Taylor-Gorilla troops optimized deep learning network for surface roughness estimation.

In order to guarantee the desired quality of machined products, a reliable surface roughness assessment is essential. Using a surface profile metre with a contact stylus, which can produce accurate measurements of surface profiles, is the most popular technique for determining the surface roughness of machined items. One of the limitations of this technique is the work piece surface degradation brought on by mechanical contact between the stylus and the surface. Hence, in this paper, a roughness assessment technique based on the suggested Taylor-Gorilla troops optimizer-based Deep Neuro-Fuzzy Network (Taylor-GTO based DNFN) is proposed for estimating the surface roughness. Pre-processing, data augmentation, feature extraction, feature fusion, and roughness estimation are the procedures that the suggested technique uses to complete the roughness estimate procedure. Roughness estimation is performed using DNFN that has been trained using Taylor-GTO, which was created by combining the Taylor series with the Gorilla troop's optimizer. The created Taylor-GTO based DNFN model has minimum Mean Absolute Error, Mean Square Error, and RMSE of 0.403, 0.416, and 1.149, respectively.

Low Power S-Box Architecture for AES Algorithm using Programmable Second Order Reversible Cellular Automata: An Application to WBAN.

In this paper, we presented a novel approach of low energy consumption architecture of S-Box used in Advanced Encryption Standard (AES) algorithm using programmable second order reversible cellular automata (RCA 2). The architecture entails a low power implementation with minimal delay overhead and the performance of proposed RCA 2 based S-Box in terms of security is evaluated using the cryptographic properties such as nonlinearity, correlation immunity bias, strict avalanche criteria, entropy and also found that the proposed architecture is secure enough for cryptographic applications. Moreover, the proposed AES algorithm architecture simulation studies show that energy consumption of 68.726 nJ, power dissipation of 3.856 mW for 0.18- µm at 13.69 MHz and energy consumption of 29.408 nJ, power dissipation of 1.65 mW for 0.13- µm at 13.69 MHz. The proposed AES algorithm with RCA 2 based S-Box shows a reduction power consumption by 50 % and energy consumption by 5 % compared to best classical S-Box and composite field arithmetic based AES algorithm. Apart from that, it is also shown that RCA 2 based S-Boxes are dynamic in nature, invertible, low power dissipation compared to that of LUT based S-Box and hence suitable for Wireless Body Area Network (WBAN) applications.

Differential regulation of genes by retrotransposons in rice promoters.

Rice genome harbors genes and promoters with retrotransposon insertions. There is very little information about their function. The effect of retrotransposon insertions in four rice promoter regions on gene regulation, was investigated using promoter-reporter gene constructs with and without retrotransposons. Differences in expression levels of gus and egfp reporter genes in forward orientation and rfp in reverse orientation were evaluated in rice plants with transient expression employing quantitative RT-PCR analysis, histochemical GUS staining, and eGFP and RFP fluorescent microscopy. The presence of SINE in the promoter 1 (P1) resulted in higher expression levels of the reporter genes, whereas the presence of LINE in P2 or gypsy LTR retrotransposon in P3 reduced expression of the reporter genes. Furthermore, the SINE in P1 acts as an enhancer in contrast with the LINE in P2 and the gypsy LTR retrotransposon in P3 which act as silencers. CTAA and CGG motifs in these retrotransposons are the likely candidates for the downregulation compared to TCTT motif (SINE) which is a candidate for the upregulation of gene expression. The effect of retrotransposons on gene regulation correlated with the earlier investigation of conservation patterns of these four retrotransposon insertions in several rice accessions implying their evolutionary significance.

Machine learning approaches distinguish multiple stress conditions using stress-responsive genes and identify candidate genes for broad resistance in rice.

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.

Integrated metabolomic and proteomic approaches dissect the effect of metal-resistant bacteria on maize biomass and copper uptake.

Marginal soils arise due to various industrial and agricultural practices reducing crop productivity. Pseudomonas sp. TLC 6-6.5-4 is a free-living multiple-metal-resistant plant-growth-promoting bacteria (PGPB) isolated from Torch Lake sediment that promotes maize growth and nutrient uptake. In this study, we examined both PGPB-soil and PGPB-plant interactions. PGPB inoculation resulted in significant increase in maize biomass. Soil inoculation before sowing seeds and coating seeds with the PGPB resulted in higher copper uptake by maize compared to other methods. The PGPB-soil interaction improved phosphorus uptake by maize and led to significant decrease in organic bound copper in marginal soil and a notable increase in exchangeable copper. PGPB improved soil health based on soil enzyme activities. Metabolomic analysis of maize revealed that PGPB inoculation upregulated photosynthesis, hormone biosynthesis, and tricarboxylic acid cycle metabolites. Proteomic analysis identified upregulation of proteins related to plant development and stress response. Further, the activity of antioxidant enzymes and total phenolics decreased in plants grown in marginal soil suggesting alleviation of metal stress in presence of PGPB. The ability of PGPB to modulate interconnected biochemical pathways could be exploited to increase crop productivity in marginal soils, phytoremediation of metal contaminated soils, and organic agriculture.

