The Experts below are selected from a list of 309 Experts worldwide ranked by ideXlab platform
Brett P Fors - One of the best experts on this subject based on the ideXlab platform.
-
shifting boundaries controlling molecular weight Distribution Shape for mechanically enhanced thermoplastic elastomers
Macromolecules, 2020Co-Authors: Stephanie I Rosenbloom, Brett P ForsAbstract:Thermoplastic elastomers (TPEs) based on polystyrene (PS) are commonplace in society. The elastomeric properties of these materials are often sacrificed to increase mechanical properties such as st...
-
molecular weight Distribution Shape as a versatile approach to tailoring block copolymer phase behavior
ACS Macro Letters, 2018Co-Authors: Dillon T Gentekos, Brett P ForsAbstract:The molecular weight Distributions (MWDs) of block copolymers significantly impact their morphological phase behavior, but exploiting these features as a means to tune material properties has been limited to the MWD breadth, or dispersity (Đ). Manipulation of the entire MWD has promising potential to address this challenge by providing a convenient and versatile route toward tailoring polymer nanostructure. Herein, we describe the self-assembly of poly(styrene)-block-poly(2-vinylpyridine) (PS-b-P2VP) where the PS blocks have systematically deviating compositions of molecular weights. We find that controlling the MWD Shape, breadth and skew, afforded access to different morphologies in samples with the same molecular characteristics, including Đ. As such, we illustrate the generality and effectiveness of this strategy and anticipate that it will facilitate the increased deployment of disperse polymer compositions in advanced materials applications.
-
exploiting molecular weight Distribution Shape to tune domain spacing in block copolymer thin films
Journal of the American Chemical Society, 2018Co-Authors: Dillon T Gentekos, Junteng Jia, Erika S Tirado, Katherine P Barteau, Detlefm Smilgies, Robert A Distasio, Brett P ForsAbstract:We report a method for tuning the domain spacing (Dsp) of self-assembled block copolymer thin films of poly(styrene-block-methyl methacrylate) (PS-b-PMMA) over a large range of lamellar periods. By modifying the molecular weight Distribution (MWD) Shape (including both the breadth and skew) of the PS block via temporal control of polymer chain initiation in anionic polymerization, we observe increases of up to 41% in Dsp for polymers with the same overall molecular weight (Mn ≈ 125 kg mol–1) without significantly changing the overall morphology or chemical composition of the final material. In conjunction with our experimental efforts, we have utilized concepts from population statistics and least-squares analysis to develop a model for predicting Dsp based on the first three moments of the MWDs. This statistical model reproduces experimental Dsp values with high fidelity (with mean absolute errors of 1.2 nm or 1.8%) and provides novel physical insight into the individual and collective roles played by th...
-
Exploiting Molecular Weight Distribution Shape to Tune Domain Spacing in Block Copolymer Thin Films
2018Co-Authors: Dillon T Gentekos, Junteng Jia, Erika S Tirado, Katherine P Barteau, Detlefm Smilgies, Robert A Distasio, Brett P ForsAbstract:We report a method for tuning the domain spacing (Dsp) of self-assembled block copolymer thin films of poly(styrene-block-methyl methacrylate) (PS-b-PMMA) over a large range of lamellar periods. By modifying the molecular weight Distribution (MWD) Shape (including both the breadth and skew) of the PS block via temporal control of polymer chain initiation in anionic polymerization, we observe increases of up to 41% in Dsp for polymers with the same overall molecular weight (Mn ≈ 125 kg mol–1) without significantly changing the overall morphology or chemical composition of the final material. In conjunction with our experimental efforts, we have utilized concepts from population statistics and least-squares analysis to develop a model for predicting Dsp based on the first three moments of the MWDs. This statistical model reproduces experimental Dsp values with high fidelity (with mean absolute errors of 1.2 nm or 1.8%) and provides novel physical insight into the individual and collective roles played by the MWD moments in determining this property of interest. This work demonstrates that both MWD breadth and skew have a profound influence over Dsp, thereby providing an experimental and conceptual platform for exploiting MWD Shape as a simple and modular handle for fine-tuning Dsp in block copolymer thin films
