Plants are simultaneously exposed to multiple stresses resulting in enormous changes in the molecular landscape within the cell. Identification and characterization of the synergistic and antagonistic components of stress response mechanisms contributing to the cross talk between stresses is of high priority to explore and enhance multiple stress responses. To this end, we performed meta-analysis of drought (abiotic), bacterial (biotic) stress response in rice and Arabidopsis by analyzing a total of 386 microarray samples belonging to 20 microarray studies and identified approximately 3100 and 900 DEGs in rice and Arabidopsis, respectively. About 38.5% (1214) and 28.7% (272) DEGs were common to drought and bacterial stresses in rice and Arabidopsis, respectively. A majority of these common DEGs showed conserved expression status in both stresses. Gene ontology enrichment analysis clearly demarcated the response and regulation of various plant hormones and related biological processes. Fatty acid metabolism and biosynthesis of alkaloids were upregulated and, nitrogen metabolism and photosynthesis was downregulated in both stress conditions. WRKY transcription family genes were highly enriched in all upregulated gene sets while ‘CO-like’ TF family showed inverse relationship of expression between drought and bacterial stresses. Weighted gene co-expression network analysis divided DEG sets into multiple modules that show high co-expression and identified stress specific hub genes with high connectivity. Detection of consensus modules based on DEGs common to drought and bacterial stress revealed 9 and 4 modules in rice and Arabidopsis, respectively, with conserved and reversed co-expression patterns.

Abiotic and biotic stress responses are traditionally thought to be regulated by discrete signaling mechanisms. Recent experimental evidence revealed a more complex picture where these mechanisms are highly entangled and can have synergistic and antagonistic effects on each other. In this study, we identified shared stress-responsive genes between abiotic and biotic stresses in rice (Oryza sativa) by performing meta-analyses of microarray studies. About 70% of the 1,377 common differentially expressed genes showed conserved expression status, and the majority of the rest were down-regulated in abiotic stresses and up-regulated in biotic stresses. Using dimension reduction techniques, principal component analysis, and partial least squares discriminant analysis, we were able to segregate abiotic and biotic stresses into separate entities. The supervised machine learning model, recursive-support vector machine, could classify abiotic and biotic stresses with 100% accuracy using a subset of differentially expressed genes. Furthermore, using a random forests decision tree model, eight out of 10 stress conditions were classified with high accuracy. Comparison of genes contributing most to the accurate classification by partial least squares discriminant analysis, recursive-support vector machine, and random forests revealed 196 common genes with a dynamic range of expression levels in multiple stresses. Functional enrichment and coexpression network analysis revealed the different roles of transcription factors and genes responding to phytohormones or modulating hormone levels in the regulation of stress responses. We envisage the top-ranked genes identified in this study, which highly discriminate abiotic and biotic stresses, as key components to further our understanding of the inherently complex nature of multiple stress responses in plants.

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In this paper, several simple and efficient sign and error nonlinearity-based adaptive filters, which are computationally superior having multiplier free weight update loops are used for cancellation of noise in electrocardiographic (ECG) signals. The proposed implementation is suitable for applications such as biotelemetry, where large signal to noise ratios with less computational complexity are required. These schemes mostly employ simple addition, shift operations and achieve considerable speed up over the other least mean square (LMS)-based realizations. Simulation studies shows that the proposed realization gives better performance compared to existing realizations in terms of signal-to-noise ratio and computational complexity.Index Terms-Adaptive filtering, artifact, electrocardiographic (ECG), least mean square (LMS) algorithm, noise cancellation, telecardiology.