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Abstract: Herein, a novel DNA-templated Au nanoparticles (Au-DNA) nanoconjugate was prepared by using the combination of metallization and DNA compaction. The electrostatic interaction between Au(Ш) and the phosphate backbone of DNA formed the netlike coordination compound of Au(Ш)-DNA, and then the complex was chemically reduced to form Au nanoparticles in this network-like DNA conformation. The negatively charged nanoconjugate was used as the matrix for immobilization of horseradish peroxidase (HRP). A stable and well-defined redox peaks of HRP were observed on the Au-DNA nanoconjugate modified glassy carbon (GC) electrode, which indicated that the modified enzyme electrode displayed good direct electron transfer behavior and excellent reducing ability toward hydrogen peroxide (H2O2) with the apparent Michaelis–Menten constant (Km ) estimated to be 0.147mM. [Copyright &y& Elsevier]

Background: Parkinson's disease (PD) is a neurodegenerative disease of the elderly, which leads to patients' motor and non-motor disabilities and affects patients' quality of daily life. Timely and effective detection of PD is a key step to medical intervention. Recently, computer aided methods for PD detection have gained lots of attention in artificial intelligence domain.Methods: This paper proposed a novel ensemble learning model fusing Random Forest (RF) classifiers and Principal Component Analysis (PCA) technique to differentiate PD patients from healthy controls (HC). Six different RF models were separately constructed to generate the corresponding class probability vectors which represent an individual's category predictions on 6 different handwritten exams, and the final prediction result for an individual was obtained through voting strategy of all RF models. Stratified k-fold cross validation was performed to split the exam datasets and evaluate the classification performances.Results: Experimental results prove that our proposed ensemble model on six handwritten exams has achieved better classification performances than a single RF based method on a single handwritten exam. Our ensemble of RF model based on multiple handwritten exams has promising accuracy (89.4 %), specificity (93.7 %), sensitivity (84.5 %) and F1-score (87.7 %). Compared with Logistic Regression (LR) and Support Vector Machines (SVM), the ensemble model based on RF can achieve better classification results.Conclusion: A computer-assisted PD diagnosis model on small handwritten dynamics dataset is proposed, and it provides a potential way for assisting diagnosis of PD in clinical setting. [ABSTRACT FROM AUTHOR]

Preparation of porous cement-based materials via freeze casting is a process with great potential. However, freeze-drying and steam curing complicate the preparation process and increase the preparation period. To simplify the process, heat-dry curing was used for replacing freeze-drying and steam curing during operation. The new preparation method combined freeze casting and heat-dry curing possesses characteristics of low-energy, simplicity, and time-saving. Compared with the traditional preparation method of porous cement-based materials, the novel preparation process can shorten preparation time by at least 16 % and retrench energy consumption by at least 18 %. As the improvement condition, temperature of heat-drying curing has great influence on the structure and performance of the samples. Therefore, it is of great significance to discuss the relationship between heat-dry temperatures and structural parameters by SEM, N 2 -adsorption/desorption, TG, and ATR-FTIR. Lamellar pores of membranes can be easier observed at low temperatures than that at 100 °C. The separation performance of oil-water is generally improved with the increase of temperature. Meanwhile, the molecular dynamics simulation was operated to illustrate the reaction mechanism of hydration with three temperatures. This study probably provides more theoretical basis for expanding the application of porous silicate cement membranes. [Display omitted] • Novel coupled method simplifies the fabricating process complexity of cement-based samples. • Influences of heat-dry curing temperatures on porous silicate cement membranes are discussed. • Silicate cement membranes own excellent permeation, oil-water separation and mechanical strengths. • Influences of heat-dry curing temperatures are illustrated by using molecular dynamics. [ABSTRACT FROM AUTHOR]

Preparation of porous cement-based materials via freeze casting is a process with great potential. However, freeze-drying and steam curing complicate the preparation process and increase the preparation period. To simplify the process, heat-dry curing was used for replacing freeze-drying and steam curing during operation. The new preparation method combined freeze casting and heat-dry curing possesses characteristics of low-energy, simplicity, and time-saving. Compared with the traditional preparation method of porous cement-based materials, the novel preparation process can shorten preparation time by at least 16 % and retrench energy consumption by at least 18 %. As the improvement condition, temperature of heat-drying curing has great influence on the structure and performance of the samples. Therefore, it is of great significance to discuss the relationship between heat-dry temperatures and structural parameters by SEM, N 2 -adsorption/desorption, TG, and ATR-FTIR. Lamellar pores of membranes can be easier observed at low temperatures than that at 100 °C. The separation performance of oil-water is generally improved with the increase of temperature. Meanwhile, the molecular dynamics simulation was operated to illustrate the reaction mechanism of hydration with three temperatures. This study probably provides more theoretical basis for expanding the application of porous silicate cement membranes. [Display omitted] • Novel coupled method simplifies the fabricating process complexity of cement-based samples. • Influences of heat-dry curing temperatures on porous silicate cement membranes are discussed. • Silicate cement membranes own excellent permeation, oil-water separation and mechanical strengths. • Influences of heat-dry curing temperatures are illustrated by using molecular dynamics. [ABSTRACT FROM AUTHOR]

