The Experts below are selected from a list of 32883 Experts worldwide ranked by ideXlab platform

James Whelan - One of the best experts on this subject based on the ideXlab platform.

  • developmental normalization of phenomics data generated by high throughput plant phenotyping systems
    2020
    Co-Authors: Diego Lozanoclaros, Xiangxiang Meng, Eddie Custovic, Guang Deng, Oliver Berkowitz, James Whelan
    Abstract:

    Sowing time is commonly used as the temporal reference for Arabidopsis thaliana (Arabidopsis) experiments in high throughput plant phenotyping (HTPP) systems. This relies on the assumption that germination and seedling establishment are uniform across the population. However, individual seeds have different development trajectories even under uniform environmental conditions. This leads to increased variance in quantitative phenotyping approaches. We developed the Digital Adjustment of Plant Development (DAPD) normalization method. It normalizes time-series HTPP measurements by reference to an early developmental stage and in an automated manner. The timeline of each measurement series is shifted to a reference time. The normalization is determined by cross-correlation at multiple time points of the time-series measurements, which may include rosette area, leaf size, and number. The DAPD method improved the accuracy of phenotyping measurements by decreasing the Statistical Dispersion of quantitative traits across a time-series. We applied DAPD to evaluate the relative growth rate in Arabidopsis plants and demonstrated that it improves uniformity in measurements, permitting a more informative comparison between individuals. Application of DAPD decreased variance of phenotyping measurements by up to 2.5 times compared to sowing-time normalization. The DAPD method also identified more outliers than any other central tendency technique applied to the non-normalized dataset. DAPD is an effective method to control for temporal differences in development within plant phenotyping datasets. In principle, it can be applied to HTPP data from any species/trait combination for which a relevant developmental scale can be defined.

  • developmental normalization of phenomics data generated by high throughput plant phenotyping systems
    2020
    Co-Authors: Diego Lozanoclaros, Xiangxiang Meng, Eddie Custovic, Guang Deng, Oliver Berkowitz, James Whelan, Mathew G Lewsey
    Abstract:

    Sowing time is commonly used as the temporal reference for Arabidopsis thaliana (Arabidopsis) experiments in high throughput plant phenotyping (HTPP) systems. This relies on the assumption that germination and seedling establishment are uniform across the population. However, individual seeds have different development trajectories even under uniform environmental conditions. This leads to increased variance in quantitative phenotyping approaches. We developed the Digital Adjustment of Plant Development (DAPD) normalization method. It normalizes time-series HTPP measurements by reference to an early developmental stage and in an automated manner. The timeline of each measurement series is shifted to a reference time. The normalization is determined by cross-correlation at multiple time points of the time-series measurements, which may include rosette area, leaf size, and number. The DAPD method improved the accuracy of phenotyping measurements by decreasing the Statistical Dispersion of quantitative traits across a time-series. We applied DAPD to evaluate the relative growth rate in A. thaliana plants and demonstrated that it improves uniformity in measurements, permitting a more informative comparison between individuals. Application of DAPD decreased variance of phenotyping measurements by up to 2.5 times compared to sowing-time normalization. The DAPD method also identified more outliers than any other central tendency technique applied to the non-normalized dataset.

Xiangxiang Meng - One of the best experts on this subject based on the ideXlab platform.

  • developmental normalization of phenomics data generated by high throughput plant phenotyping systems
    2020
    Co-Authors: Diego Lozanoclaros, Xiangxiang Meng, Eddie Custovic, Guang Deng, Oliver Berkowitz, James Whelan
    Abstract:

    Sowing time is commonly used as the temporal reference for Arabidopsis thaliana (Arabidopsis) experiments in high throughput plant phenotyping (HTPP) systems. This relies on the assumption that germination and seedling establishment are uniform across the population. However, individual seeds have different development trajectories even under uniform environmental conditions. This leads to increased variance in quantitative phenotyping approaches. We developed the Digital Adjustment of Plant Development (DAPD) normalization method. It normalizes time-series HTPP measurements by reference to an early developmental stage and in an automated manner. The timeline of each measurement series is shifted to a reference time. The normalization is determined by cross-correlation at multiple time points of the time-series measurements, which may include rosette area, leaf size, and number. The DAPD method improved the accuracy of phenotyping measurements by decreasing the Statistical Dispersion of quantitative traits across a time-series. We applied DAPD to evaluate the relative growth rate in Arabidopsis plants and demonstrated that it improves uniformity in measurements, permitting a more informative comparison between individuals. Application of DAPD decreased variance of phenotyping measurements by up to 2.5 times compared to sowing-time normalization. The DAPD method also identified more outliers than any other central tendency technique applied to the non-normalized dataset. DAPD is an effective method to control for temporal differences in development within plant phenotyping datasets. In principle, it can be applied to HTPP data from any species/trait combination for which a relevant developmental scale can be defined.

