Extracted Trans-Resveratrol from Arachis hypogaea Enhances Expression of Sirtuin Gene and Replicative Life Span in Saccharomyces cerevisiae

Aims: Biotic stress given by Aspergillus niger enhances trans-resveratrol production in Arachis hypogaea plant. This plant extract increases sir2 gene expression and Replicative Life Span in Saccharomyces cerevisiae. Design of Study: Peanut plant was grown in aseptic environment, infected by Aspergillus niger. Plant extract used for quantification of trans-resveratrol by RP-HPLC. Yeast culture was grown in Potato dextrose media along with plant extract. Sir2 gene expression fold calculated by real time pcr. Replicative Life Span of yeast was measured by spectrophotometer. Place and Duration of Study: Allele Life Sciences Pvt. Ltd., Department of Biotechnology between February 2017 to March 2020. Methodology: Biotic stress in Arachis hypogaea plant was induced by wounding the leaves and introducing Aspergillus niger to enhance trans-resveratrol production. Tran-resveratrol was quantified by Reverse Phase High Pressure Liquid Chromatography (RP-HPLC). Two methods conducted to check reverse ageing, first one epigenetic based, when extracted trans-resveratrol from infected Arachis hypogaea plant extract added to Saccharomyces cerevisiae culture, it enhanced expression of Sir2 gene in Saccharomyces cerevisiae measured by qPCR, ABI applied biosystem. Process included RNA isolation, cDNA synthesis and thereafter qPCR. Enhanced expression of sirtuin responsible for gene silencing as sirtuin (Sir2 gene product) is a class of Histone deacetylase transferase enzyme. Second method, Replicative Life Span of Saccharomyces cerevisiae culture increased when Aspergillus niger infected peanut plant extract added to yeast culture which was measured through spectrophotometer at 600nm and showed high absorbance value. Results: Tran-resveratrol was quantified by Reverse Phase High Pressure Liquid Chromatography (RP-HPLC) and yield was 2.24 mg/g. Sir2 gene expression increased by 1.56 fold in yeast grown in infected peanut plant extract. Absorbance of yeast culture grown in infected peanut plant extract was 0.522±0.008 which was higher than control. Conclusion: Sir2 gene expression enhances along with replicative life span in yeast in presence of peanut plant extract.

Development of a fuzzy model for differentiating peanut plant from broadleaf weeds using image features

A combination of decision tree (DT) and fuzzy logic techniques was used to develop a fuzzy model for differentiating peanut plant from weeds. Color features and wavelet-based texture features were extracted from images of peanut plant and its three common weeds. Two feature selection techniques namely Principal Component Analysis (PCA) and Correlation-based Feature Selection (CFS) were applied on input dataset and three Decision Trees (DTs) including J48, Random Tree (RT), and Reduced Error Pruning (REP) were used to distinguish between different plants. In all cases, the best overall classification accuracies were achieved when CFS-selected features were used as input data. The obtained accuracies of J48-CFS, REP-CFS, and RT-CFS trees for classification of the four plant categories namely peanut plant, Velvetleaf, False daisy, and Nicandra, were 80.83%, 80.00% and 79.17% respectively. Along with these almost low accuracies, the structures of the decision trees were complex making them unsuitable for developing a fuzzy inference system. The classifiers were also used for differentiating peanut plant from the group of weeds. The overall accuracies on training and testing datasets were respectively 95.56% and 93.75% for J48-CFS; 92.78% and 91.67% for REP-CFS; and 93.33% and 92.59% for RT-CFS DTs. The results showed that the J48-CFS and REP-CFS were the most appropriate models to set the membership functions and rules of the fuzzy classifier system. Based on the results, it can be concluded that the developed DT-based fuzzy logic model can be used effectively to discriminate weeds from peanut plant in the form of machine vision-based cultivating systems.

Development of a Fuzzy Model for Differentiating Peanut Plant from Broadleaf Weeds Using Image Features

A combination of decision tree (DT) and fuzzy logic techniques was used to develop a fuzzy model for differentiating peanut plant from weeds. Color features and wavelet-based texture features were extracted from images of peanut plant and its three common weeds. Two feature selection techniques namely Principal Component Analysis (PCA) and Correlation-based Feature Selection (CFS) were applied on input dataset and three Decision Trees (DTs) including J48, Random Tree (RT), and Reduced Error Pruning (REP) were used to distinguish between different plants. In all cases, the best overall classification accuracies were achieved when CFS-selected features were used as input data. The obtained accuracies of J48-CFS, REP-CFS, and RT-CFS trees for classification of the four plant categories namely peanut plant, Velvetleaf, False daisy, and Nicandra, were 80.83%, 80.00% and 79.17% respectively. Along with these almost low accuracies, the structures of the decision trees were complex making them unsuitable for developing a fuzzy inference system. The classifiers were also used for differentiating peanut plant from the group of weeds. The overall accuracies on training and testing datasets were respectively 95.56% and 93.75% for J48-CFS; 92.78% and 91.67% for REP-CFS; and 93.33% and 92.59% for RT-CFS DTs. The results showed that the J48-CFS and REP-CFS were the most appropriate models to set the membership functions and rules of the fuzzy classifier system. Based on the results, it can be concluded that the developed DT-based fuzzy logic model can be used effectively to discriminate weeds from peanut plant in the form of machine vision-based cultivating systems.

Development of a Fuzzy Model for Differentiating Peanut Plant from Broadleaf Weeds Using Image Features

A combination of decision tree (DT) and fuzzy logic techniques was used to develop a fuzzy model for differentiating peanut plant from weeds. Color features and wavelet-based texture features were extracted from images of peanut plant and its three common weeds. Two feature selection techniques namely Principal Component Analysis (PCA) and Correlation-based Feature Selection (CFS) were applied on input dataset and three Decision Trees (DTs) including J48, Random Tree (RT), and Reduced Error Pruning (REP) were used to distinguish between different plants. In all cases, the best overall classification accuracies were achieved when CFS-selected features were used as input data. The obtained accuracies of J48-CFS, REP-CFS, and RT-CFS trees for classification of the four plant categories namely peanut plant, Velvetleaf, False daisy, and Nicandra, were 80.83%, 80.00% and 79.17% respectively. Along with these almost low accuracies, the structures of the decision trees were complex making them unsuitable for developing a fuzzy inference system. The classifiers were also used for differentiating peanut plant from the group of weeds. The overall accuracies were 95.56% for J48-CFS, 92.78% for REP-CFS, and 93.33% for RT-CFS DTs. The results showed that the J48-CFS and REP-CFS were the most appropriate models to set the membership functions and rules of the fuzzy classifier system. Based on the results, it can be concluded that the developed DT-based fuzzy logic model can be used effectively to discriminate weeds from peanut plant in the form of machine vision-based cultivating systems.