-
manipulation of molecular weight Distribution Shape as a new strategy to control processing parameters
Macromolecular Rapid Communications, 2017Co-Authors: Milena Nadgorny, Brett P Fors, Dillon T Gentekos, Zeyun Xiao, Parker S Singleton, Luke A ConnalAbstract:Molecular weight and dispersity (Ð) influence physical and rheological properties of polymers, which are of significant importance in polymer processing technologies. However, these parameters provide only partial information about the precise composition of polymers, which is reflected by the Shape and symmetry of molecular weight Distribution (MWD). In this work, the effect of MWD symmetry on thermal and rheological properties of polymers with identical molecular weights and Ð is demonstrated. Remarkably, when the MWD is skewed to higher molecular weight, a higher glass transition temperature (Tg ), increased stiffness, increased thermal stability, and higher apparent viscosities are observed. These observed differences are attributed to the chain length composition of the polymers, easily controlled by the synthetic strategy. This work demonstrates a versatile approach to engineer the properties of polymers using controlled synthesis to skew the Shape of MWD.
Dillon T Gentekos - One of the best experts on this subject based on the ideXlab platform.
-
molecular weight Distribution Shape as a versatile approach to tailoring block copolymer phase behavior
ACS Macro Letters, 2018Co-Authors: Dillon T Gentekos, Brett P ForsAbstract:The molecular weight Distributions (MWDs) of block copolymers significantly impact their morphological phase behavior, but exploiting these features as a means to tune material properties has been limited to the MWD breadth, or dispersity (Đ). Manipulation of the entire MWD has promising potential to address this challenge by providing a convenient and versatile route toward tailoring polymer nanostructure. Herein, we describe the self-assembly of poly(styrene)-block-poly(2-vinylpyridine) (PS-b-P2VP) where the PS blocks have systematically deviating compositions of molecular weights. We find that controlling the MWD Shape, breadth and skew, afforded access to different morphologies in samples with the same molecular characteristics, including Đ. As such, we illustrate the generality and effectiveness of this strategy and anticipate that it will facilitate the increased deployment of disperse polymer compositions in advanced materials applications.
-
exploiting molecular weight Distribution Shape to tune domain spacing in block copolymer thin films
Journal of the American Chemical Society, 2018Co-Authors: Dillon T Gentekos, Junteng Jia, Erika S Tirado, Katherine P Barteau, Detlefm Smilgies, Robert A Distasio, Brett P ForsAbstract:We report a method for tuning the domain spacing (Dsp) of self-assembled block copolymer thin films of poly(styrene-block-methyl methacrylate) (PS-b-PMMA) over a large range of lamellar periods. By modifying the molecular weight Distribution (MWD) Shape (including both the breadth and skew) of the PS block via temporal control of polymer chain initiation in anionic polymerization, we observe increases of up to 41% in Dsp for polymers with the same overall molecular weight (Mn ≈ 125 kg mol–1) without significantly changing the overall morphology or chemical composition of the final material. In conjunction with our experimental efforts, we have utilized concepts from population statistics and least-squares analysis to develop a model for predicting Dsp based on the first three moments of the MWDs. This statistical model reproduces experimental Dsp values with high fidelity (with mean absolute errors of 1.2 nm or 1.8%) and provides novel physical insight into the individual and collective roles played by th...
