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Sets Containing (set + containing)
Kinds of Sets Containing Selected AbstractsThe evolution of rewards: seed dispersal, seed size and elaiosome sizeJOURNAL OF ECOLOGY, Issue 3 2006WILL EDWARDS Summary 1We examine the relationship between the reward offered to ants to disperse seeds (elaiosome size) and seed size, and the possible mechanisms that may generate this relationship in Australian plant species. 2We used seed and elaiosome sizes from our own data set containing 87 Acacia species, supplemented with 22 species from a previously published data set, and 98 ,Other species' from 51 genera in 25 families, also from published data. 3The relationship between ln(elaiosome size) and ln(seed size) was determined using standard major axis (SMA) regression for both data sets. For the Other data set we also determined the relationship among species independent of the differences between genera, among genera independent of the differences between families, among genera and among families. We used SMA to test for differences in slopes between groups. 4We found a significant common slope amongst all subsets of the larger data set. The estimated common slope and the 95% confidence interval for the relationship between ln(elaiosome size) and ln(seed size) across all data sets fell above one (1.24, 95%CI = 1.17,1.32), suggesting positive allometry. Slopes were also significantly positive and strikingly similar between the Acacia species data set and the Other species data sets. Similar positive allometry was shown in the ,other' species data set among genera and families, and among species independent of genus means (,species effects'). 5Significant and consistent relationships between taxonomic levels, independent of relationships at other levels, along with significant relationships at the species level, and similarity of slopes, suggest independent convergence towards an underlying functional relationship that has persisted over long evolutionary periods. Our results therefore suggest that ants have been agents of selection on seed traits. 6Such a functional relationship might result from a trade-off in ant foraging behaviour between the benefit of the reward (elaiosome) and the cost of the dispersal (determined by seed size). Slopes > 1 would then suggest that ants need more than proportionally larger rewards to remove larger seeds. [source] Can Public Housing Authorities Attract and Hold Upwardly Mobile Households?: A Report from CincinnatiJOURNAL OF URBAN AFFAIRS, Issue 3-4 2001David P. Varady Logistic regression analysis is applied to a pooled, cross sectional data set containing results from approximately 1,300 interviews with Cincinnati Metropolitan Housing Authority (CMHA) residents, carried out between 1995 and 1998 to determine overall levels of residential attachment, and to test whether socially mobile householders had especially weak attachments to their locations. The results highlighted a strong propensity to move among CMHA residents generally. Although most residents stated that they were satisfied with their home, nearly three-fifths said that they expected to move within five years. Multivariate results suggested that socially mobile residents (college educated householders, workers, moderate-income households) were using the CMHA stock as a stepping-stone to better rental housing or homeownership. Public housing officials need to decide whether to make a special effort to hold these upwardly mobile households. A more realistic goal would be to minimize residential turnover caused by environmental problems (e.g., crime), regardless of income level. Policies to achieve this goal are discussed. [source] Analysis of multilocus fingerprinting data sets containing missing dataMOLECULAR ECOLOGY RESOURCES, Issue 2 2006PHILIPP M. SCHLÜTER Abstract Missing data are commonly encountered using multilocus, fragment-based (dominant) fingerprinting methods, such as random amplified polymorphic DNA (RAPD) or amplified fragment length polymorphism (AFLP). Data sets containing missing data have been analysed by eliminating those bands or samples with missing data, assigning values to missing data or ignoring the problem. Here, we present a method that uses random assignments of band presence,absence to the missing data, implemented by the computer program famd (available from http://homepage.univie.ac.at/philipp.maria.schlueter/famd.html), for analyses based on pairwise similarity and Shannon's index. When missing values group in a data set, sample or band elimination is likely to be the most appropriate action. However, when missing values are scattered across the data set, minimum, maximum and average similarity coefficients are a simple means of visualizing the effects of missing data on tree structure. Our approach indicates the range of values that a data set containing missing data points might generate, and