A systematic review of research on cheating in online exams from 2010 to 2021.

In recent years, online learning has received more attention than ever before. One of the most challenging aspects of online education is the students' assessment since academic integrity could be violated due to various cheating behaviors in online examinations. Although a considerable number of literature reviews exist about online learning, there is no such review study to provide comprehensive insight into cheating motivations, cheating types, cheating detection, and cheating prevention in the online setting. The current study is a review of 58 publications about online cheating, published from January 2010 to February 2021. We present the categorization of the research and show topic trends in the field of online exam cheating. The study can be a valuable reference for educators and researchers working in the field of online learning to obtain a comprehensive view of cheating mitigation, detection, and prevention.

Expanding the Cross-Link Coverage of a Carboxyl-Group Specific Chemical Cross-Linking Strategy for Structural Proteomics Applications.

Carboxyl-group specific chemical cross-linking is gaining an increased interest as a structural mass spectrometry/structural proteomics technique that is complementary to the more commonly used amine-specific chemistry using succinimide esters. One of these protocols uses a combination of dihydrazide linkers and the coupling reagent DMTMM [4-(4,6-dimethoxy-1,3,5-triazin-2-yl)-4-methylmorpholinium] chloride, which allows performing the reaction at neutral pH. The reaction yields two types of products, carboxyl-carboxyl cross-links that incorporate the dihydrazide linker and zero-length carboxyl-amine cross-links induced by DMTMM alone. Until now, it has not been systematically investigated how the balance between the two products is affected by experimental conditions. Here, we studied the role of the ratios of the two reagents (using pimelic dihydrazide and DMTMM) and demonstrate that the concentration of the two reagents can be systematically adjusted to favor one reaction product over the other. Using a set of five model proteins, we observed that the number of identified cross-linked peptides could be more than doubled by a combination of three different reaction conditions. We also applied this strategy to the bovine 20S proteasome and the Escherichia coli 70S ribosome, again demonstrating complementarity and increased cross-link coverage.

Chemical cross-linking/mass spectrometry maps the amyloid ß peptide binding region on both apolipoprotein E domains.

Apolipoprotein E (apoE) binds the amyloid ß peptide (Aß), one of the major culprits in Alzheimer's disease development. The formation of apoE:Aß complexes is implicated in both Aß clearance and fibrillization. However, the binding interface between apoE and Aß is poorly defined despite substantial previous research efforts, and the exact role of apoE in the pathology of Alzheimer's disease remains largely elusive. Here, we compared the three main isoforms of apoE (E2, E3, and E4) for their interaction with Aß1-42 in an early stage of aggregation and at near physiological conditions. Using electron microscopy and Western blots, we showed that all three isoforms are able to prevent Aß fibrillization and form a noncovalent complex, with one molecule of Aß bound per apoE. Using chemical cross-linking coupled to mass spectrometry, we further examined the interface of interaction between apoE2/3/4 and Aß. Multiple high-confidence intermolecular apoE2/3/4:Aß cross-links confirmed that Lys16 is located in the region of Aß binding to apoE2/3/4. Further, we demonstrated that both N- and C-terminal domains of apoE2/3/4 are interacting with Aß. The cross-linked sites were mapped onto and evaluated in light of a recent structure of apoE. Our results support binding of the hydrophobic Aß at the apoE domain-domain interaction interface, which would explain how apoE is able to stabilize Aß and thereby prevent its subsequent aggregation.

Identification of disease-causing genes using microarray data mining and Gene Ontology.

BACKGROUND: One of the best and most accurate methods for identifying disease-causing genes is monitoring gene expression values in different samples using microarray technology. One of the shortcomings of microarray data is that they provide a small quantity of samples with respect to the number of genes. This problem reduces the classification accuracy of the methods, so gene selection is essential to improve the predictive accuracy and to identify potential marker genes for a disease. Among numerous existing methods for gene selection, support vector machine-based recursive feature elimination (SVMRFE) has become one of the leading methods, but its performance can be reduced because of the small sample size, noisy data and the fact that the method does not remove redundant genes. METHODS: We propose a novel framework for gene selection which uses the advantageous features of conventional methods and addresses their weaknesses. In fact, we have combined the Fisher method and SVMRFE to utilize the advantages of a filtering method as well as an embedded method. Furthermore, we have added a redundancy reduction stage to address the weakness of the Fisher method and SVMRFE. In addition to gene expression values, the proposed method uses Gene Ontology which is a reliable source of information on genes. The use of Gene Ontology can compensate, in part, for the limitations of microarrays, such as having a small number of samples and erroneous measurement results. RESULTS: The proposed method has been applied to colon, Diffuse Large B-Cell Lymphoma (DLBCL) and prostate cancer datasets. The empirical results show that our method has improved classification performance in terms of accuracy, sensitivity and specificity. In addition, the study of the molecular function of selected genes strengthened the hypothesis that these genes are involved in the process of cancer growth. CONCLUSIONS: The proposed method addresses the weakness of conventional methods by adding a redundancy reduction stage and utilizing Gene Ontology information. It predicts marker genes for colon, DLBCL and prostate cancer with a high accuracy. The predictions made in this study can serve as a list of candidates for subsequent wet-lab verification and might help in the search for a cure for cancers.