Gene profiling reveals hydrogen sulphide recruits death signaling via the N-methyl-D-aspartate receptor identifying commonalities with excitotoxicity

Recently the role of hydrogen sulphide (H₂S) as a gasotransmitter stimulated wide interest owing to its involvement in Alzheimer's disease and ischemic stroke. Previously we demonstrated the importance of functional ionotropic glutamate receptors (GluRs) by neurons is critical for H₂S-mediated dose- and time-dependent injury. Moreover N-methyl-D-aspartate receptor (NMDAR) antagonists abolished the consequences of H₂S-induced neuronal death. This study focuses on deciphering the downstream effects activation of NMDAR on H₂S-mediated neuronal injury by analyzing the time-course of global gene profiling (5, 15, and 24 h) to provide a comprehensive description of the recruitment of NMDAR-mediated signaling. Microarray analyses were performed on RNA from cultured mouse primary cortical neurons treated with 200 µM sodium hydrosulphide (NaHS) or NMDA over a time-course of 5–24 h. Data were validated via real-time PCR, western blotting, and global proteomic analysis. A substantial overlap of 1649 genes, accounting for over 80% of NMDA global gene profile present in that of H₂S and over 50% vice versa, was observed. Within these commonly occurring genes, the percentage of transcriptional consistency at each time-point ranged from 81 to 97%. Gene families involved included those related to cell death, endoplasmic reticulum stress, calcium homeostasis, cell cycle, heat shock proteins, and chaperones. Examination of genes exclusive to H₂S-mediated injury (43%) revealed extensive dysfunction of the ubiquitin-proteasome system. These data form a foundation for the development of screening platforms and define targets for intervention in H₂S neuropathologies where NMDAR-activated signaling cascades played a substantial role. J. Cell. Physiol. 226: 1308–1322, 2011.

Boosting performance for 2D linear discriminant analysis via regression

Two Dimensional Linear Discriminant Analysis (2DLDA) has received much interest in recent years. However, 2DLDA could make pairwise distances between any two classes become significantly unbalanced, which may affect its performance. Moreover 2DLDA could also suffer from the small sample size problem. Based on these observations, we propose two novel algorithms called Regularized 2DLDA and Ridge Regression for 2DLDA (RR-2DLDA). Regularized 2DLDA is an extension of 2DLDA with the introduction of a regularization parameter to deal with the small sample size problem. RR-2DLDA integrates ridge regression into Regularized 2DLDA to balance the distances among different classes after the transformation. These proposed algorithms overcome the limitations of 2DLDA and boost recognition accuracy. The experimental results on the Yale, PIE and FERET databases showed that RR-2DLDA is superior not only to 2DLDA but also other state-of-the-art algorithms.

Ridge regression for two dimensional locality preserving projection

Two Dimensional Locality Preserving Projection (2D-LPP) is a recent extension of LPP, a popular face recognition algorithm. It has been shown that 2D-LPP performs better than PCA, 2D-PCA and LPP. However, the computational cost of 2D-LPP is high. This paper proposes a novel algorithm called Ridge Regression for Two Dimensional Locality Preserving Projection (RR- 2DLPP), which is an extension of 2D-LPP with the use of ridge regression. RR-2DLPP is comparable to 2DLPP in performance whilst having a lower computational cost. The experimental results on three benchmark face data sets - the ORL, Yale and FERET databases - demonstrate the effectiveness and efficiency of RR-2DLPP compared with other face recognition algorithms such as PCA, LPP, SR, 2D-PCA and 2D-LPP.