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.

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.

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

Apolipoprotein E (apoE) binds the amyloid β peptide (Aβ), one of the major culprits in Alzheimer's disease development. The formation of apoE:Aβ complexes is implicated in both Aβ clearance and fibrillization. However, the binding interface between apoE and Aβ is poorly defined despite substantial previous research efforts, and the exact role of apoE in the pathology of Alzheimer's disease remains largely elusive.

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.