The Experts below are selected from a list of 228 Experts worldwide ranked by ideXlab platform
Barbara M. Reed - One of the best experts on this subject based on the ideXlab platform.
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nitrogen ions and nitrogen ion proportions impact the growth of apricot prunus armeniaca shoot cultures
Plant Cell Tissue and Organ Culture, 2018Co-Authors: I Kovalchuk, Zinat Mukhitdinova, Timur Turdiyev, Gulnara Madiyeva, Meleksen Akin, Ecevit Eyduran, Barbara M. ReedAbstract:Nitrogen is a major driver of plant growth and the nitrogen source can be critical to good growth in vitro. A response surface methodology Mixture-Component design and a data mining algorithm were applied to nitrogen (N) nutrition for improving the micropropagation of Prunus armeniaca Lam. Data taken on shoot cultures included a subjective quality rating, shoot number, shoot length, leaf characteristics and physiological disorders. Data were analyzed using the Classification and Regression Tree data mining algorithm. The best overall shoot quality as well as leaf color were on medium with NO3− > 25 mM and NH4+/Ca+ > 0.8. Improving shoot length to15 mm required 25 25 mM and NH4+/Ca2+ ≤ 0.8, but there were 5–10 shoots at other NO3− concentrations regardless of NH4+/Ca2+ proportion. Leaves increased in size with higher NO3− concentrations (> 55 mM). Physiological disorders were also influenced by the nitrogen Components. Shoot tip necrosis was rarely present with NO3− > 45 mM. Callus production decreased somewhat with NH4+/Ca2+ > 2.33. Suggested concentrations for an improved medium considering all of these growth characteristics would be 25 < NO3− ≤ 35 mM and NH4+/Ca+ ≤ 0.8. Validation experiments comparing WPM and three trial media showed improvements in several shoot growth parameters on medium with optimized mesos and optimized nitrogen Components.
Laurent Rouviere - One of the best experts on this subject based on the ideXlab platform.
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On clustering procedures and nonparametric Mixture estimation
Electronic journal of statistics, 2015Co-Authors: Stéphane Auray, Nicolas Klutchnikoff, Laurent RouviereAbstract:This paper deals with nonparametric estimation of conditional den-sities in Mixture models in the case when additional covariates are available. The proposed approach consists of performing a prelim-inary clustering algorithm on the additional covariates to guess the Mixture Component of each observation. Conditional densities of the Mixture model are then estimated using kernel density estimates ap-plied separately to each cluster. We investigate the expected L 1 -error of the resulting estimates and derive optimal rates of convergence over classical nonparametric density classes provided the clustering method is accurate. Performances of clustering algorithms are measured by the maximal misclassification error. We obtain upper bounds of this quantity for a single linkage hierarchical clustering algorithm. Lastly, applications of the proposed method to Mixture models involving elec-tricity distribution data and simulated data are presented.
Jim E. Riviere - One of the best experts on this subject based on the ideXlab platform.
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Mixture Component effects on the in vitro dermal absorption of pentachlorophenol.
Archives of toxicology, 2001Co-Authors: Jim E. Riviere, Guilin Qiao, Ronald E. Baynes, James D. Brooks, Moiz MumtazAbstract:Interactions between chemicals in a Mixture and interactions of Mixture Components with the skin can significantly alter the rate and extent of percutaneous absorption, as well as the cutaneous disposition of a topically applied chemical. The predictive ability of dermal absorption models, and consequently the dermal risk assessment process, would be greatly improved by the elucidation and characterization of these interactions. Pentachlorophenol (PCP), a compound known to penetrate the skin readily, was used as a marker compound to examine Mixture Component effects using in vitro porcine skin models. PCP was administered in ethanol or in a 40% ethanol/60% water Mixture or a 40% ethanol/60% water Mixture containing either the rubefacient methyl nicotinate (MNA) or the surfactant sodium lauryl sulfate (SLS), or both MNA and SLS. Experiments were also conducted with 14C-labelled 3,3',4,4'-tetrachlorobiphenyl (TCB) and 3,3',4,4',5-pentachlorobiphenyl (PCB). Maximal PCP absorption was 14.12% of the applied dose from the Mixture containing SLS, MNA, ethanol and water. However, when PCP was administered in ethanol only, absorption was only 1.12% of the applied dose. There were also qualitative differences among the absorption profiles for the different PCP Mixtures. In contrast with the PCP results, absorption of TCB or PCB was negligible in perfused porcine skin, with only 0.14% of the applied TCB dose and 0.05% of the applied PCB dose being maximally absorbed. The low absorption levels for the PCB congeners precluded the identification of Mixture Component effects. These results suggest that dermal absorption estimates from a single chemical exposure may not reflect absorption seen after exposure as a chemical Mixture and that absorption of both TCB and PCB are minimal in this model system.
