Genetic architecture and sexual conflict in the life history of Drosophila

Because males and females of a species express many homologous traits, sex-specific selection on these traits can shift the opposite sex away from its phenotypic optimum. This mode of sexually antagonistic selection, known as intralocus sexual conflict (IaSC), arises when the evolution of sexual dimorphism is constrained by the two sexes sharing a common gene pool. As IaSC has been historically overlooked, many outstanding questions remain. For example, what is its contribution in maintaining genetic variation for fitness in populations? What characters underlie this variation in fitness? How does the selection history of the population influence the standing genetic variation? I used the model organism Drosophila melanogaster to attempt to resolve some of these questions. The first part of my Master’s project involved assessing the detectability of sexually antagonistic alleles in populations at different stages of adaptation to the laboratory. For the second part of my Master’s project, I looked for evidence of conflict during the development of body size, a well-known sexually dimorphic trait. While the first part of my thesis proved inconclusive, the second part revealed a surprising source of sexual conflict in pre-adult stages of D. melanogaster.

Classifying And Predicting Software Security Vulnerabilities based on Reproducibility

Security defects are common in large software systems because of their size and complexity. Although efficient development processes, testing, and maintenance policies are applied to software systems, there are still a large number of vulnerabilities that can remain, despite these measures. Some vulnerabilities stay in a system from one release to the next one because they cannot be easily reproduced through testing. These vulnerabilities endanger the security of the systems. We propose vulnerability classification and prediction frameworks based on vulnerability reproducibility. The frameworks are effective to identify the types and locations of vulnerabilities in the earlier stage, and improve the security of software in the next versions (referred to as releases). We expand an existing concept of software bug classification to vulnerability classification (easily reproducible and hard to reproduce) to develop a classification framework for differentiating between these vulnerabilities based on code fixes and textual reports. We then investigate the potential correlations between the vulnerability categories and the classical software metrics and some other runtime environmental factors of reproducibility to develop a vulnerability prediction framework. The classification and prediction frameworks help developers adopt corresponding mitigation or elimination actions and develop appropriate test cases. Also, the vulnerability prediction framework is of great help for security experts focus their effort on the top-ranked vulnerability-prone files. As a result, the frameworks decrease the number of attacks that exploit security vulnerabilities in the next versions of the software. To build the classification and prediction frameworks, different machine learning techniques (C4.5 Decision Tree, Random Forest, Logistic Regression, and Naive Bayes) are employed. The effectiveness of the proposed frameworks is assessed based on collected software security defects of Mozilla Firefox.