-
Exploiting Molecular Weight Distribution Shape to Tune Domain Spacing in Block Copolymer Thin Films
2018Co-Authors: Dillon T Gentekos, Junteng Jia, Erika S Tirado, Katherine P Barteau, Detlefm Smilgies, Robert A Distasio, Brett P ForsAbstract:We report a method for tuning the domain spacing (Dsp) of self-assembled block copolymer thin films of poly(styrene-block-methyl methacrylate) (PS-b-PMMA) over a large range of lamellar periods. By modifying the molecular weight Distribution (MWD) Shape (including both the breadth and skew) of the PS block via temporal control of polymer chain initiation in anionic polymerization, we observe increases of up to 41% in Dsp for polymers with the same overall molecular weight (Mn ≈ 125 kg mol–1) without significantly changing the overall morphology or chemical composition of the final material. In conjunction with our experimental efforts, we have utilized concepts from population statistics and least-squares analysis to develop a model for predicting Dsp based on the first three moments of the MWDs. This statistical model reproduces experimental Dsp values with high fidelity (with mean absolute errors of 1.2 nm or 1.8%) and provides novel physical insight into the individual and collective roles played by the MWD moments in determining this property of interest. This work demonstrates that both MWD breadth and skew have a profound influence over Dsp, thereby providing an experimental and conceptual platform for exploiting MWD Shape as a simple and modular handle for fine-tuning Dsp in block copolymer thin films
-
manipulation of molecular weight Distribution Shape as a new strategy to control processing parameters
Macromolecular Rapid Communications, 2017Co-Authors: Milena Nadgorny, Brett P Fors, Dillon T Gentekos, Zeyun Xiao, Parker S Singleton, Luke A ConnalAbstract:Molecular weight and dispersity (Ð) influence physical and rheological properties of polymers, which are of significant importance in polymer processing technologies. However, these parameters provide only partial information about the precise composition of polymers, which is reflected by the Shape and symmetry of molecular weight Distribution (MWD). In this work, the effect of MWD symmetry on thermal and rheological properties of polymers with identical molecular weights and Ð is demonstrated. Remarkably, when the MWD is skewed to higher molecular weight, a higher glass transition temperature (Tg ), increased stiffness, increased thermal stability, and higher apparent viscosities are observed. These observed differences are attributed to the chain length composition of the polymers, easily controlled by the synthetic strategy. This work demonstrates a versatile approach to engineer the properties of polymers using controlled synthesis to skew the Shape of MWD.
Jessica C. Mar - One of the best experts on this subject based on the ideXlab platform.
-
The Shape of gene expression Distributions matter: how incorporating Distribution Shape improves the interpretation of cancer transcriptomic data.
BMC bioinformatics, 2020Co-Authors: L. De Torrente, Masako Suzuki, Maximilian Christopeit, John M. Greally, Samuel Zimmerman, Jessica C. MarAbstract:In genomics, we often assume that continuous data, such as gene expression, follow a specific kind of Distribution. However we rarely stop to question the validity of this assumption, or consider how broadly applicable it may be to all genes that are in the transcriptome. Our study investigated the prevalence of a range of gene expression Distributions in three different tumor types from the Cancer Genome Atlas (TCGA). Surprisingly, the expression of less than 50% of all genes was Normally-distributed, with other Distributions including Gamma, Bimodal, Cauchy, and Lognormal also represented. Most of the Distribution categories contained genes that were significantly enriched for unique biological processes. Different assumptions based on the Shape of the expression profile were used to identify genes that could discriminate between patients with good versus poor survival. The prognostic marker genes that were identified when the Shape of the Distribution was accounted for reflected functional insights into cancer biology that were not observed when standard assumptions were applied. We showed that when multiple types of Distributions were permitted, i.e. the Shape of the expression profile was used, the statistical classifiers had greater predictive accuracy for determining the prognosis of a patient versus those that assumed only one type of gene expression Distribution. Our results highlight the value of studying a gene's Distribution Shape to model heterogeneity of transcriptomic data and the impact on using analyses that permit more than one type of gene expression Distribution. These insights would have been overlooked when using standard approaches that assume all genes follow the same type of Distribution in a patient cohort.
-
The Shape of gene expression Distributions matter: how incorporating Distribution Shape improves the interpretation of cancer transcriptomic data.
