Descriptor selection for banana accessions based on univariate and multivariate analysis.

PubMed

Brandão, L P; Souza, C P F; Pereira, V M; Silva, S O; Santos-Serejo, J A; Ledo, C A S; Amorim, E P

2013-05-14

Our objective was to establish a minimum number of morphological descriptors for the characterization of banana germplasm and evaluate the efficiency of removal of redundant characters, based on univariate and multivariate statistical analyses. Phenotypic characterization was made of 77 accessions from Bahia, Brazil, using 92 descriptors. The selection of the descriptors was carried out by principal components analysis (quantitative) and by entropy (multi-category). Efficiency of elimination was analyzed by a comparative study between the clusters formed, taking into consideration all 92 descriptors and smaller groups. The selected descriptors were analyzed with the Ward-MLM procedure and a combined matrix formed by the Gower algorithm. We were able to reduce the number of descriptors used for characterizing the banana germplasm (42%). The correlation between the matrices considering the 92 descriptors and the selected ones was 0.82, showing that the reduction in the number of descriptors did not influence estimation of genetic variability between the banana accessions. We conclude that removing these descriptors caused no loss of information, considering the groups formed from pre-established criteria, including subgroup/subspecies.

Design of an optimal preview controller for linear discrete-time descriptor systems with state delay

NASA Astrophysics Data System (ADS)

Cao, Mengjuan; Liao, Fucheng

2015-04-01

In this paper, the linear discrete-time descriptor system with state delay is studied, and a design method for an optimal preview controller is proposed. First, by using the discrete lifting technique, the original system is transformed into a general descriptor system without state delay in form. Then, taking advantage of the first-order forward difference operator, we construct a descriptor augmented error system, including the state vectors of the lifted system, error vectors, and desired target signals. Rigorous mathematical proofs are given for the regularity, stabilisability, causal controllability, and causal observability of the descriptor augmented error system. Based on these, the optimal preview controller with preview feedforward compensation for the original system is obtained by using the standard optimal regulator theory of the descriptor system. The effectiveness of the proposed method is shown by numerical simulation.

A physically interpretable quantum-theoretic QSAR for some carbonic anhydrase inhibitors with diverse aromatic rings, obtained by a new QSAR procedure.

PubMed

Clare, Brian W; Supuran, Claudiu T

2005-03-15

A QSAR based almost entirely on quantum theoretically calculated descriptors has been developed for a large and heterogeneous group of aromatic and heteroaromatic carbonic anhydrase inhibitors, using orbital energies, nodal angles, atomic charges, and some other intuitively appealing descriptors. Most calculations have been done at the B3LYP/6-31G* level of theory. For the first time we have treated five-membered rings by the same means that we have used for benzene rings in the past. Our flip regression technique has been expanded to encompass automatic variable selection. The statistical quality of the results, while not equal to those we have had with benzene derivatives, is very good considering the noncongeneric nature of the compounds. The most significant correlation was with charge on the atoms of the sulfonamide group, followed by the nodal orientation and the solvation energy calculated by COSMO and the charge polarization of the molecule calculated as the mean absolute Mulliken charge over all atoms.

A conceptual DFT study of the molecular properties of glycating carbonyl compounds.

PubMed

Frau, Juan; Glossman-Mitnik, Daniel

2017-01-01

Several glycating carbonyl compounds have been studied by resorting to the latest Minnesota family of density functional with the objective of determinating their molecular properties. In particular, the chemical reactivity descriptors that arise from conceptual density functional theory and chemical reactivity theory have been calculated through a [Formula: see text]SCF protocol. The validity of the KID (Koopmans' in DFT) procedure has been checked by comparing the reactivity descriptors obtained from the values of the HOMO and LUMO with those calculated through vertical energy values. The reactivity sites have been determined by means of the calculation of the Fukui function indices, the condensed dual descriptor [Formula: see text] and the electrophilic and nucleophilic Parr functions. The glycating power of the studied compounds have been compared with the same property for simple carbohydrates.Graphical abstractSeveral glycating carbonyl compounds have been studied by resorting to the latest Minnesota family of density functional with the objective of determinating their molecular properties, the chemical reactivity descriptors and the validity of the KID (Koopmans' in DFT) procedure.

Quantitative structure-retention relationship studies with immobilized artificial membrane chromatography II: partial least squares regression.

PubMed

Li, Jie; Sun, Jin; He, Zhonggui

2007-01-26

We aimed to establish quantitative structure-retention relationship (QSRR) with immobilized artificial membrane (IAM) chromatography using easily understood and obtained physicochemical molecular descriptors and to elucidate which descriptors are critical to affect the interaction process between solutes and immobilized phospholipid membranes. The retention indices (logk(IAM)) of 55 structurally diverse drugs were determined on an immobilized artificial membrane column (IAM.PC.DD2) directly or obtained by extrapolation method for highly hydrophobic compounds. Ten simple physicochemical property descriptors (clogP, rings, rotatory bond, hydro-bond counting, etc.) of these drugs were collected and used to establish QSRR and predict the retention data by partial least squares regression (PLSR). Five descriptors, clogP, rotatory bond (RotB), rings, molecular weight (MW) and total surface area (TSA), were reserved by using the Variable Importance for Projection (VIP) values as criterion to build the final PLSR model. An external test set was employed to verify the QSRR based on the training set with the five variables, and QSRR by PLSR exhibited a satisfying predictive ability with R(p)=0.902 and RMSE(p)=0.400. Comparison of coefficients of centered and scaled variables by PLSR demonstrated that, for the descriptors studied, clogP and TSA have the most significant positive effect but the rotatable bond has significant negative effect on drug IAM chromatographic retention.

A Molecular Electron Density Theory Study of the Chemical Reactivity of Cis- and Trans-Resveratrol.

PubMed

Frau, Juan; Muñoz, Francisco; Glossman-Mitnik, Daniel

2016-12-01

The chemical reactivity of resveratrol isomers with the potential to play a role as inhibitors of the nonenzymatic glycation of amino acids and proteins, both acting as antioxidants and as chelating agents for metallic ions such as Cu, Al and Fe, have been studied by resorting to the latest family of Minnesota density functionals. The chemical reactivity descriptors have been calculated through Molecular Electron Density Theory encompassing Conceptual DFT. The active sites for nucleophilic and electrophilic attacks have been chosen by relating them to the Fukui function indices, the dual descriptor f ( 2 ) ( r ) and the electrophilic and nucleophilic Parr functions. The validity of "Koopmans' theorem in DFT" has been assessed by means of a comparison between the descriptors calculated through vertical energy values and those arising from the HOMO and LUMO values.

The QSAR study of flavonoid-metal complexes scavenging rad OH free radical

NASA Astrophysics Data System (ADS)

Wang, Bo-chu; Qian, Jun-zhen; Fan, Ying; Tan, Jun

2014-10-01

Flavonoid-metal complexes have antioxidant activities. However, quantitative structure-activity relationships (QSAR) of flavonoid-metal complexes and their antioxidant activities has still not been tackled. On the basis of 21 structures of flavonoid-metal complexes and their antioxidant activities for scavenging rad OH free radical, we optimised their structures using Gaussian 03 software package and we subsequently calculated and chose 18 quantum chemistry descriptors such as dipole, charge and energy. Then we chose several quantum chemistry descriptors that are very important to the IC50 of flavonoid-metal complexes for scavenging rad OH free radical through method of stepwise linear regression, Meanwhile we obtained 4 new variables through the principal component analysis. Finally, we built the QSAR models based on those important quantum chemistry descriptors and the 4 new variables as the independent variables and the IC50 as the dependent variable using an Artificial Neural Network (ANN), and we validated the two models using experimental data. These results show that the two models in this paper are reliable and predictable.

Intrinsic movement variability at work. How long is the path from motor control to design engineering?

PubMed

Gaudez, C; Gilles, M A; Savin, J

2016-03-01

For several years, increasing numbers of studies have highlighted the existence of movement variability. Before that, it was neglected in movement analysis and it is still almost completely ignored in workstation design. This article reviews motor control theories and factors influencing movement execution, and indicates how intrinsic movement variability is part of task completion. These background clarifications should help ergonomists and workstation designers to gain a better understanding of these concepts, which can then be used to improve design tools. We also question which techniques--kinematics, kinetics or muscular activity--and descriptors are most appropriate for describing intrinsic movement variability and for integration into design tools. By this way, simulations generated by designers for workstation design should be closer to the real movements performed by workers. This review emphasises the complexity of identifying, describing and processing intrinsic movement variability in occupational activities. Copyright © 2015 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Food product design: emerging evidence for food policy.

PubMed

Al-Hamdani, Mohammed; Smith, Steven

2017-03-01

The research on the impact of specific brand elements such as food descriptors and package colors is underexplored. We tested whether a "light" color and a "low-calorie" descriptor on food packages gain favorable consumer perception ratings as compared with regular packages. Our online experiment recruited 406 adults in a 3 (product type: Chips versus Juice versus Yoghurt) × 2 (descriptor type: regular versus low-calorie) × 2 (color type: regular versus light) mixed design. Dependent variables were sensory (evaluations of the product's nutritional value and quality), product-based (evaluations of the product's physical appeal), and consumer-based (evaluations of the potential consumers of the product) scales. "Low-calorie" descriptors were found to increase sensory ratings as compared with regular descriptors and light-colored packages received higher product-based ratings as compared with their regular-colored counterparts. Food package color and descriptors present a promising venue for understanding preventative measures against obesity.[Formula: see text].

Lagrangian descriptors of driven chemical reaction manifolds.

PubMed

Craven, Galen T; Junginger, Andrej; Hernandez, Rigoberto

2017-08-01

The persistence of a transition state structure in systems driven by time-dependent environments allows the application of modern reaction rate theories to solution-phase and nonequilibrium chemical reactions. However, identifying this structure is problematic in driven systems and has been limited by theories built on series expansion about a saddle point. Recently, it has been shown that to obtain formally exact rates for reactions in thermal environments, a transition state trajectory must be constructed. Here, using optimized Lagrangian descriptors [G. T. Craven and R. Hernandez, Phys. Rev. Lett. 115, 148301 (2015)PRLTAO0031-900710.1103/PhysRevLett.115.148301], we obtain this so-called distinguished trajectory and the associated moving reaction manifolds on model energy surfaces subject to various driving and dissipative conditions. In particular, we demonstrate that this is exact for harmonic barriers in one dimension and this verification gives impetus to the application of Lagrangian descriptor-based methods in diverse classes of chemical reactions. The development of these objects is paramount in the theory of reaction dynamics as the transition state structure and its underlying network of manifolds directly dictate reactivity and selectivity.

Structure-Activity Relationships for Rates of Aromatic Amine Oxidation by Manganese Dioxide.

PubMed

Salter-Blanc, Alexandra J; Bylaska, Eric J; Lyon, Molly A; Ness, Stuart C; Tratnyek, Paul G

2016-05-17

New energetic compounds are designed to minimize their potential environmental impacts, which includes their transformation and the fate and effects of their transformation products. The nitro groups of energetic compounds are readily reduced to amines, and the resulting aromatic amines are subject to oxidation and coupling reactions. Manganese dioxide (MnO2) is a common environmental oxidant and model system for kinetic studies of aromatic amine oxidation. In this study, a training set of new and previously reported kinetic data for the oxidation of model and energetic-derived aromatic amines was assembled and subjected to correlation analysis against descriptor variables that ranged from general purpose [Hammett σ constants (σ(-)), pKas of the amines, and energies of the highest occupied molecular orbital (EHOMO)] to specific for the likely rate-limiting step [one-electron oxidation potentials (Eox)]. The selection of calculated descriptors (pKa, EHOMO, and Eox) was based on validation with experimental data. All of the correlations gave satisfactory quantitative structure-activity relationships (QSARs), but they improved with the specificity of the descriptor. The scope of correlation analysis was extended beyond MnO2 to include literature data on aromatic amine oxidation by other environmentally relevant oxidants (ozone, chlorine dioxide, and phosphate and carbonate radicals) by correlating relative rate constants (normalized to 4-chloroaniline) to EHOMO (calculated with a modest level of theory).

Fruit morphological descriptors as a tool for discrimination of Daucus L. germplasm

USDA-ARS?s Scientific Manuscript database

Morphological diversity of a Daucus L. germplasm collection maintained at the National Gene Bank of Tunisia was assessed using fourteen morphological descriptors related to mature fruits. Quantification of variability for each character was investigated using the standardized Shannon-Weaver Diversit...

Deconstructing field-induced ketene isomerization through Lagrangian descriptors.

PubMed

Craven, Galen T; Hernandez, Rigoberto

2016-02-07

The time-dependent geometrical separatrices governing state transitions in field-induced ketene isomerization are constructed using the method of Lagrangian descriptors. We obtain the stable and unstable manifolds of time-varying transition states as dynamic phase space objects governing configurational changes when the ketene molecule is subjected to an oscillating electric field. The dynamics of the isomerization reaction are modeled through classical trajectory studies on the Gezelter-Miller potential energy surface and an approximate dipole moment model which is coupled to a time-dependent electric field. We obtain a representation of the reaction geometry, over varying field strengths and oscillation frequencies, by partitioning an initial phase space into basins labeled according to which product state is reached at a given time. The borders between these basins are in agreement with those obtained using Lagrangian descriptors, even in regimes exhibiting chaotic dynamics. Major outcomes of this work are: validation and extension of a transition state theory framework built from Lagrangian descriptors, elaboration of the applicability for this theory to periodically- and aperiodically-driven molecular systems, and prediction of regimes in which isomerization of ketene and its derivatives may be controlled using an external field.

A Quantum Chemical and Statistical Study of Phenolic Schiff Bases with Antioxidant Activity against DPPH Free Radical

PubMed Central

Anouar, El Hassane

2014-01-01

Phenolic Schiff bases are known as powerful antioxidants. To select the electronic, 2D and 3D descriptors responsible for the free radical scavenging ability of a series of 30 phenolic Schiff bases, a set of molecular descriptors were calculated by using B3P86 (Becke’s three parameter hybrid functional with Perdew 86 correlation functional) combined with 6-31 + G(d,p) basis set (i.e., at the B3P86/6-31 + G(d,p) level of theory). The chemometric methods, simple and multiple linear regressions (SLR and MLR), principal component analysis (PCA) and hierarchical cluster analysis (HCA) were employed to reduce the dimensionality and to investigate the relationship between the calculated descriptors and the antioxidant activity. The results showed that the antioxidant activity mainly depends on the first and second bond dissociation enthalpies of phenolic hydroxyl groups, the dipole moment and the hydrophobicity descriptors. The antioxidant activity is inversely proportional to the main descriptors. The selected descriptors discriminate the Schiff bases into active and inactive antioxidants. PMID:26784873

A quantum chemical study of molecular properties and QSPR modeling of oximes, amidoximes and hydroxamic acids with nucleophilic activity against toxic organophosphorus agents

NASA Astrophysics Data System (ADS)

Alencar Filho, Edilson B.; Santos, Aline A.; Oliveira, Boaz G.

2017-04-01

The proposal of this work includes the use of quantum chemical methods and cheminformatics strategies in order to understand the structural profile and reactivity of α-nucleophiles compounds such as oximes, amidoximes and hydroxamic acids, related to hydrolysis rate of organophosphates. Theoretical conformational study of 41 compounds were carried out through the PM3 semiempirical Hamiltonian, followed by the geometry optimization at the B3LYP/6-31+G(d,p) level of theory, complemented by Polarized Continuum Model (PCM) to simulate the aqueous environment. In line with the experimental hypothesis about hydrolytic power, the strength of the Intramolecular Hydrogen Bonds (IHBs) at light of the Bader's Quantum Theory of Atoms in Molecules (QTAIM) is related to the preferential conformations of α-nucleophiles. A set of E-Dragon descriptors (1,666) were submitted to a variable selection through Ordered Predictor Selection (OPS) algorithm. Five descriptors, including atomic charges obtained from the Natural Bond Orbitals (NBO) protocol jointly with a fragment index associated to the presence/absence of IHBs, provided a Quantitative Structure-Property Relationship (QSPR) model via Multiple Linear Regression (MLR). This model showed good validation parameters (R2 = 0.80, Qloo2 = 0.67 and Qext2 = 0.81) and allowed the identification of significant physicochemical features on the molecular scaffold in order to design compounds potentially more active against organophosphorus poisoning.

Molecular descriptor subset selection in theoretical peptide quantitative structure-retention relationship model development using nature-inspired optimization algorithms.

PubMed

Žuvela, Petar; Liu, J Jay; Macur, Katarzyna; Bączek, Tomasz

2015-10-06

In this work, performance of five nature-inspired optimization algorithms, genetic algorithm (GA), particle swarm optimization (PSO), artificial bee colony (ABC), firefly algorithm (FA), and flower pollination algorithm (FPA), was compared in molecular descriptor selection for development of quantitative structure-retention relationship (QSRR) models for 83 peptides that originate from eight model proteins. The matrix with 423 descriptors was used as input, and QSRR models based on selected descriptors were built using partial least squares (PLS), whereas root mean square error of prediction (RMSEP) was used as a fitness function for their selection. Three performance criteria, prediction accuracy, computational cost, and the number of selected descriptors, were used to evaluate the developed QSRR models. The results show that all five variable selection methods outperform interval PLS (iPLS), sparse PLS (sPLS), and the full PLS model, whereas GA is superior because of its lowest computational cost and higher accuracy (RMSEP of 5.534%) with a smaller number of variables (nine descriptors). The GA-QSRR model was validated initially through Y-randomization. In addition, it was successfully validated with an external testing set out of 102 peptides originating from Bacillus subtilis proteomes (RMSEP of 22.030%). Its applicability domain was defined, from which it was evident that the developed GA-QSRR exhibited strong robustness. All the sources of the model's error were identified, thus allowing for further application of the developed methodology in proteomics.

Local chemical potential, local hardness, and dual descriptors in temperature dependent chemical reactivity theory.

PubMed

Franco-Pérez, Marco; Ayers, Paul W; Gázquez, José L; Vela, Alberto

2017-05-31

In this work we establish a new temperature dependent procedure within the grand canonical ensemble, to avoid the Dirac delta function exhibited by some of the second order chemical reactivity descriptors based on density functional theory, at a temperature of 0 K. Through the definition of a local chemical potential designed to integrate to the global temperature dependent electronic chemical potential, the local chemical hardness is expressed in terms of the derivative of this local chemical potential with respect to the average number of electrons. For the three-ground-states ensemble model, this local hardness contains a term that is equal to the one intuitively proposed by Meneses, Tiznado, Contreras and Fuentealba, which integrates to the global hardness given by the difference in the first ionization potential, I, and the electron affinity, A, at any temperature. However, in the present approach one finds an additional temperature-dependent term that introduces changes at the local level and integrates to zero. Additionally, a τ-hard dual descriptor and a τ-soft dual descriptor given in terms of the product of the global hardness and the global softness multiplied by the dual descriptor, respectively, are derived. Since all these reactivity indices are given by expressions composed of terms that correspond to products of the global properties multiplied by the electrophilic or nucleophilic Fukui functions, they may be useful for studying and comparing equivalent sites in different chemical environments.

Visual analytics in cheminformatics: user-supervised descriptor selection for QSAR methods.

PubMed

Martínez, María Jimena; Ponzoni, Ignacio; Díaz, Mónica F; Vazquez, Gustavo E; Soto, Axel J

2015-01-01

The design of QSAR/QSPR models is a challenging problem, where the selection of the most relevant descriptors constitutes a key step of the process. Several feature selection methods that address this step are concentrated on statistical associations among descriptors and target properties, whereas the chemical knowledge is left out of the analysis. For this reason, the interpretability and generality of the QSAR/QSPR models obtained by these feature selection methods are drastically affected. Therefore, an approach for integrating domain expert's knowledge in the selection process is needed for increase the confidence in the final set of descriptors. In this paper a software tool, which we named Visual and Interactive DEscriptor ANalysis (VIDEAN), that combines statistical methods with interactive visualizations for choosing a set of descriptors for predicting a target property is proposed. Domain expertise can be added to the feature selection process by means of an interactive visual exploration of data, and aided by statistical tools and metrics based on information theory. Coordinated visual representations are presented for capturing different relationships and interactions among descriptors, target properties and candidate subsets of descriptors. The competencies of the proposed software were assessed through different scenarios. These scenarios reveal how an expert can use this tool to choose one subset of descriptors from a group of candidate subsets or how to modify existing descriptor subsets and even incorporate new descriptors according to his or her own knowledge of the target property. The reported experiences showed the suitability of our software for selecting sets of descriptors with low cardinality, high interpretability, low redundancy and high statistical performance in a visual exploratory way. Therefore, it is possible to conclude that the resulting tool allows the integration of a chemist's expertise in the descriptor selection process with a low cognitive effort in contrast with the alternative of using an ad-hoc manual analysis of the selected descriptors. Graphical abstractVIDEAN allows the visual analysis of candidate subsets of descriptors for QSAR/QSPR. In the two panels on the top, users can interactively explore numerical correlations as well as co-occurrences in the candidate subsets through two interactive graphs.

Prediction of Radical Scavenging Activities of Anthocyanins Applying Adaptive Neuro-Fuzzy Inference System (ANFIS) with Quantum Chemical Descriptors

PubMed Central

Jhin, Changho; Hwang, Keum Taek

2014-01-01

Radical scavenging activity of anthocyanins is well known, but only a few studies have been conducted by quantum chemical approach. The adaptive neuro-fuzzy inference system (ANFIS) is an effective technique for solving problems with uncertainty. The purpose of this study was to construct and evaluate quantitative structure-activity relationship (QSAR) models for predicting radical scavenging activities of anthocyanins with good prediction efficiency. ANFIS-applied QSAR models were developed by using quantum chemical descriptors of anthocyanins calculated by semi-empirical PM6 and PM7 methods. Electron affinity (A) and electronegativity (χ) of flavylium cation, and ionization potential (I) of quinoidal base were significantly correlated with radical scavenging activities of anthocyanins. These descriptors were used as independent variables for QSAR models. ANFIS models with two triangular-shaped input fuzzy functions for each independent variable were constructed and optimized by 100 learning epochs. The constructed models using descriptors calculated by both PM6 and PM7 had good prediction efficiency with Q-square of 0.82 and 0.86, respectively. PMID:25153627

PyGlobal: A toolkit for automated compilation of DFT-based descriptors.

