bluuebunny/crossref_metadata_2025_split huggingface dataset:
Global protein expression analysis in apicomplexan parasites: Current status
Authors: Belli Sabina I. , Walker Robert A. , Flowers Sarah A. 
Published: March 2005

AbstractMembers of the phylum Apicomplexa are important protozoan parasites that cause some of the most serious, and in some cases, deadly diseases in humans and animals. They include species from the genus Plasmodium, Toxoplasma, Eimeria, Neospora, Cryptosporidium, Babesia and Theileria. The medical, veterinary and economic impact of these pathogens on a global scale is enormous. Although chemo‐ and immuno‐prophylactic strategies are available to control some of these parasites, they are inadequate. Currently, there is an urgent need to design new vaccines or chemotherapeutics for apicomplexan diseases. High‐throughput global protein expression analyses using gel or non‐gel based protein separation technologies coupled with mass spectrometry and bioinformatics provide a means to identify new drug and vaccine targets in these pathogens. Protein identification based proteomic projects in apicomplexan parasites is currently underway, with the most significant progress made in the malaria parasite, Plasmodium falciparum. More recently, preliminary two‐dimensional gel electrophoresis maps of Toxoplasma gondii and Neospora caninum tachyzoites and Eimeria tenella sporozoites, have been produced, as well as for micronemes in E. tenella. In this review, the status of proteomics in the analysis of global protein expression in apicomplexan parasites will be compared and the challenges associated with these investigations discussed.
Multiscale modeling of viscoelastic properties of polymer nanocomposites
Authors: Borodin Oleg , Bedrov Dmitry , Smith Grant D. , Nairn John , Bardenhagen Scott 
Published: March 2005

AbstractA methodology for simple multiscale modeling of mechanical properties of polymer nanocomposites has been developed. This methodology consists of three steps: (1) obtaining from molecular dynamics simulations the viscoelastic properties of the bulklike polymer and approximating the position‐dependent shear modulus of the interfacial polymer on the basis of the polymer‐bead mean‐square displacements as a function of the distance from the nanoparticle surface, (2) using bulk‐ and interfacial‐polymer properties obtained from molecular dynamics simulations and performing stress–relaxation simulations of the nanocomposites with material‐point‐method simulations to extract the nanocomposite viscoelastic properties, and (3) performing direct validation of the average composite viscoelastic properties obtained from material‐point‐method simulations with those obtained from the molecular dynamics simulations of the nanocomposites. © 2005 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 43: 1005‐1013, 2005
Quantitative structure property relationship models for the prediction of liquid heat capacity
Authors: Yao Xiaojun , Fan Botao , Doucet J. P. , Panaye A. , Liu Mancang , Zhang Ruisheng , Zhang Xiaoyun , Hu Zhide 
Published: March 2005

AbstractQuantitative Structure‐Property Relationship (QSPR) models based on molecular descriptors derived from molecular structures have been developed for the prediction of liquid heat capacity at 25 °C using a diverse set of 871 organic compounds. The molecular descriptors used to represent molecular structures include constitutional and topological indices and quantum chemical parameters. Forward stepwise regression and radial basis function neural networks (RBFNNs) were used to construct the QSPR models. The root mean square errors in liquid heat capacity predictions for the training, test and overall data sets are 16.857, 18.744 and 17.141 heat capacity units, respectively. The prediction results are in agreement with the experimental values, but the RBFNN model seems to be better than stepwise regression method.
Bioconcentration potential predictions based on molecular attributes – an early warning approach for chemicals found in humans, birds, fish and wildlife
Authors: Dimitrov Sabcho D. , Dimitrova Nadezhda C. , Walker John D. , Veith Gilman D. , Mekenyan Ovanes G. 
Published: March 2005