forces the investigator to consider the effects of missing values on data interpretation. [source] Neural network modeling of physical properties of chemical compoundsINTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY, Issue 1 2001J. Kozio Abstract Three different models relating structural descriptors to normal boiling points, melting points, and refractive indexes of organic compounds have been developed using artificial neural networks. A newly elaborated set of molecular descriptors was evaluated to determine their utility in quantitative structure,property relationship (QSPR) studies. Applying two data sets containing 190 amines and 393 amides, neural networks were trained to predict physical properties with close to experimental accuracy, using the conjugated gradient algorithm. Obtained results have shown a high predictive ability of learned neural networks models. The fit error for the predicted properties values compared to experimental data is relatively small. © 2001 John Wiley & Sons, Inc. Int J Quant Chem 84: 117,126, 2001 [source] Analysis of multilocus fingerprinting data sets containing missing dataMOLECULAR ECOLOGY RESOURCES, Issue 2 2006PHILIPP M. SCHLÜTER Abstract Missing data are commonly encountered using multilocus, fragment-based (dominant) fingerprinting methods, such as random amplified polymorphic DNA (RAPD) or amplified fragment length polymorphism (AFLP). Data sets containing missing data have been analysed by eliminating those bands or samples with missing data, assigning values to missing data or ignoring the problem. Here, we present a method that uses random assignments of band presence,absence to the missing data, implemented by the computer program famd (available from http://homepage.univie.ac.at/philipp.maria.schlueter/famd.html), for analyses based on pairwise similarity and Shannon's index. When missing values group in a data set, sample or band elimination is likely to be the most appropriate action. However, when missing values are scattered across the data set, minimum, maximum and average similarity coefficients are a simple means of visualizing the effects of missing data on tree structure. Our approach indicates the range of values that a data set containing missing data points might generate, and forces the investigator to consider the effects of missing values on data interpretation. [source] QSPR Analysis of Copolymers by Recursive Neural Networks: Prediction of the Glass Transition Temperature of (Meth)acrylic Random CopolymersMOLECULAR INFORMATICS, Issue 8-9 2010Carlo Giuseppe Bertinetto Abstract The glass transition temperature (Tg) of acrylic and methacrylic random copolymers was investigated by means of Quantitative Structure-Property Relationship (QSPR) methodology based on Recursive Neural Networks (RNN). This method can directly take molecular structures as input, in the form of labelled trees, without needing predefined descriptors. It was applied to three data sets containing up to 615 polymers (340 homopolymers and 275,copolymers). The adopted representation was able to account for the structure of the repeating unit as well as average macromolecular characteristics, such as stereoregularity and molar composition. The best result, obtained on a data set focused on copolymers, showed a Mean Average Residual (MAR) of 4.9,K, a standard error of prediction (S) of 6.1,K and a squared correlation coefficient (R2) of 0.98 for the test set, with an optimal rate with respect to the training error. Through the treatment of homopolymers and copolymers both as separated and merged data sets, we also showed that the proposed approach is particularly suited for generalizing prediction of polymer properties to various types of chemical structures in a uniform setting. [source] A Study of CDK2 Inhibitors Using a Novel 3D-QSAR Method Exploiting Receptor FlexibilityMOLECULAR INFORMATICS, Issue 8 2009Michael Abstract A new 3D-QSAR method based on the novel molecular dynamics methodology, Active Site Pressurization (ASP), has been validated using two cyclin-dependent kinase 2 data sets containing 65 purines and 91 oxindoles. ASP allows the construction of cavity casts that represent the maximal energetically feasible 3D distortion of protein binding sites potentially achievable by induced fit upon binding of ligands. The ASP-QSAR method entails many components of traditional 3D-QSAR strategies but additionally correlates the biological activity of ligand sets with features of ASP-derived binding site cavity casts, thus taking target protein flexibility into account implicitly. Both of the data sets used to validate the ASP-QSAR method resulted in QSAR models that were of exceptional quality and predictivity. A non-cross-validated variance coefficient (R2) between 0.959 and 0.99 and a cross-validated variance coefficient (Q2) of between 0.927 and 0.929 were obtained for these ASP-QSAR models. [source] | |||||||||||||