Prediction of disulfide connectivity in proteins with support vector machine

Enhanced selectivity of the CO2 reverse water-gas reaction over a Ni2P/CeO2 catalyst

Ni catalysts tend to easily undergo methanation and metal sintering at high temperatures during the CO₂ hydrogenation reaction. Herein, Ni₂P, a typical kind of transition metal phosphide, had been demonstrated to be efficient for use in the reverse water gas (RWGS) reaction. Under weight hourly space velocity (WHSV) values of 150 000 and 300 000 mL g^-1 h^-1, the Ni₂P/CeO₂ catalyst presented a high CO selectivity, better than that of the conventional Ni/CeO₂ catalyst. The activity was also well maintained in a 20 h stability test. Detailed physicochemical characterization proved that the moderate adsorption strength of CO₂, as well as more strong adsorption active sites for H₂ on the Ni₂P/CeO₂ surface, prevented the CO₂ from further hydrogenating to CH₄, which accounted for the remarkable CO selectivity and stability of the CO₂ RWGS reaction.

AIBH: Accurate Identification of Brain Hemorrhage Using Genetic Algorithm Based Feature Selection and Stacking

Brain hemorrhage is a type of stroke which is caused by a ruptured artery, resulting in localized bleeding in or around the brain tissues. Among a variety of imaging tests, a computerized tomography (CT) scan of the brain enables the accurate detection and diagnosis of a brain hemorrhage. In this work, we developed a practical approach to detect the existence and type of brain hemorrhage in a CT scan image of the brain, called Accurate Identification of Brain Hemorrhage, abbreviated as AIBH. The steps of the proposed method consist of image preprocessing, image segmentation, feature extraction, feature selection, and design of an advanced classification framework. The image preprocessing and segmentation steps involve removing the skull region from the image and finding out the region of interest (ROI) using Otsu’s method, respectively. Subsequently, feature extraction includes the collection of a comprehensive set of features from the ROI, such as the size of the ROI, centroid of the ROI, perimeter of the ROI, the distance between the ROI and the skull, and more. Furthermore, a genetic algorithm (GA)-based feature selection algorithm is utilized to select relevant features for improved performance. These features are then used to train the stacking-based machine learning framework to predict different types of a brain hemorrhage. Finally, the evaluation results indicate that the proposed predictor achieves a 10-fold cross-validation (CV) accuracy (ACC), precision (PR), Recall, F1-score, and Matthews correlation coefficient (MCC) of 99.5%, 99%, 98.9%, 0.989, and 0.986, respectively, on the benchmark CT scan dataset. While comparing AIBH with the existing state-of-the-art classification method of the brain hemorrhage type, AIBH provides an improvement of 7.03%, 7.27%, and 7.38% based on PR, Recall, and F1-score, respectively. Therefore, the proposed approach considerably outperforms the existing brain hemorrhage classification approach and can be useful for the effective prediction of brain hemorrhage types from CT scan images (The code and data can be found here: http://cs.uno.edu/~tamjid/Software/AIBH/code_data.zip).

Soft Computing Methods for Disulfide Connectivity Prediction

The problem of protein structure prediction (PSP) is one of the main challenges in structural bioinformatics. To tackle this problem, PSP can be divided into several subproblems. One of these subproblems is the prediction of disulfide bonds. The disulfide connectivity prediction problem consists in identifying which nonadjacent cysteines would be cross-linked from all possible candidates. Determining the disulfide bond connectivity between the cysteines of a protein is desirable as a previous step of the 3D PSP, as the protein conformational search space is highly reduced. The most representative soft computing approaches for the disulfide bonds connectivity prediction problem of the last decade are summarized in this paper. Certain aspects, such as the different methodologies based on soft computing approaches (artificial neural network or support vector machine) or features of the algorithms, are used for the classification of these methods.

Gas phase selective hydrogenation over oxide supported Ni–Au

Ni–Au synergism on Al₂O₃ and TiO₂ generates increased surface reactive hydrogen with elevated reaction rates in the hydrogenation of nitroarenes.