Cancellation of artifacts in ECG signals using block adaptive filtering techniques.

In this chapter, various block-based adaptive filter structures are presented, which estimate the deterministic components of the electrocardiogram (ECG) signal and remove the noise. The familiar Block LMS algorithm (BLMS) and its fast implementation, Fast Block LMS (FBLMS) algorithm, is proposed for removing artifacts preserving the low frequency components and tiny features of the ECG. The proposed implementation is suitable for applications requiring large signal-to-noise ratios with fast convergence rate. Finally, we have applied these algorithms on real ECG signals obtained from the MIT-BIH database and compared its performance with the conventional LMS algorithm. The results show that the performance of the block-based algorithms is superior than the LMS algorithm.

Genes and co-expression modules common to drought and bacterial stress responses in Arabidopsis and rice.

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.

Coordinate rotation based low complexity architecture for 3D Single Channel Independent Component Analysis.

In this paper, we have proposed a low complexity architecture of the Single Channel Independent Component Analysis Algorithm (SCICA) targeted for remote cardiac health monitoring by introducing the concept of coordinate rotation. The ECG is widely used for the diagnosis of cardiovascular diseases, and in remote healthcare applications, it is necessary to obtain clean ECG data by separating noise from the obtained signal. Independent Component Analysis is popularly used to solve the signal separation problem, however it requires as many signal sensors as the number of independent signals involved, and also requires the number of signals involved to be known a priori. This is not always possible in remote healthcare environments and thus, this motivates us to explore the use of the SCICA algorithm which requires only one input sensor for signal separation. SCICA is computationally intensive and thus there is a need for a low-complexity implementation of the algorithm for the development of healthcare devices. The proposed architecture is validated in terms of functionality and computational complexity and it has been observed that its performance compares favorably with the conventional algorithm.

Bioinformatic analysis of epigenetic and microRNA mediated regulation of drought responsive genes in rice.

Drought stress response is a complex trait regulated at multiple levels. Changes in the epigenetic and miRNA regulatory landscape can dramatically alter the outcome of a stress response. However, little is known about the scope and extent of these regulatory factors on drought related cellular processes and functions. To this end, we selected a list of 5468 drought responsive genes (DRGs) of rice identified in multiple microarray studies and mapped the DNA methylation regions found in a genome wide methylcytosine immunoprecipitation and sequencing (mCIP-Seq) study to their genic and promoter regions, identified the chromatin remodeling genes and the genes that are targets of miRNAs. We found statistically significant enrichment of DNA methylation reads and miRNA target sequences in DRGs compared to a random set of genes. About 75% of the DRGs annotated to be involved in chromatin remodeling were downregulated. We found one-third of the DRGs are targeted by two-thirds of all known/predicted miRNAs in rice which include many transcription factors targeted by more than five miRNAs. Clustering analysis of the DRGs with epigenetic and miRNA features revealed, upregulated cluster was enriched in drought tolerance mechanisms while the downregulated cluster was enriched in drought resistance mechanisms evident by their unique gene ontologies (GOs), protein-protein interactions (PPIs), specific transcription factors, protein domains and metabolic pathways. Further, we analyzed the proteome of two weeks old young rice plants treated with a global demethylating agent, 5-azacytidine (5-azaC), subjected to drought stress and identified 56 protein spots that are differentially expressed. Out of the 56 spots, 35 were differently expressed in the sample with both demethylation and drought stress treatments and 28 (50%) were part of DRGs considered in the bioinformatic analysis.

Antibacterial activity of synthesized 2,4,5-trisubstituted imidazole derivatives.

Some novel chemically synthesized 2,4,5-trisubstituted imidazoles from aryl aldehydes and 1,2-diketones or alpha-hydroxyketone were screened against eight different human pathogenic bacteria and fungi. Seven compounds were found to be active against different bacteria. These compounds showed variation in activity and were found to be active against Gram-positive as well as Gram-negative bacteria. Compound 4-(4,5-diphenyl-1H-imidazol-2-yl)-phenol, 3d was the only compound which showed activity against Klebsiella pneumoniae while rest of the compounds did not show significant activity against this micro-organism. Minimum inhibitory concentrations of the compounds were in the range of 0.50 to 6.1 microg/mL and minimum bactericidal concentration ranges from 1.11 to 12.9 microg/mL. The candidature of active compounds to be an effective and novel drug was examined based on Lipinski's rule of Five which explained ClogP, LogS, H-bond acceptors, H-Bond donors and rotational bonds. Compounds 3a-d and 3f satisfies Lipinski's rule of Five and could be proposed as potent new antibacterial drugs.