BMC Bioinformatics, 2020Co-Authors: L. De Torrente, Samuel E. Zimmerman, Masako Suzuki, Maximilian Christopeit, John M. Greally, Jessica C. MarAbstract:Background In genomics, we often assume that continuous data, such as gene expression, follow a specific kind of Distribution. However we rarely stop to question the validity of this assumption, or consider how broadly applicable it may be to all genes that are in the transcriptome. Our study investigated the prevalence of a range of gene expression Distributions in three different tumor types from the Cancer Genome Atlas (TCGA). Results Surprisingly, the expression of less than 50% of all genes was Normally-distributed, with other Distributions including Gamma, Bimodal, Cauchy, and Lognormal also represented. Most of the Distribution categories contained genes that were significantly enriched for unique biological processes. Different assumptions based on the Shape of the expression profile were used to identify genes that could discriminate between patients with good versus poor survival. The prognostic marker genes that were identified when the Shape of the Distribution was accounted for reflected functional insights into cancer biology that were not observed when standard assumptions were applied. We showed that when multiple types of Distributions were permitted, i.e. the Shape of the expression profile was used, the statistical classifiers had greater predictive accuracy for determining the prognosis of a patient versus those that assumed only one type of gene expression Distribution. Conclusions Our results highlight the value of studying a gene's Distribution Shape to model heterogeneity of transcriptomic data and the impact on using analyses that permit more than one type of gene expression Distribution. These insights would have been overlooked when using standard approaches that assume all genes follow the same type of Distribution in a patient cohort.
-
The Shape of gene expression Distributions matter: how incorporating Distribution Shape improves the interpretation of cancer transcriptomic data
2019Co-Authors: L. De Torrente, Samuel E. Zimmerman, Masako Suzuki, Maximilian Christopeit, John M. Greally, Jessica C. MarAbstract:Abstract In genomics, we often impose the assumption that gene expression data follows a specific Distribution. However, rarely do we stop to question this assumption or consider its applicability to all genes in the transcriptome. Our study investigated the prevalence of genes with expression Distributions that are non-Normal in three different tumor types from the Cancer Genome Atlas (TCGA). Surprisingly, less than 50% of all genes were Normally-distributed, with other Distributions including Gamma, Bimodal, Cauchy, and Lognormal were represented. Relevant information about cancer biology was captured by the genes with non-Normal gene expression. When used for classification, the set of non-Normal genes were able to discriminate between cancer patients with poor versus good survival status. Our results highlight the value of studying a gene’s Distribution Shape to model heterogeneity of transcriptomic data. These insights would have been overlooked when using standard approaches that assume all genes follow the same type of Distribution in a patient cohort.
L. De Torrente - One of the best experts on this subject based on the ideXlab platform.
-
The Shape of gene expression Distributions matter: how incorporating Distribution Shape improves the interpretation of cancer transcriptomic data.
BMC bioinformatics, 2020Co-Authors: L. De Torrente, Masako Suzuki, Maximilian Christopeit, John M. Greally, Samuel Zimmerman, Jessica C. MarAbstract:In genomics, we often assume that continuous data, such as gene expression, follow a specific kind of Distribution. However we rarely stop to question the validity of this assumption, or consider how broadly applicable it may be to all genes that are in the transcriptome. Our study investigated the prevalence of a range of gene expression Distributions in three different tumor types from the Cancer Genome Atlas (TCGA). Surprisingly, the expression of less than 50% of all genes was Normally-distributed, with other Distributions including Gamma, Bimodal, Cauchy, and Lognormal also represented. Most of the Distribution categories contained genes that were significantly enriched for unique biological processes. Different assumptions based on the Shape of the expression profile were used to identify genes that could discriminate between patients with good versus poor survival. The prognostic marker genes that were identified when the Shape of the Distribution was accounted for reflected functional insights into cancer biology that were not observed when standard assumptions were applied. We showed that when multiple types of Distributions were permitted, i.e. the Shape of the expression profile was used, the statistical classifiers had greater predictive accuracy for determining the prognosis of a patient versus those that assumed only one type of gene expression Distribution. Our results highlight the value of studying a gene's Distribution Shape to model heterogeneity of transcriptomic data and the impact on using analyses that permit more than one type of gene expression Distribution. These insights would have been overlooked when using standard approaches that assume all genes follow the same type of Distribution in a patient cohort.