PubMed

Nath, Shilpa R; Kurup, Sudheer S; Joshi, Kaustubh A

2016-06-15

Density Functional Theory (DFT)-based Global reactivity descriptor calculations have emerged as powerful tools for studying the reactivity, selectivity, and stability of chemical and biological systems. A Python-based module, PyGlobal has been developed for systematically parsing a typical Gaussian outfile and extracting the relevant energies of the HOMO and LUMO. Corresponding global reactivity descriptors are further calculated and the data is saved into a spreadsheet compatible with applications like Microsoft Excel and LibreOffice. The efficiency of the module has been accounted by measuring the time interval for randomly selected Gaussian outfiles for 1000 molecules. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Chemical reactivity indices for the complete series of chlorinated benzenes: solvent effect.

PubMed

Padmanabhan, J; Parthasarathi, R; Subramanian, V; Chattaraj, P K

2006-03-02

We present a comprehensive analysis to probe the effect of solvation on the reactivity of the complete series of chlorobenzenes through the conceptual density functional theory (DFT)-based global and local descriptors. We propose a multiphilic descriptor in this study to explore the nature of attack at a particular site in a molecule. It is defined as the difference between nucleophilic and electrophilic condensed philicity functions. This descriptor is capable of explaining both the nucleophilicity and electrophilicity of the given atomic sites in the molecule simultaneously. The predictive ability of this descriptor is tested on the complete series of chlorobenzenes in gas and solvent media. A structure-toxicity analysis of these entire sets of chlorobenzenes toward aquatic organisms demonstrates the importance of the electrophilicity index in the prediction of the reactivity/toxicity.

Structure-Activity Relationships for Rates of Aromatic Amine Oxidation by Manganese Dioxide

DOE PAGES

Salter-Blanc, Alexandra J.; Bylaska, Eric J.; Lyon, Molly A.; ...

2016-04-13

New energetic compounds are designed to minimize their potential environmental impacts, which includes their transformation and the fate and effects of their transformation products. The nitro groups of energetic compounds are readily reduced to amines, and the resulting aromatic amines are subject to oxidation and coupling reactions. Manganese dioxide (MnO 2) is a common environmental oxidant and model system for kinetic studies of aromatic amine oxidation. Here in this study, a training set of new and previously reported kinetic data for the oxidation of model and energetic-derived aromatic amines was assembled and subjected to correlation analysis against descriptor variables that ranged from general purpose [Hammettmore » $$\\sigma$$ constants ($$\\sigma^-$$), pK as of the amines, and energies of the highest occupied molecular orbital (E HOMO)] to specific for the likely rate-limiting step [one-electron oxidation potentials (E ox)]. The selection of calculated descriptors (pK a), E HOMO, and E ox) was based on validation with experimental data. All of the correlations gave satisfactory quantitative structure-activity relationships (QSARs), but they improved with the specificity of the descriptor. The scope of correlation analysis was extended beyond MnO 2 to include literature data on aromatic amine oxidation by other environmentally relevant oxidants (ozone, chlorine dioxide, and phosphate and carbonate radicals) by correlating relative rate constants (normalized to 4-chloroaniline) to E HOMO (calculated with a modest level of theory).« less

Structure-Activity Relationships for Rates of Aromatic Amine Oxidation by Manganese Dioxide

DOE Office of Scientific and Technical Information (OSTI.GOV)

Salter-Blanc, Alexandra J.; Bylaska, Eric J.; Lyon, Molly A.

New energetic compounds are designed to minimize their potential environmental impacts, which includes their transformation and the fate and effects of their transformation products. The nitro groups of energetic compounds are readily reduced to amines, and the resulting aromatic amines are subject to oxidation and coupling reactions. Manganese dioxide (MnO 2) is a common environmental oxidant and model system for kinetic studies of aromatic amine oxidation. Here in this study, a training set of new and previously reported kinetic data for the oxidation of model and energetic-derived aromatic amines was assembled and subjected to correlation analysis against descriptor variables that ranged from general purpose [Hammettmore » $$\\sigma$$ constants ($$\\sigma^-$$), pK as of the amines, and energies of the highest occupied molecular orbital (E HOMO)] to specific for the likely rate-limiting step [one-electron oxidation potentials (E ox)]. The selection of calculated descriptors (pK a), E HOMO, and E ox) was based on validation with experimental data. All of the correlations gave satisfactory quantitative structure-activity relationships (QSARs), but they improved with the specificity of the descriptor. The scope of correlation analysis was extended beyond MnO 2 to include literature data on aromatic amine oxidation by other environmentally relevant oxidants (ozone, chlorine dioxide, and phosphate and carbonate radicals) by correlating relative rate constants (normalized to 4-chloroaniline) to E HOMO (calculated with a modest level of theory).« less

Calculation of the octanol-water partition coefficient of armchair polyhex BN nanotubes

NASA Astrophysics Data System (ADS)

Mohammadinasab, E.; Pérez-Sánchez, H.; Goodarzi, M.

2017-12-01

A predictive model for determination partition coefficient (log P) of armchair polyhex BN nanotubes by using simple descriptors was built. The relationship between the octanol-water log P and quantum chemical descriptors, electric moments, and topological indices of some armchair polyhex BN nanotubes with various lengths and fixed circumference are represented. Based on density functional theory electric moments and physico-chemical properties of those nanotubes are calculated.

Density functional theory and surface reactivity study of bimetallic AgnYm (n+m = 10) clusters

NASA Astrophysics Data System (ADS)

Hussain, Riaz; Hussain, Abdullah Ijaz; Chatha, Shahzad Ali Shahid; Hussain, Riaz; Hanif, Usman; Ayub, Khurshid

2018-06-01

Density functional theory calculations have been performed on pure silver (Agn), yttrium (Ym) and bimetallic silver yttrium clusters AgnYm (n + m = 2-10) for reactivity descriptors in order to realize sites for nucleophilic and electrophilic attack. The reactivity descriptors of the clusters, studied as a function of cluster size and shape, reveal the presence of different type of reactive sites in a cluster. The size and shape of the pure silver, yttrium and bimetallic silver yttrium cluster (n = 2-10) strongly influences the number and position of active sites for an electrophilic and/or nucleophilic attack. The trends of reactivities through reactivity descriptors are confirmed through comparison with experimental data for CO binding with silver clusters. Moreover, the adsorption of CO on bimetallic silver yttrium clusters is also evaluated. The trends of binding energies support the reactivity descriptors values. Doping of pure cluster with the other element also influence the hardness, softness and chemical reactivity of the clusters. The softness increases as we increase the number of silver atoms in the cluster, whereas the hardness decreases. The chemical reactivity increases with silver doping whereas it decreases by increasing yttrium concentration. Silver atoms are nucleophilic in small clusters but changed to electrophilic in large clusters.

High-throughput screening for thermoelectric sulphides by using crystal structure features as descriptors

NASA Astrophysics Data System (ADS)

Zhang, Ruizhi; Du, Baoli; Chen, Kan; Reece, Mike; Materials Research Insititute Team

With the increasing computational power and reliable databases, high-throughput screening is playing a more and more important role in the search of new thermoelectric materials. Rather than the well established density functional theory (DFT) calculation based methods, we propose an alternative approach to screen for new TE materials: using crystal structural features as 'descriptors'. We show that a non-distorted transition metal sulphide polyhedral network can be a good descriptor for high power factor according to crystal filed theory. By using Cu/S containing compounds as an example, 1600+ Cu/S containing entries in the Inorganic Crystal Structure Database (ICSD) were screened, and of those 84 phases are identified as promising thermoelectric materials. The screening results are validated by both electronic structure calculations and experimental results from the literature. We also fabricated some new compounds to test our screening results. Another advantage of using crystal structure features as descriptors is that we can easily establish structural relationships between the identified phases. Based on this, two material design approaches are discussed: 1) High-pressure synthesis of metastable phase; 2) In-situ 2-phase composites with coherent interface. This work was supported by a Marie Curie International Incoming Fellowship of the European Community Human Potential Program.

New Fukui, dual and hyper-dual kernels as bond reactivity descriptors.

PubMed

Franco-Pérez, Marco; Polanco-Ramírez, Carlos-A; Ayers, Paul W; Gázquez, José L; Vela, Alberto

2017-06-21

We define three new linear response indices with promising applications for bond reactivity using the mathematical framework of τ-CRT (finite temperature chemical reactivity theory). The τ-Fukui kernel is defined as the ratio between the fluctuations of the average electron density at two different points in the space and the fluctuations in the average electron number and is designed to integrate to the finite-temperature definition of the electronic Fukui function. When this kernel is condensed, it can be interpreted as a site-reactivity descriptor of the boundary region between two atoms. The τ-dual kernel corresponds to the first order response of the Fukui kernel and is designed to integrate to the finite temperature definition of the dual descriptor; it indicates the ambiphilic reactivity of a specific bond and enriches the traditional dual descriptor by allowing one to distinguish between the electron-accepting and electron-donating processes. Finally, the τ-hyper dual kernel is defined as the second-order derivative of the Fukui kernel and is proposed as a measure of the strength of ambiphilic bonding interactions. Although these quantities have never been proposed, our results for the τ-Fukui kernel and for τ-dual kernel can be derived in zero-temperature formulation of the chemical reactivity theory with, among other things, the widely-used parabolic interpolation model.

Molecular Descriptors

NASA Astrophysics Data System (ADS)

Consonni, Viviana; Todeschini, Roberto

In the last decades, several scientific researches have been focused on studying how to encompass and convert - by a theoretical pathway - the information encoded in the molecular structure into one or more numbers used to establish quantitative relationships between structures and properties, biological activities, or other experimental properties. Molecular descriptors are formally mathematical representations of a molecule obtained by a well-specified algorithm applied to a defined molecular representation or a well-specified experimental procedure. They play a fundamental role in chemistry, pharmaceutical sciences, environmental protection policy, toxicology, ecotoxicology, health research, and quality control. Evidence of the interest of the scientific community in the molecular descriptors is provided by the huge number of descriptors proposed up today: more than 5000 descriptors derived from different theories and approaches are defined in the literature and most of them can be calculated by means of dedicated software applications. Molecular descriptors are of outstanding importance in the research fields of quantitative structure-activity relationships (QSARs) and quantitative structure-property relationships (QSPRs), where they are the independent chemical information used to predict the properties of interest. Along with the definition of appropriate molecular descriptors, the molecular structure representation and the mathematical tools for deriving and assessing models are other fundamental components of the QSAR/QSPR approach. The remarkable progress during the last few years in chemometrics and chemoinformatics has led to new strategies for finding mathematical meaningful relationships between the molecular structure and biological activities, physico-chemical, toxicological, and environmental properties of chemicals. Different approaches for deriving molecular descriptors here reviewed and some of the most relevant descriptors are presented in detail with numerical examples.

Bio-activity of aminosulfonyl ureas in the light of nucleic acid bases and DNA base pair interaction.

PubMed

Mondal Roy, Sutapa

2018-08-01

The quantum chemical descriptors based on density functional theory (DFT) are applied to predict the biological activity (log IC 50 ) of one class of acyl-CoA: cholesterol O-acyltransferase (ACAT) inhibitors, viz. aminosulfonyl ureas. ACAT are very effective agents for reduction of triglyceride and cholesterol levels in human body. Successful two parameter quantitative structure-activity relationship (QSAR) models are developed with a combination of relevant global and local DFT based descriptors for prediction of biological activity of aminosulfonyl ureas. The global descriptors, electron affinity of the ACAT inhibitors (EA) and/or charge transfer (ΔN) between inhibitors and model biosystems (NA bases and DNA base pairs) along with the local group atomic charge on sulfonyl moiety (∑Q Sul ) of the inhibitors reveals more than 90% efficacy of the selected descriptors for predicting the experimental log (IC 50 ) values. Copyright © 2018 Elsevier Ltd. All rights reserved.

A climate index indicative of cloudiness derived from satellite infrared sounder data

NASA Technical Reports Server (NTRS)

Abel, M. D.; Cox, S. K.

1981-01-01

In many current studies conducted to enhance the usefulness of meteorological satellite radiance data, one common objective is to infer conventional weather variables. The present investigation, on the other hand, is mainly concerned with the efficient retrieval (minimization of errors) of a nonstandard atmospheric descriptor. The atmosphere's Vertical Infrared Radiative Emitting Structure (VIRES) is retrieved. VIRES is described by the broadband infrared weighting function curve. The shapes of these weighting curves are primarily a function of the three-dimensional cloud structure. The weighting curves are retrieved by a method which uses satellite spectral radiance data. The basic theory involved in the VIRES retrieval procedure parallels the technique used to retrieve temperature soundings.

Gold-standard for computer-assisted morphological sperm analysis.

PubMed

Chang, Violeta; Garcia, Alejandra; Hitschfeld, Nancy; Härtel, Steffen

2017-04-01

Published algorithms for classification of human sperm heads are based on relatively small image databases that are not open to the public, and thus no direct comparison is available for competing methods. We describe a gold-standard for morphological sperm analysis (SCIAN-MorphoSpermGS), a dataset of sperm head images with expert-classification labels in one of the following classes: normal, tapered, pyriform, small or amorphous. This gold-standard is for evaluating and comparing known techniques and future improvements to present approaches for classification of human sperm heads for semen analysis. Although this paper does not provide a computational tool for morphological sperm analysis, we present a set of experiments for comparing sperm head description and classification common techniques. This classification base-line is aimed to be used as a reference for future improvements to present approaches for human sperm head classification. The gold-standard provides a label for each sperm head, which is achieved by majority voting among experts. The classification base-line compares four supervised learning methods (1- Nearest Neighbor, naive Bayes, decision trees and Support Vector Machine (SVM)) and three shape-based descriptors (Hu moments, Zernike moments and Fourier descriptors), reporting the accuracy and the true positive rate for each experiment. We used Fleiss' Kappa Coefficient to evaluate the inter-expert agreement and Fisher's exact test for inter-expert variability and statistical significant differences between descriptors and learning techniques. Our results confirm the high degree of inter-expert variability in the morphological sperm analysis. Regarding the classification base line, we show that none of the standard descriptors or classification approaches is best suitable for tackling the problem of sperm head classification. We discovered that the correct classification rate was highly variable when trying to discriminate among non-normal sperm heads. By using the Fourier descriptor and SVM, we achieved the best mean correct classification: only 49%. We conclude that the SCIAN-MorphoSpermGS will provide a standard tool for evaluation of characterization and classification approaches for human sperm heads. Indeed, there is a clear need for a specific shape-based descriptor for human sperm heads and a specific classification approach to tackle the problem of high variability within subcategories of abnormal sperm cells. Copyright © 2017 Elsevier Ltd. All rights reserved.

A promising tool to achieve chemical accuracy for density functional theory calculations on Y-NO homolysis bond dissociation energies.

PubMed

Li, Hong Zhi; Hu, Li Hong; Tao, Wei; Gao, Ting; Li, Hui; Lu, Ying Hua; Su, Zhong Min

2012-01-01

A DFT-SOFM-RBFNN method is proposed to improve the accuracy of DFT calculations on Y-NO (Y = C, N, O, S) homolysis bond dissociation energies (BDE) by combining density functional theory (DFT) and artificial intelligence/machine learning methods, which consist of self-organizing feature mapping neural networks (SOFMNN) and radial basis function neural networks (RBFNN). A descriptor refinement step including SOFMNN clustering analysis and correlation analysis is implemented. The SOFMNN clustering analysis is applied to classify descriptors, and the representative descriptors in the groups are selected as neural network inputs according to their closeness to the experimental values through correlation analysis. Redundant descriptors and intuitively biased choices of descriptors can be avoided by this newly introduced step. Using RBFNN calculation with the selected descriptors, chemical accuracy (≤1 kcal·mol(-1)) is achieved for all 92 calculated organic Y-NO homolysis BDE calculated by DFT-B3LYP, and the mean absolute deviations (MADs) of the B3LYP/6-31G(d) and B3LYP/STO-3G methods are reduced from 4.45 and 10.53 kcal·mol(-1) to 0.15 and 0.18 kcal·mol(-1), respectively. The improved results for the minimal basis set STO-3G reach the same accuracy as those of 6-31G(d), and thus B3LYP calculation with the minimal basis set is recommended to be used for minimizing the computational cost and to expand the applications to large molecular systems. Further extrapolation tests are performed with six molecules (two containing Si-NO bonds and two containing fluorine), and the accuracy of the tests was within 1 kcal·mol(-1). This study shows that DFT-SOFM-RBFNN is an efficient and highly accurate method for Y-NO homolysis BDE. The method may be used as a tool to design new NO carrier molecules.

A Promising Tool to Achieve Chemical Accuracy for Density Functional Theory Calculations on Y-NO Homolysis Bond Dissociation Energies

PubMed Central

Li, Hong Zhi; Hu, Li Hong; Tao, Wei; Gao, Ting; Li, Hui; Lu, Ying Hua; Su, Zhong Min

2012-01-01

A DFT-SOFM-RBFNN method is proposed to improve the accuracy of DFT calculations on Y-NO (Y = C, N, O, S) homolysis bond dissociation energies (BDE) by combining density functional theory (DFT) and artificial intelligence/machine learning methods, which consist of self-organizing feature mapping neural networks (SOFMNN) and radial basis function neural networks (RBFNN). A descriptor refinement step including SOFMNN clustering analysis and correlation analysis is implemented. The SOFMNN clustering analysis is applied to classify descriptors, and the representative descriptors in the groups are selected as neural network inputs according to their closeness to the experimental values through correlation analysis. Redundant descriptors and intuitively biased choices of descriptors can be avoided by this newly introduced step. Using RBFNN calculation with the selected descriptors, chemical accuracy (≤1 kcal·mol−1) is achieved for all 92 calculated organic Y-NO homolysis BDE calculated by DFT-B3LYP, and the mean absolute deviations (MADs) of the B3LYP/6-31G(d) and B3LYP/STO-3G methods are reduced from 4.45 and 10.53 kcal·mol−1 to 0.15 and 0.18 kcal·mol−1, respectively. The improved results for the minimal basis set STO-3G reach the same accuracy as those of 6-31G(d), and thus B3LYP calculation with the minimal basis set is recommended to be used for minimizing the computational cost and to expand the applications to large molecular systems. Further extrapolation tests are performed with six molecules (two containing Si-NO bonds and two containing fluorine), and the accuracy of the tests was within 1 kcal·mol−1. This study shows that DFT-SOFM-RBFNN is an efficient and highly accurate method for Y-NO homolysis BDE. The method may be used as a tool to design new NO carrier molecules. PMID:22942689

Density functional theory fragment descriptors to quantify the reactivity of a molecular family: application to amino acids.

PubMed

Senet, P; Aparicio, F

2007-04-14

By using the exact density functional theory, one demonstrates that the value of the local electronic softness of a molecular fragment is directly related to the polarization charge (Coulomb hole) induced by a test electron removed (or added) from (at) the fragment. Our finding generalizes to a chemical group a formal relation between these molecular descriptors recently obtained for an atom in a molecule using an approximate atomistic model [P. Senet and M. Yang, J. Chem. Sci. 117, 411 (2005)]. In addition, a practical ab initio computational scheme of the Coulomb hole and related local descriptors of reactivity of a molecular family having in common a similar fragment is presented. As a blind test, the method is applied to the lateral chains of the 20 isolated amino acids. One demonstrates that the local softness of the lateral chain is a quantitative measure of the similarity of the amino acids. It predicts the separation of amino acids in different biochemical groups (aliphatic, basic, acidic, sulfur contained, and aromatic). The present approach may find applications in quantitative structure activity relationship methodology.

QSPR models for half-wave reduction potential of steroids: a comparative study between feature selection and feature extraction from subsets of or entire set of descriptors.

PubMed

Hemmateenejad, Bahram; Yazdani, Mahdieh

2009-02-16

Steroids are widely distributed in nature and are found in plants, animals, and fungi in abundance. A data set consists of a diverse set of steroids have been used to develop quantitative structure-electrochemistry relationship (QSER) models for their half-wave reduction potential. Modeling was established by means of multiple linear regression (MLR) and principle component regression (PCR) analyses. In MLR analysis, the QSPR models were constructed by first grouping descriptors and then stepwise selection of variables from each group (MLR1) and stepwise selection of predictor variables from the pool of all calculated descriptors (MLR2). Similar procedure was used in PCR analysis so that the principal components (or features) were extracted from different group of descriptors (PCR1) and from entire set of descriptors (PCR2). The resulted models were evaluated using cross-validation, chance correlation, application to prediction reduction potential of some test samples and accessing applicability domain. Both MLR approaches represented accurate results however the QSPR model found by MLR1 was statistically more significant. PCR1 approach produced a model as accurate as MLR approaches whereas less accurate results were obtained by PCR2 approach. In overall, the correlation coefficients of cross-validation and prediction of the QSPR models resulted from MLR1, MLR2 and PCR1 approaches were higher than 90%, which show the high ability of the models to predict reduction potential of the studied steroids.

Computational modeling of high performance steel fiber reinforced concrete using a micromorphic approach

NASA Astrophysics Data System (ADS)

Huespe, A. E.; Oliver, J.; Mora, D. F.

2013-12-01

A finite element methodology for simulating the failure of high performance fiber reinforced concrete composites (HPFRC), with arbitrarily oriented short fibers, is presented. The composite material model is based on a micromorphic approach. Using the framework provided by this theory, the body configuration space is described through two kinematical descriptors. At the structural level, the displacement field represents the standard kinematical descriptor. Additionally, a morphological kinematical descriptor, the micromorphic field, is introduced. It describes the fiber-matrix relative displacement, or slipping mechanism of the bond, observed at the mesoscale level. In the first part of this paper, we summarize the model formulation of the micromorphic approach presented in a previous work by the authors. In the second part, and as the main contribution of the paper, we address specific issues related to the numerical aspects involved in the computational implementation of the model. The developed numerical procedure is based on a mixed finite element technique. The number of dofs per node changes according with the number of fiber bundles simulated in the composite. Then, a specific solution scheme is proposed to solve the variable number of unknowns in the discrete model. The HPFRC composite model takes into account the important effects produced by concrete fracture. A procedure for simulating quasi-brittle fracture is introduced into the model and is described in the paper. The present numerical methodology is assessed by simulating a selected set of experimental tests which proves its viability and accuracy to capture a number of mechanical phenomenon interacting at the macro- and mesoscale and leading to failure of HPFRC composites.

