The consumption of low-dose aspirin (LDA) to prevent cardiovascular disease continues to increase worldwide. Consequently, the number of chronic LDA users seeking dental procedures that require complementary acute anti-inflammatory medication has also grown. Considering the lack of literature evaluating this interaction, we analyzed the gastric and renal effects caused by a selective COX-2 inhi...

Linear Discriminant Analysis (LDA) is among the most optimal dimension reduction methods for classification, which provides a high degree of class separability for numerous applications from science and engineering. However, problems arise with this classical method when one or both of the scatter matrices is singular. Singular scatter matrices are not unusual in many applications, especially f...

Linear discriminant analysis (LDA) is one of the most popular methods of classification. For high-dimensional microarray data classification, due to the small number of samples and large number of features, classical LDA has sub-optimal performance corresponding to the singularity and instability of the within-group covariance matrix. Two modified LDA approaches (MLDA and NLDA) were applied for...

Lithium diisopropylamide (LDA)-mediated ortholithiations of 2-fluoropyridine and 2,6-difluoropyridine in tetrahydrofuran at -78 °C were studied using a combination of IR and NMR spectroscopic and computational methods. Rate studies show that a substrate-assisted deaggregation of LDA dimer occurs parallel to an unprecedented tetramer-based pathway. Standard and competitive isotope effects confir...

Linear discriminant analysis (LDA) is one of the most popular classification algorithms for brain-computer interfaces (BCI). LDA assumes Gaussian distribution of the data, with equal covariance matrices for the concerned classes, however, the assumption is not usually held in actual BCI applications, where the heteroscedastic class distributions are usually observed. This paper proposes an enha...

Singularity problems of scatter matrices in Linear Discriminant Analysis (LDA) are challenging and have obtained attention during the last decade. Linear Discriminant Analysis via QR decomposition (LDA/QR) and Direct Linear Discriminant analysis (DLDA) are two popular algorithms to solve the singularity problem. This paper establishes the equivalent relationship between LDA/QR and DLDA. They ca...

The microscopic structure of a silicon vacancy is studied theoretically using first-principles supercell calculations. Both the standard Kohn–Sham local-density approximation (LDA) scheme and the generalized Kohn–Sham screened-exchange local-density approximation (sX-LDA) scheme are used. The latter approximation is expected to improve the description of electronic levels in the gap region subs...