Human activity recognition (HAR) has gained lots of attention in recent years due to its high demand in different domains. In this paper, a novel HAR system based on a cascade ensemble learning (CELearning) model is proposed. Each layer of the proposed model is comprised of Extremely Gradient Boosting Trees (XGBoost), Random Forest, Extremely Randomized Trees (ExtraTrees) and Softmax Regression, and the model goes deeper layer by layer. The initial input vectors sampled from smartphone accelerometer and gyroscope sensor are trained separately by four different classifiers in the first layer, and the probability vectors representing different classes to which each sample belongs are obtained. Both the initial input data and the probability vectors are concatenated together and considered as input to the next layer's classifiers, and eventually the final prediction is obtained according to the classifiers of the last layer. This system achieved satisfying classification accuracy on two public datasets of HAR based on smartphone accelerometer and gyroscope sensor. The experimental results show that the proposed approach has gained better classification accuracy for HAR compared to existing state-of-the-art methods, and the training process of the model is simple and efficient. [ABSTRACT FROM AUTHOR]

Abstract: A simple method for polyaniline nanofibers modified glassy carbon electrode was developed. Polyaniline nanofibers (4-toluenesulfonic acid as dopant) were synthesized by interface polymerization. The polyaniline nanofibers had better processability than conventional polyaniline and were easily dispersed in chitosan solution. The mixed dispersion of polyaniline nanofibers and horseradish peroxidase was cast onto the glassy carbon electrode by a drop-coating method. The use of polyaniline nanofibers led to efficient enzyme loading and rapid electron transfer rate between the active centers of enzymes and electrodes. Horseradish peroxidase entrapped in the polyaniline nanofibers/chitosan film could keep its native bioactivity and effectively catalyze the reduction of hydrogen peroxide. The proposed biosensor exhibited good analytical performance due to the benefits of nanostructured polyaniline. This biosensor had a wide linear range of 1×10−5 to 1.5×10−3 M and correlation coefficient of 0.998 (n =17), with the detection limit of 5×10−7 M based on signal-to-noise ratio of 3. [Copyright &y& Elsevier]

Abstract: NiO hollow nanospheres were synthesized by controlled precipitation of metal ions with urea using carbon microspheres as templates, which were for the first time adopted to construct a novel amperometric glucose biosensor. Glucose oxidase was immobilized on the surface of hollow nanospheres through chitosan-assisted cross-linking technique. Due to the high specific active sites and high electrocatalytic activity of NiO hollow nanospheres, the constructed glucose biosensors exhibited a high sensitivity of 3.43μA/mM. The low detection limit was estimated to be 47μM (S/N=3), and the Michaelis–Menten constant was found to be 7.76mM, indicating the high affinity of enzyme on NiO hollow nanospheres to glucose. These results show that the NiO hollow nanospheres are a promising material to construct enzyme biosensors. [Copyright &y& Elsevier]

Abstract: Colloidal carbon microspheres (CMS) are dispersed in chitosan (CHIT) solution to form an organic–inorganic hybrid with excellent micro-environment for the immobilization of biomolecules. A novel amperometric biosensor for the determination of hydrogen peroxide (H2O2) has been constructed by entrapping horseradish peroxidase (HRP) in as-synthesized CMS/CHIT hybrid. The modification of glassy carbon electrode is made by a simple solution-evaporation method. The electrochemical properties of the biosensor are characterized in electrochemical methods. The proposed biosensor shows high sensitive determination and fast response to H2O2 at −0.15V. The constructed HRP/CHIT/CMS/GC electrode also exhibits a fine linear correlation with H2O2 concentration. The calculated value of the apparent Michaelis–Menten constant, 2.33mM, suggests that the HRP in CMS/CHIT hybrid keeps its native bioactivity and has high affinity for H2O2. [Copyright &y& Elsevier]