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The use of mechanistically defined chemical Mixtures (MDCM) to assess Mixture Component effects on the percutaneous absorption and cutaneous disposition of topically exposed chemicals. II. Development of a general dermatopharmacokinetic model for use
Toxicology and applied pharmacology, 1996Co-Authors: Patrick L. Williams, Guilin Qiao, Denis Thompson, Nancy A. Monteiro-riviere, Jim E. RiviereAbstract:We present a conceptual approach to a general comprehensive mathematical model to quantify percutaneous absorption of topically applied chemicals in complex Mixtures on the basis of biophysical parameters estimated or measured using in vitro and ex vivo perfused skin preparations. This model addresses mechanistically defined chemical Mixtures (MDCM) which consist of Components selected because of their potential to modulate by various mechanisms the absorption of a marker toxic penetrant. This model accounts for observed toxicodynamic general and specific effects of chemicals, acting single or in concert, on the absorption of any or all Components in a defined Mixture. We have also included experimental data from an isolated perfused porcine skin flap study with topically applied parathion as the marker penetrant and acetone or DMSO as solvent, with methyl nicotinate as a potential rubefacient, sodium laurel sulfate as a surfactant, and stannous chloride as a reducing agent in order to provide an illustration of the application and performance of the model. This model supports the MDCM concept that defining and then simulating those Components of a complex Mixture that could have a significant impact on the absorption of a marker toxic compound would be a useful screening approach in the risk assessment of topical chemical Mixtures. It may also be used to identify critical pathways where chemical Mixture Component interactions significantly modify the absorption of the penetrant of interest.
Stéphane Auray - One of the best experts on this subject based on the ideXlab platform.
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On clustering procedures and nonparametric Mixture estimation
Electronic journal of statistics, 2015Co-Authors: Stéphane Auray, Nicolas Klutchnikoff, Laurent RouviereAbstract:This paper deals with nonparametric estimation of conditional den-sities in Mixture models in the case when additional covariates are available. The proposed approach consists of performing a prelim-inary clustering algorithm on the additional covariates to guess the Mixture Component of each observation. Conditional densities of the Mixture model are then estimated using kernel density estimates ap-plied separately to each cluster. We investigate the expected L 1 -error of the resulting estimates and derive optimal rates of convergence over classical nonparametric density classes provided the clustering method is accurate. Performances of clustering algorithms are measured by the maximal misclassification error. We obtain upper bounds of this quantity for a single linkage hierarchical clustering algorithm. Lastly, applications of the proposed method to Mixture models involving elec-tricity distribution data and simulated data are presented.
Eric Po Xing - One of the best experts on this subject based on the ideXlab platform.
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UAI - Integrating document clustering and topic modeling
2013Co-Authors: Pengtao Xie, Eric Po XingAbstract:Document clustering and topic modeling are two closely related tasks which can mutually benefit each other. Topic modeling can project documents into a topic space which facilitates effective document clustering. Cluster labels discovered by document clustering can be incorporated into topic models to extract local topics specific to each cluster and global topics shared by all clusters. In this paper, we propose a multi-grain clustering topic model (MGCTM) which integrates document clustering and topic modeling into a unified framework and jointly performs the two tasks to achieve the overall best performance. Our model tightly couples two Components: a Mixture Component used for discovering latent groups in document collection and a topic model Component used for mining multi-grain topics including local topics specific to each cluster and global topics shared across clusters. We employ variational inference to approximate the posterior of hidden variables and learn model parameters. Experiments on two datasets demonstrate the effectiveness of our model.
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Integrating Document Clustering and Topic Modeling
Proceedings of the 29th conference on uncertainty in artificial intelligence, 2013Co-Authors: Pengtao Xie, Eric Po XingAbstract:Document clustering and topic modeling are two closely related tasks which can mutually benefit each other. Topic modeling can project documents into a topic space which facilitates effective document clustering. Cluster labels discovered by document clustering can be incorporated into topic models to extract local topics specific to each cluster and global topics shared by all clusters. In this paper, we propose a multi-grain clustering topic model (MGCTM) which integrates document clustering and topic modeling into a unified framework and jointly performs the two tasks to achieve the overall best performance. Our model tightly couples two Components: a Mixture Component used for discovering latent groups in document collection and a topic model Component used for mining multi-grain topics including local topics specific to each cluster and global topics shared across clusters.We employ variational inference to approximate the posterior of hidden variables and learn model parameters. Experiments on two datasets demonstrate the effectiveness of our model.
