Ammi

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D Madhankumar - One of the best experts on this subject based on the ideXlab platform.

  • one step green synthesis of silver nano microparticles using extracts of trachyspermum Ammi and papaver somniferum
    Colloids and Surfaces B: Biointerfaces, 2012
    Co-Authors: Krish Vijayaraghavan, S Kamala P Nalini, N Udaya K Prakash, D Madhankumar
    Abstract:

    a b s t r a c t A novel biosynthesis route for silver nanoparticles (Ag-NPs) was attempted in this present investigation using aqueous extracts of Trachyspermum Ammi and Papaver somniferum. The main constituents in T. Ammi are thymol, p-cymene and -terpinene, while P. somniferum consists of morphine and codeine. The essential oil in T. Ammi was found to be a good reducing agent than the alkaloids present in P. somniferum for the formation of biocompatible Ag-NPs. The effectiveness of both the extracts was investigated by using same dosage of extract in the synthesis of silver nanoparticle. The results showed that for the same dosage of extracts the T. Ammi synthesized various size triangular shaped nanoparticles measur- ing from 87 nm, to a fewer nanoparticles having a size of 998 nm diagonally. P. somniferum resulted in almost spherical shaped particle ranging in size between 3.2 and 7.6 m diagonally. Future research based on synthesis of size specific nanoparticle based on the optimization of reaction condition would be an interesting area.

  • One step green synthesis of silver nano/microparticles using extracts of Trachyspermum Ammi and Papaver somniferum
    Colloids and Surfaces B: Biointerfaces, 2012
    Co-Authors: Krish Vijayaraghavan, S Kamala P Nalini, N. K. Udaya Prakash, D Madhankumar
    Abstract:

    a b s t r a c t A novel biosynthesis route for silver nanoparticles (Ag-NPs) was attempted in this present investigation using aqueous extracts of Trachyspermum Ammi and Papaver somniferum. The main constituents in T. Ammi are thymol, p-cymene and -terpinene, while P. somniferum consists of morphine and codeine. The essential oil in T. Ammi was found to be a good reducing agent than the alkaloids present in P. somniferum for the formation of biocompatible Ag-NPs. The effectiveness of both the extracts was investigated by using same dosage of extract in the synthesis of silver nanoparticle. The results showed that for the same dosage of extracts the T. Ammi synthesized various size triangular shaped nanoparticles measur- ing from 87 nm, to a fewer nanoparticles having a size of 998 nm diagonally. P. somniferum resulted in almost spherical shaped particle ranging in size between 3.2 and 7.6 m diagonally. Future research based on synthesis of size specific nanoparticle based on the optimization of reaction condition would be an interesting area.

Krish Vijayaraghavan - One of the best experts on this subject based on the ideXlab platform.

  • one step green synthesis of silver nano microparticles using extracts of trachyspermum Ammi and papaver somniferum
    Colloids and Surfaces B: Biointerfaces, 2012
    Co-Authors: Krish Vijayaraghavan, S Kamala P Nalini, N Udaya K Prakash, D Madhankumar
    Abstract:

    a b s t r a c t A novel biosynthesis route for silver nanoparticles (Ag-NPs) was attempted in this present investigation using aqueous extracts of Trachyspermum Ammi and Papaver somniferum. The main constituents in T. Ammi are thymol, p-cymene and -terpinene, while P. somniferum consists of morphine and codeine. The essential oil in T. Ammi was found to be a good reducing agent than the alkaloids present in P. somniferum for the formation of biocompatible Ag-NPs. The effectiveness of both the extracts was investigated by using same dosage of extract in the synthesis of silver nanoparticle. The results showed that for the same dosage of extracts the T. Ammi synthesized various size triangular shaped nanoparticles measur- ing from 87 nm, to a fewer nanoparticles having a size of 998 nm diagonally. P. somniferum resulted in almost spherical shaped particle ranging in size between 3.2 and 7.6 m diagonally. Future research based on synthesis of size specific nanoparticle based on the optimization of reaction condition would be an interesting area.

  • One step green synthesis of silver nano/microparticles using extracts of Trachyspermum Ammi and Papaver somniferum
    Colloids and Surfaces B: Biointerfaces, 2012
    Co-Authors: Krish Vijayaraghavan, S Kamala P Nalini, N. K. Udaya Prakash, D Madhankumar
    Abstract:

    a b s t r a c t A novel biosynthesis route for silver nanoparticles (Ag-NPs) was attempted in this present investigation using aqueous extracts of Trachyspermum Ammi and Papaver somniferum. The main constituents in T. Ammi are thymol, p-cymene and -terpinene, while P. somniferum consists of morphine and codeine. The essential oil in T. Ammi was found to be a good reducing agent than the alkaloids present in P. somniferum for the formation of biocompatible Ag-NPs. The effectiveness of both the extracts was investigated by using same dosage of extract in the synthesis of silver nanoparticle. The results showed that for the same dosage of extracts the T. Ammi synthesized various size triangular shaped nanoparticles measur- ing from 87 nm, to a fewer nanoparticles having a size of 998 nm diagonally. P. somniferum resulted in almost spherical shaped particle ranging in size between 3.2 and 7.6 m diagonally. Future research based on synthesis of size specific nanoparticle based on the optimization of reaction condition would be an interesting area.