Theories of quantum dissipation and nonlinear coupling bath descriptors

NASA Astrophysics Data System (ADS)

Xu, Rui-Xue; Liu, Yang; Zhang, Hou-Dao; Yan, YiJing

2018-03-01

The quest of an exact and nonperturbative treatment of quantum dissipation in nonlinear coupling environments remains in general an intractable task. In this work, we address the key issues toward the solutions to the lowest nonlinear environment, a harmonic bath coupled both linearly and quadratically with an arbitrary system. To determine the bath coupling descriptors, we propose a physical mapping scheme, together with the prescription reference invariance requirement. We then adopt a recently developed dissipaton equation of motion theory [R. X. Xu et al., Chin. J. Chem. Phys. 30, 395 (2017)], with the underlying statistical quasi-particle ("dissipaton") algebra being extended to the quadratic bath coupling. We report the numerical results on a two-level system dynamics and absorption and emission line shapes.

Web-4D-QSAR: A web-based application to generate 4D-QSAR descriptors.

PubMed

Ataide Martins, João Paulo; Rougeth de Oliveira, Marco Antônio; Oliveira de Queiroz, Mário Sérgio

2018-06-05

A web-based application is developed to generate 4D-QSAR descriptors using the LQTA-QSAR methodology, based on molecular dynamics (MD) trajectories and topology information retrieved from the GROMACS package. The LQTAGrid module calculates the intermolecular interaction energies at each grid point, considering probes and all aligned conformations resulting from MD simulations. These interaction energies are the independent variables or descriptors employed in a QSAR analysis. A friendly front end web interface, built using the Django framework and Python programming language, integrates all steps of the LQTA-QSAR methodology in a way that is transparent to the user, and in the backend, GROMACS and LQTAGrid are executed to generate 4D-QSAR descriptors to be used later in the process of QSAR model building. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.

Positive bias is a defining characteristic of aging to the same extent as declining performance.

PubMed

Simón, Teresa; Suengas, Aurora G; Ruiz-Gallego-Largo, Trinidad; Bandrés, Javier

2013-01-01

The aim of this study was to analyze whether one of the supposed gains of aging--positive bias--discriminates between young and older participants to the same extent as some of the losses in cognitive performance--recall and source monitoring--that come with age. Two age groups (N = 120)--young (M = 22.08, SD = 3.30) and older (M = 72.78, SD = 6.57)--carried out three tasks with varying levels of difficulty that included recall, recognition, and source monitoring using pictures, faces, and personal descriptors exchanged in a conversation as stimuli. The results of the discriminant analysis performed on 20 dependent variables indicated that six of them were key in discriminating between young and older participants. Younger participants outperformed older participants in recalling pictures, and in recognizing the descriptors exchanged in a conversation, as well as in monitoring their source. Just as important in discriminating between the two groups were the ability to recognize previously seen pictures, the likability rating they produced, and the recognition of faces with positive expressions--all superior in older participants. Thus, variables related to a positive bias--likability ratings and recognition of positive expressions--characterize the differences as a function of age as well as variables related to cognitive performance, such as recall and source monitoring. In addition, the likability ratings evoked by both pictures and faces were also significantly higher in the older participants with better cognitive performance than in those who performed poorly. This effect was not present in younger participants. The results are interpreted within the framework of socioemotional selectivity theory as evidence for a positive bias in old age. The connection between a positive bias and the maintenance of cognitive performance is also discussed.

A new texture descriptor based on local micro-pattern for detection of architectural distortion in mammographic images

NASA Astrophysics Data System (ADS)

de Oliveira, Helder C. R.; Moraes, Diego R.; Reche, Gustavo A.; Borges, Lucas R.; Catani, Juliana H.; de Barros, Nestor; Melo, Carlos F. E.; Gonzaga, Adilson; Vieira, Marcelo A. C.

2017-03-01

This paper presents a new local micro-pattern texture descriptor for the detection of Architectural Distortion (AD) in digital mammography images. AD is a subtle contraction of breast parenchyma that may represent an early sign of breast cancer. Due to its subtlety and variability, AD is more difficult to detect compared to microcalcifications and masses, and is commonly found in retrospective evaluations of false-negative mammograms. Several computer-based systems have been proposed for automatic detection of AD, but their performance are still unsatisfactory. The proposed descriptor, Local Mapped Pattern (LMP), is a generalization of the Local Binary Pattern (LBP), which is considered one of the most powerful feature descriptor for texture classification in digital images. Compared to LBP, the LMP descriptor captures more effectively the minor differences between the local image pixels. Moreover, LMP is a parametric model which can be optimized for the desired application. In our work, the LMP performance was compared to the LBP and four Haralick's texture descriptors for the classification of 400 regions of interest (ROIs) extracted from clinical mammograms. ROIs were selected and divided into four classes: AD, normal tissue, microcalcifications and masses. Feature vectors were used as input to a multilayer perceptron neural network, with a single hidden layer. Results showed that LMP is a good descriptor to distinguish AD from other anomalies in digital mammography. LMP performance was slightly better than the LBP and comparable to Haralick's descriptors (mean classification accuracy = 83%).

Grounded theory in music therapy research.

PubMed

O'Callaghan, Clare

2012-01-01

Grounded theory is one of the most common methodologies used in constructivist (qualitative) music therapy research. Researchers use the term "grounded theory" when denoting varying research designs and theoretical outcomes. This may be challenging for novice researchers when considering whether grounded theory is appropriate for their research phenomena. This paper examines grounded theory within music therapy research. Grounded theory is briefly described, including some of its "contested" ideas. A literature search was conducted using the descriptor "music therapy and grounded theory" in Pubmed, CINAHL PsychlNFO, SCOPUS, ERIC (CSA), Web of Science databases, and a music therapy monograph series. A descriptive analysis was performed on the uncovered studies to examine researched phenomena, grounded theory methods used, and how findings were presented, Thirty music therapy research projects were found in refereed journals and monographs from 1993 to "in press." The Strauss and Corbin approach to grounded theory dominates the field. Descriptors to signify grounded theory components in the studies greatly varied. Researchers have used partial or complete grounded theory methods to examine clients', family members', staff, music therapy "overhearers," music therapists', and students' experiences, as well as music therapy creative products and professional views, issues, and literature. Seven grounded theories were offered. It is suggested that grounded theory researchers clarify what and who inspired their design, why partial grounded theory methods were used (when relevant), and their ontology. By elucidating assumptions underpinning the data collection, analysis, and findings' contribution, researchers will continue to improve music therapy research using grounded theory methods.

Landmark-free statistical analysis of the shape of plant leaves.

PubMed

Laga, Hamid; Kurtek, Sebastian; Srivastava, Anuj; Miklavcic, Stanley J

2014-12-21

The shapes of plant leaves are important features to biologists, as they can help in distinguishing plant species, measuring their health, analyzing their growth patterns, and understanding relations between various species. Most of the methods that have been developed in the past focus on comparing the shape of individual leaves using either descriptors or finite sets of landmarks. However, descriptor-based representations are not invertible and thus it is often hard to map descriptor variability into shape variability. On the other hand, landmark-based techniques require automatic detection and registration of the landmarks, which is very challenging in the case of plant leaves that exhibit high variability within and across species. In this paper, we propose a statistical model based on the Squared Root Velocity Function (SRVF) representation and the Riemannian elastic metric of Srivastava et al. (2011) to model the observed continuous variability in the shape of plant leaves. We treat plant species as random variables on a non-linear shape manifold and thus statistical summaries, such as means and covariances, can be computed. One can then study the principal modes of variations and characterize the observed shapes using probability density models, such as Gaussians or Mixture of Gaussians. We demonstrate the usage of such statistical model for (1) efficient classification of individual leaves, (2) the exploration of the space of plant leaf shapes, which is important in the study of population-specific variations, and (3) comparing entire plant species, which is fundamental to the study of evolutionary relationships in plants. Our approach does not require descriptors or landmarks but automatically solves for the optimal registration that aligns a pair of shapes. We evaluate the performance of the proposed framework on publicly available benchmarks such as the Flavia, the Swedish, and the ImageCLEF2011 plant leaf datasets. Copyright © 2014 Elsevier Ltd. All rights reserved.

3D Riesz-wavelet based Covariance descriptors for texture classification of lung nodule tissue in CT.

PubMed

Cirujeda, Pol; Muller, Henning; Rubin, Daniel; Aguilera, Todd A; Loo, Billy W; Diehn, Maximilian; Binefa, Xavier; Depeursinge, Adrien

2015-01-01

In this paper we present a novel technique for characterizing and classifying 3D textured volumes belonging to different lung tissue types in 3D CT images. We build a volume-based 3D descriptor, robust to changes of size, rigid spatial transformations and texture variability, thanks to the integration of Riesz-wavelet features within a Covariance-based descriptor formulation. 3D Riesz features characterize the morphology of tissue density due to their response to changes in intensity in CT images. These features are encoded in a Covariance-based descriptor formulation: this provides a compact and flexible representation thanks to the use of feature variations rather than dense features themselves and adds robustness to spatial changes. Furthermore, the particular symmetric definite positive matrix form of these descriptors causes them to lay in a Riemannian manifold. Thus, descriptors can be compared with analytical measures, and accurate techniques from machine learning and clustering can be adapted to their spatial domain. Additionally we present a classification model following a "Bag of Covariance Descriptors" paradigm in order to distinguish three different nodule tissue types in CT: solid, ground-glass opacity, and healthy lung. The method is evaluated on top of an acquired dataset of 95 patients with manually delineated ground truth by radiation oncology specialists in 3D, and quantitative sensitivity and specificity values are presented.

Prediction of blood-brain partitioning: a model based on molecular electronegativity distance vector descriptors.

PubMed

Zhang, Yong-Hong; Xia, Zhi-Ning; Qin, Li-Tang; Liu, Shu-Shen

2010-09-01

The objective of this paper is to build a reliable model based on the molecular electronegativity distance vector (MEDV) descriptors for predicting the blood-brain barrier (BBB) permeability and to reveal the effects of the molecular structural segments on the BBB permeability. Using 70 structurally diverse compounds, the partial least squares regression (PLSR) models between the BBB permeability and the MEDV descriptors were developed and validated by the variable selection and modeling based on prediction (VSMP) technique. The estimation ability, stability, and predictive power of a model are evaluated by the estimated correlation coefficient (r), leave-one-out (LOO) cross-validation correlation coefficient (q), and predictive correlation coefficient (R(p)). It has been found that PLSR model has good quality, r=0.9202, q=0.7956, and R(p)=0.6649 for M1 model based on the training set of 57 samples. To search the most important structural factors affecting the BBB permeability of compounds, we performed the values of the variable importance in projection (VIP) analysis for MEDV descriptors. It was found that some structural fragments in compounds, such as -CH(3), -CH(2)-, =CH-, =C, triple bond C-, -CH<, =C<, =N-, -NH-, =O, and -OH, are the most important factors affecting the BBB permeability. (c) 2010. Published by Elsevier Inc.

Prediction on the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase based on gene expression programming.

PubMed

Li, Yuqin; You, Guirong; Jia, Baoxiu; Si, Hongzong; Yao, Xiaojun

2014-01-01

Quantitative structure-activity relationships (QSAR) were developed to predict the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase via heuristic method (HM) and gene expression programming (GEP). The descriptors of 33 pyrrolidine derivatives were calculated by the software CODESSA, which can calculate quantum chemical, topological, geometrical, constitutional, and electrostatic descriptors. HM was also used for the preselection of 5 appropriate molecular descriptors. Linear and nonlinear QSAR models were developed based on the HM and GEP separately and two prediction models lead to a good correlation coefficient (R (2)) of 0.93 and 0.94. The two QSAR models are useful in predicting the inhibition ratio of pyrrolidine derivatives on matrix metalloproteinase during the discovery of new anticancer drugs and providing theory information for studying the new drugs.

Genetic divergence among accessions of melon from traditional agriculture of the Brazilian Northeast.

PubMed

Aragão, F A S; Torres Filho, J; Nunes, G H S; Queiróz, M A; Bordallo, P N; Buso, G S C; Ferreira, M A; Costa, Z P; Bezerra Neto, F

2013-12-06

The genetic divergence of 38 melon accessions from traditional agriculture of the Brazilian Northeast and three commercial hybrids were evaluated using fruit descriptors and microsatellite markers. The melon germplasm belongs to the botanic varieties cantalupensis (19), momordica (7), conomon (4), and inodorus (3), and to eight genotypes that were identified only at the species level. The fruit descriptors evaluated were: number of fruits per plant (NPF), fruit mass (FM; kg), fruit longitudinal diameter (LD; cm), fruit transversal diameter (TD; cm), shape index based on the LD/TD ratio, flesh pulp thickness, cavity thickness (CT; cm), firmness fruit pulp (N), and soluble solids (SS; °Brix). The results showed high variability for all descriptors, especially for NPF, LD, and FM. The grouping analysis based on fruit descriptors produced eight groups without taxonomic criteria. The LD (22.52%), NPF (19.70%), CT (16.13%), and SS (9.57%) characteristics were the descriptors that contributed the most to genotype dissimilarity. The 17 simple sequence repeat polymorphic markers amplified 41 alleles with an average of 2.41 alleles and three genotypes per locus. Some markers presented a high frequency for the main allele. The genetic diversity ranged from 0.07 to 0.60, the observed heterozygosity had very low values, and the mean polymorphism information content was 0.32. Molecular genetic similarity analyses clustered the accessions in 13 groups, also not following taxonomic ranks. There was no association between morphoagronomic and molecular groupings. In conclusion, there was great variability among the accessions and among and within botanic groups.

Foot morphometric phenomena.

PubMed

Agić, Ante

2007-06-01

Knowledge of the foot morphometry is important for proper foot structure and function. Foot structure as a vital part of human body is important for many reasons. The foot anthropometric and morphology phenomena are analyzed together with hidden biomechanical descriptors in order to fully characterize foot functionality. For Croatian student population the scatter data of the individual foot variables were interpolated by multivariate statistics. Foot morphometric descriptors are influenced by many factors, such as life style, climate, and things of great importance in human society. Dominant descriptors related to fit and comfort are determined by the use 3D foot shape and advanced foot biomechanics. Some practical recommendations and conclusions for medical, sportswear and footwear practice are highlighted.

Effectiveness and consistency of a suite of descriptors for assessing the ecological status of seagrass meadows (Posidonia oceanica L. Delile)

NASA Astrophysics Data System (ADS)

Rotini, Alice; Belmonte, Alessandro; Barrote, Isabel; Micheli, Carla; Peirano, Andrea; Santos, Rui O.; Silva, João; Migliore, Luciana

2013-09-01

The increasing rate of human-induced environmental changes on coastal marine ecosystems has created a demand for effective descriptors, in particular for those suitable for monitoring the status of seagrass meadows. Growing evidence has supported the useful application of biochemical and genetic descriptors such as secondary metabolite synthesis, photosynthetic activity and genetic diversity. In the present study, we have investigated the effectiveness of different descriptors (traditional, biochemical and genetic) in monitoring seagrass meadow conservation status. The Posidonia oceanica meadow of Monterosso al Mare (Ligurian sea, NW Mediterranean) was subjected to the measurement of bed density, leaf biometry, total phenols, soluble protein and photosynthetic pigment content as well as to RAPD marker analysis. This suite of descriptors provided evidence of their effectiveness and convenient application as markers of the conservation status of P. oceanica and/or other seagrasses. Biochemical/genetic descriptors and those obtained by traditional methods depicted a well conserved meadow with seasonal variability and, particularly in summer, indicated a healthier condition in a portion of the bed (station C), which was in agreement with the physical and sedimentological features of the station. Our results support the usefulness of introducing biochemical and genetic approaches to seagrass monitoring programs since they are effective indicators of plant physiological stress and environmental disturbance.

Genetic variability in Brazilian Capsicum baccatum germplasm collection assessed by morphological fruit traits and AFLP markers

PubMed Central

Giacomin, Renata M.; Ruas, Paulo M.; Ruas, Eduardo A.; Barbieri, Rosa L.; Rodrigues, Rosana

2018-01-01

Capsicum baccatum is one of the main pepper species grown and consumed in South America. In Brazil, it is commonly cultivated by family farmers, using mostly the genotypes bishop's hat genotypes (locally cambuci) and red chili pepper (dedo-de-moça). This study had the objective of characterizing 116 C. baccatum accessions from different regions of Brazil, based on morphological fruit descriptors and AFLP (Amplified Fragment Length Polymorphisms) markers. Broad phenotypic variability among the C. baccatum accessions was detected when using morphological fruit descriptors. The Ward modified location model (Ward-MLM) discriminated five groups, based mainly on fruit shape. Six combinations of AFLP primers detected polymorphism in 97.93% of the 2466 identified bands, indicating the high genetic variability in the accessions. The UPGMA coincided with the Bayesian clustering analysis and three large groups were formed, separating the wild variety C. baccatum var. praetermissum from the other accessions. There was no relation between genetic distance and geographical origin of the accessions, probably due to the intense exchange of fruits and seeds between farmers. Morphological descriptors used together with AFLP markers proved efficient in detecting the levels of genetic variability among the accessions maintained in the germplasm collections. These results can be used as an additional source of helpful information to be exploited in C. baccatum breeding programs. PMID:29758023

A participatory approach to the study of lifting demands and musculoskeletal symptoms among Hong Kong workers

PubMed Central

Yeung, S; Genaidy, A; Deddens, J; Shoaf, C; Leung, P

2003-01-01

Aims: To investigate the use of a worker based methodology to assess the physical stresses of lifting tasks on effort expended, and to associate this loading with musculoskeletal outcomes (MO). Methods: A cross sectional study was conducted on 217 male manual handling workers from the Hong Kong area. The effects of four lifting variables (weight of load, horizontal distance, twisting angle, and vertical travel distance) on effort were examined using a linguistic approach (that is, characterising variables in descriptors such as "heavy" for weight of load). The numerical interpretations of linguistic descriptors were established. In addition, the associations between on the job effort and MO were investigated for 10 body regions including the spine, and both upper and lower extremities. Results: MO were prevalent in multiple body regions (range 12–58%); effort was significantly associated with MO in 8 of 10 body regions (odds ratios with age adjusted ranged from 1.31 for low back to 1.71 for elbows and forearm). The lifting task variables had significant effects on effort, with the weight of load having twice the effect of other variables; each linguistic descriptor was better described by a range of numerical values rather than a single numerical value. Conclusions: The participatory worker based approach on musculoskeletal outcomes is a promising methodology. Further testing of this approach is recommended. PMID:14504360

Neural network approach to quantum-chemistry data: accurate prediction of density functional theory energies.

PubMed

Balabin, Roman M; Lomakina, Ekaterina I

2009-08-21

Artificial neural network (ANN) approach has been applied to estimate the density functional theory (DFT) energy with large basis set using lower-level energy values and molecular descriptors. A total of 208 different molecules were used for the ANN training, cross validation, and testing by applying BLYP, B3LYP, and BMK density functionals. Hartree-Fock results were reported for comparison. Furthermore, constitutional molecular descriptor (CD) and quantum-chemical molecular descriptor (QD) were used for building the calibration model. The neural network structure optimization, leading to four to five hidden neurons, was also carried out. The usage of several low-level energy values was found to greatly reduce the prediction error. An expected error, mean absolute deviation, for ANN approximation to DFT energies was 0.6+/-0.2 kcal mol(-1). In addition, the comparison of the different density functionals with the basis sets and the comparison of multiple linear regression results were also provided. The CDs were found to overcome limitation of the QD. Furthermore, the effective ANN model for DFT/6-311G(3df,3pd) and DFT/6-311G(2df,2pd) energy estimation was developed, and the benchmark results were provided.

Unveiling the nature of post-linear response Z-vector method for time-dependent density functional theory.

PubMed

Pastore, Mariachiara; Assfeld, Xavier; Mosconi, Edoardo; Monari, Antonio; Etienne, Thibaud

2017-07-14

We report a theoretical study on the analysis of the relaxed one-particle difference density matrix characterizing the passage from the ground to the excited state of a molecular system, as obtained from time-dependent density functional theory. In particular, this work aims at using the physics contained in the so-called Z-vector, which differentiates between unrelaxed and relaxed difference density matrices to analyze excited states' nature. For this purpose, we introduce novel quantum-mechanical quantities, based on the detachment/attachment methodology, for analysing the Z-vector transformation for different molecules and density functional theory functionals. A derivation pathway of these novel descriptors is reported, involving a numerical integration to be performed in the Euclidean space on the density functions. This topological analysis is then applied to two sets of chromophores, and the correlation between the level of theory and the behavior of our descriptors is properly rationalized. In particular, the effect of range-separation on the relaxation amplitude is discussed. The relaxation term is finally shown to be system-specific (for a given level of theory) and independent of the number of electrons (i.e., the relaxation amplitude is not simply the result of a collective phenomenon).

Developing an instrument to assess the endoscopic severity of ulcerative colitis: the Ulcerative Colitis Endoscopic Index of Severity (UCEIS).

