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Bug localization is the task of recommending source code locations (typically files) that contain the cause of a bug and hence need to be changed to fix the bug. Along these lines, information retrieval-based bug localization (IRBL) approaches have been adopted, which identify the most bug-prone files from the source code space. In current practice, a series of state-of-the-art IRBL techniques leverage the combination of different components (e.g., similar reports, version history, and code structure) to achieve better performance. ABLoTS is a recently proposed approach with the core component, TraceScore, that utilizes requirements and traceability information between different issue reports (i.e., feature requests and bug reports) to identify buggy source code snippets with promising results. To evaluate the accuracy of these results and obtain additional insights into the practical applicability of ABLoTS, we conducted a replication study of this approach with the original dataset and also on two extended datasets (i.e., additional Java dataset and Python dataset). The original dataset consists of 11 open source Java projects with 8,494 bug reports. The extended Java dataset includes 16 more projects comprising 25,893 bug reports and corresponding source code commits. The extended Python dataset consists of 12 projects with 1,289 bug reports. While we find that the TraceScore component, which is the core of ABLoTS, produces comparable or even better results with the extended datasets, we also find that we cannot reproduce the ABLoTS results, as reported in its original paper, due to an overlooked side effect of incorrectly choosing a cut-off date that led to test data leaking into training data with significant effects on performance. Additionally, we conduct experiments to assess the performance of various composers that aggregate scores from different components, revealing that Logistic Regression, fixed weight, and CombSUM outperform the other composers across all three datasets, while decision tree and random forest exhibited subpar performance.

The emergence of triploid oysters has freed the oyster market from the problem of interruptions in the breeding season, allowing for year-round availability. However, according to recent statistics, the phenomenon of fertility in triploid oysters has been increasing yearly, perhaps eventually leading to the loss of the sterility characteristic. This has in turn affected the oyster farming industry. Therefore, we attempted to mine key genes through transcriptomics to find the causes of triploid fertility. During our surveys and sampling, we found that most of the fertile triploid Crassostrea gigaswere females, with a much smaller percentage of males. Therefore, the main objective of this study was to analyze the differences between fertile and sterile triploid female C. gigas. We performed transcriptome analysis of fertile female triploid C. gigasand sterile female triploid C. gigasand identified 2361 up-regulated differentially expressed genes (DEGs) and 1541 down-regulated DEGs, respectively. Enrichment analysis of these DEGs revealed that most GO terms and KEGG pathways were associated with mitosis. We then created a protein–protein interaction (PPI) network using genes enriched in mitosis-associated KEGG pathways. Finally, we screened and validated 26 key genes by quantitative RT-PCR and all were found to be downregulated in expression in sterile female triploids. The main cause of sterility is the suppression of the expression of genes that control the process of germ cell proliferation, the failure of the mitotic cycle to proceed smoothly, and the inability to produce an oogonium. These results can be used to explore ways to reduce the fertility of triploid C. gigasand to promote the breeding of higher quality triploid oysters.

Bone defects are encountered substantially in clinical practice, and bionic scaffolds represent a promising solution for repairing bone defects. However, it is difficult to fabricate scaffolds with bionic structures and reconstruct the microenvironment to fulfill the satisfying repair effects. In this review article, we first discuss various strategies for the design and construction of bionic scaffolds to promote bone defect repair, especially including the structural construction of the scaffold and the integration of bioactive substances together with the application of external stimuli. We then discuss the roles of artificial intelligence and medical imaging in aiding clinical treatment. Finally, we point out the challenges and future outlooks in developing multifunctional bone repair scaffolds, aiming to provide insights for improving bone regeneration efficacy and accelerating clinical translation. Schematic showing pivotal factors involved in bone regeneration, including the bionic design and structural construction of scaffolds, the advantages of loading bioactive substances and applying external stimuli, as well as intelligent and enhanced imaging systems for the assistance of diagnosis and treatment, aiming to explore solutions to facilitate the development of ideal bone grafts and accelerate clinical research.