S Kamala P Nalini - One of the best experts on this subject based on the ideXlab platform.

  • one step green synthesis of silver nano microparticles using extracts of trachyspermum Ammi and papaver somniferum
    Colloids and Surfaces B: Biointerfaces, 2012
    Co-Authors: Krish Vijayaraghavan, S Kamala P Nalini, N Udaya K Prakash, D Madhankumar
    Abstract:

    a b s t r a c t A novel biosynthesis route for silver nanoparticles (Ag-NPs) was attempted in this present investigation using aqueous extracts of Trachyspermum Ammi and Papaver somniferum. The main constituents in T. Ammi are thymol, p-cymene and -terpinene, while P. somniferum consists of morphine and codeine. The essential oil in T. Ammi was found to be a good reducing agent than the alkaloids present in P. somniferum for the formation of biocompatible Ag-NPs. The effectiveness of both the extracts was investigated by using same dosage of extract in the synthesis of silver nanoparticle. The results showed that for the same dosage of extracts the T. Ammi synthesized various size triangular shaped nanoparticles measur- ing from 87 nm, to a fewer nanoparticles having a size of 998 nm diagonally. P. somniferum resulted in almost spherical shaped particle ranging in size between 3.2 and 7.6 m diagonally. Future research based on synthesis of size specific nanoparticle based on the optimization of reaction condition would be an interesting area.

  • One step green synthesis of silver nano/microparticles using extracts of Trachyspermum Ammi and Papaver somniferum
    Colloids and Surfaces B: Biointerfaces, 2012
    Co-Authors: Krish Vijayaraghavan, S Kamala P Nalini, N. K. Udaya Prakash, D Madhankumar
    Abstract:

    a b s t r a c t A novel biosynthesis route for silver nanoparticles (Ag-NPs) was attempted in this present investigation using aqueous extracts of Trachyspermum Ammi and Papaver somniferum. The main constituents in T. Ammi are thymol, p-cymene and -terpinene, while P. somniferum consists of morphine and codeine. The essential oil in T. Ammi was found to be a good reducing agent than the alkaloids present in P. somniferum for the formation of biocompatible Ag-NPs. The effectiveness of both the extracts was investigated by using same dosage of extract in the synthesis of silver nanoparticle. The results showed that for the same dosage of extracts the T. Ammi synthesized various size triangular shaped nanoparticles measur- ing from 87 nm, to a fewer nanoparticles having a size of 998 nm diagonally. P. somniferum resulted in almost spherical shaped particle ranging in size between 3.2 and 7.6 m diagonally. Future research based on synthesis of size specific nanoparticle based on the optimization of reaction condition would be an interesting area.

Abouzar Abbasian - One of the best experts on this subject based on the ideXlab platform.

  • evaluation of genotype environment interaction in rice based on Ammi model in iran
    Rice Science, 2017
    Co-Authors: Peyman Sharifi, Hashem Aminpanah, Rahman Erfani, Ali Mohaddesi, Abouzar Abbasian
    Abstract:

    Abstract Identification of high-yielding stable promising rice lines and determination of suitable areas for rice lines would be done by additive main effects and multiplicative interaction (Ammi) model. Seven promising rice genotypes plus two check varieties Shiroudi and 843 were analyzed using a randomized complete block design with three replications in three consecutive years (2012, 2013 and 2014). Homogenous error variance was indicated in the nine environments for grain yield. The combined analysis of variance indicated significant effects of environment, genotype and genotype × environment (GE) interactions on grain yield. The significant effect of GE interaction reflected on the differential response of genotypes in various environments and demonstrated that GE interaction had remarkable effect on genotypic performance in different environments. The application of Ammi model for partitioning the GE interaction effects showed that only the first two terms of Ammi were significant based on Gollob's F-test. The lowest Ammi-1 was observed for G7, G2 and G6. G7 and G6 had higher grain yield. According to the first eigenvalue, which benefits only the first interaction principal component scores, G1, G6, G2 and G9 were the most stable genotypes. The values of the sum of first two interaction principal component scores could be useful in identifying genotype stability, and G6, G5 and G2 were the most dynamic stable genotypes. Ammi stability value introduced G6 as the most stable one. According to Ammi biplot view, G6 was high yielding and highly stable genotype. In conclusion, this study revealed that GE interactions were an important source of rice yield variation, and its Ammi biplots were forceful for visualizing the response of genotypes to environments.