-
The Shape of gene expression Distributions matter: how incorporating Distribution Shape improves the interpretation of cancer transcriptomic data.
BMC Bioinformatics, 2020Co-Authors: L. De Torrente, Samuel E. Zimmerman, Masako Suzuki, Maximilian Christopeit, John M. Greally, Jessica C. MarAbstract:Background In genomics, we often assume that continuous data, such as gene expression, follow a specific kind of Distribution. However we rarely stop to question the validity of this assumption, or consider how broadly applicable it may be to all genes that are in the transcriptome. Our study investigated the prevalence of a range of gene expression Distributions in three different tumor types from the Cancer Genome Atlas (TCGA). Results Surprisingly, the expression of less than 50% of all genes was Normally-distributed, with other Distributions including Gamma, Bimodal, Cauchy, and Lognormal also represented. Most of the Distribution categories contained genes that were significantly enriched for unique biological processes. Different assumptions based on the Shape of the expression profile were used to identify genes that could discriminate between patients with good versus poor survival. The prognostic marker genes that were identified when the Shape of the Distribution was accounted for reflected functional insights into cancer biology that were not observed when standard assumptions were applied. We showed that when multiple types of Distributions were permitted, i.e. the Shape of the expression profile was used, the statistical classifiers had greater predictive accuracy for determining the prognosis of a patient versus those that assumed only one type of gene expression Distribution. Conclusions Our results highlight the value of studying a gene's Distribution Shape to model heterogeneity of transcriptomic data and the impact on using analyses that permit more than one type of gene expression Distribution. These insights would have been overlooked when using standard approaches that assume all genes follow the same type of Distribution in a patient cohort.
-
The Shape of gene expression Distributions matter: how incorporating Distribution Shape improves the interpretation of cancer transcriptomic data
2019Co-Authors: L. De Torrente, Samuel E. Zimmerman, Masako Suzuki, Maximilian Christopeit, John M. Greally, Jessica C. MarAbstract:Abstract In genomics, we often impose the assumption that gene expression data follows a specific Distribution. However, rarely do we stop to question this assumption or consider its applicability to all genes in the transcriptome. Our study investigated the prevalence of genes with expression Distributions that are non-Normal in three different tumor types from the Cancer Genome Atlas (TCGA). Surprisingly, less than 50% of all genes were Normally-distributed, with other Distributions including Gamma, Bimodal, Cauchy, and Lognormal were represented. Relevant information about cancer biology was captured by the genes with non-Normal gene expression. When used for classification, the set of non-Normal genes were able to discriminate between cancer patients with poor versus good survival status. Our results highlight the value of studying a gene’s Distribution Shape to model heterogeneity of transcriptomic data. These insights would have been overlooked when using standard approaches that assume all genes follow the same type of Distribution in a patient cohort.
Masako Suzuki - One of the best experts on this subject based on the ideXlab platform.
-
The Shape of gene expression Distributions matter: how incorporating Distribution Shape improves the interpretation of cancer transcriptomic data.
