Ethylene copolymers with different polar comonomers,such as vinyl acetate,methyl acrylate,glycidyl methacrylate,and maleic anhydride,were used for the preparation of polymer/clay nanocomposites by statically annealing...Ethylene copolymers with different polar comonomers,such as vinyl acetate,methyl acrylate,glycidyl methacrylate,and maleic anhydride,were used for the preparation of polymer/clay nanocomposites by statically annealing their mechanical mixtures with different commercial or home-made organically modified montmorillonites containing only one long alkyl tail.The nanostructure of the products was monitored by X-ray diffraction,and the dispersion of the silicate particles within the polymer matrix was qualitatively evaluated through microscopic analyses.The effect of the preparation conditions on the structure and the morphology of the composites was also addressed through the characterization of selected samples with similar composition prepared by melt compounding.In agreement with the findings reported in a previous paper for the composites filled with two-tailed organoclays,intercalation of the copolymer chains within the tighter galleries of the one-tailed clays occurs easily,independent of the application of a mechanical stress.However,the shear-driven break-up of the intercalated clay particles into smaller platelets(exfoliation)seems more hindered.A collapse of the organoclay interlayer spacing was only observed clearly for a commercial one-tailed organoclay-Cloisite®30B–whereas the same effect was almost negligible for a home-made organoclay with similar structure.展开更多
Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subse...Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.展开更多
An important problem with null hypothesis significance testing, as it is normally performed, is that it is uninformative to reject a point null hypothesis [1]. A way around this problem is to use range null hypotheses...An important problem with null hypothesis significance testing, as it is normally performed, is that it is uninformative to reject a point null hypothesis [1]. A way around this problem is to use range null hypotheses [2]. But the use of range null hypotheses also is problematic. Aside from the usual issues of whether null hypothesis significance tests can be justified at all, there is an issue that is specific to range null hypotheses. It is not straightforward how to calculate the probability of the data given a range null hypothesis. The traditional way is to use the single point that maximizes the obtained p-value. The Bayesian alternative is to propose a prior probability distribution and integrate across it. Because frequentists and Bayesians disagree about a variety of issues, especially those pertaining to whether it is permissible to assign probabilities to hypotheses, and what gets lost in the shuffle is that the two camps actually come to different answers for the probability of the data given a range null hypothesis. Because the probability of the data given the hypothesis is a precursor for both camps, for drawing conclusions about hypotheses, different values for this probability for the different camps is crucial but seldom acknowledged. The goal of the present article is to bring out the problem in a manner accessible to researchers without strong mathematical or statistical backgrounds.展开更多
文摘Ethylene copolymers with different polar comonomers,such as vinyl acetate,methyl acrylate,glycidyl methacrylate,and maleic anhydride,were used for the preparation of polymer/clay nanocomposites by statically annealing their mechanical mixtures with different commercial or home-made organically modified montmorillonites containing only one long alkyl tail.The nanostructure of the products was monitored by X-ray diffraction,and the dispersion of the silicate particles within the polymer matrix was qualitatively evaluated through microscopic analyses.The effect of the preparation conditions on the structure and the morphology of the composites was also addressed through the characterization of selected samples with similar composition prepared by melt compounding.In agreement with the findings reported in a previous paper for the composites filled with two-tailed organoclays,intercalation of the copolymer chains within the tighter galleries of the one-tailed clays occurs easily,independent of the application of a mechanical stress.However,the shear-driven break-up of the intercalated clay particles into smaller platelets(exfoliation)seems more hindered.A collapse of the organoclay interlayer spacing was only observed clearly for a commercial one-tailed organoclay-Cloisite®30B–whereas the same effect was almost negligible for a home-made organoclay with similar structure.
基金supported in part by NIH grants R01NS39600,U01MH114829RF1MH128693(to GAA)。
文摘Many fields,such as neuroscience,are experiencing the vast prolife ration of cellular data,underscoring the need fo r organizing and interpreting large datasets.A popular approach partitions data into manageable subsets via hierarchical clustering,but objective methods to determine the appropriate classification granularity are missing.We recently introduced a technique to systematically identify when to stop subdividing clusters based on the fundamental principle that cells must differ more between than within clusters.Here we present the corresponding protocol to classify cellular datasets by combining datadriven unsupervised hierarchical clustering with statistical testing.These general-purpose functions are applicable to any cellular dataset that can be organized as two-dimensional matrices of numerical values,including molecula r,physiological,and anatomical datasets.We demonstrate the protocol using cellular data from the Janelia MouseLight project to chara cterize morphological aspects of neurons.
文摘An important problem with null hypothesis significance testing, as it is normally performed, is that it is uninformative to reject a point null hypothesis [1]. A way around this problem is to use range null hypotheses [2]. But the use of range null hypotheses also is problematic. Aside from the usual issues of whether null hypothesis significance tests can be justified at all, there is an issue that is specific to range null hypotheses. It is not straightforward how to calculate the probability of the data given a range null hypothesis. The traditional way is to use the single point that maximizes the obtained p-value. The Bayesian alternative is to propose a prior probability distribution and integrate across it. Because frequentists and Bayesians disagree about a variety of issues, especially those pertaining to whether it is permissible to assign probabilities to hypotheses, and what gets lost in the shuffle is that the two camps actually come to different answers for the probability of the data given a range null hypothesis. Because the probability of the data given the hypothesis is a precursor for both camps, for drawing conclusions about hypotheses, different values for this probability for the different camps is crucial but seldom acknowledged. The goal of the present article is to bring out the problem in a manner accessible to researchers without strong mathematical or statistical backgrounds.