PubMed

Travis, Simon P L; Schnell, Dan; Krzeski, Piotr; Abreu, Maria T; Altman, Douglas G; Colombel, Jean-Frédéric; Feagan, Brian G; Hanauer, Stephen B; Lémann, Marc; Lichtenstein, Gary R; Marteau, Phillippe R; Reinisch, Walter; Sands, Bruce E; Yacyshyn, Bruce R; Bernhardt, Christian A; Mary, Jean-Yves; Sandborn, William J

2012-04-01

Variability in endoscopic assessment necessitates rigorous investigation of descriptors for scoring severity of ulcerative colitis (UC). To evaluate variation in the overall endoscopic assessment of severity, the intra- and interindividual variation of descriptive terms and to create an Ulcerative Colitis Endoscopic Index of Severity which could be validated. A two-phase study used a library of 670 video sigmoidoscopies from patients with Mayo Clinic scores 0-11, supplemented by 10 videos from five people without UC and five hospitalised patients with acute severe UC. In phase 1, each of 10 investigators viewed 16/24 videos to assess agreement on the Baron score with a central reader and agreed definitions of 10 endoscopic descriptors. In phase 2, each of 30 different investigators rated 25/60 different videos for the descriptors and assessed overall severity on a 0-100 visual analogue scale. κ Statistics tested inter- and intraobserver variability for each descriptor. A general linear mixed regression model based on logit link and β distribution of variance was used to predict overall endoscopic severity from descriptors. There was 76% agreement for 'severe', but 27% agreement for 'normal' appearances between phase I investigators and the central reader. In phase 2, weighted κ values ranged from 0.34 to 0.65 and 0.30 to 0.45 within and between observers for the 10 descriptors. The final model incorporated vascular pattern, (normal/patchy/complete obliteration) bleeding (none/mucosal/luminal mild/luminal moderate or severe), erosions and ulcers (none/erosions/superficial/deep), each with precise definitions, which explained 90% of the variance (pR(2), Akaike Information Criterion) in the overall assessment of endoscopic severity, predictions varying from 4 to 93 on a 100-point scale (from normal to worst endoscopic severity). The Ulcerative Colitis Endoscopic Index of Severity accurately predicts overall assessment of endoscopic severity of UC. Validity and responsiveness need further testing before it can be applied as an outcome measure in clinical trials or clinical practice.

Role of physicochemical properties in the activation of peroxisome proliferator-activated receptor δ.

PubMed

Maltarollo, Vinícius G; Homem-de-Mello, Paula; Honorio, Káthia M

2011-10-01

Current researches on treatments for metabolic diseases involve a class of biological receptors called peroxisome proliferator-activated receptors (PPARs), which control the metabolism of carbohydrates and lipids. A subclass of these receptors, PPARδ, regulates several metabolic processes, and the substances that activate them are being studied as new drug candidates for the treatment of diabetes mellitus and metabolic syndrome. In this study, several PPARδ agonists with experimental biological activity were selected for a structural and chemical study. Electronic, stereochemical, lipophilic and topological descriptors were calculated for the selected compounds using various theoretical methods, such as density functional theory (DFT). Fisher's weight and principal components analysis (PCA) methods were employed to select the most relevant variables for this study. The partial least squares (PLS) method was used to construct the multivariate statistical model, and the best model obtained had 4 PCs, q ( 2 ) = 0.80 and r ( 2 ) = 0.90, indicating a good internal consistency. The prediction residues calculated for the compounds in the test set had low values, indicating the good predictive capability of our PLS model. The model obtained in this study is reliable and can be used to predict the biological activity of new untested compounds. Docking studies have also confirmed the importance of the molecular descriptors selected for this system.

Characterization of Mixtures. Part 2: QSPR Models for Prediction of Excess Molar Volume and Liquid Density Using Neural Networks.

PubMed

Ajmani, Subhash; Rogers, Stephen C; Barley, Mark H; Burgess, Andrew N; Livingstone, David J

2010-09-17

In our earlier work, we have demonstrated that it is possible to characterize binary mixtures using single component descriptors by applying various mixing rules. We also showed that these methods were successful in building predictive QSPR models to study various mixture properties of interest. Here in, we developed a QSPR model of an excess thermodynamic property of binary mixtures i.e. excess molar volume (V(E) ). In the present study, we use a set of mixture descriptors which we earlier designed to specifically account for intermolecular interactions between the components of a mixture and applied successfully to the prediction of infinite-dilution activity coefficients using neural networks (part 1 of this series). We obtain a significant QSPR model for the prediction of excess molar volume (V(E) ) using consensus neural networks and five mixture descriptors. We find that hydrogen bond and thermodynamic descriptors are the most important in determining excess molar volume (V(E) ), which is in line with the theory of intermolecular forces governing excess mixture properties. The results also suggest that the mixture descriptors utilized herein may be sufficient to model a wide variety of properties of binary and possibly even more complex mixtures. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Description of 3D digital curves using the theory free groups

NASA Astrophysics Data System (ADS)

Imiya, Atsushi; Oosawa, Muneaki

1999-09-01

In this paper, we propose a new descriptor for two- and three- dimensional digital curves using the theory of free groups. A spatial digital curve is expressed as a word which is an element of the free group which consists from three elements. These three symbols correspond to the directions of the orthogonal coordinates, respectively. Since a digital curve is treated as a word which is a sequence of alphabetical symbols, this expression permits us to describe any geometric operation as rewriting rules for words. Furthermore, the symbolic derivative of words yields geometric invariants of digital curves for digital Euclidean motion. These invariants enable us to design algorithms for the matching and searching procedures of partial structures of digital curves. Moreover, these symbolic descriptors define the global and local distances for digital curves as an editing distance.

Bias-Free Chemically Diverse Test Sets from Machine Learning.

PubMed

Swann, Ellen T; Fernandez, Michael; Coote, Michelle L; Barnard, Amanda S

2017-08-14

Current benchmarking methods in quantum chemistry rely on databases that are built using a chemist's intuition. It is not fully understood how diverse or representative these databases truly are. Multivariate statistical techniques like archetypal analysis and K-means clustering have previously been used to summarize large sets of nanoparticles however molecules are more diverse and not as easily characterized by descriptors. In this work, we compare three sets of descriptors based on the one-, two-, and three-dimensional structure of a molecule. Using data from the NIST Computational Chemistry Comparison and Benchmark Database and machine learning techniques, we demonstrate the functional relationship between these structural descriptors and the electronic energy of molecules. Archetypes and prototypes found with topological or Coulomb matrix descriptors can be used to identify smaller, statistically significant test sets that better capture the diversity of chemical space. We apply this same method to find a diverse subset of organic molecules to demonstrate how the methods can easily be reapplied to individual research projects. Finally, we use our bias-free test sets to assess the performance of density functional theory and quantum Monte Carlo methods.

Inductive generalization with familiar categories: developmental changes in children's reliance on perceptual similarity and kind information

PubMed Central

Godwin, Karrie E.; Fisher, Anna V.

2015-01-01

Inductive generalization is ubiquitous in human cognition; however, the factors underpinning this ability early in development remain contested. The present study was designed to (1) test the predictions of the naïve theory and a similarity-based account and (2) examine the mechanism by which labels promote induction. In Experiment 1, 3- to 5-year-old children made inferences about highly familiar categories. The results were not fully consistent with either theoretical account. In contrast to the predictions of the naïve theory approach, the youngest children in the study did not ignore perceptually compelling lures in favor of category-match items; in contrast to the predictions of the similarity-based account, no group of participants favored perceptually compelling lures in the presence of dissimilar-looking category-match items. In Experiment 2 we investigated the mechanisms by which labels promote induction by examining the influence of different label types, namely category labels (e.g., the target and category-match both labeled as bird) and descriptor labels (e.g., the target and the perceptual lure both labeled as brown) on induction performance. In contrast to the predictions of the naïve theory approach, descriptor labels but not category labels affected induction in 3-year-old children. Consistent with the predictions of the similarity-based account, descriptor labels affected the performance of children in all age groups included in the study. The implications of these findings for the developmental account of induction are discussed. PMID:26217254

Inductive generalization with familiar categories: developmental changes in children's reliance on perceptual similarity and kind information.

PubMed

Godwin, Karrie E; Fisher, Anna V

2015-01-01

Inductive generalization is ubiquitous in human cognition; however, the factors underpinning this ability early in development remain contested. The present study was designed to (1) test the predictions of the naïve theory and a similarity-based account and (2) examine the mechanism by which labels promote induction. In Experiment 1, 3- to 5-year-old children made inferences about highly familiar categories. The results were not fully consistent with either theoretical account. In contrast to the predictions of the naïve theory approach, the youngest children in the study did not ignore perceptually compelling lures in favor of category-match items; in contrast to the predictions of the similarity-based account, no group of participants favored perceptually compelling lures in the presence of dissimilar-looking category-match items. In Experiment 2 we investigated the mechanisms by which labels promote induction by examining the influence of different label types, namely category labels (e.g., the target and category-match both labeled as bird) and descriptor labels (e.g., the target and the perceptual lure both labeled as brown) on induction performance. In contrast to the predictions of the naïve theory approach, descriptor labels but not category labels affected induction in 3-year-old children. Consistent with the predictions of the similarity-based account, descriptor labels affected the performance of children in all age groups included in the study. The implications of these findings for the developmental account of induction are discussed.

Dissecting the space-time structure of tree-ring datasets using the partial triadic analysis.

PubMed

Rossi, Jean-Pierre; Nardin, Maxime; Godefroid, Martin; Ruiz-Diaz, Manuela; Sergent, Anne-Sophie; Martinez-Meier, Alejandro; Pâques, Luc; Rozenberg, Philippe

2014-01-01

Tree-ring datasets are used in a variety of circumstances, including archeology, climatology, forest ecology, and wood technology. These data are based on microdensity profiles and consist of a set of tree-ring descriptors, such as ring width or early/latewood density, measured for a set of individual trees. Because successive rings correspond to successive years, the resulting dataset is a ring variables × trees × time datacube. Multivariate statistical analyses, such as principal component analysis, have been widely used for extracting worthwhile information from ring datasets, but they typically address two-way matrices, such as ring variables × trees or ring variables × time. Here, we explore the potential of the partial triadic analysis (PTA), a multivariate method dedicated to the analysis of three-way datasets, to apprehend the space-time structure of tree-ring datasets. We analyzed a set of 11 tree-ring descriptors measured in 149 georeferenced individuals of European larch (Larix decidua Miller) during the period of 1967-2007. The processing of densitometry profiles led to a set of ring descriptors for each tree and for each year from 1967-2007. The resulting three-way data table was subjected to two distinct analyses in order to explore i) the temporal evolution of spatial structures and ii) the spatial structure of temporal dynamics. We report the presence of a spatial structure common to the different years, highlighting the inter-individual variability of the ring descriptors at the stand scale. We found a temporal trajectory common to the trees that could be separated into a high and low frequency signal, corresponding to inter-annual variations possibly related to defoliation events and a long-term trend possibly related to climate change. We conclude that PTA is a powerful tool to unravel and hierarchize the different sources of variation within tree-ring datasets.

Prediction of chromatographic relative retention time of polychlorinated biphenyls from the molecular electronegativity distance vector.

PubMed

Liu, Shu-Shen; Liu, Yan; Yin, Da-Qian; Wang, Xiao-Dong; Wang, Lian-Sheng

2006-02-01

Using the molecular electronegativity distance vector (MEDV) descriptors derived directly from the molecular topological structures, the gas chromatographic relative retention times (RRTs) of 209 polychlorinated biphenyls (PCBs) on the SE-54 stationary phase were predicted. A five-variable regression equation with the correlation coefficient of 0.9964 and the root mean square errors of 0.0152 was developed. The descriptors included in the equation represent degree of chlorination (nCl), nonortho index (Ino), and interactions between three pairs of atom types, i.e., atom groups -C= and -C=, -C= and >C=, -C= and -Cl. It has been proved that the retention times of all 209 PCB congeners can be accurately predicted as long as there are more than 50 calibration compounds. In the same way, the MEDV descriptors are also used to develop the five- or six-variable models of RRTs of PCBs on other 18 stationary phases and the correlation coefficients in both modeling stage and LOO cross-validation step are not lower than 0.99 except two models.

Application of a methodology for categorizing and differentiating urban soundscapes using acoustical descriptors and semantic-differential attributes.

PubMed

Torija, Antonio J; Ruiz, Diego P; Ramos-Ridao, A F

2013-07-01

A subjective and physical categorization of an ambient sound is the first step to evaluate the soundscape and provides a basis for designing or adapting this ambient sound to match people's expectations. For this reason, the main goal of this work is to develop a categorization and differentiation analysis of soundscapes on the basis of acoustical and perceptual variables. A hierarchical cluster analysis, using 15 semantic-differential attributes and acoustical descriptors to include an equivalent sound-pressure level, maximum-minimum sound-pressure level, impulsiveness of the sound-pressure level, sound-pressure level time course, and spectral composition, was conducted to classify soundscapes into different typologies. This analysis identified 15 different soundscape typologies. Furthermore, based on a discriminant analysis the acoustical descriptors, the crest factor (impulsiveness of the sound-pressure level), and the sound level at 125 Hz were found to be the acoustical variables with the highest impact in the differentiation of the recognized types of soundscapes. Finally, to determine how the different soundscape typologies differed from each other, both subjectively and acoustically, a study was performed.

Quantitative structure-property relationship analysis for the retention index of fragrance-like compounds on a polar stationary phase.

PubMed

Rojas, Cristian; Duchowicz, Pablo R; Tripaldi, Piercosimo; Pis Diez, Reinaldo

2015-11-27

A quantitative structure-property relationship (QSPR) was developed for modeling the retention index of 1184 flavor and fragrance compounds measured using a Carbowax 20M glass capillary gas chromatography column. The 4885 molecular descriptors were calculated using Dragon software, and then were simultaneously analyzed through multivariable linear regression analysis using the replacement method (RM) variable subset selection technique. We proceeded in three steps, the first one by considering all descriptor blocks, the second one by excluding conformational descriptor blocks, and the last one by analyzing only 3D-descriptor families. The models were validated through an external test set of compounds. Cross-validation methods such as leave-one-out and leave-many-out were applied, together with Y-randomization and applicability domain analysis. The developed model was used to estimate the I of a set of 22 molecules. The results clearly suggest that 3D-descriptors do not offer relevant information for modeling the retention index, while a topological index such as the Randić-like index from reciprocal squared distance matrix has a high relevance for this purpose. Copyright © 2015 Elsevier B.V. All rights reserved.

Magnostics: Image-Based Search of Interesting Matrix Views for Guided Network Exploration.

PubMed

Behrisch, Michael; Bach, Benjamin; Hund, Michael; Delz, Michael; Von Ruden, Laura; Fekete, Jean-Daniel; Schreck, Tobias

2017-01-01

In this work we address the problem of retrieving potentially interesting matrix views to support the exploration of networks. We introduce Matrix Diagnostics (or Magnostics), following in spirit related approaches for rating and ranking other visualization techniques, such as Scagnostics for scatter plots. Our approach ranks matrix views according to the appearance of specific visual patterns, such as blocks and lines, indicating the existence of topological motifs in the data, such as clusters, bi-graphs, or central nodes. Magnostics can be used to analyze, query, or search for visually similar matrices in large collections, or to assess the quality of matrix reordering algorithms. While many feature descriptors for image analyzes exist, there is no evidence how they perform for detecting patterns in matrices. In order to make an informed choice of feature descriptors for matrix diagnostics, we evaluate 30 feature descriptors-27 existing ones and three new descriptors that we designed specifically for MAGNOSTICS-with respect to four criteria: pattern response, pattern variability, pattern sensibility, and pattern discrimination. We conclude with an informed set of six descriptors as most appropriate for Magnostics and demonstrate their application in two scenarios; exploring a large collection of matrices and analyzing temporal networks.

Molecular and agronomic analysis of intraspecific variability in Capsicum baccatum var. pendulum accessions.

PubMed

Leite, P S S; Rodrigues, R; Silva, R N O; Pimenta, S; Medeiros, A M; Bento, C S; Gonçalves, L S A

2016-10-05

Capsicum baccatum is one of the most important chili peppers in South America, since this region is considered to be the center of origin and diversity of this species. In Brazil, C. baccatum has been widely explored by family farmers and there are different local names for each fruit phenotype, such as cambuci and dedo-de-moça (lady's finger). Although very popular among farmers and consumers, C. baccatum has been less extensively studied than other Capsicum species. This study describes the phenotypic and genotypic variability in C. baccatum var. pendulum accessions. Twenty-nine accessions from the Universidade Estadual do Norte Fluminense Darcy Ribeiro gene bank, and one commercial genotype ('BRS-Mari') were evaluated for 53 morphoagronomic descriptors (31 qualitative and 22 quantitative traits). In addition, accessions were genotyped using 30 microsatellite primers. Three accessions from the C. annuum complex were included in the molecular characterization. Nine of 31 qualitative descriptors were monomorphic, while all quantitative descriptors were highly significant different between accessions (P < 0.01). Using the unweighted pair group method using arithmetic averages, four groups were obtained based on multicategoric variables and five groups were obtained based on quantitative variables. In the genotyping analysis, 12 polymorphic simple sequence repeat primers amplified in C. baccatum with dissimilarity between accessions ranging from 0.13 to 0.91, permitting the formation of two distinct groups for Bayesian analysis. These results indicate wide variability among the accessions comparing phenotypic and genotypic data and revealed distinct patterns of dissimilarity between matrices, indicating that both steps are valuable for the characterization of C. baccatum var. pendulum accessions.

Sensitivity of temporal heart rate variability in Poincaré plot to changes in parasympathetic nervous system activity.

PubMed

Karmakar, Chandan K; Khandoker, Ahsan H; Voss, Andreas; Palaniswami, Marimuthu

2011-03-03

A novel descriptor (Complex Correlation Measure (CCM)) for measuring the variability in the temporal structure of Poincaré plot has been developed to characterize or distinguish between Poincaré plots with similar shapes. This study was designed to assess the changes in temporal structure of the Poincaré plot using CCM during atropine infusion, 70° head-up tilt and scopolamine administration in healthy human subjects. CCM quantifies the point-to-point variation of the signal rather than gross description of the Poincaré plot. The physiological relevance of CCM was demonstrated by comparing the changes in CCM values with autonomic perturbation during all phases of the experiment. The sensitivities of short term variability (SD1), long term variability (SD2) and variability in temporal structure (CCM) were analyzed by changing the temporal structure by shuffling the sequences of points of the Poincaré plot. Surrogate analysis was used to show CCM as a measure of changes in temporal structure rather than random noise and sensitivity of CCM with changes in parasympathetic activity. CCM was found to be most sensitive to changes in temporal structure of the Poincaré plot as compared to SD1 and SD2. The values of all descriptors decreased with decrease in parasympathetic activity during atropine infusion and 70° head-up tilt phase. In contrast, values of all descriptors increased with increase in parasympathetic activity during scopolamine administration. The concordant reduction and enhancement in CCM values with parasympathetic activity indicates that the temporal variability of Poincaré plot is modulated by the parasympathetic activity which correlates with changes in CCM values. CCM is more sensitive than SD1 and SD2 to changes of parasympathetic activity.

Combining multiple features for color texture classification

NASA Astrophysics Data System (ADS)

Cusano, Claudio; Napoletano, Paolo; Schettini, Raimondo

2016-11-01

The analysis of color and texture has a long history in image analysis and computer vision. These two properties are often considered as independent, even though they are strongly related in images of natural objects and materials. Correlation between color and texture information is especially relevant in the case of variable illumination, a condition that has a crucial impact on the effectiveness of most visual descriptors. We propose an ensemble of hand-crafted image descriptors designed to capture different aspects of color textures. We show that the use of these descriptors in a multiple classifiers framework makes it possible to achieve a very high classification accuracy in classifying texture images acquired under different lighting conditions. A powerful alternative to hand-crafted descriptors is represented by features obtained with deep learning methods. We also show how the proposed combining strategy hand-crafted and convolutional neural networks features can be used together to further improve the classification accuracy. Experimental results on a food database (raw food texture) demonstrate the effectiveness of the proposed strategy.

Prediction of Mutagenicity of Chemicals from Their Calculated Molecular Descriptors: A Case Study with Structurally Homogeneous versus Diverse Datasets.

PubMed

Basak, Subhash C; Majumdar, Subhabrata

2015-01-01

Variation in high-dimensional data is often caused by a few latent factors, and hence dimension reduction or variable selection techniques are often useful in gathering useful information from the data. In this paper we consider two such recent methods: Interrelated two-way clustering and envelope models. We couple these methods with traditional statistical procedures like ridge regression and linear discriminant analysis, and apply them on two data sets which have more predictors than samples (i.e. n < p scenario) and several types of molecular descriptors. One of these datasets consists of a congeneric group of Amines while the other has a much diverse collection compounds. The difference of prediction results between these two datasets for both the methods supports the hypothesis that for a congeneric set of compounds, descriptors of a certain type are enough to provide good QSAR models, but as the data set grows diverse including a variety of descriptors can improve model quality considerably.

Shape model of the maxillary dental arch using Fourier descriptors with an application in the rehabilitation for edentulous patient.

PubMed

Rijal, Omar M; Abdullah, Norli A; Isa, Zakiah M; Noor, Norliza M; Tawfiq, Omar F

2013-01-01

The knowledge of teeth positions on the maxillary arch is useful in the rehabilitation of the edentulous patient. A combination of angular (θ), and linear (l) variables representing position of four teeth were initially proposed as the shape descriptor of the maxillary dental arch. Three categories of shape were established, each having a multivariate normal distribution. It may be argued that 4 selected teeth on the standardized digital images of the dental casts could be considered as insufficient with respect to representing shape. However, increasing the number of points would create problems with dimensions and proof of existence of the multivariate normal distribution is extremely difficult. This study investigates the ability of Fourier descriptors (FD) using all maxillary teeth to find alternative shape models. Eight FD terms were sufficient to represent 21 points on the arch. Using these 8 FD terms as an alternative shape descriptor, three categories of shape were verified, each category having the complex normal distribution.

Contemporary group estimates adjusted for climatic effects provide a finer definition of the unknown environmental challenges experienced by growing pigs.