BMC bioinformatics, 2020Co-Authors: L. De Torrente, Masako Suzuki, Maximilian Christopeit, John M. Greally, Samuel Zimmerman, Jessica C. MarAbstract:In genomics, we often assume that continuous data, such as gene expression, follow a specific kind of Distribution. However we rarely stop to question the validity of this assumption, or consider how broadly applicable it may be to all genes that are in the transcriptome. Our study investigated the prevalence of a range of gene expression Distributions in three different tumor types from the Cancer Genome Atlas (TCGA). Surprisingly, the expression of less than 50% of all genes was Normally-distributed, with other Distributions including Gamma, Bimodal, Cauchy, and Lognormal also represented. Most of the Distribution categories contained genes that were significantly enriched for unique biological processes. Different assumptions based on the Shape of the expression profile were used to identify genes that could discriminate between patients with good versus poor survival. The prognostic marker genes that were identified when the Shape of the Distribution was accounted for reflected functional insights into cancer biology that were not observed when standard assumptions were applied. We showed that when multiple types of Distributions were permitted, i.e. the Shape of the expression profile was used, the statistical classifiers had greater predictive accuracy for determining the prognosis of a patient versus those that assumed only one type of gene expression Distribution. Our results highlight the value of studying a gene's Distribution Shape to model heterogeneity of transcriptomic data and the impact on using analyses that permit more than one type of gene expression Distribution. These insights would have been overlooked when using standard approaches that assume all genes follow the same type of Distribution in a patient cohort.
-
The Shape of gene expression Distributions matter: how incorporating Distribution Shape improves the interpretation of cancer transcriptomic data.
BMC Bioinformatics, 2020Co-Authors: L. De Torrente, Samuel E. Zimmerman, Masako Suzuki, Maximilian Christopeit, John M. Greally, Jessica C. MarAbstract:Background In genomics, we often assume that continuous data, such as gene expression, follow a specific kind of Distribution. However we rarely stop to question the validity of this assumption, or consider how broadly applicable it may be to all genes that are in the transcriptome. Our study investigated the prevalence of a range of gene expression Distributions in three different tumor types from the Cancer Genome Atlas (TCGA). Results Surprisingly, the expression of less than 50% of all genes was Normally-distributed, with other Distributions including Gamma, Bimodal, Cauchy, and Lognormal also represented. Most of the Distribution categories contained genes that were significantly enriched for unique biological processes. Different assumptions based on the Shape of the expression profile were used to identify genes that could discriminate between patients with good versus poor survival. The prognostic marker genes that were identified when the Shape of the Distribution was accounted for reflected functional insights into cancer biology that were not observed when standard assumptions were applied. We showed that when multiple types of Distributions were permitted, i.e. the Shape of the expression profile was used, the statistical classifiers had greater predictive accuracy for determining the prognosis of a patient versus those that assumed only one type of gene expression Distribution. Conclusions Our results highlight the value of studying a gene's Distribution Shape to model heterogeneity of transcriptomic data and the impact on using analyses that permit more than one type of gene expression Distribution. These insights would have been overlooked when using standard approaches that assume all genes follow the same type of Distribution in a patient cohort.
-
The Shape of gene expression Distributions matter: how incorporating Distribution Shape improves the interpretation of cancer transcriptomic data
2019Co-Authors: L. De Torrente, Samuel E. Zimmerman, Masako Suzuki, Maximilian Christopeit, John M. Greally, Jessica C. MarAbstract:Abstract In genomics, we often impose the assumption that gene expression data follows a specific Distribution. However, rarely do we stop to question this assumption or consider its applicability to all genes in the transcriptome. Our study investigated the prevalence of genes with expression Distributions that are non-Normal in three different tumor types from the Cancer Genome Atlas (TCGA). Surprisingly, less than 50% of all genes were Normally-distributed, with other Distributions including Gamma, Bimodal, Cauchy, and Lognormal were represented. Relevant information about cancer biology was captured by the genes with non-Normal gene expression. When used for classification, the set of non-Normal genes were able to discriminate between cancer patients with poor versus good survival status. Our results highlight the value of studying a gene’s Distribution Shape to model heterogeneity of transcriptomic data. These insights would have been overlooked when using standard approaches that assume all genes follow the same type of Distribution in a patient cohort.