A real-time quantitative polymerase chain reaction (qPCR) was developed for detection and discrimination of Mycobacterium tuberculosis (H37Rv and H37Ra) and M. bovis bacillus Calmette-Guérin (BCG) of the Mycobacterium tuberculosis complex (MTBC) from mycobacterial other than tuberculosis (MOTT). It was based on the melting curve (Tm) analysis of the gyrB gene using SYBR(®) Green I detection dye and the LightCycler 1.5 system. The optimal conditions for the assay were 0.25 μmol/L of primers with 3.1 mmol/L of MgCl(2) and 45 cycles of amplification. For M. tuberculosis (H37Rv and H37Ra) and M. bovis BCG of the MTBC, we detected the crossing points (Cp) at cycles of 16.96 ± 0.07, 18.02 ± 0.14, and 18.62 ± 0.09, respectively, while the Tm values were 90.19 ± 0.06 °C, 90.27 ± 0.09 °C, and 89.81 ± 0.04 °C, respectively. The assay was sensitive and rapid with a detection limit of 10 pg of the DNA template within 35 min. In this study, the Tm analysis of the qPCR assay was applied for the detection and discrimination of MTBC from MOTT.
The performance of the defect prediction model by using balanced and imbalanced datasets makes a big impact on the discovery of future defects. Current resampling techniques only address the imbalanced datasets without taking into consideration redundancy and noise inherent to the imbalanced datasets. To address the imbalance issue, we propose Kernel Crossover Oversampling (KCO), an oversampling technique based on kernel analysis and crossover interpolation. Specifically, the proposed technique aims to generate balanced datasets by increasing data diversity in order to reduce redundancy and noise. KCO first represents multidimensional features into two-dimensional features by employing Kernel Principal Component Analysis (KPCA). KCO then divides the plotted data distribution by deploying spectral clustering to select the best region for interpolation. Lastly, KCO generates the new defect data by interpolating different data templates within the selected data clusters. According to the prediction evaluation conducted, KCO consistently produced F-scores ranging from 21% to 63% across six datasets, on average. According to the experimental results presented in this study, KCO provides more effective prediction performance than other baseline techniques. The experimental results show that KCO within project and cross project predictions especially consistently achieve higher performance of F-score results.