  • developmental normalization of phenomics data generated by high throughput plant phenotyping systems
    2020
    Co-Authors: Diego Lozanoclaros, Xiangxiang Meng, Eddie Custovic, Guang Deng, Oliver Berkowitz, James Whelan, Mathew G Lewsey
    Abstract:

    Sowing time is commonly used as the temporal reference for Arabidopsis thaliana (Arabidopsis) experiments in high throughput plant phenotyping (HTPP) systems. This relies on the assumption that germination and seedling establishment are uniform across the population. However, individual seeds have different development trajectories even under uniform environmental conditions. This leads to increased variance in quantitative phenotyping approaches. We developed the Digital Adjustment of Plant Development (DAPD) normalization method. It normalizes time-series HTPP measurements by reference to an early developmental stage and in an automated manner. The timeline of each measurement series is shifted to a reference time. The normalization is determined by cross-correlation at multiple time points of the time-series measurements, which may include rosette area, leaf size, and number. The DAPD method improved the accuracy of phenotyping measurements by decreasing the Statistical Dispersion of quantitative traits across a time-series. We applied DAPD to evaluate the relative growth rate in A. thaliana plants and demonstrated that it improves uniformity in measurements, permitting a more informative comparison between individuals. Application of DAPD decreased variance of phenotyping measurements by up to 2.5 times compared to sowing-time normalization. The DAPD method also identified more outliers than any other central tendency technique applied to the non-normalized dataset.

Amit K. Naskar - One of the best experts on this subject based on the ideXlab platform.

  • poly ethylene oxide assisted macromolecular self assembly of lignin in abs matrix for sustainable composite applications
    2015
    Co-Authors: Kokouvi Akato, Chau D. Tran, Jihua Chen, Amit K. Naskar
    Abstract:

    In this effort, we report the compatibilization of biomass-derived lignin polymer in acrylonitrile butadiene styrene (ABS) thermoplastic matrix without loss of mechanical properties via poly(ethylene oxide) (PEO)-mediated macromolecular self-assembly. ABS was blended with lignin in different concentrations, and blends with 10 wt % PEO (relative to lignin) were prepared. The relative tensile strength improved slightly at low lignin content but diminished rapidly as the lignin content was increased. However, the inclusion of PEO as an interfacial adhesion promoter helped avoid deleterious effects. Dynamic mechanical analysis showed that PEO plasticized the hard phase and thus lowered the activation energy (Ea) for its relaxation but caused stiffening of the soft phase and increased its Ea. Microscopy revealed that incorporating lignin in ABS led to the Statistical Dispersion of discrete lignin domains (300–1000 nm) which, after PEO addition, were reduced to smaller interconnected particles (200–500 nm). The...

  • Poly(ethylene oxide)-Assisted Macromolecular Self-Assembly of Lignin in ABS Matrix for Sustainable Composite Applications
    2015
    Co-Authors: Kokouvi Akato, Chau D. Tran, Jihua Chen, Amit K. Naskar
    Abstract:

    In this effort, we report the compatibilization of biomass-derived lignin polymer in acrylonitrile butadiene styrene (ABS) thermoplastic matrix without loss of mechanical properties via poly­(ethylene oxide) (PEO)-mediated macromolecular self-assembly. ABS was blended with lignin in different concentrations, and blends with 10 wt % PEO (relative to lignin) were prepared. The relative tensile strength improved slightly at low lignin content but diminished rapidly as the lignin content was increased. However, the inclusion of PEO as an interfacial adhesion promoter helped avoid deleterious effects. Dynamic mechanical analysis showed that PEO plasticized the hard phase and thus lowered the activation energy (Ea) for its relaxation but caused stiffening of the soft phase and increased its Ea. Microscopy revealed that incorporating lignin in ABS led to the Statistical Dispersion of discrete lignin domains (300–1000 nm) which, after PEO addition, were reduced to smaller interconnected particles (200–500 nm). The lignin-extended partially renewable ABS resins showed shear-thinning behavior and reduced viscosity compared to neat ABS. The preferred lignin-loaded compositions reinforced with 20 vol % chopped carbon fibers exhibited mechanical performances (77–80 MPa) equivalent to those of reinforced ABS materials reportedly used in 3D printing applications. This approach could lower the cost of ABS while reducing its carbon footprint

Oliver Berkowitz - One of the best experts on this subject based on the ideXlab platform.

  • developmental normalization of phenomics data generated by high throughput plant phenotyping systems
    2020
    Co-Authors: Diego Lozanoclaros, Xiangxiang Meng, Eddie Custovic, Guang Deng, Oliver Berkowitz, James Whelan
    Abstract:

    Sowing time is commonly used as the temporal reference for Arabidopsis thaliana (Arabidopsis) experiments in high throughput plant phenotyping (HTPP) systems. This relies on the assumption that germination and seedling establishment are uniform across the population. However, individual seeds have different development trajectories even under uniform environmental conditions. This leads to increased variance in quantitative phenotyping approaches. We developed the Digital Adjustment of Plant Development (DAPD) normalization method. It normalizes time-series HTPP measurements by reference to an early developmental stage and in an automated manner. The timeline of each measurement series is shifted to a reference time. The normalization is determined by cross-correlation at multiple time points of the time-series measurements, which may include rosette area, leaf size, and number. The DAPD method improved the accuracy of phenotyping measurements by decreasing the Statistical Dispersion of quantitative traits across a time-series. We applied DAPD to evaluate the relative growth rate in Arabidopsis plants and demonstrated that it improves uniformity in measurements, permitting a more informative comparison between individuals. Application of DAPD decreased variance of phenotyping measurements by up to 2.5 times compared to sowing-time normalization. The DAPD method also identified more outliers than any other central tendency technique applied to the non-normalized dataset. DAPD is an effective method to control for temporal differences in development within plant phenotyping datasets. In principle, it can be applied to HTPP data from any species/trait combination for which a relevant developmental scale can be defined.