AbstractThe bioaccumulation potential of chemicals is used to indicate when chemicals are likely to contaminate fish, birds and other wildlife, and humans. Together with knowledge of the persistence of chemicals, the bioaccumulation potential is useful in setting priorities for hazard identification as well as environmental monitoring. Because the measurement of the bioaccumulation potential is costly, developing reliable estimates of this important indicator directly from chemical structure has long been a goal of Quantitative Structure Activity Relationship (QSAR) practitioners. Many previous models for predicting bioconcentration factors (BCF) for organic chemicals have been based on linear and bilinear relationships between log(BCF) and octanol‐water partition coefficient (log(Kow)) , some of which also included other structural parameters such as structural correction factors or molecular connectivity indices, Fujita's characters, etc. Most of these BCF models have been derived for predicting passive diffusion of chemicals with log. octanol‐water partition coefficients log(Kow) <7. Most previous models showed large discrepancy for large number of chemicals (predominantly highly lipophilic) found in humans and fish. The effect of steric molecular attributes on predicting BCF was studied using 694 chemicals with available experimental BCF and Kowvalues. It was found that maximum cross sectional diameters and conformational flexibility of chemicals affect significantly bioconcentration and could be used to explain identification of certain highly hydrophobic chemicals in humans and fish.
Modeling mode of action of industrial chemicals: Application using chemicals on Canada's Domestic Substances List (DSL)
Authors: Dimitrov S. , Koleva Y. , Lewis M. , Breton R. , Veith G. , Mekenyan O. 
Published: March 2005

AbstractTraditionally quantitative structure activity relationships (QSARs) are derived on the assumption that similar chemicals should behave in a toxicologically similar manner. The development of mechanistic models for acute toxicity requires the chemical classification to be based on similar mode of toxic action. Currently, the classification approaches used to predict the mode of toxic action are predominantly based on chemical fragments and, to a lesser extent, on their electronic properties. The discrete nature of the fragment‐based approach, however, yields a classification that does not consider the electronic features of the entire chemical that has “continuous character”. The present study is based on the assumption that chemicals with the same mode of toxic action should possess commonality in their steric and electronic structure. The COmmon REactivity PAtterns (COREPA) approach has been applied to define the global physical properties and quantum‐chemical descriptors best clustering chemicals according to their behavioural mode of action (MOA). The derived COREPA models for each mode were translated into decision trees and applied to screen the organic chemicals on Canada's Domestic Substances List (DSL) for their mode of toxic action.
The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models
Authors: Tropsha Alexander , Gramatica Paola , Gombar Vijay K. 
Published: March 2005

AbstractThis paper emphasizes the importance of rigorous validation as a crucial, integral component of Quantitative Structure Property Relationship (QSPR) model development. We consider some examples of published QSPR models, which in spite of their high fitted accuracy for the training sets and apparent mechanistic appeal, fail rigorous validation tests, and, thus, may lack practical utility as reliable screening tools. We present a set of simple guidelines for developing validated and predictive QSPR models. To this end, we discuss several validation strategies including (1) randomization of the modelled property, also called Y‐scrambling, (2) multiple leave‐many‐out cross‐validations, and (3) external validation using rational division of a dataset into training and test sets. We also highlight the need to establish the domain of model applicability in the chemical space to flag molecules for which predictions may be unreliable, and discuss some algorithms that can be used for this purpose. We advocate the broad use of these guidelines in the development of predictive QSPR models.
QSAR Models of the in vitro Estrogen Activity of Bisphenol A Analogs
Authors: Coleman Kelly P. , Toscano William A. , Wiese Thomas E. 
Published: March 2005