  • Evaluation of Genotype × Environment Interaction in Rice Based on Ammi Model in Iran
    Rice Science, 2017
    Co-Authors: Peyman Sharifi, Hashem Aminpanah, Rahman Erfani, Ali Mohaddesi, Abouzar Abbasian
    Abstract:

    Abstract Identification of high-yielding stable promising rice lines and determination of suitable areas for rice lines would be done by additive main effects and multiplicative interaction (Ammi) model. Seven promising rice genotypes plus two check varieties Shiroudi and 843 were analyzed using a randomized complete block design with three replications in three consecutive years (2012, 2013 and 2014). Homogenous error variance was indicated in the nine environments for grain yield. The combined analysis of variance indicated significant effects of environment, genotype and genotype × environment (GE) interactions on grain yield. The significant effect of GE interaction reflected on the differential response of genotypes in various environments and demonstrated that GE interaction had remarkable effect on genotypic performance in different environments. The application of Ammi model for partitioning the GE interaction effects showed that only the first two terms of Ammi were significant based on Gollob's F-test. The lowest Ammi-1 was observed for G7, G2 and G6. G7 and G6 had higher grain yield. According to the first eigenvalue, which benefits only the first interaction principal component scores, G1, G6, G2 and G9 were the most stable genotypes. The values of the sum of first two interaction principal component scores could be useful in identifying genotype stability, and G6, G5 and G2 were the most dynamic stable genotypes. Ammi stability value introduced G6 as the most stable one. According to Ammi biplot view, G6 was high yielding and highly stable genotype. In conclusion, this study revealed that GE interactions were an important source of rice yield variation, and its Ammi biplots were forceful for visualizing the response of genotypes to environments.

Hugh G. Gauch - One of the best experts on this subject based on the ideXlab platform.

  • Ammi analysis of four way genotype location management year data from a wheat trial in poland
    Crop Science, 2016
    Co-Authors: Jakub Paderewski, Hugh G. Gauch, Wieslaw Mądry, Edward Gacek
    Abstract:

    Grain yield data of winter wheat (Triticum aestivum L.) trials in Poland had a four-way factorial design of 24 genotypes by 20 locations by two managements by 3 yr. The experimental design had genotype–management strip plots with two replications for genotypes, with somewhat more genotypes than the 24 having no missing data. The research objectives were to extend additive main effects and multiplicative interactions (Ammi) analysis from two-way to higher-way datasets to reduce spurious complexity originating from noise, delineate wheat mega-environments in Poland, and make genotype recommendations within each mega-environment. Statistical analysis began with adjusting the yield estimates using the strip-plot experimental design and then combining the results in a genotype × location × management × year (GLMY) table. This table was analyzed by a four-way ANOVA mixed model. Next the GLMY dataset was reorganized into a two-way classification, namely a genotype × environment (G × E) dataset, where the 120 environments were defined as combinations of location, management, and year. This two-way dataset was analyzed by Ammi, with practical limitations of working with only a few mega-environments focusing interest on the Ammi1 member of this model family. The first principal component had an evident geographical interpretation, contrasting northeast Poland (colder climate) and southwest Poland (warmer climate). Suitable genotypes were recommended within each of these two mega-environments. The methodological significance of this paper is the extension of Ammi analysis from the customary two-way G × E datasets to higher-way datasets, such as the present four-way GLMY dataset.

  • A weighted Ammi algorithm to study genotype-by-environment interaction and QTL-by-environment interaction
    Crop Science, 2014
    Co-Authors: Paulo Canas Rodrigues, Hugh G. Gauch, Marcos Malosetti, Fred A. Van Eeuwijk
    Abstract:

