An Exploratory Study to Find Motives Behind Cross-platform Forks from Software Heritage Dataset

The fork-based development mechanism provides the flexibility and the unified processes for software teams to collaborate easily in a distributed setting without too much coordination overhead.Currently, multiple social coding platforms support fork-based development, such as GitHub, GitLab, and Bitbucket. Although these different platforms virtually share the same features, they have different emphasis. As GitHub is the most popular platform and the corresponding data is publicly available, most of the current studies are focusing on GitHub hosted projects. However, we observed anecdote evidences that people are confused about choosing among these platforms, and some projects are migrating from one platform to another, and the reasons behind these activities remain unknown.With the advances of Software Heritage Graph Dataset (SWHGD),we have the opportunity to investigate the forking activities across platforms. In this paper, we conduct an exploratory study on 10popular open-source projects to identify cross-platform forks and investigate the motivation behind. Preliminary result shows that cross-platform forks do exist. For the 10 subject systems in this study, we found 81,357 forks in total among which 179 forks are on GitLab. Based on our qualitative analysis, we found that most of the cross-platform forks that we identified are mirrors of the repositories on another platform, but we still find cases that were created due to preference of using certain functionalities (e.g. Continuous Integration (CI)) supported by different platforms. This study lays the foundation of future research directions, such as understanding the differences between platforms and supporting cross-platform collaboration.Comment: Accepted at 17th International Conference on Mining Software Repositories, October 5--6, 2020, Seoul, Republic of Kore

Salivary Metabolomics for Oral Precancerous Lesions: A Comprehensive Narrative Review

Oral submucous fibrosis (OSMF) is a chronic, potentially malignant disorder of the oral cavity, primarily associated with the consumption of areca nut products and other risk factors. Early and accurate diagnosis of OSMF is crucial to prevent its progression to oral cancer. In recent years, the field of metabolomics has gained momentum as a promising approach for disease detection and monitoring. Salivary metabolomics, a non-invasive and easily accessible diagnostic tool, has shown potential in identifying biomarkers associated with various oral diseases, including OSMF. This review synthesizes current literature on the application of salivary metabolomics in the context of OSMF detection. The review encompasses a comprehensive analysis of studies conducted over the past decade, highlighting advancements in analytical techniques, metabolomic profiling, and identified biomarkers linked to OSMF progression. The primary objective of this review is to provide a critical assessment of the feasibility and reliability of salivary metabolomics as a diagnostic tool for OSMF, along with its potential to differentiate OSMF from other oral disorders. In conclusion, salivary metabolomics holds great promise in revolutionizing OSMF detection through the identification of reliable biomarkers and the development of robust diagnostic models. However, challenges such as sample variability, validation of biomarkers, and standardization need to be addressed before its widespread clinical implementation. This review contributes to a comprehensive understanding of the current status, challenges, and future directions of salivary metabolomics in the realm of OSMF detection, emphasizing its potential impact on early intervention and improved patient outcomes

Forest fire susceptibility prediction using machine learning models with resampling algorithms, Northern part of Eastern Ghat Mountain range (India)

Periodic forest fires destruct to biodiversity, ecosystem productivity and multiple ecosystem services. Forest fires are currently turning a leading cause of forest degradation. The principal objective of this research is to predict forest fire vulnerable zones over Similipal biosphere reserve (SBR; Odisha) using different machine learning (ML) models, such as support vector machine (SVM), random forest (RF) and multivariate adaptive regression splines (MARS). Different resampling methods (CV and bootstrap) have also been applied for optimizing the result and better accuracy. Results show that 10-fold cross validation (CV) technique performed best on SVM model (AUC = 0.83) whereas bootstrap performed best on RF (AUC = 0.80) and MARS model (AUC= 0.84). The main advantage of MARS model is that it only uses input variable and significantly increases the performance of the model. The novelty of this research is application of various ML algorithms through resampling techniques to reduce the biasness and improves the reliability of the models

Polymorphisms of CYP1A1, GSTM1 and GSTT1 Loci as the Genetic Predispositions of Oral Cancers and Other Oral Pathologies: Tobacco and Alcohol as Risk Modifiers

Polycyclic aromatic hydrocarbons of tobacco require activation by phase I enzymes, such as cytochrome-P4501A1 (CYP1A1) to become an ultimate carcinogen, which are subjected to detoxification by phase II enzymes, especially glutathione S-transferases (GSTs). A study was designed to find whether genetic predisposition are risk modifiers of oral pathologies. The study included 102 cases with Oral Cancers (OCs), 68 cases with nonmalignant pathologies, 100 cases as control group. GSTM1 null genotype was associated with increased risk of OCs but not with benign pathologies. Deleted GSTT1 was associated with all pathologies. Both m1m2 and m2m2 polymorphisms of CYP1A1 were associated with oral pathologies