer and crosslinker, calcination temperature and calcination time on ZnMoO₄ materials were studied by single variable method, the preparation process was optimized, and its characterization and electrochemical tests were carried out. After 100 cycles, the optimized ZnMoO₄ electrode has a discharge capacity of 374.0 mAh· g^?1, 332.5, 263.5 and 177.1 mAh · g^?1 at current densities of 0.1, 0.5, 1.0 and 2.0 A g^?1, respectively. The electrochemical results show that the optimized ZnMoO₄ has high capacity, large rate capability and excellent cycle stability.