PubMed

Guy, S Z Y; Li, L; Thomson, P C; Hermesch, S

2017-12-01

Environmental descriptors derived from mean performances of contemporary groups (CGs) are assumed to capture any known and unknown environmental challenges. The objective of this paper was to obtain a finer definition of the unknown challenges, by adjusting CG estimates for the known climatic effects of monthly maximum air temperature (MaxT), minimum air temperature (MinT) and monthly rainfall (Rain). As the unknown component could include infection challenges, these refined descriptors may help to better model varying responses of sire progeny to environmental infection challenges for the definition of disease resilience. Data were recorded from 1999 to 2013 at a piggery in south-east Queensland, Australia (n = 31,230). Firstly, CG estimates of average daily gain (ADG) and backfat (BF) were adjusted for MaxT, MinT and Rain, which were fitted as splines. In the models used to derive CG estimates for ADG, MaxT and MinT were significant variables. The models that contained these significant climatic variables had CG estimates with a lower variance compared to models without significant climatic variables. Variance component estimates were similar across all models, suggesting that these significant climatic variables accounted for some known environmental variation captured in CG estimates. No climatic variables were significant in the models used to derive the CG estimates for BF. These CG estimates were used to categorize environments. There was no observable sire by environment interaction (Sire×E) for ADG when using the environmental descriptors based on CG estimates on BF. For the environmental descriptors based on CG estimates of ADG, there was significant Sire×E only when MinT was included in the model (p = .01). Therefore, this new definition of the environment, preadjusted by MinT, increased the ability to detect Sire×E. While the unknown challenges captured in refined CG estimates need verification for infection challenges, this may provide a practical approach for the genetic improvement of disease resilience. © 2017 Blackwell Verlag GmbH.

Dr. Mom Project.

ERIC Educational Resources Information Center

Selke, Mary J.; Collins, Martha D.

This study explored what impact mothers having doctorates and being professors has on their children. A survey of 55 women professors with doctoral degrees was conducted, examining number of children, age and marital status, variables related to doctoral degrees, institutional variables, rank and tenure status, position descriptors and related…

Probabilistic modeling of anatomical variability using a low dimensional parameterization of diffeomorphisms.

PubMed

Zhang, Miaomiao; Wells, William M; Golland, Polina

2017-10-01

We present an efficient probabilistic model of anatomical variability in a linear space of initial velocities of diffeomorphic transformations and demonstrate its benefits in clinical studies of brain anatomy. To overcome the computational challenges of the high dimensional deformation-based descriptors, we develop a latent variable model for principal geodesic analysis (PGA) based on a low dimensional shape descriptor that effectively captures the intrinsic variability in a population. We define a novel shape prior that explicitly represents principal modes as a multivariate complex Gaussian distribution on the initial velocities in a bandlimited space. We demonstrate the performance of our model on a set of 3D brain MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our model yields a more compact representation of group variation at substantially lower computational cost than the state-of-the-art method such as tangent space PCA (TPCA) and probabilistic principal geodesic analysis (PPGA) that operate in the high dimensional image space. Copyright © 2017 Elsevier B.V. All rights reserved.

Pain Quality Descriptors in Community-Dwelling Older Adults with Nonmalignant Pain

PubMed Central

Thakral, Manu; Shi, Ling; Foust, Janice B.; Patel, Kushang V.; Shmerling, Robert H.; Bean, Jonathan F.; Leveille, Suzanne G.

2016-01-01

This study aimed to characterize the prevalence of various pain qualities in older adults with chronic non-malignant pain and determine the association of pain quality to other pain characteristics namely: severity, interference distribution, and pain-associated conditions. In the population-based MOBILIZE Boston Study, 560 participants aged≥70 years reported chronic pain in the baseline assessment, which included a home interview and clinic exam. Pain quality was assessed using a modified version of the McGill Pain Questionnaire (MPQ) consisting of 20 descriptors, from which 3 categories were derived: cognitive/affective, sensory and neuropathic. Presence of ≥2 pain-associated conditions was significantly associated with 18 of the 20 pain quality descriptors. Sensory descriptors were endorsed by nearly all older adults with chronic pain (93%), followed by cognitive/affective (83.4%) and neuropathic descriptors (68.6%). Neuropathic descriptors were associated with the greatest number of pain-associated conditions including osteoarthritis of the hand and knee. More than half of participants (59%) endorsed descriptors in all 3 categories and had more severe pain and interference, and multi-site or widespread pain than those endorsing 1 or 2 categories. Strong associations were observed between pain quality and measures of pain severity, interference, and distribution (p<.0001). Findings from this study indicate that older adults have multiple pain-associated conditions which likely reflect multiple physiological mechanisms for pain. Linking pain qualities with other associated pain characteristics serves to develop a multidimensional approach to geriatric pain assessment. Future research is needed to investigate the physiological mechanisms responsible for the variability in pain qualities endorsed by older adults. PMID:27842050

Pain quality descriptors in community-dwelling older adults with nonmalignant pain.

PubMed

Thakral, Manu; Shi, Ling; Foust, Janice B; Patel, Kushang V; Shmerling, Robert H; Bean, Jonathan F; Leveille, Suzanne G

2016-12-01

This study aimed to characterize the prevalence of various pain qualities in older adults with chronic nonmalignant pain and determine the association of pain quality to other pain characteristics namely: severity, interference, distribution, and pain-associated conditions. In the population-based MOBILIZE Boston Study, 560 participants aged ≥70 years reported chronic pain in the baseline assessment, which included a home interview and clinic exam. Pain quality was assessed using a modified version of the McGill Pain Questionnaire (MPQ) consisting of 20 descriptors from which 3 categories were derived: cognitive/affective, sensory, and neuropathic. Presence of ≥2 pain-associated conditions was significantly associated with 18 of the 20 pain quality descriptors. Sensory descriptors were endorsed by nearly all older adults with chronic pain (93%), followed by cognitive/affective (83.4%) and neuropathic descriptors (68.6%). Neuropathic descriptors were associated with the greatest number of pain-associated conditions including osteoarthritis of the hand and knee. More than half of participants (59%) endorsed descriptors in all 3 categories and had more severe pain and interference, and multisite or widespread pain than those endorsing 1 or 2 categories. Strong associations were observed between pain quality and measures of pain severity, interference, and distribution (P < 0.0001). Findings from this study indicate that older adults have multiple pain-associated conditions that likely reflect multiple physiological mechanisms for pain. Linking pain qualities with other associated pain characteristics serve to develop a multidimensional approach to geriatric pain assessment. Future research is needed to investigate the physiological mechanisms responsible for the variability in pain qualities endorsed by older adults.

A robust H∞-tracking design for uncertain Takagi-Sugeno fuzzy systems with unknown premise variables using descriptor redundancy approach

NASA Astrophysics Data System (ADS)

Hassan Asemani, Mohammad; Johari Majd, Vahid

2015-12-01

This paper addresses a robust H∞ fuzzy observer-based tracking design problem for uncertain Takagi-Sugeno fuzzy systems with external disturbances. To have a practical observer-based controller, the premise variables of the system are assumed to be not measurable in general, which leads to a more complex design process. The tracker is synthesised based on a fuzzy Lyapunov function approach and non-parallel distributed compensation (non-PDC) scheme. Using the descriptor redundancy approach, the robust stability conditions are derived in the form of strict linear matrix inequalities (LMIs) even in the presence of uncertainties in the system, input, and output matrices simultaneously. Numerical simulations are provided to show the effectiveness of the proposed method.

Low-Dimensional Statistics of Anatomical Variability via Compact Representation of Image Deformations.

PubMed

Zhang, Miaomiao; Wells, William M; Golland, Polina

2016-10-01

Using image-based descriptors to investigate clinical hypotheses and therapeutic implications is challenging due to the notorious "curse of dimensionality" coupled with a small sample size. In this paper, we present a low-dimensional analysis of anatomical shape variability in the space of diffeomorphisms and demonstrate its benefits for clinical studies. To combat the high dimensionality of the deformation descriptors, we develop a probabilistic model of principal geodesic analysis in a bandlimited low-dimensional space that still captures the underlying variability of image data. We demonstrate the performance of our model on a set of 3D brain MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Our model yields a more compact representation of group variation at substantially lower computational cost than models based on the high-dimensional state-of-the-art approaches such as tangent space PCA (TPCA) and probabilistic principal geodesic analysis (PPGA).

Theory of Knowledge Aims, Objectives and Assessment Criteria: An Analysis of Critical Thinking Descriptors

ERIC Educational Resources Information Center

Hughes, Conrad

2014-01-01

This article analyses the construct validity of the International Baccalaureate Diploma Programme's Theory of Knowledge course in the light of claims that it is a course in critical thinking. After discussion around critical thinking--what it is and why it is valuable educationally--the article analyses the extent to which the course aims,…

Gain-Loss Framing and Choice: Separating Outcome Formulations from Descriptor Formulations.

PubMed

Mandel, David R.

2001-05-01

This article reexamines the assumptions underlying the disease problem used by Tversky and Kahneman (1981) to illustrate gain-loss formulation effects. It is argued that their reported effect may have been due to asymmetries in the ambiguity of the sure and risky prospects and to the entanglement of two distinct types of formulation manipulations: one having to do with the expected outcomes that are made explicit (positive vs negative) and the other having to do with the descriptors used to convey the relevant expected outcomes (lives saved/not saved vs lives lost/not lost). Two experiments using a formally equivalent problem in which these confounds were eliminated revealed no significant predictive effect of either descriptor or outcomes frames on choice, although a marginally significant framing effect was obtained in Experiment 1 when the signs of the two framing manipulations were congruent. Implications for prospect theory are discussed. Copyright 2001 Academic Press.

Compositional descriptor-based recommender system for the materials discovery

NASA Astrophysics Data System (ADS)

Seko, Atsuto; Hayashi, Hiroyuki; Tanaka, Isao

2018-06-01

Structures and properties of many inorganic compounds have been collected historically. However, it only covers a very small portion of possible inorganic crystals, which implies the presence of numerous currently unknown compounds. A powerful machine-learning strategy is mandatory to discover new inorganic compounds from all chemical combinations. Herein we propose a descriptor-based recommender-system approach to estimate the relevance of chemical compositions where crystals can be formed [i.e., chemically relevant compositions (CRCs)]. In addition to data-driven compositional similarity used in the literature, the use of compositional descriptors as a prior knowledge is helpful for the discovery of new compounds. We validate our recommender systems in two ways. First, one database is used to construct a model, while another is used for the validation. Second, we estimate the phase stability for compounds at expected CRCs using density functional theory calculations.

Uncovering the Geometry of Barrierless Reactions Using Lagrangian Descriptors.

PubMed

Junginger, Andrej; Hernandez, Rigoberto

2016-03-03

Transition-state theories describing barrierless chemical reactions, or more general activated problems, are often hampered by the lack of a saddle around which the dividing surface can be constructed. For example, the time-dependent transition-state trajectory uncovering the nonrecrossing dividing surface in thermal reactions in the framework of the Langevin equation has relied on perturbative approaches in the vicinity of the saddle. We recently obtained an alternative approach using Lagrangian descriptors to construct time-dependent and recrossing-free dividing surfaces. This is a nonperturbative approach making no reference to a putative saddle. Here we show how the Lagrangian descriptor can be used to obtain the transition-state geometry of a dissipated and thermalized reaction across barrierless potentials. We illustrate the method in the case of a 1D Brownian motion for both barrierless and step potentials; however, the method is not restricted and can be directly applied to different kinds of potentials and higher dimensional systems.

Quantitative structure-activity relationship study of antioxidative peptide by using different sets of amino acids descriptors

NASA Astrophysics Data System (ADS)

Li, Yao-Wang; Li, Bo; He, Jiguo; Qian, Ping

2011-07-01

A database consisting of 214 tripeptides which contain either His or Tyr residue was applied to study quantitative structure-activity relationships (QSAR) of antioxidative tripeptides. Partial Least-Squares Regression analysis (PLSR) was conducted using parameters individually of each amino acid descriptor, including Divided Physico-chemical Property Scores (DPPS), Hydrophobic, Electronic, Steric, and Hydrogen (HESH), Vectors of Hydrophobic, Steric, and Electronic properties (VHSE), Molecular Surface-Weighted Holistic Invariant Molecular (MS-WHIM), isotropic surface area-electronic charge index (ISA-ECI) and Z-scale, to describe antioxidative tripeptides as X-variables and antioxidant activities measured with ferric thiocyanate methods were as Y-variable. After elimination of outliers by Hotelling's T 2 method and residual analysis, six significant models were obtained describing the entire data set. According to cumulative squared multiple correlation coefficients ( R2), cumulative cross-validation coefficients ( Q2) and relative standard deviation for calibration set (RSD c), the qualities of models using DPPS, HESH, ISA-ECI, and VHSE descriptors are better ( R2 > 0.6, Q2 > 0.5, RSD c < 0.39) than that of models using MS-WHIM and Z-scale descriptors ( R2 < 0.6, Q2 < 0.5, RSD c > 0.44). Furthermore, the predictive ability of models using DPPS descriptor is best among the six descriptors systems (cumulative multiple correlation coefficient for predict set ( Rext2) > 0.7). It was concluded that the DPPS is better to describe the amino acid of antioxidative tripeptides. The results of DPPS descriptor reveal that the importance of the center amino acid and the N-terminal amino acid are far more than the importance of the C-terminal amino acid for antioxidative tripeptides. The hydrophobic (positively to activity) and electronic (negatively to activity) properties of the N-terminal amino acid are suggested to play the most important significance to activity, followed by the hydrogen bond (positively to activity) of the center amino acid. The N-terminal amino acid should be a high hydrophobic and low electronic amino acid (such as Ala, Gly, Val, and Leu); the center amino acid would be an amino acid that possesses high hydrogen bond property (such as base amino acid Arg, Lys, and His). The structural characteristics of antioxidative peptide be found in this paper may contribute to the further research of antioxidative mechanism.

Chemical graphs, molecular matrices and topological indices in chemoinformatics and quantitative structure-activity relationships.

PubMed

Ivanciuc, Ovidiu

2013-06-01

Chemical and molecular graphs have fundamental applications in chemoinformatics, quantitative structureproperty relationships (QSPR), quantitative structure-activity relationships (QSAR), virtual screening of chemical libraries, and computational drug design. Chemoinformatics applications of graphs include chemical structure representation and coding, database search and retrieval, and physicochemical property prediction. QSPR, QSAR and virtual screening are based on the structure-property principle, which states that the physicochemical and biological properties of chemical compounds can be predicted from their chemical structure. Such structure-property correlations are usually developed from topological indices and fingerprints computed from the molecular graph and from molecular descriptors computed from the three-dimensional chemical structure. We present here a selection of the most important graph descriptors and topological indices, including molecular matrices, graph spectra, spectral moments, graph polynomials, and vertex topological indices. These graph descriptors are used to define several topological indices based on molecular connectivity, graph distance, reciprocal distance, distance-degree, distance-valency, spectra, polynomials, and information theory concepts. The molecular descriptors and topological indices can be developed with a more general approach, based on molecular graph operators, which define a family of graph indices related by a common formula. Graph descriptors and topological indices for molecules containing heteroatoms and multiple bonds are computed with weighting schemes based on atomic properties, such as the atomic number, covalent radius, or electronegativity. The correlation in QSPR and QSAR models can be improved by optimizing some parameters in the formula of topological indices, as demonstrated for structural descriptors based on atomic connectivity and graph distance.

Many-Body Descriptors for Predicting Molecular Properties with Machine Learning: Analysis of Pairwise and Three-Body Interactions in Molecules.

PubMed

Pronobis, Wiktor; Tkatchenko, Alexandre; Müller, Klaus-Robert

2018-06-12

Machine learning (ML) based prediction of molecular properties across chemical compound space is an important and alternative approach to efficiently estimate the solutions of highly complex many-electron problems in chemistry and physics. Statistical methods represent molecules as descriptors that should encode molecular symmetries and interactions between atoms. Many such descriptors have been proposed; all of them have advantages and limitations. Here, we propose a set of general two-body and three-body interaction descriptors which are invariant to translation, rotation, and atomic indexing. By adapting the successfully used kernel ridge regression methods of machine learning, we evaluate our descriptors on predicting several properties of small organic molecules calculated using density-functional theory. We use two data sets. The GDB-7 set contains 6868 molecules with up to 7 heavy atoms of type CNO. The GDB-9 set is composed of 131722 molecules with up to 9 heavy atoms containing CNO. When trained on 5000 random molecules, our best model achieves an accuracy of 0.8 kcal/mol (on the remaining 1868 molecules of GDB-7) and 1.5 kcal/mol (on the remaining 126722 molecules of GDB-9) respectively. Applying a linear regression model on our novel many-body descriptors performs almost equal to a nonlinear kernelized model. Linear models are readily interpretable: a feature importance ranking measure helps to obtain qualitative and quantitative insights on the importance of two- and three-body molecular interactions for predicting molecular properties computed with quantum-mechanical methods.

Functional Constructivism: In Search of Formal Descriptors.

PubMed

Trofimova, Irina

2017-10-01

The Functional Constructivism (FC) paradigm is an alternative to behaviorism and considers behavior as being generated every time anew, based on an individual's capacities, environmental resources and demands. Walter Freeman's work provided us with evidence supporting the FC principles. In this paper we make parallels between gradual construction processes leading to the formation of individual behavior and habits, and evolutionary processes leading to the establishment of biological systems. Referencing evolutionary theory, several formal descriptors of such processes are proposed. These FC descriptors refer to the most universal aspects for constructing consistent structures: expansion of degrees of freedom, integration processes based on internal and external compatibility between systems and maintenance processes, all given in four different classes of systems: (a) Zone of Proximate Development (poorly defined) systems; (b) peer systems with emerging reproduction of multiple siblings; (c) systems with internalized integration of behavioral elements ('cruise controls'); and (d) systems capable of handling low-probability, not yet present events. The recursive dynamics within this set of descriptors acting on (traditional) downward, upward and horizontal directions of evolution, is conceptualized as diagonal evolution, or di-evolution. Two examples applying these FC descriptors to taxonomy are given: classification of the functionality of neuro-transmitters and temperament traits; classification of mental disorders. The paper is an early step towards finding a formal language describing universal tendencies in highly diverse, complex and multi-level transient systems known in ecology and biology as 'contingency cycles.'

Dynamic clustering detection through multi-valued descriptors of dermoscopic images.

PubMed

Cozza, Valentina; Guarracino, Maria Rosario; Maddalena, Lucia; Baroni, Adone

2011-09-10

This paper introduces a dynamic clustering methodology based on multi-valued descriptors of dermoscopic images. The main idea is to support medical diagnosis to decide if pigmented skin lesions belonging to an uncertain set are nearer to malignant melanoma or to benign nevi. Melanoma is the most deadly skin cancer, and early diagnosis is a current challenge for clinicians. Most data analysis algorithms for skin lesions discrimination focus on segmentation and extraction of features of categorical or numerical type. As an alternative approach, this paper introduces two new concepts: first, it considers multi-valued data that scalar variables not only describe but also intervals or histogram variables; second, it introduces a dynamic clustering method based on Wasserstein distance to compare multi-valued data. The overall strategy of analysis can be summarized into the following steps: first, a segmentation of dermoscopic images allows to identify a set of multi-valued descriptors; second, we performed a discriminant analysis on a set of images where there is an a priori classification so that it is possible to detect which features discriminate the benign and malignant lesions; and third, we performed the proposed dynamic clustering method on the uncertain cases, which need to be associated to one of the two previously mentioned groups. Results based on clinical data show that the grading of specific descriptors associated to dermoscopic characteristics provides a novel way to characterize uncertain lesions that can help the dermatologist's diagnosis. Copyright © 2011 John Wiley & Sons, Ltd.

Rate constants of hydroxyl radical oxidation of polychlorinated biphenyls in the gas phase: A single-descriptor based QSAR and DFT study.

PubMed

Yang, Zhihui; Luo, Shuang; Wei, Zongsu; Ye, Tiantian; Spinney, Richard; Chen, Dong; Xiao, Ruiyang

2016-04-01

The second-order rate constants (k) of hydroxyl radical (·OH) with polychlorinated biphenyls (PCBs) in the gas phase are of scientific and regulatory importance for assessing their global distribution and fate in the atmosphere. Due to the limited number of measured k values, there is a need to model the k values for unknown PCBs congeners. In the present study, we developed a quantitative structure-activity relationship (QSAR) model with quantum chemical descriptors using a sequential approach, including correlation analysis, principal component analysis, multi-linear regression, validation, and estimation of applicability domain. The result indicates that the single descriptor, polarizability (α), plays an important role in determining the reactivity with a global standardized function of lnk = -0.054 × α ‒ 19.49 at 298 K. In order to validate the QSAR predicted k values and expand the current k value database for PCBs congeners, an independent method, density functional theory (DFT), was employed to calculate the kinetics and thermodynamics of the gas-phase ·OH oxidation of 2,4',5-trichlorobiphenyl (PCB31), 2,2',4,4'-tetrachlorobiphenyl (PCB47), 2,3,4,5,6-pentachlorobiphenyl (PCB116), 3,3',4,4',5,5'-hexachlorobiphenyl (PCB169), and 2,3,3',4,5,5',6-heptachlorobiphenyl (PCB192) at 298 K at B3LYP/6-311++G**//B3LYP/6-31 + G** level of theory. The QSAR predicted and DFT calculated k values for ·OH oxidation of these PCB congeners exhibit excellent agreement with the experimental k values, indicating the robustness and predictive power of the single-descriptor based QSAR model we developed. Copyright © 2015 Elsevier Ltd. All rights reserved.