  • developmental normalization of phenomics data generated by high throughput plant phenotyping systems
    2020
    Co-Authors: Diego Lozanoclaros, Xiangxiang Meng, Eddie Custovic, Guang Deng, Oliver Berkowitz, James Whelan, Mathew G Lewsey
    Abstract:

    Sowing time is commonly used as the temporal reference for Arabidopsis thaliana (Arabidopsis) experiments in high throughput plant phenotyping (HTPP) systems. This relies on the assumption that germination and seedling establishment are uniform across the population. However, individual seeds have different development trajectories even under uniform environmental conditions. This leads to increased variance in quantitative phenotyping approaches. We developed the Digital Adjustment of Plant Development (DAPD) normalization method. It normalizes time-series HTPP measurements by reference to an early developmental stage and in an automated manner. The timeline of each measurement series is shifted to a reference time. The normalization is determined by cross-correlation at multiple time points of the time-series measurements, which may include rosette area, leaf size, and number. The DAPD method improved the accuracy of phenotyping measurements by decreasing the Statistical Dispersion of quantitative traits across a time-series. We applied DAPD to evaluate the relative growth rate in A. thaliana plants and demonstrated that it improves uniformity in measurements, permitting a more informative comparison between individuals. Application of DAPD decreased variance of phenotyping measurements by up to 2.5 times compared to sowing-time normalization. The DAPD method also identified more outliers than any other central tendency technique applied to the non-normalized dataset.

Diego Lozanoclaros - One of the best experts on this subject based on the ideXlab platform.

  • developmental normalization of phenomics data generated by high throughput plant phenotyping systems
    2020
    Co-Authors: Diego Lozanoclaros, Xiangxiang Meng, Eddie Custovic, Guang Deng, Oliver Berkowitz, James Whelan
    Abstract:

    Sowing time is commonly used as the temporal reference for Arabidopsis thaliana (Arabidopsis) experiments in high throughput plant phenotyping (HTPP) systems. This relies on the assumption that germination and seedling establishment are uniform across the population. However, individual seeds have different development trajectories even under uniform environmental conditions. This leads to increased variance in quantitative phenotyping approaches. We developed the Digital Adjustment of Plant Development (DAPD) normalization method. It normalizes time-series HTPP measurements by reference to an early developmental stage and in an automated manner. The timeline of each measurement series is shifted to a reference time. The normalization is determined by cross-correlation at multiple time points of the time-series measurements, which may include rosette area, leaf size, and number. The DAPD method improved the accuracy of phenotyping measurements by decreasing the Statistical Dispersion of quantitative traits across a time-series. We applied DAPD to evaluate the relative growth rate in Arabidopsis plants and demonstrated that it improves uniformity in measurements, permitting a more informative comparison between individuals. Application of DAPD decreased variance of phenotyping measurements by up to 2.5 times compared to sowing-time normalization. The DAPD method also identified more outliers than any other central tendency technique applied to the non-normalized dataset. DAPD is an effective method to control for temporal differences in development within plant phenotyping datasets. In principle, it can be applied to HTPP data from any species/trait combination for which a relevant developmental scale can be defined.

  • developmental normalization of phenomics data generated by high throughput plant phenotyping systems
    2020
    Co-Authors: Diego Lozanoclaros, Xiangxiang Meng, Eddie Custovic, Guang Deng, Oliver Berkowitz, James Whelan, Mathew G Lewsey
    Abstract:

    Sowing time is commonly used as the temporal reference for Arabidopsis thaliana (Arabidopsis) experiments in high throughput plant phenotyping (HTPP) systems. This relies on the assumption that germination and seedling establishment are uniform across the population. However, individual seeds have different development trajectories even under uniform environmental conditions. This leads to increased variance in quantitative phenotyping approaches. We developed the Digital Adjustment of Plant Development (DAPD) normalization method. It normalizes time-series HTPP measurements by reference to an early developmental stage and in an automated manner. The timeline of each measurement series is shifted to a reference time. The normalization is determined by cross-correlation at multiple time points of the time-series measurements, which may include rosette area, leaf size, and number. The DAPD method improved the accuracy of phenotyping measurements by decreasing the Statistical Dispersion of quantitative traits across a time-series. We applied DAPD to evaluate the relative growth rate in A. thaliana plants and demonstrated that it improves uniformity in measurements, permitting a more informative comparison between individuals. Application of DAPD decreased variance of phenotyping measurements by up to 2.5 times compared to sowing-time normalization. The DAPD method also identified more outliers than any other central tendency technique applied to the non-normalized dataset.