    Genotype-by-environment (G × E) interaction (GEI) and quantitative trait locus (QTL)-by-environment interaction (QEI) are common phenomena in multiple-environment trials and represent a major challenge to breeders. The additive main effects and multiplicative interaction (Ammi) model is a widely used tool for the analysis of multiple-environment trials, where the data are represented by a two-way table of G × E means. For complete tables, least squares estimation for the Ammi model is equivalent to fitting an additive two-way ANOVA model for the main effects and applying a singular value decomposition to the interaction residuals, thereby implicitly assuming equal weights for all G × E means. However, multiple-environment data with strong GEI are often also characterized by strong heterogeneous error variation. To improve the performance of the Ammi model in the latter situation, we introduce a generalized estimation scheme, the weighted Ammi or W-Ammi algorithm. This algorithm is useful for studying GEI and QEI. For QEI, the W-Ammi algorithm can be used to create predicted values per environment that are subjected to QTL analysis. We compare the performance of this combined W-Ammi and QTL mapping strategy to direct QTL mapping on G × E means and to QTL mapping on Ammi-predicted values, again with QTL analyses for individual environments. Finally, we compare the W-Ammi QTL mapping strategy, with a multi-environment mixed model QTL mapping approach. Two data sets are used: (i) data from a simulated pepper (Capsicum annuum L.) back cross population using a crop growth model to relate genotypes to phenotypes in a nonlinear way, and (ii) the doubled-haploid Steptoe × Morex barley (Hordeum vulgare L.) population. The QTL analyses on the W-Ammi-predicted values outperformed the QTL analyses on the G × E means and on the Ammi-predicted values, and were very similar to the mixed model QTL mapping approach with regard to the number and location of the true positive QTLs detected, especially for QTLs associated with the interaction and for environments with higher error variance. W-Ammi analysis for GEI and QEI provides an easy-to-use and robust tool with wide applicability.

  • evaluation of genotype environment interaction in barley hordeum vulgare l based on Ammi model using developed sas program
    Journal of Agricultural Science and Technology, 2014
    Co-Authors: Omid Ali Akbarpour, Hamid Dehghani, B Sorkhi, Hugh G. Gauch
    Abstract:

    Understanding the implication of genotype-by-environment interaction (GEI) and improving stability of crop yield in a target production environment is important in plant breeding. In this research, we used the Ammi (Additive Main Effects and Multiplicative Interaction) model to identify the stable genotype(s) by predictive accuracy of yield data. Results of this study indicated that the FGH tests were useful to identify the best truncated Ammi model. In general, FGH1 and FGH2 tests had similar results. The findings of this study confirmed that the Ammi-4 was the best truncated Ammi model to distinguish the general and specific stability of genotypes across environments for recommending them to farmers. Based on Ammi-4 yield prediction, G15 and G17 were identified as useful genotypes for some environments, while G14 was found as a stable genotype in all environments.

  • Direct Validation of Ammi Predictions in Turfgrass Trials
    Crop Science, 2011
    Co-Authors: J. S. Ebdon, Hugh G. Gauch
    Abstract:

    Previous studies using turfgrass quality (TQ) data from the National Turfgrass Evaluation Program (NTEP) used cross validation with additive main effects and multiplicative interactions (Ammi) models to demonstrate accuracy gain within a single trial. The objective of this study is to test Ammi predictive accuracy for new years or new years × locations. Five winners recommended by the raw data AmmiF and five winners recommended by the more accurate Ammi5 model were selected, resulting in a 10-entry roster at six validation sites for evaluation over 5 yr. Average TQ for Ammi5 selections was 5.57 and was significantly greater than AmmiF selections averaging only 5.31. In five of six validation locations, higher TQ was observed by planting Ammi5 selections over AmmiF. The correlation between the 60 validation observations and Ammi5 predictions was 0.628 but for AmmiF was 0.479. Better predictive success indicated by a significantly lower mean squared deviation (MSD) was observed with Ammi5 (0.453) than AmmiF (0.608). The Ammi5 model predicted winners were five times as effective as AmmiF in predicting observed winners from the top group (rank one and two). There was decisive evidence of a parsimonious Ammi model increasing predictive success across years despite a meager 1.24 statistical efficiency with Ammi5.

  • statistical analysis of yield trials by Ammi and gge further considerations
    Crop Science, 2008
    Co-Authors: Hugh G. Gauch, Hanspeter Piepho, Paolo Annicchiarico
    Abstract:

    Recent review articles in this journal have compared the relative merits of two prominent statistical models for analyzing yield-trial data: Additive main effects and multiplicative interaction (Ammi) and genotype main effects and genotype × environment interaction (GGE). This review addresses more than 20 issues that require clarification after controversial statements and contrasting conclusions have appeared in those recent reviews. The Ammi2 mega-environment display incorporates more of the genotype main effect and captures more of the genotype × environment (GE) interaction than does GGE2, thereby displaying the which-won-where pattern more accurately for complex datasets. When the GE interaction is captured well by one principal component, the Ammi1 display of genotype nominal yields describes winning genotypes and adaptive responses more simply and clearly than the GGE2 biplot. For genotype evaluation within a single mega-environment, a simple scatterplot of mean and stability is more straightforward than the mean vs. stability view of a GGE2 biplot. Diagnosing the most predictively accurate member of a model family is vital for either Ammi or GGE, both for gaining accuracy and delineating mega-environments.