Modeling Biophysical and Biological Properties From the Characteristics of the Molecular Electron Density, Electron Localization and Delocalization Matrices, and the Electrostatic Potential

PubMed Central

Matta*, Chérif F

2014-01-01

The electron density and the electrostatic potential are fundamentally related to the molecular hamiltonian, and hence are the ultimate source of all properties in the ground- and excited-states. The advantages of using molecular descriptors derived from these fundamental scalar fields, both accessible from theory and from experiment, in the formulation of quantitative structure-to-activity and structure-to-property relationships, collectively abbreviated as QSAR, are discussed. A few such descriptors encode for a wide variety of properties including, for example, electronic transition energies, pKa's, rates of ester hydrolysis, NMR chemical shifts, DNA dimers binding energies, π-stacking energies, toxicological indices, cytotoxicities, hepatotoxicities, carcinogenicities, partial molar volumes, partition coefficients (log P), hydrogen bond donor capacities, enzyme–substrate complementarities, bioisosterism, and regularities in the genetic code. Electronic fingerprinting from the topological analysis of the electron density is shown to be comparable and possibly superior to Hammett constants and can be used in conjunction with traditional bulk and liposolubility descriptors to accurately predict biological activities. A new class of descriptors obtained from the quantum theory of atoms in molecules' (QTAIM) localization and delocalization indices and bond properties, cast in matrix format, is shown to quantify transferability and molecular similarity meaningfully. Properties such as “interacting quantum atoms (IQA)” energies which are expressible into an interaction matrix of two body terms (and diagonal one body “self” terms, as IQA energies) can be used in the same manner. The proposed QSAR-type studies based on similarity distances derived from such matrix representatives of molecular structure necessitate extensive investigation before their utility is unequivocally established. © 2014 The Author and the Journal of Computational Chemistry Published by Wiley Periodicals, Inc. PMID:24777743

Development of bovine serum albumin-water partition coefficients predictive models for ionogenic organic chemicals based on chemical form adjusted descriptors.

PubMed

Ding, Feng; Yang, Xianhai; Chen, Guosong; Liu, Jining; Shi, Lili; Chen, Jingwen

2017-10-01

The partition coefficients between bovine serum albumin (BSA) and water (K BSA/w ) for ionogenic organic chemicals (IOCs) were different greatly from those of neutral organic chemicals (NOCs). For NOCs, several excellent models were developed to predict their logK BSA/w . However, it was found that the conventional descriptors are inappropriate for modeling logK BSA/w of IOCs. Thus, alternative approaches are urgently needed to develop predictive models for K BSA/w of IOCs. In this study, molecular descriptors that can be used to characterize the ionization effects (e.g. chemical form adjusted descriptors) were calculated and used to develop predictive models for logK BSA/w of IOCs. The models developed had high goodness-of-fit, robustness, and predictive ability. The predictor variables selected to construct the models included the chemical form adjusted averages of the negative potentials on the molecular surface (V s-adj - ), the chemical form adjusted molecular dipole moment (dipolemoment adj ), the logarithm of the n-octanol/water distribution coefficient (logD). As these molecular descriptors can be calculated from their molecular structures directly, the developed model can be easily used to fill the logK BSA/w data gap for other IOCs within the applicability domain. Furthermore, the chemical form adjusted descriptors calculated in this study also could be used to construct predictive models on other endpoints of IOCs. Copyright © 2017 Elsevier Inc. All rights reserved.

Grading system to categorize breast MRI using BI-RADS 5th edition: a statistical study of non-mass enhancement descriptors in terms of probability of malignancy.

PubMed

Asada, Tatsunori; Yamada, Takayuki; Kanemaki, Yoshihide; Fujiwara, Keishi; Okamoto, Satoko; Nakajima, Yasuo

2018-03-01

To analyze the association of breast non-mass enhancement descriptors in the BI-RADS 5th edition with malignancy, and to establish a grading system and categorization of descriptors. This study was approved by our institutional review board. A total of 213 patients were enrolled. Breast MRI was performed with a 1.5-T MRI scanner using a 16-channel breast radiofrequency coil. Two radiologists determined internal enhancement and distribution of non-mass enhancement by consensus. Corresponding pathologic diagnoses were obtained by either biopsy or surgery. The probability of malignancy by descriptor was analyzed using Fisher's exact test and multivariate logistic regression analysis. The probability of malignancy by category was analyzed using Fisher's exact and multi-group comparison tests. One hundred seventy-eight lesions were malignant. Multivariate model analysis showed that internal enhancement (homogeneous vs others, p < 0.001, heterogeneous and clumped vs clustered ring, p = 0.003) and distribution (focal and linear vs segmental, p < 0.001) were the significant explanatory variables. The descriptors were classified into three grades of suspicion, and the categorization (3, 4A, 4B, 4C, and 5) by sum-up grades showed an incremental increase in the probability of malignancy (p < 0.0001). The three-grade criteria and categorization by sum-up grades of descriptors appear valid for non-mass enhancement.

Translations on Eastern Europe Political, Sociological, and Military Affairs No. 1361

DTIC Science & Technology

1977-03-07

focusing on theories and methods of late bourgeois, logical positivistic trends of a structuralistic mold reducing the complex and muUi...and articles on military and civil defense, organization, theory , budgets, and hardware. 17. Key Words and Document Analysis. 17a. Descriptors...unanimously emphasized its support for consolidation of administration for non-proliferation of nuclear arms. The political advisory committee, however

Improving Large-Scale Image Retrieval Through Robust Aggregation of Local Descriptors.

PubMed

Husain, Syed Sameed; Bober, Miroslaw

2017-09-01

Visual search and image retrieval underpin numerous applications, however the task is still challenging predominantly due to the variability of object appearance and ever increasing size of the databases, often exceeding billions of images. Prior art methods rely on aggregation of local scale-invariant descriptors, such as SIFT, via mechanisms including Bag of Visual Words (BoW), Vector of Locally Aggregated Descriptors (VLAD) and Fisher Vectors (FV). However, their performance is still short of what is required. This paper presents a novel method for deriving a compact and distinctive representation of image content called Robust Visual Descriptor with Whitening (RVD-W). It significantly advances the state of the art and delivers world-class performance. In our approach local descriptors are rank-assigned to multiple clusters. Residual vectors are then computed in each cluster, normalized using a direction-preserving normalization function and aggregated based on the neighborhood rank. Importantly, the residual vectors are de-correlated and whitened in each cluster before aggregation, leading to a balanced energy distribution in each dimension and significantly improved performance. We also propose a new post-PCA normalization approach which improves separability between the matching and non-matching global descriptors. This new normalization benefits not only our RVD-W descriptor but also improves existing approaches based on FV and VLAD aggregation. Furthermore, we show that the aggregation framework developed using hand-crafted SIFT features also performs exceptionally well with Convolutional Neural Network (CNN) based features. The RVD-W pipeline outperforms state-of-the-art global descriptors on both the Holidays and Oxford datasets. On the large scale datasets, Holidays1M and Oxford1M, SIFT-based RVD-W representation obtains a mAP of 45.1 and 35.1 percent, while CNN-based RVD-W achieve a mAP of 63.5 and 44.8 percent, all yielding superior performance to the state-of-the-art.

Foot anthropometry and morphology phenomena.

PubMed

Agić, Ante; Nikolić, Vasilije; Mijović, Budimir

2006-12-01

Foot structure description is important for many reasons. The foot anthropometric morphology phenomena are analyzed together with hidden biomechanical functionality in order to fully characterize foot structure and function. For younger Croatian population the scatter data of the individual foot variables were interpolated by multivariate statistics. Foot structure descriptors are influenced by many factors, as a style of life, race, climate, and things of the great importance in human society. Dominant descriptors are determined by principal component analysis. Some practical recommendation and conclusion for medical, sportswear and footwear practice are highlighted.

Synthesis and DFT calculations of some 2-aminothiazoles

NASA Astrophysics Data System (ADS)

Rezania, Jafar; Behzadi, Hadi; Shockravi, Abbas; Ehsani, Morteza; Akbarzadeh, Elahe

2018-04-01

A series of 2-aminothiazole derivatives have been synthesized by the reaction of acetyl compounds with thiourea and iodine as catalyst under solvent-free condition, a green chemistry method. The quantum chemical calculations at the DFT/B3LYP level of theory in gas phase were carried out for starting acetyl derivatives. The highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) and related reactivity descriptor of acetyl derivatives, as well as, enthalpy of reactions are calculated in order to investigate the reaction properties of acetyl compounds and yields of the reactions. The calculated reactivity descriptors are well correlated to activity of different acetyl derivatives.

Local and linear chemical reactivity response functions at finite temperature in density functional theory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Franco-Pérez, Marco, E-mail: francopj@mcmaster.ca, E-mail: ayers@mcmaster.ca, E-mail: jlgm@xanum.uam.mx, E-mail: avela@cinvestav.mx; Departamento de Química, Universidad Autónoma Metropolitana-Iztapalapa, Av. San Rafael Atlixco 186, México, D.F. 09340; Ayers, Paul W., E-mail: francopj@mcmaster.ca, E-mail: ayers@mcmaster.ca, E-mail: jlgm@xanum.uam.mx, E-mail: avela@cinvestav.mx

2015-12-28

We explore the local and nonlocal response functions of the grand canonical potential density functional at nonzero temperature. In analogy to the zero-temperature treatment, local (e.g., the average electron density and the local softness) and nonlocal (e.g., the softness kernel) intrinsic response functions are defined as partial derivatives of the grand canonical potential with respect to its thermodynamic variables (i.e., the chemical potential of the electron reservoir and the external potential generated by the atomic nuclei). To define the local and nonlocal response functions of the electron density (e.g., the Fukui function, the linear density response function, and the dualmore » descriptor), we differentiate with respect to the average electron number and the external potential. The well-known mathematical relationships between the intrinsic response functions and the electron-density responses are generalized to nonzero temperature, and we prove that in the zero-temperature limit, our results recover well-known identities from the density functional theory of chemical reactivity. Specific working equations and numerical results are provided for the 3-state ensemble model.« less

Local and linear chemical reactivity response functions at finite temperature in density functional theory.

PubMed

Franco-Pérez, Marco; Ayers, Paul W; Gázquez, José L; Vela, Alberto

2015-12-28

We explore the local and nonlocal response functions of the grand canonical potential density functional at nonzero temperature. In analogy to the zero-temperature treatment, local (e.g., the average electron density and the local softness) and nonlocal (e.g., the softness kernel) intrinsic response functions are defined as partial derivatives of the grand canonical potential with respect to its thermodynamic variables (i.e., the chemical potential of the electron reservoir and the external potential generated by the atomic nuclei). To define the local and nonlocal response functions of the electron density (e.g., the Fukui function, the linear density response function, and the dual descriptor), we differentiate with respect to the average electron number and the external potential. The well-known mathematical relationships between the intrinsic response functions and the electron-density responses are generalized to nonzero temperature, and we prove that in the zero-temperature limit, our results recover well-known identities from the density functional theory of chemical reactivity. Specific working equations and numerical results are provided for the 3-state ensemble model.

A stochastic context free grammar based framework for analysis of protein sequences

PubMed Central

Dyrka, Witold; Nebel, Jean-Christophe

2009-01-01

Background In the last decade, there have been many applications of formal language theory in bioinformatics such as RNA structure prediction and detection of patterns in DNA. However, in the field of proteomics, the size of the protein alphabet and the complexity of relationship between amino acids have mainly limited the application of formal language theory to the production of grammars whose expressive power is not higher than stochastic regular grammars. However, these grammars, like other state of the art methods, cannot cover any higher-order dependencies such as nested and crossing relationships that are common in proteins. In order to overcome some of these limitations, we propose a Stochastic Context Free Grammar based framework for the analysis of protein sequences where grammars are induced using a genetic algorithm. Results This framework was implemented in a system aiming at the production of binding site descriptors. These descriptors not only allow detection of protein regions that are involved in these sites, but also provide insight in their structure. Grammars were induced using quantitative properties of amino acids to deal with the size of the protein alphabet. Moreover, we imposed some structural constraints on grammars to reduce the extent of the rule search space. Finally, grammars based on different properties were combined to convey as much information as possible. Evaluation was performed on sites of various sizes and complexity described either by PROSITE patterns, domain profiles or a set of patterns. Results show the produced binding site descriptors are human-readable and, hence, highlight biologically meaningful features. Moreover, they achieve good accuracy in both annotation and detection. In addition, findings suggest that, unlike current state-of-the-art methods, our system may be particularly suited to deal with patterns shared by non-homologous proteins. Conclusion A new Stochastic Context Free Grammar based framework has been introduced allowing the production of binding site descriptors for analysis of protein sequences. Experiments have shown that not only is this new approach valid, but produces human-readable descriptors for binding sites which have been beyond the capability of current machine learning techniques. PMID:19814800

A novel model for DNA sequence similarity analysis based on graph theory.

PubMed

Qi, Xingqin; Wu, Qin; Zhang, Yusen; Fuller, Eddie; Zhang, Cun-Quan

2011-01-01

Determination of sequence similarity is one of the major steps in computational phylogenetic studies. As we know, during evolutionary history, not only DNA mutations for individual nucleotide but also subsequent rearrangements occurred. It has been one of major tasks of computational biologists to develop novel mathematical descriptors for similarity analysis such that various mutation phenomena information would be involved simultaneously. In this paper, different from traditional methods (eg, nucleotide frequency, geometric representations) as bases for construction of mathematical descriptors, we construct novel mathematical descriptors based on graph theory. In particular, for each DNA sequence, we will set up a weighted directed graph. The adjacency matrix of the directed graph will be used to induce a representative vector for DNA sequence. This new approach measures similarity based on both ordering and frequency of nucleotides so that much more information is involved. As an application, the method is tested on a set of 0.9-kb mtDNA sequences of twelve different primate species. All output phylogenetic trees with various distance estimations have the same topology, and are generally consistent with the reported results from early studies, which proves the new method's efficiency; we also test the new method on a simulated data set, which shows our new method performs better than traditional global alignment method when subsequent rearrangements happen frequently during evolutionary history.

Evaluation of structure-reactivity descriptors and biological activity spectra of 4-(6-methoxy-2-naphthyl)-2-butanone using spectroscopic techniques

NASA Astrophysics Data System (ADS)

Agrawal, Megha; Deval, Vipin; Gupta, Archana; Sangala, Bagvanth Reddy; Prabhu, S. S.

2016-10-01

The structure and several spectroscopic features along with reactivity parameters of the compound 4-(6-methoxy-2-naphthyl)-2-butanone (Nabumetone) have been studied using experimental techniques and tools derived from quantum chemical calculations. Structure optimization is followed by force field calculations based on density functional theory (DFT) at the B3LYP/6-311++G(d,p) level of theory. The vibrational spectra have been interpreted with the aid of normal coordinate analysis. UV-visible spectrum and the effect of solvent have been discussed. The electronic properties such as HOMO and LUMO energies have been determined by TD-DFT approach. In order to understand various aspects of pharmacological sciences several new chemical reactivity descriptors - chemical potential, global hardness and electrophilicity have been evaluated. Local reactivity descriptors - Fukui functions and local softnesses have also been calculated to find out the reactive sites within molecule. Aqueous solubility and lipophilicity have been calculated which are crucial for estimating transport properties of organic molecules in drug development. Estimation of biological effects, toxic/side effects has been made on the basis of prediction of activity spectra for substances (PASS) prediction results and their analysis by Pharma Expert software. Using the THz-TDS technique, the frequency-dependent absorptions of NBM have been measured in the frequency range up to 3 THz.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Plessow, Philipp N.; Bajdich, Michal; Greene, Joshua

The formation of thin oxide films on metal supports is an important phenomenon, especially in the context of strong metal support interaction (SMSI). Computational predictions of the stability of these films are hampered by their structural complexity and a varying lattice mismatch with different supports. In this study, we report a large combination of supports and ultrathin oxide films studied with density functional theory (DFT). Trends in stability are investigated through a descriptor-based analysis. Since the studied films are bound to the support exclusively through metal–metal interaction, the adsorption energy of the oxide-constituting metal atom can be expected to bemore » a reasonable descriptor for the stability of the overlayers. If the same supercell is used for all supports, the overlayers experience different amounts of stress. Using supercells with small lattice mismatch for each system leads to significantly improved scaling relations for the stability of the overlayers. Finally, this approach works well for the studied systems and therefore allows the descriptor-based exploration of the thermodynamic stability of supported thin oxide layers.« less

Maximal Predictability Approach for Identifying the Right Descriptors for Electrocatalytic Reactions.

PubMed

Krishnamurthy, Dilip; Sumaria, Vaidish; Viswanathan, Venkatasubramanian

2018-02-01

Density functional theory (DFT) calculations are being routinely used to identify new material candidates that approach activity near fundamental limits imposed by thermodynamics or scaling relations. DFT calculations are associated with inherent uncertainty, which limits the ability to delineate materials (distinguishability) that possess high activity. Development of error-estimation capabilities in DFT has enabled uncertainty propagation through activity-prediction models. In this work, we demonstrate an approach to propagating uncertainty through thermodynamic activity models leading to a probability distribution of the computed activity and thereby its expectation value. A new metric, prediction efficiency, is defined, which provides a quantitative measure of the ability to distinguish activity of materials and can be used to identify the optimal descriptor(s) ΔG opt . We demonstrate the framework for four important electrochemical reactions: hydrogen evolution, chlorine evolution, oxygen reduction and oxygen evolution. Future studies could utilize expected activity and prediction efficiency to significantly improve the prediction accuracy of highly active material candidates.

Demonstration of SST value as EBVs descriptor in the Mediterranean Sea

NASA Astrophysics Data System (ADS)

Valentini, E.; Filipponi, F.; Nguyen Xuan, A.; Taramelli, A.

2017-12-01

Sea Surface Temperature is an Essential Climate and Ocean Variable (ECV - EOV) able to capture critical scales in the seascape warming patterns and to highlight the exceeding of thresholds. This presentation addresses the changes of the SST in the last three decades over the Mediterranean Sea, a "Large Marine Ecosystem (LME)", in order to speculate the value of such powerful variable, as proxy for the assessment of ecosystem state in terms of ecosystem structures, functions and composition key descriptor. Time series of daily SST for the period 1982-2016, estimated from multi-sensor satellite data and provided by Copernicus Marine Environment Monitoring Service (CMEMS-EU) are used to perform different statistical analysis on common fish species. Results highlight the critical conditions, the general trends as well as the spatial and temporal patterns, in terms of thermal growth, vitality and stress influence on selected fish species. Results confirm a constant increasing trend in SST with an average rise of 1.4° C in the past thirty years. The variance associated to the average trend is not constant across the entire Mediterranean Sea opening the way to multiple scenarios for fish growth and vitality in the diverse sub-basins. A major effort is oriented in addressing the cross-scale ecological interactions to assess the feasibility of using SST as descriptor for Essential Biodiversity Variables, able to prioritize areas and to feed operational tools for planning and management in the Mediterranean LME.

Explanatory Power of Multi-scale Physical Descriptors in Modeling Benthic Indices Across Nested Ecoregions of the Pacific Northwest

NASA Astrophysics Data System (ADS)

Holburn, E. R.; Bledsoe, B. P.; Poff, N. L.; Cuhaciyan, C. O.

2005-05-01

Using over 300 R/EMAP sites in OR and WA, we examine the relative explanatory power of watershed, valley, and reach scale descriptors in modeling variation in benthic macroinvertebrate indices. Innovative metrics describing flow regime, geomorphic processes, and hydrologic-distance weighted watershed and valley characteristics are used in multiple regression and regression tree modeling to predict EPT richness, % EPT, EPT/C, and % Plecoptera. A nested design using seven ecoregions is employed to evaluate the influence of geographic scale and environmental heterogeneity on the explanatory power of individual and combined scales. Regression tree models are constructed to explain variability while identifying threshold responses and interactions. Cross-validated models demonstrate differences in the explanatory power associated with single-scale and multi-scale models as environmental heterogeneity is varied. Models explaining the greatest variability in biological indices result from multi-scale combinations of physical descriptors. Results also indicate that substantial variation in benthic macroinvertebrate response can be explained with process-based watershed and valley scale metrics derived exclusively from common geospatial data. This study outlines a general framework for identifying key processes driving macroinvertebrate assemblages across a range of scales and establishing the geographic extent at which various levels of physical description best explain biological variability. Such information can guide process-based stratification to avoid spurious comparison of dissimilar stream types in bioassessments and ensure that key environmental gradients are adequately represented in sampling designs.

Quantum chemical and statistical study of megazol-derived compounds with trypanocidal activity

NASA Astrophysics Data System (ADS)

Rosselli, F. P.; Albuquerque, C. N.; da Silva, A. B. F.

In this work we performed a structure-activity relationship (SAR) study with the aim to correlate molecular properties of the megazol compound and 10 of its analogs with the biological activity against Trypanosoma cruzi (trypanocidal or antichagasic activity) presented by these molecules. The biological activity indication was obtained from in vitro tests and the molecular properties (variables or descriptors) were obtained from the optimized chemical structures by using the PM3 semiempirical method. It was calculated ˜80 molecular properties selected among steric, constitutional, electronic, and lipophilicity properties. In order to reduce dimensionality and investigate which subset of variables (descriptors) would be more effective in classifying the compounds studied, according to their degree of trypanocidal activity, we employed statistical methodologies (pattern recognition and classification techniques) such as principal component analysis (PCA), hierarchical cluster analysis (HCA), K-nearest neighbor (KNN), and discriminant function analysis (DFA). These methods showed that the descriptors molecular mass (MM), energy of the second lowest unoccupied molecular orbital (LUMO+1), charge on the first nitrogen at substituent 2 (qN'), dihedral angles (D1 and D2), bond length between atom C4 and its substituent (L4), Moriguchi octanol-partition coefficient (MLogP), and length-to-breadth ratio (L/Bw) were the variables responsible for the separation between active and inactive compounds against T. cruzi. Afterwards, the PCA, KNN, and DFA models built in this work were used to perform trypanocidal activity predictions for eight new megazol analog compounds.

The Development of Novel Chemical Fragment-Based Descriptors Using Frequent Common Subgraph Mining Approach and Their Application in QSAR Modeling.

PubMed

Khashan, Raed; Zheng, Weifan; Tropsha, Alexander

2014-03-01

We present a novel approach to generating fragment-based molecular descriptors. The molecules are represented by labeled undirected chemical graph. Fast Frequent Subgraph Mining (FFSM) is used to find chemical-fragments (subgraphs) that occur in at least a subset of all molecules in a dataset. The collection of frequent subgraphs (FSG) forms a dataset-specific descriptors whose values for each molecule are defined by the number of times each frequent fragment occurs in this molecule. We have employed the FSG descriptors to develop variable selection k Nearest Neighbor (kNN) QSAR models of several datasets with binary target property including Maximum Recommended Therapeutic Dose (MRTD), Salmonella Mutagenicity (Ames Genotoxicity), and P-Glycoprotein (PGP) data. Each dataset was divided into training, test, and validation sets to establish the statistical figures of merit reflecting the model validated predictive power. The classification accuracies of models for both training and test sets for all datasets exceeded 75 %, and the accuracy for the external validation sets exceeded 72 %. The model accuracies were comparable or better than those reported earlier in the literature for the same datasets. Furthermore, the use of fragment-based descriptors affords mechanistic interpretation of validated QSAR models in terms of essential chemical fragments responsible for the compounds' target property. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Constructive alignment of a research-informed teaching activity within an undergraduate diagnostic radiography curriculum: A reflection.