AbstractBisphenol A is a monomer constituent of epoxy and polycarbonate resins used in consumer products. Many studies have shown that bisphenol A is a weak estrogen receptor agonist with endocrine disrupting potential in exposed organisms. Presented here is a series of quantitative structure‐activity relationship models to describe the in vitro hormone activity (estrogen receptor binding, reporter gene induction, and cell proliferation) of bisphenol A and 24 of its analogs. The hormone activity ranged over four orders of magnitude, with bisphenol A displaying intermediate activity. Comparative molecular field analysis, comparative molecular similarity indices, and hologram quantitative structure activity models were generated using SYBYL 6.8. Bisphenols with optimal estrogen activity contained two unencumbered phenolic groups in the para orientation, and multiple alkyl substituents extending from the carbon linking phenolic rings. Bisphenols with methyl group hydrogens replaced by halogens also produced strong estrogenic analogs. These studies suggest that it may be possible to use such structure activities to develop bisphenols that are useful monomers with reduced hormone activity.
From Science to Innovation and From Data to Knowledge: eScience in the Dutch Polymer Institute's High‐Throughput Experimentation Cluster
Authors: Adams Nico , Schubert Ulrich S. 
Published: March 2005

AbstractModern materials research increasingly relies on the use of combinatorial and high‐throughput synthesis and screening methods in order to shorten product development times and to maintain filled product pipelines. In order to derive maximum benefit from such technologies, an informatics infrastructure, which supports the experimental work, is indispensable. This paper discusses the efforts made at the Dutch Polymer Institute (DPI) to develop such a system. A detailed requirements and workflow analysis is presented, together with the solution implemented at the DPI. In our approach, we have focussed on the use of existing and proven enterprise technologies, based on open standards, such as JAVA, J2EE, XML and SQL. This allows the rapid development of interfaces between applications as required.
Integrated Informatics in Life and Materials Sciences: An Oxymoron?
Authors: Gilardoni François , Curcin Vasa , Karunanayake Kanishka , Norgaard Jonas , Guo Yike 
Published: March 2005

AbstractThe pharmaceutical and chemical industries are facing significant internal and external pressure to boost the experimental efficiency and effectiveness by cutting the direct research costs and reducing the time to market for new sustainable products. Other key issues to secure a competitive return on investment are to enable the rescue of stalled product development projects, abort failing projects early, enhance collaborative multidisciplinary ventures, and ensure an effectual risk management and cost savings through safety testing and failure analysis. Typically, modern organizations rely on a variety of informatics solutions from well‐established software vendors. These typically operate on different platforms and are controlled by different management systems using different data types and proprietary formats. Maintaining, integrating, updating and monitoring these powerful, but disparate, ensembles of tools, are an intricate and expensive operation. We present the concept of the Enterprise Discovery Planning (EDP) software platform design to facilitate the optimization of discovery activities at an enterprise level. We also briefly present how Inforsense addresses these issues.
Effect of the Genetic Algorithm Parameters on the Optimisation of Heterogeneous Catalysts
Authors: Pereira Silvia R. M. , Clerc Frederic , Farrusseng David , van der Waal Jan C. , Maschmeyer Thomas , Mirodatos Claude 
Published: March 2005

AbstractA study of the effect of Genetic Algorithm (GA) configurations on the performance of heterogeneous catalyst optimisation is reported. The GA optimisation procedure is validated on real case studies. Experimental data to construct the benchmarks were collected by means of High‐Throughput Experimentation (HTE) on CO oxidation (COox) and selective CO oxidation (Selox) reactions. For the search space mapping, 189 catalysts were tested for the two reaction conditions at different temperatures, resulting in 1134 test reactions from which two benchmarks were derived. For the algorithm configuration, an in‐house‐implemented GA platform was used enabling a large variety of operator combinations. Because of the typical limitations in the number of parallel experiments that can be carried out in heterogeneous catalysis, the effects of the population size on the robustness and convergence speed were investigated. From this study, general considerations about the algorithm settings (crossover, selection and mutation) to use for the optimisation of heterogeneous catalysts are addressed.
Application of Data Mining and Evolutionary Optimization in Catalyst Discovery and High‐Throughput Experimentation – Techniques, Strategies, and Software
Authors: Ohrenberg Arne , von Törne Christian , Schuppert Andreas , Knab Bernhard 
Published: March 2005

