In this study, we present the application of a novel capillary electrophoresis (CE) method in combination with label-free quantitation and support vector machine-based feature selection (support vector machine-estimated recursive feature elimination or SVM-RFE) to identify potential glycan alterations in Parkinson’s disease. Specific focus was placed on the use of neutral coated capillaries, by a dynamic capillary coating strategy, to ensure stable and repeatable separations without the need of non-mass spectrometry (MS) friendly additives within the separation electrolyte. The developed online dynamic coating strategy was applied to identify serum N-glycosylation by CE-MS/MS in combination with exoglycosidase sequencing. The annotated structures were quantified in 15 controls and 15 Parkinson’s disease patients by label-free quantitation. Lower sialylation and increased fucosylation were found in Parkinson’s disease patients on tri-antennary glycans with 2 and 3 terminal sialic acids. The set of potential glycan alterations was narrowed by a recursive feature elimination algorithm resulting in the efficient classification of male patients.
Recently, massive open online courses (MOOCs) have been offering a new online approach in the field of distance learning and online education. A typical MOOC course consists of video lectures, reading material and easily accessible tests for students. For a computer programming course, it is important to provide interactive, dynamic, online coding exercises and more complex programming assignments for learners. It is expedient for the students to receive prompt feedback on their coding submissions. Although MOOC automated programme evaluation subsystem is capable of assessing source programme files that are in learning management systems, in MOOC systems there is a grader that is responsible for evaluating students' assignments with the result that course staff would be required to assess thousands of programmes submitted by the participants of the course without the benefit of an automatic grader. This paper presents a new concept for grading programming submissions of students and improved techniques based on the Java unit testing framework that enables automatic grading of code chunks. Some examples are also given such as the creation of unique exercises by dynamically generating the parameters of the assignment in a MOOC programming course combined with the kind of coding style recognition to teach coding standards.
Software defect prediction (SDP) plays a vital role in enhancing the quality of software projects and reducing maintenance-based risks through the ability to detect defective software components. SDP refers to using historical defect data to construct a relationship between software metrics and defects via diverse methodologies. Several prediction models, such as machine learning (ML) and deep learning (DL), have been developed and adopted to recognize software module defects, and many methodologies and frameworks have been presented. Class imbalance is one of the most challenging problems these models face in binary classification. However, When the distribution of classes is imbalanced, the accuracy may be high, but the models cannot recognize data instances in the minority class, leading to weak classifications. So far, little research has been done in the previous studies that address the problem of class imbalance in SDP. In this study, the data sampling method is introduced to address the class imbalance problem and improve the performance of ML models in SDP. The proposed approach is based on a convolutional neural network (CNN) and gated recurrent unit (GRU) combined with a synthetic minority oversampling technique plus the Tomek link (SMOTE Tomek) to predict software defects. To establish the efficiency of the proposed models, the experiments have been conducted on benchmark datasets obtained from the PROMISE repository. The experimental results have been compared and evaluated in terms of accuracy, precision, recall, F-measure, Matthew’s correlation coefficient (MCC), the area under the ROC curve (AUC), the area under the precision-recall curve (AUCPR), and mean square error (MSE). The experimental results showed that the proposed models predict the software defects more effectively on the balanced datasets than the original datasets, with an improvement of up to 19% for the CNN model and 24% for the GRU model in terms of AUC. We compared our proposed approach with existing SDP approaches based on several standard performance measures. The comparison results demonstrated that the proposed approach significantly outperforms existing state-of-the-art SDP approaches on most datasets.
The most commonly applied industrial synthesis of 4,4′-methylene diphenyl diamine (4,4′-MDA), an important polyurethane intermediate, is the reaction of aniline and formaldehyde. Molecular understanding of the 4,4′-MDA formation can provide strategy to prevent from side reactions. In this work, a molecular mechanism consisted of eight consecutive, elementary reaction steps from anilines and formaldehyde to the formation of 4,4′-MDA in acidic media is proposed using accurate G3MP2B3 composite quantum chemical method. Then G3MP2B3-SMD results in aqueous and aniline solutions were compared to the gas phase mechanism. Based on the gas phase calculations standard enthalpy of formation, entropy and heat capacity values were evaluated using G3MP2B3 results for intermediates The proposed mechanism was critically evaluated and important side reactions are considered: the competition of formation of protonated p-aminobenzylaniline (PABAH+), protonated aminal (AMH+) and o-aminobenzylaniline (OABAH+). Competing reactions of the 4,4′-MDA formation is also thermodynamically analyzed such as the formation of 2,4-MDAH+, 3,4-MDAH+. AMH+ can be formed through loose transition state, but it becomes kinetic dead-end, while formation of significant amount of 2,4-MDA is plausible through low-lying transition state. The acid strength of the key intermediates such as N-methylenebenzeneanilium, PABAH+, 4-methylidenecyclohexa-2,5-diene-1-iminium, and AMH+ was estimated by relative pKa calculation.
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