PubMed

Higgins, R; Hogg, P; Robinson, L

2017-09-01

To evaluate the learning experience of a level 5 (year 2) student cohort within a research-informed teaching (RiT) activity and to map findings against learning outcomes and level descriptors using constructive alignment. An online questionnaire was used to explore the level 5 student experience of a Research-informed Teaching (RiT) activity. Responses were retrospectively mapped against Framework for Higher Education Qualifications (FHEQ) level descriptors for level 5 using constructive alignment. Thirty one out of 46 level 5 students completed the questionnaire (67% response rate). Analysis of the questionnaire supported the integration of this RiT activity within the curriculum in terms of learning and research skill development by students. However, it was identified that this activity could be revised further to better align with level 5 descriptors and incorporate additional higher level cognitive processes. Learning outcomes for this RiT activity were constructively aligned with FHEQ level 5 descriptors. Recommendations are provided on how these could be further refined to ensure students undertake a more critical approach to the application of theory into practice. Discussion also considers how this process could be used to develop a similar RiT activity at level 6 (year 3). Copyright © 2016 The College of Radiographers. Published by Elsevier Ltd. All rights reserved.

Clar theory and resonance energy

NASA Astrophysics Data System (ADS)

Gutman, Ivan; Gojak, Sabina; Furtula, Boris

2005-09-01

A mathematical model, referred here as the Zhang-Zhang polynomial ζ( x), that embraces all the main concepts encountered in the Clar aromatic sextet theory of benzenoid hydrocarbons, was recently put forward by Zhang and Zhang. We now show that ζ( x) is related to resonance energy, and that ln ζ( x) and RE are best correlated when x ≈ 1. This indicates that ζ(1) could be viewed as a (novel) structure-descriptor, playing a role analogous to the Kekulé structure count in Kekulé-structure-based theories. Some basic properties of ζ(1) are established.

Imidazole derivatives as angiotensin II AT1 receptor blockers: Benchmarks, drug-like calculations and quantitative structure-activity relationships modeling

NASA Astrophysics Data System (ADS)

Alloui, Mebarka; Belaidi, Salah; Othmani, Hasna; Jaidane, Nejm-Eddine; Hochlaf, Majdi

2018-03-01

We performed benchmark studies on the molecular geometry, electron properties and vibrational analysis of imidazole using semi-empirical, density functional theory and post Hartree-Fock methods. These studies validated the use of AM1 for the treatment of larger systems. Then, we treated the structural, physical and chemical relationships for a series of imidazole derivatives acting as angiotensin II AT1 receptor blockers using AM1. QSAR studies were done for these imidazole derivatives using a combination of various physicochemical descriptors. A multiple linear regression procedure was used to design the relationships between molecular descriptor and the activity of imidazole derivatives. Results validate the derived QSAR model.

Scaling left ventricular mass in adolescent boys aged 11-15 years.

PubMed

Valente-Dos-Santos, João; Coelho-E-Silva, Manuel J; Ferraz, António; Castanheira, Joaquim; Ronque, Enio R; Sherar, Lauren B; Elferink-Gemser, Marije T; Malina, Robert M

2014-01-01

Normalizing left ventricular mass (LVM) for inter-individual variation in body size is a central issue in human biology. During the adolescent growth spurt, variability in body size descriptors needs to be interpreted in combination with biological maturation. To examine the contribution of biological maturation, stature, sitting height, body mass, fat-free mass (FFM) and fat mass (FM) to inter-individual variability in LVM in boys, using proportional allometric modelling. The cross-sectional sample included 110 boys of 11-15 years (12.9-1.0 years). Stature, sitting height, body mass, cardiac chamber dimensions and LVM were measured. Age at peak height velocity (APHV) was predicted and used as an indicator of biological maturation. Percentage fat was estimated from triceps and subscapular skinfolds; FM and FFM were derived. Exponents for body size descriptors were k = 2.33 for stature, k = 2.18 for sitting height, k = 0.68 for body mass, k = 0.17 for FM and k = 0.80 for FFM (adjusted R(2 )= 19-62%). The combination of body descriptors and APHV increased the explained variance in LVM (adjusted R(2)( )= 56-69%). Stature, FM and FFM are the best combination for normalizing LVM in adolescent boys; when body composition is not available, an indicator of biological maturity should be included with stature.

Recurrent Education. A Resource Guide.

ERIC Educational Resources Information Center

Rochte, Newton C.

To assist both practitioner and reader to find answers to questions on the theory and practice of recurrent education, this resource guide compiles 715 abstracts of relevant articles, books, and monographs, from many countries. Descriptors and identifiers, used in computer searches to identify the materials, are arranged alphabetically in the…

Trends in Selective Hydrogen Peroxide Production on Transition Metal Surfaces from First Principles

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rankin, Rees B.; Greeley, Jeffrey P.

2012-10-19

We present a comprehensive, Density Functional Theory-based analysis of the direct synthesis of hydrogen peroxide, H2O2, on twelve transition metal surfaces. We determine the full thermodynamics and selected kinetics of the reaction network on these metals, and we analyze these energetics with simple, microkinetically motivated rate theories to assess the activity and selectivity of hydrogen peroxide production on the surfaces of interest. By further exploiting Brønsted-Evans-Polanyi relationships and scaling relationships between the binding energies of different adsorbates, we express the results in the form of a two dimensional contour volcano plot, with the activity and selectivity being determined as functionsmore » of two independent descriptors, the atomic hydrogen and oxygen adsorption free energies. We identify both a region of maximum predicted catalytic activity, which is near Pt and Pd in descriptor space, and a region of selective hydrogen peroxide production, which includes Au. The optimal catalysts represent a compromise between activity and selectivity and are predicted to fall approximately between Au and Pd in descriptor space, providing a compact explanation for the experimentally known performance of Au-Pd alloys for hydrogen peroxide synthesis, and suggesting a target for future computational screening efforts to identify improved direct hydrogen peroxide synthesis catalysts. Related methods of combining activity and selectivity analysis into a single volcano plot may be applicable to, and useful for, other aqueous phase heterogeneous catalytic reactions where selectivity is a key catalytic criterion.« less

QSAR STUDY OF THE REDUCTION OF NITROAROMATICS BY FE (II) SPECIES

EPA Science Inventory

The development of predictive models for the reductive transformation of nitroaromatics requires further clarification of the effect of environmentally relevant variables on reaction kinetics and the identification of readily available molecular descriptors for calculating reacti...

Nonparametric regression applied to quantitative structure-activity relationships

PubMed

Constans; Hirst

2000-03-01

Several nonparametric regressors have been applied to modeling quantitative structure-activity relationship (QSAR) data. The simplest regressor, the Nadaraya-Watson, was assessed in a genuine multivariate setting. Other regressors, the local linear and the shifted Nadaraya-Watson, were implemented within additive models--a computationally more expedient approach, better suited for low-density designs. Performances were benchmarked against the nonlinear method of smoothing splines. A linear reference point was provided by multilinear regression (MLR). Variable selection was explored using systematic combinations of different variables and combinations of principal components. For the data set examined, 47 inhibitors of dopamine beta-hydroxylase, the additive nonparametric regressors have greater predictive accuracy (as measured by the mean absolute error of the predictions or the Pearson correlation in cross-validation trails) than MLR. The use of principal components did not improve the performance of the nonparametric regressors over use of the original descriptors, since the original descriptors are not strongly correlated. It remains to be seen if the nonparametric regressors can be successfully coupled with better variable selection and dimensionality reduction in the context of high-dimensional QSARs.

T-scale as a novel vector of topological descriptors for amino acids and its application in QSARs of peptides

NASA Astrophysics Data System (ADS)

Tian, Feifei; Zhou, Peng; Li, Zhiliang

2007-03-01

In this paper, a new topological descriptor T-scale is derived from principal component analysis (PCA) on the collected 67 kinds of structural and topological variables of 135 amino acids. Applying T-scale to three peptide panels as 58 angiotensin-converting enzyme (ACE) inhibitors, 20 thromboplastin inhibitors (TI) and 28 bovine lactoferricin-(17-31)-pentadecapeptides (LFB), the resulting QSAR models, constructed by partial least squares (PLS), are all superior to reference reports, with correlative coefficient r2 and cross-validated q2 of 0.845, 0.786; 0.996, 0.782 (0.988, 0.961); 0.760, 0.627, respectively.

LQTA-QSAR: a new 4D-QSAR methodology.

PubMed

Martins, João Paulo A; Barbosa, Euzébio G; Pasqualoto, Kerly F M; Ferreira, Márcia M C

2009-06-01

A novel 4D-QSAR approach which makes use of the molecular dynamics (MD) trajectories and topology information retrieved from the GROMACS package is presented in this study. This new methodology, named LQTA-QSAR (LQTA, Laboratório de Quimiometria Teórica e Aplicada), has a module (LQTAgrid) that calculates intermolecular interaction energies at each grid point considering probes and all aligned conformations resulting from MD simulations. These interaction energies are the independent variables or descriptors employed in a QSAR analysis. The comparison of the proposed methodology to other 4D-QSAR and CoMFA formalisms was performed using a set of forty-seven glycogen phosphorylase b inhibitors (data set 1) and a set of forty-four MAP p38 kinase inhibitors (data set 2). The QSAR models for both data sets were built using the ordered predictor selection (OPS) algorithm for variable selection. Model validation was carried out applying y-randomization and leave-N-out cross-validation in addition to the external validation. PLS models for data set 1 and 2 provided the following statistics: q(2) = 0.72, r(2) = 0.81 for 12 variables selected and 2 latent variables and q(2) = 0.82, r(2) = 0.90 for 10 variables selected and 5 latent variables, respectively. Visualization of the descriptors in 3D space was successfully interpreted from the chemical point of view, supporting the applicability of this new approach in rational drug design.

Bonding reactivity descriptor from conceptual density functional theory and its applications to elucidate bonding formation

NASA Astrophysics Data System (ADS)

Zhou, Pan-Pan; Liu, Shubin; Ayers, Paul W.; Zhang, Rui-Qin

2017-10-01

Condensed-to-atom Fukui functions which reflect the atomic reactivity like the tendency susceptible to either nucleophilic or electrophilic attack demonstrate the bonding trend of an atom in a molecule. Accordingly, Fukui functions based concepts, that is, bonding reactivity descriptors which reveal the bonding properties of molecules in the reaction were put forward and then applied to pericyclic and cluster reactions to confirm their effectiveness and reliability. In terms of the results from the bonding descriptors, a covalent bond can readily be predicted between two atoms with large Fukui functions (i.e., one governs nucleophilic attack while the other one governs electrophilic attack, or both of them govern radical attacks) for pericyclic reactions. For SinOm clusters' reactions, the clusters with a low O atom ratio readily form a bond between two Si atoms with big values of their Fukui functions in which they respectively govern nucleophilic and electrophilic attacks or both govern radical attacks. Also, our results from bonding descriptors show that Si—Si bonds can be formed via the radical mechanism between two Si atoms, and formations of Si—O and O—O bonds are possible when the O content is high. These results conform with experimental findings and can help experimentalists design appropriate clusters to synthesize Si nanowires with high yields. The approach established in this work could be generalized and applied to study reactivity properties for other systems.

A Theoretical Framework for Lagrangian Descriptors

NASA Astrophysics Data System (ADS)

Lopesino, C.; Balibrea-Iniesta, F.; García-Garrido, V. J.; Wiggins, S.; Mancho, A. M.

This paper provides a theoretical background for Lagrangian Descriptors (LDs). The goal of achieving rigorous proofs that justify the ability of LDs to detect invariant manifolds is simplified by introducing an alternative definition for LDs. The definition is stated for n-dimensional systems with general time dependence, however we rigorously prove that this method reveals the stable and unstable manifolds of hyperbolic points in four particular 2D cases: a hyperbolic saddle point for linear autonomous systems, a hyperbolic saddle point for nonlinear autonomous systems, a hyperbolic saddle point for linear nonautonomous systems and a hyperbolic saddle point for nonlinear nonautonomous systems. We also discuss further rigorous results which show the ability of LDs to highlight additional invariants sets, such as n-tori. These results are just a simple extension of the ergodic partition theory which we illustrate by applying this methodology to well-known examples, such as the planar field of the harmonic oscillator and the 3D ABC flow. Finally, we provide a thorough discussion on the requirement of the objectivity (frame-invariance) property for tools designed to reveal phase space structures and their implications for Lagrangian descriptors.

Trends in the thermodynamic stability of ultrathin supported oxide films

DOE PAGES

Plessow, Philipp N.; Bajdich, Michal; Greene, Joshua; ...

2016-05-05

The formation of thin oxide films on metal supports is an important phenomenon, especially in the context of strong metal support interaction (SMSI). Computational predictions of the stability of these films are hampered by their structural complexity and a varying lattice mismatch with different supports. In this study, we report a large combination of supports and ultrathin oxide films studied with density functional theory (DFT). Trends in stability are investigated through a descriptor-based analysis. Since the studied films are bound to the support exclusively through metal–metal interaction, the adsorption energy of the oxide-constituting metal atom can be expected to bemore » a reasonable descriptor for the stability of the overlayers. If the same supercell is used for all supports, the overlayers experience different amounts of stress. Using supercells with small lattice mismatch for each system leads to significantly improved scaling relations for the stability of the overlayers. Finally, this approach works well for the studied systems and therefore allows the descriptor-based exploration of the thermodynamic stability of supported thin oxide layers.« less

Real-time action recognition using a multilayer descriptor with variable size

NASA Astrophysics Data System (ADS)

Alcantara, Marlon F.; Moreira, Thierry P.; Pedrini, Helio

2016-01-01

Video analysis technology has become less expensive and more powerful in terms of storage resources and resolution capacity, promoting progress in a wide range of applications. Video-based human action detection has been used for several tasks in surveillance environments, such as forensic investigation, patient monitoring, medical training, accident prevention, and traffic monitoring, among others. We present a method for action identification based on adaptive training of a multilayer descriptor applied to a single classifier. Cumulative motion shapes (CMSs) are extracted according to the number of frames present in the video. Each CMS is employed as a self-sufficient layer in the training stage but belongs to the same descriptor. A robust classification is achieved through individual responses of classifiers for each layer, and the dominant result is used as a final outcome. Experiments are conducted on five public datasets (Weizmann, KTH, MuHAVi, IXMAS, and URADL) to demonstrate the effectiveness of the method in terms of accuracy in real time.

Movement characteristics of persons with prader-willi syndrome rising from supine.

PubMed

Belt, A B; Hertel, T A; Mante, J R; Marks, T; Rockett, V L; Wade, C; Clayton-Krasinski, D

2001-01-01

The purposes of this study were to: 1) determine if previously published descriptors of the supine to stand rising task in healthy individuals could be applied to the movements of persons with Prader-Willi Syndrome (PWS); and 2) assess upper extremity (UE), axial region (AX), and lower extremity (LE) movements among subjects with PWS compared with controls. Nine subjects with PWS (seven-36 years of age) and matched controls were videotaped performing 10 rising trials. The UE, AX, and LE movements were classified using published descriptors. Occurrence frequencies of movement patterns, duration of movement, and the relationships among body region movement score, BMI, and age were determined. Subjects with PWS utilized developmentally less advanced asymmetrical rising patterns, took longer to rise, and demonstrated less within subject variability than controls. Categorical descriptors, with minor modifications, can be used to describe rising movements in persons with PWS. Knowledge of successful rising patterns may assist PTs when examining or planning intervention strategies for teaching the rising task.

QSAR modeling of acute toxicity on mammals caused by aromatic compounds: the case study using oral LD50 for rats.

PubMed

Rasulev, Bakhtiyor; Kusić, Hrvoje; Leszczynska, Danuta; Leszczynski, Jerzy; Koprivanac, Natalija

2010-05-01

The goal of the study was to predict toxicity in vivo caused by aromatic compounds structured with a single benzene ring and the presence or absence of different substituent groups such as hydroxyl-, nitro-, amino-, methyl-, methoxy-, etc., by using QSAR/QSPR tools. A Genetic Algorithm and multiple regression analysis were applied to select the descriptors and to generate the correlation models. The most predictive model is shown to be the 3-variable model which also has a good ratio of the number of descriptors and their predictive ability to avoid overfitting. The main contributions to the toxicity were shown to be the polarizability weighted MATS2p and the number of certain groups C-026 descriptors. The GA-MLRA approach showed good results in this study, which allows the building of a simple, interpretable and transparent model that can be used for future studies of predicting toxicity of organic compounds to mammals.

Method of data communications with reduced latency

DOEpatents

Blocksome, Michael A; Parker, Jeffrey J

2013-11-05

Data communications with reduced latency, including: writing, by a producer, a descriptor and message data into at least two descriptor slots of a descriptor buffer, the descriptor buffer comprising allocated computer memory segmented into descriptor slots, each descriptor slot having a fixed size, the descriptor buffer having a header pointer that identifies a next descriptor slot to be processed by a DMA controller, the descriptor buffer having a tail pointer that identifies a descriptor slot for entry of a next descriptor in the descriptor buffer; recording, by the producer, in the descriptor a value signifying that message data has been written into descriptor slots; and setting, by the producer, in dependence upon the recorded value, a tail pointer to point to a next open descriptor slot.

Global QSAR modeling of logP values of phenethylamines acting as adrenergic alpha-1 receptor agonists.

PubMed

Yadav, Mukesh; Joshi, Shobha; Nayarisseri, Anuraj; Jain, Anuja; Hussain, Aabid; Dubey, Tushar

2013-06-01

Global QSAR models predict biological response of molecular structures which are generic in particular class. A global QSAR dataset admits structural features derived from larger chemical space, intricate to model but more applicable in medicinal chemistry. The present work is global in either sense of structural diversity in QSAR dataset or large number of descriptor input. Forty phenethylamine structure derivatives were selected from a large pool (904) of similar phenethylamines available in Pubchem database. LogP values of selected candidates were collected from physical properties database (PHYSPROP) determined in identical set of conditions. Attempts to model logP value have produced significant QSAR models. MLR aided linear one-variable and two-variable QSAR models with their respective R(2) (0.866, 0.937), R(2)A (0.862, 0.932), F-stat (181.936, 199.812) and Standard Error (0.365, 0.255) are statistically fit and found predictive after internal validation and external validation. The descriptors chosen after improvisation and optimization reveal mechanistic part of work in terms of Verhaar model of Fish base-line toxicity from MLOGP, i.e. (BLTF96) and 3D-MoRSE -signal 15 /unweighted molecular descriptor calculated by summing atom weights viewed by a different angular scattering function (Mor15u) are crucial in regulation of logP values of phenethylamines.

Physicochemical properties/descriptors governing the solubility and partitioning of chemicals in water-solvent-gas systems. Part 1. Partitioning between octanol and air.

PubMed

Raevsky, O A; Grigor'ev, V J; Raevskaja, O E; Schaper, K-J

2006-06-01

QSPR analyses of a data set containing experimental partition coefficients in the three systems octanol-water, water-gas, and octanol-gas for 98 chemicals have shown that it is possible to calculate any partition coefficient in the system 'gas phase/octanol/water' by three different approaches: (1) from experimental partition coefficients obtained in the corresponding two other subsystems. However, in many cases these data may not be available. Therefore, a solution may be approached (2), a traditional QSPR analysis based on e.g. HYBOT descriptors (hydrogen bond acceptor and donor factors, SigmaCa and SigmaCd, together with polarisability alpha, a steric bulk effect descriptor) and supplemented with substructural indicator variables. (3) A very promising approach which is a combination of the similarity concept and QSPR based on HYBOT descriptors. In this approach observed partition coefficients of structurally nearest neighbours of a compound-of-interest are used. In addition, contributions arising from differences in alpha, SigmaCa, and SigmaCd values between the compound-of-interest and its nearest neighbour(s), respectively, are considered. In this investigation highly significant relationships were obtained by approaches (1) and (3) for the octanol/gas phase partition coefficient (log Log).

Prediction of blood-brain barrier permeation of α-adrenergic and imidazoline receptor ligands using PAMPA technique and quantitative-structure permeability relationship analysis.

PubMed

Vucicevic, Jelica; Nikolic, Katarina; Dobričić, Vladimir; Agbaba, Danica

2015-02-20

Imidazoline receptor ligands are a numerous family of biologically active compounds known to produce central hypotensive effect by interaction with both α2-adrenoreceptors (α2-AR) and imidazoline receptors (IRs). Recent hypotheses connect those ligands with several neurological disorders. Therefore some IRs ligands are examined as novel centrally acting antihypertensives and drug candidates for treatment of various neurological diseases. Effective Blood-Brain Barrier (BBB) permeability (P(e)) of 18 IRs/α-ARs ligands and 22 Central Nervous System (CNS) drugs was experimentally determined using Parallel Artificial Membrane Permeability Assay (PAMPA) and studied by the Quantitative-Structure-Permeability Relationship (QSPR) methodology. The dominant molecules/cations species of compounds have been calculated at pH = 7.4. The analyzed ligands were optimized using Density Functional Theory (B3LYP/6-31G(d,p)) included in ChemBio3D Ultra 13.0 program and molecule descriptors for optimized compounds were calculated using ChemBio3D Ultra 13.0, Dragon 6.0 and ADMET predictor 6.5 software. Effective permeability of compounds was used as dependent variable (Y), while calculated molecular parametres were used as independent variables (X) in the QSPR study. SIMCA P+ 12.0 was used for Partial Least Square (PLS) analysis, while the stepwise Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) modeling were performed using STASTICA Neural Networks 4.0. Predictive potential of the formed models was confirmed by Leave-One-Out Cross- and external-validation and the most reliable models were selected. The descriptors that are important for model building are identified as well as their influence on BBB permeability. Results of the QSPR studies could be used as time and cost efficient screening tools for evaluation of BBB permeation of novel α-adrenergic/imidazoline receptor ligands, as promising drug candidates for treatment of hypertension or neurological diseases. Copyright © 2014 Elsevier B.V. All rights reserved.