AbstractData‐mining and evolutionary optimization techniques are powerful tools to improve the efficiency of high‐throughput experimentation (HTE) to discover new materials, drugs, or catalysts.
Robust Material Design: A New Workflow for High‐Throughput Experimentation and Analysis
Authors: Nicolaides David 
Published: March 2005

AbstractThe analysis of highly replicated experiment sets is described. These experiments are often performed as part of validating high‐throughput experimentation (HTE) apparatus. Their analysis has been greatly simplified by the application of traditional regression methods in combination with the development of novel multi‐objective optimisers. These methods allow the analysis to treat the problem of “robust design” inherent in materials and product development. Robust design tries to ensure that a material performs to a specification in spite of manufacturing or usage conditions which are not under the control of the designer. This problem is especially amenable to solution by a method combining HTE and analysis.
Split‐Plot Experimental Designs for Combinatorial and High‐Throughput Experimentation
Authors: Castillo Flor A. , Sweeney Jeff , Margl Peter , Zirk Wayne 
Published: March 2005

AbstractIn the last few years, high‐throughput reactors have small received significant attention due to the potential they offer for fast material development. While many experimental design techniques are proposed, statistical issues related to experimentation in this type of equipment are emerging. One of the experimental design techniques needed is the split‐plot approach, given the randomization restrictions imposed by the equipment. This paper presents the use of split‐plot experimental designs in a high‐throughput reactor. We discuss the unique error structure of these designs and the special statistical analysis that considers two different types of errors. A case study in the Dow Chemical Company is presented. The main advantage of the split‐plot approach related to high throughput is that reactor‐well utilization can be maximized, while randomization restrictions can be addressed correctly and simultaneously. The results obtained indicate the success of this strategy in maximizing the chance of detecting a lead and making the right conclusions, which is of key importance given the speed of data generation of high‐throughput reactors.
Theory of Monte Carlo simulations of the magnetic circular dichroism spectra of alkali metal/rare gas systems
Authors: Kenney John W. , Boatz Jerry A. , Terrill Vosbein Heidi A. 
Published: March 2005

AbstractThe history of magnetic circular dichroism (MCD) spectroscopy in the study of alkali metal/rare gas (M/Rg) cryogenic systems is reviewed in the context of developing a better understanding of alkali metal/hydrogen systems of current interest to the U.S. Air Force as enhanced‐performance cryogenic rocket propellants. A new theory for simulating the MCD spectra of M/Rg systems is presented together with a careful discussion of the theory's implicit and explicit approximations and their implications. This theory uses a classical Monte Carlo (MC) simulation scheme to model the perturbing effects of the Rg environment on the 2S → 2P MCD‐active transition of the M atom. The theory sets up the MC–MCD simulation as a 6 × 6 matrix eigenvalue/eigenvector problem in the 2P manifold in which are included the effects of M–Rg interactions, metal atom spin‐orbit coupling in the 2P manifold, magnetic Zeeman perturbations of the 2S and 2P manifolds, Boltzmann temperature factors, and electric dipole transition moment integrals for left circularly polarized (LCP) and right circularly polarized (RCP) light. The theory may be applied to any type of trapping site of the host M in the guest Rg matrix; a single atom substitutional metal atom trapping site (one host Rg atom is replaced by one guest M atom) is modeled in this study for M = Na and Rg = Ar. Two temperature factors are used in these simulations; a lattice temperature to model the mobility of the Rg lattice and a magnetic temperature to model Boltzmann factors in the 2S ground manifold. The 6 × 6 eigenvalue/eigenvector problem is solved for a number of randomly generated and suitably averaged Rg configurations to yield the simulated MC‐MCD spectrum for the single substitutional Na/Ar system. The MC–MCD simulations of Na/Ar give the characteristic triplet MCD spectrum with the correct Boltzmann temperature dependence. The simulated MC–MCD spectrum correctly inverts when the direction of the applied magnetic field is reversed. Addition of the LCP and RCP absorbances gives rise to a characteristic 2S → 2P triplet absorption feature. © 2005 Wiley Periodicals, Inc. Int J Quantum Chem, 2005
Kernel energy method illustrated with peptides
Authors: Huang Lulu , Massa Lou , Karle Jerome 
Published: March 2005

AbstractWe describe a kernel energy method (KEM) for applying quantum crystallography to large molecules, with an emphasis on the calculation of the molecular energy of peptides. The computational difficulty of representing the system increases only modestly with the number of atoms. The calculations are carried out on modern parallel supercomputers. By adopting the approximation that a full biological molecule can be represented by smaller “kernels” of atoms, the calculations are greatly simplified. Moreover, collections of kernels are, from a computational point of view, well suited for parallel computation. The result is a modest increase in computational time as the number of atoms increases, while retaining the ab initio character of the calculations. We describe a test of our method, and establish its accuracy using 15 different peptides of biological interest. © 2005 Wiley Periodicals, Inc. Int J Quantum Chem, 2005
Are water–aromatic complexes always stabilized due to π–H interactions? LMP2 study
Authors: Reyes Abraham , Fomina Lioudmila , Rumsh Lev , Fomine Serguei 
Published: March 2005

AbstractEight complexes of various aromatic molecules with water have been studied theoretically at the local Møller–Plesset 2nd order theory (LMP2)/aug‐cc‐pVTZ(‐f)//LMP2/6‐31+G* level of theory. Two types of complexes can be formed, depending on the electronic structure of aromatic molecules. Donor hydrocarbons form A‐type complexes, while aromatics bearing electron‐withdrawing substituents form B‐type complexes. A‐type complexes are stabilized due to π–H interactions with the OH bond pointing to the aromatic molecule plane, while B‐type complexes have geometry with the oxygen atom pointing to the aromatic molecule plane stabilized by the interaction of highest occupied molecular orbital (HOMO) of water molecule with π* orbitals of the aromatics. It has been found that a (HOMO–lowest unoccupied molecular orbital (LUMO)/2 value of aromatic molecule, which can be called “molecular electronegativity,” is useful to predict the type of complex formed by aromatic molecule and water. Aromatic hydrocarbons with “molecular electronegativity” of <0.15 tend to form A‐type complexes, while aromatic molecules with “molecular electronegativity” of <0.15 a.u. form B‐type complexes. The binding energy of water–aromatic complexes undergoes a minimum in the area of switching from A‐type to B type complexes, which can be rationalize in terms of frontier orbital interactions. © 2005 Wiley Periodicals, Inc. Int J Quantum Chem, 2005
Optimizing dam release rules to meet environmental flow targets
Authors: Harman Ciaran , Stewardson Michael 
Published: March 2005

AbstractLinking environmental flow dam release rules to environmental variables has potential to link the flow variability of a regulated stream to the environmental forces acting on the catchment. A technique is developed to examine a range of release rules related to gauged tributary inflows and optimize these with respect to total water release volumes and other criteria. A model is created consisting of three parts: (1) a Dam Release Event Generator, which generates dam releases according to a set of rules, (2) a simple routing model using linear transforms and derived time‐series for ungauged tributary inflows, (3) an algorithm to analyse the output time‐series and produce statistics on the environmental flows. This model is run for a range of rule parameters within a parameter space. The results showed that multiple strategies might be capable of achieving the required targets but that some of these may be more robust in the face of climate variability than others. Significant water savings can result from the use of the technique. However, the results also highlight the limitations of the way environmental flows are currently stipulated and point towards a new generation of environmental flows. The technique is applied to environmental flow targets in the Thomson River catchment, Victoria, Australia. Copyright © 2005 John Wiley & Sons, Ltd.