Analyzing the substitution effect on the CoMFA results within the framework of density functional theory (DFT).

PubMed

Morales-Bayuelo, Alejandro

2016-07-01

Though QSAR was originally developed in the context of physical organic chemistry, it has been applied very extensively to chemicals (drugs) which act on biological systems, in this idea one of the most important QSAR methods is the 3D QSAR model. However, due to the complexity of understanding the results it is necessary to postulate new methodologies to highlight their physical-chemical meaning. In this sense, this work postulates new insights to understand the CoMFA results using molecular quantum similarity and chemical reactivity descriptors within the framework of density functional theory. To obtain these insights a simple theoretical scheme involving quantum similarity (overlap, coulomb operators, their euclidean distances) and chemical reactivity descriptors such as chemical potential (μ), hardness (ɳ), softness (S), electrophilicity (ω), and the Fukui functions, was used to understand the substitution effect. In this sense, this methodology can be applied to analyze the biological activity and the stabilization process in the non-covalent interactions on a particular molecular set taking a reference compound.

Allometric modelling of peak oxygen uptake in male soccer players of 8-18 years of age.

PubMed

Valente-Dos-Santos, João; Coelho-E-Silva, Manuel J; Tavares, Óscar M; Brito, João; Seabra, André; Rebelo, António; Sherar, Lauren B; Elferink-Gemser, Marije T; Malina, Robert M

2015-03-01

Peak oxygen uptake (VO2peak) is routinely scaled as mL O2 per kilogram body mass despite theoretical and statistical limitations of using ratios. To examine the contribution of maturity status and body size descriptors to age-associated inter-individual variability in VO2peak and to present static allometric models to normalize VO2peak in male youth soccer players. Total body and estimates of total and regional lean mass were measured with dual energy X-ray absorptiometry in a cross-sectional sample of Portuguese male soccer players. The sample was divided into three age groups for analysis: 8-12 years, 13-15 years and 16-18 years. VO2peak was estimated using an incremental maximal exercise test on a motorized treadmill. Static allometric models were used to normalize VO2peak. The independent variables with the best statistical fit explained 72% in the younger group (lean body mass: k = 1.07), 52% in mid-adolescent players (lean body mass: k = 0.93) and 31% in the older group (body mass: k = 0.51) of variance in VO2peak. The inclusion of the exponential term pubertal status marginally increased the explained variance in VO2peak (adjusted R(2 )= 36-75%) and provided statistical adjustments to the size descriptors coefficients. The allometric coefficients and exponents evidenced the varying inter-relationship among size descriptors and maturity status with aerobic fitness from early to late-adolescence. Lean body mass, lean lower limbs mass and body mass combined with pubertal status explain most of the inter-individual variability in VO2peak among youth soccer players.

COMPARATIVE STUDY OF THREE FUNDAMENTAL ORGANIC COMPOUNDS OF CHAIN STRUCTURE OF THREE RINGS An approach based in the molecular descriptors of the DFT (Density Functional Theory)

NASA Astrophysics Data System (ADS)

Leon, Neira B. Oscar; Fabio, Mejía Elio; Elizabeth, y. Rincón B.

2008-04-01

The organic molecules of a chain structure containing phenyl, oxazole and oxadiazole rings are used in different combinations as active media for tunable lasers. From this viewpoint, we focused in the theoretical study of organic compounds of three rings, which have similar optical properties (fluorescence and laser properties). The main goal of this study is to compare the electronic structure through the analysis of molecular global descriptors defined in the DFT framework of2-[2-X-phenyl]-5-phenyl-1,3-Oxazole, 2-[2-X-phenyl]-5-phenyl-1,3,4-Oxadiazole, and 2-[2-X-phenyl]-5-phenyl-furane with X = H, F and Cl. The basis set used was 6-31G+(d).

QSPR model for bioconcentration factors of nonpolar organic compounds using molecular electronegativity distance vector descriptors.

PubMed

Qin, Li-Tang; Liu, Shu-Shen; Liu, Hai-Ling

2010-02-01

A five-variable model (model M2) was developed for the bioconcentration factors (BCFs) of nonpolar organic compounds (NPOCs) by using molecular electronegativity distance vector (MEDV) to characterize the structures of NPOCs and variable selection and modeling based on prediction (VSMP) to select the optimum descriptors. The estimated correlation coefficient (r (2)) and the leave-one-out cross-validation correlation coefficients (q (2)) of model M2 were 0.9271 and 0.9171, respectively. The model was externally validated by splitting the whole data set into a representative training set of 85 chemicals and a validation set of 29 chemicals. The results show that the main structural factors influencing the BCFs of NPOCs are -cCc, cCcc, -Cl, and -Br (where "-" refers to a single bond and "c" refers to a conjugated bond). The quantitative structure-property relationship (QSPR) model can effectively predict the BCFs of NPOCs, and the predictions of the model can also extend the current BCF database of experimental values.

Structure-activity relationships between sterols and their thermal stability in oil matrix.

PubMed

Hu, Yinzhou; Xu, Junli; Huang, Weisu; Zhao, Yajing; Li, Maiquan; Wang, Mengmeng; Zheng, Lufei; Lu, Baiyi

2018-08-30

Structure-activity relationships between 20 sterols and their thermal stabilities were studied in a model oil system. All sterol degradations were found to be consistent with a first-order kinetic model with determination of coefficient (R 2 ) higher than 0.9444. The number of double bonds in the sterol structure was negatively correlated with the thermal stability of sterol, whereas the length of the branch chain was positively correlated with the thermal stability of sterol. A quantitative structure-activity relationship (QSAR) model to predict thermal stability of sterol was developed by using partial least squares regression (PLSR) combined with genetic algorithm (GA). A regression model was built with R 2 of 0.806. Almost all sterol degradation constants can be predicted accurately with R 2 of cross-validation equals to 0.680. Four important variables were selected in optimal QSAR model and the selected variables were observed to be related with information indices, RDF descriptors, and 3D-MoRSE descriptors. Copyright © 2018 Elsevier Ltd. All rights reserved.

Chemical Reactivity Theory Study of Advanced Glycation Endproduct Inhibitors.

PubMed

Frau, Juan; Glossman-Mitnik, Daniel

2017-02-02

Several compounds with the known ability to perform as inhibitors of advanced glycation endproducts (AGE) have been studied with Density Functional Theory (DFT) through the use of anumberofdensityfunctionalswhoseaccuracyhasbeentestedacrossabroadspectrumofdatabases in Chemistry and Physics. The chemical reactivity descriptors for these systems have been calculated through Conceptual DFT in an attempt to relate their intrinsic chemical reactivity with the ability to inhibit the action of glycating carbonyl compounds on amino acids and proteins. This knowledge could be useful in the design and development of new drugs which can be potential medicines for diabetes and Alzheimer's disease.

Electron-density descriptors as predictors in quantitative structure--activity/property relationships and drug design.

PubMed

Matta, Chérif F; Arabi, Alya A

2011-06-01

The use of electron density-based molecular descriptors in drug research, particularly in quantitative structure--activity relationships/quantitative structure--property relationships studies, is reviewed. The exposition starts by a discussion of molecular similarity and transferability in terms of the underlying electron density, which leads to a qualitative introduction to the quantum theory of atoms in molecules (QTAIM). The starting point of QTAIM is the topological analysis of the molecular electron-density distributions to extract atomic and bond properties that characterize every atom and bond in the molecule. These atomic and bond properties have considerable potential as bases for the construction of robust quantitative structure--activity/property relationships models as shown by selected examples in this review. QTAIM is applicable to the electron density calculated from quantum-chemical calculations and/or that obtained from ultra-high resolution x-ray diffraction experiments followed by nonspherical refinement. Atomic and bond properties are introduced followed by examples of application of each of these two families of descriptors. The review ends with a study whereby the molecular electrostatic potential, uniquely determined by the density, is used in conjunction with atomic properties to elucidate the reasons for the biological similarity of bioisosteres.

Quantitative structure-activity relationships of the antimalarial agent artemisinin and some of its derivatives - a DFT approach.

PubMed

Rajkhowa, Sanchaita; Hussain, Iftikar; Hazarika, Kalyan K; Sarmah, Pubalee; Deka, Ramesh Chandra

2013-09-01

Artemisinin form the most important class of antimalarial agents currently available, and is a unique sesquiterpene peroxide occurring as a constituent of Artemisia annua. Artemisinin is effectively used in the treatment of drug-resistant Plasmodium falciparum and because of its rapid clearance of cerebral malaria, many clinically useful semisynthetic drugs for severe and complicated malaria have been developed. However, one of the major disadvantages of using artemisinins is their poor solubility either in oil or water and therefore, in order to overcome this difficulty many derivatives of artemisinin were prepared. A comparative study on the chemical reactivity of artemisinin and some of its derivatives is performed using density functional theory (DFT) calculations. DFT based global and local reactivity descriptors, such as hardness, chemical potential, electrophilicity index, Fukui function, and local philicity calculated at the optimized geometries are used to investigate the usefulness of these descriptors for understanding the reactive nature and reactive sites of the molecules. Multiple regression analysis is applied to build up a quantitative structure-activity relationship (QSAR) model based on the DFT based descriptors against the chloroquine-resistant, mefloquine-sensitive Plasmodium falciparum W-2 clone.

Computational study of AuSi{sub n} (n=1-9) nanoalloy clusters invoking DFT based descriptors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ranjan, Prabhat; Kumar, Ajay; Chakraborty, Tanmoy, E-mail: tanmoy.chakraborty@jaipur.manipal.edu, E-mail: tanmoychem@gmail.com

2016-04-13

Nanoalloy clusters formed between Au and Si are topics of great interest today from both scientific and technological point of view. Due to its remarkable catalytic, electronic, mechanical and magnetic properties Au-Si nanoalloy clusters have extensive applications in the field of microelectronics, catalysis, biomedicine, and jewelry industry. Density Functional Theory (DFT) is a new paradigm of quantum mechanics, which is very much popular to study the electronic properties of materials. Conceptual DFT based descriptors have been invoked to correlate the experimental properties of nanoalloy clusters. In this venture, we have systematically investigated AuSi{sub n} (n=1-9) nanoalloy clusters in the theoreticalmore » frame of the B3LYP exchange correlation. The experimental properties of AuSi{sub n} (n=1-9) nanoalloy clusters are correlated in terms of DFT based descriptors viz. HOMO-LUMO gap, Electronegativity (χ), Global Hardness (η), Global Softness (S) and Electrophilicity Index (ω). The calculated HOMO-LUMO gap exhibits interesting odd-even alteration behaviour, indicating that even numbered clusters possess higher stability as compare to their neighbour odd numbered clusters. This study also reflects a very well agreement between experimental bond length and computed data.« less

A general representation scheme for crystalline solids based on Voronoi-tessellation real feature values and atomic property data

PubMed Central

Jalem, Randy; Nakayama, Masanobu; Noda, Yusuke; Le, Tam; Takeuchi, Ichiro; Tateyama, Yoshitaka; Yamazaki, Hisatsugu

2018-01-01

Abstract Increasing attention has been paid to materials informatics approaches that promise efficient and fast discovery and optimization of functional inorganic materials. Technical breakthrough is urgently requested to advance this field and efforts have been made in the development of materials descriptors to encode or represent characteristics of crystalline solids, such as chemical composition, crystal structure, electronic structure, etc. We propose a general representation scheme for crystalline solids that lifts restrictions on atom ordering, cell periodicity, and system cell size based on structural descriptors of directly binned Voronoi-tessellation real feature values and atomic/chemical descriptors based on the electronegativity of elements in the crystal. Comparison was made vs. radial distribution function (RDF) feature vector, in terms of predictive accuracy on density functional theory (DFT) material properties: cohesive energy (CE), density (d), electronic band gap (BG), and decomposition energy (Ed). It was confirmed that the proposed feature vector from Voronoi real value binning generally outperforms the RDF-based one for the prediction of aforementioned properties. Together with electronegativity-based features, Voronoi-tessellation features from a given crystal structure that are derived from second-nearest neighbor information contribute significantly towards prediction. PMID:29707064

A general representation scheme for crystalline solids based on Voronoi-tessellation real feature values and atomic property data.

PubMed

Jalem, Randy; Nakayama, Masanobu; Noda, Yusuke; Le, Tam; Takeuchi, Ichiro; Tateyama, Yoshitaka; Yamazaki, Hisatsugu

2018-01-01

Increasing attention has been paid to materials informatics approaches that promise efficient and fast discovery and optimization of functional inorganic materials. Technical breakthrough is urgently requested to advance this field and efforts have been made in the development of materials descriptors to encode or represent characteristics of crystalline solids, such as chemical composition, crystal structure, electronic structure, etc. We propose a general representation scheme for crystalline solids that lifts restrictions on atom ordering, cell periodicity, and system cell size based on structural descriptors of directly binned Voronoi-tessellation real feature values and atomic/chemical descriptors based on the electronegativity of elements in the crystal. Comparison was made vs. radial distribution function (RDF) feature vector, in terms of predictive accuracy on density functional theory (DFT) material properties: cohesive energy (CE), density ( d ), electronic band gap (BG), and decomposition energy (Ed). It was confirmed that the proposed feature vector from Voronoi real value binning generally outperforms the RDF-based one for the prediction of aforementioned properties. Together with electronegativity-based features, Voronoi-tessellation features from a given crystal structure that are derived from second-nearest neighbor information contribute significantly towards prediction.

Bond-based linear indices of the non-stochastic and stochastic edge-adjacency matrix. 1. Theory and modeling of ChemPhys properties of organic molecules.

PubMed

Marrero-Ponce, Yovani; Martínez-Albelo, Eugenio R; Casañola-Martín, Gerardo M; Castillo-Garit, Juan A; Echevería-Díaz, Yunaimy; Zaldivar, Vicente Romero; Tygat, Jan; Borges, José E Rodriguez; García-Domenech, Ramón; Torrens, Francisco; Pérez-Giménez, Facundo

2010-11-01

Novel bond-level molecular descriptors are proposed, based on linear maps similar to the ones defined in algebra theory. The kth edge-adjacency matrix (E(k)) denotes the matrix of bond linear indices (non-stochastic) with regard to canonical basis set. The kth stochastic edge-adjacency matrix, ES(k), is here proposed as a new molecular representation easily calculated from E(k). Then, the kth stochastic bond linear indices are calculated using ES(k) as operators of linear transformations. In both cases, the bond-type formalism is developed. The kth non-stochastic and stochastic total linear indices are calculated by adding the kth non-stochastic and stochastic bond linear indices, respectively, of all bonds in molecule. First, the new bond-based molecular descriptors (MDs) are tested for suitability, for the QSPRs, by analyzing regressions of novel indices for selected physicochemical properties of octane isomers (first round). General performance of the new descriptors in this QSPR studies is evaluated with regard to the well-known sets of 2D/3D MDs. From the analysis, we can conclude that the non-stochastic and stochastic bond-based linear indices have an overall good modeling capability proving their usefulness in QSPR studies. Later, the novel bond-level MDs are also used for the description and prediction of the boiling point of 28 alkyl-alcohols (second round), and to the modeling of the specific rate constant (log k), partition coefficient (log P), as well as the antibacterial activity of 34 derivatives of 2-furylethylenes (third round). The comparison with other approaches (edge- and vertices-based connectivity indices, total and local spectral moments, and quantum chemical descriptors as well as E-state/biomolecular encounter parameters) exposes a good behavior of our method in this QSPR studies. Finally, the approach described in this study appears to be a very promising structural invariant, useful not only for QSPR studies but also for similarity/diversity analysis and drug discovery protocols.

Relations between water physico-chemistry and benthic algal communities in a northern Canadian watershed: defining reference conditions using multiple descriptors of community structure.

PubMed

Thomas, Kathryn E; Hall, Roland I; Scrimgeour, Garry J

2015-09-01

Defining reference conditions is central to identifying environmental effects of anthropogenic activities. Using a watershed approach, we quantified reference conditions for benthic algal communities and their relations to physico-chemical conditions in rivers in the South Nahanni River watershed, NWT, Canada, in 2008 and 2009. We also compared the ability of three descriptors that vary in terms of analytical costs to define algal community structure based on relative abundances of (i) all algal taxa, (ii) only diatom taxa, and (iii) photosynthetic pigments. Ordination analyses showed that variance in algal community structure was strongly related to gradients in environmental variables describing water physico-chemistry, stream habitats, and sub-watershed structure. Water physico-chemistry and local watershed-scale descriptors differed significantly between algal communities from sites in the Selwyn Mountain ecoregion compared to sites in the Nahanni-Hyland ecoregions. Distinct differences in algal community types between ecoregions were apparent irrespective of whether algal community structure was defined using all algal taxa, diatom taxa, or photosynthetic pigments. Two algal community types were highly predictable using environmental variables, a core consideration in the development of Reference Condition Approach (RCA) models. These results suggest that assessments of environmental impacts could be completed using RCA models for each ecoregion. We suggest that use of algal pigments, a high through-put analysis, is a promising alternative compared to more labor-intensive and costly taxonomic approaches for defining algal community structure.

Global chemical reactivity parameters for several chiral beta-blockers from the Density Functional Theory viewpoint

PubMed Central

TALMACIU, MONA MARIA; BODOKI, EDE; OPREAN, RADU

2016-01-01

Background and aim Beta-adrenergic antagonists have been established as first line treatment in the medical management of hypertension, acute coronary syndrome and other cardiovascular diseases, as well as for the prevention of initial episodes of gastrointestinal bleeding in patients with cirrhosis and esophageal varices, glaucoma, and have recently become the main form of treatment of infantile hemangiomas. The aim of the present study is to calculate for 14 beta-blockers several quantum chemical descriptors in order to interpret various molecular properties such as electronic structure, conformation, reactivity, in the interest of determining how such descriptors could have an impact on our understanding of the experimental observations and describing various aspects of chemical binding of beta-blockers in terms of these descriptors. Methods The 2D chemical structures of the beta-blockers (14 molecules with one stereogenic center) were cleaned in 3D, their geometry was preoptimized using the software MOPAC2012, by PM6 method, and then further refined using standard settings in MOE; HOMO and LUMO descriptors were calculated using semi-empirical molecular orbital methods AM1, MNDO and PM3, for the lowest energy conformers and the quantum chemical descriptors (HLG, electronegativity, chemical potential, hardness and softness, electrophilicity) were then calculated. Results According to HOMO-LUMO gap and the chemical hardness the most stable compounds are alprenolol, bisoprolol and esmolol. The softness values calculated for the study molecules revolve around 0.100. Propranolol, sotalol and timolol have among the highest electrophilicity index of the studied beta-blocker molecules. Results obtained from calculations showed that acebutolol, atenolol, timolol and sotalol have the highest values for the electronegativity index. Conclusions The future aim is to determine whether it is possible to find a valid correlation between these descriptors and the physicochemical behavior of the molecules from this class. The HLG could be correlated to the experimentally recorded electrochemical properties of the molecules. HOMO could be correlated to the observed oxidation potential, since the required voltage is related to the energy of the HOMO, because only the electron from this orbital is involved in the oxidation process. PMID:27857521

Idiopathic environmental intolerance: Part 1: A causation analysis applying Bradford Hill's criteria to the toxicogenic theory.

PubMed

Staudenmayer, Herman; Binkley, Karen E; Leznoff, Arthur; Phillips, Scott

2003-01-01

Idiopathic environmental intolerance (IEI) is a descriptor for a phenomenon that has many names including environmental illness, multiple chemical sensitivity and chemical intolerance. Toxicogenic and psychogenic theories have been proposed to explain IEI. This paper presents a causality analysis of the toxicogenic theory using Bradford Hill's nine criteria (strength, consistency, specificity, temporality, biological gradient, biological plausibility, coherence, experimental intervention and analogy) and an additional criteria (reversibility) and reviews critically the scientific literature on the topic. The results of this analysis indicate that the toxicogenic theory fails all of these criteria. There is no convincing evidence to support the fundamental postulate that IEI has a toxic aetiology; the hypothesised biological processes and mechanisms are implausible.

Chapter 5 Multiple, Localized, and Delocalized/Conjugated Bonds in the Orbital Communication Theory of Molecular Systems

NASA Astrophysics Data System (ADS)

Nalewajski, Roman F.

Information theory (IT) probe of the molecular electronic structure, within the communication theory of chemical bonds (CTCB), uses the standard entropy/information descriptors of the Shannon theory of communication to characterize a scattering of the electronic probabilities and their information content throughout the system chemical bonds generated by the occupied molecular orbitals (MO). These "communications" between the basis-set orbitals are determined by the two-orbital conditional probabilities: one- and two-electron in character. They define the molecular information system, in which the electron-allocation "signals" are transmitted between various orbital "inputs" and "outputs". It is argued, using the quantum mechanical superposition principle, that the one-electron conditional probabilities are proportional to the squares of corresponding elements of the charge and bond-order (CBO) matrix of the standard LCAO MO theory. Therefore, the probability of the interorbital connections in the molecular communication system is directly related to Wiberg's quadratic covalency indices of chemical bonds. The conditional-entropy (communication "noise") and mutual-information (information capacity) descriptors of these molecular channels generate the IT-covalent and IT-ionic bond components, respectively. The former reflects the electron delocalization (indeterminacy) due to the orbital mixing, throughout all chemical bonds in the system under consideration. The latter characterizes the localization (determinacy) in the probability scattering in the molecule. These two IT indices, respectively, indicate a fraction of the input information lost in the channel output, due to the communication noise, and its surviving part, due to deterministic elements in probability scattering in the molecular network. Together, these two components generate the system overall bond index. By a straightforward output reduction (condensation) of the molecular channel, the IT indices of molecular fragments, for example, localized bonds, functional groups, and forward and back donations accompanying the bond formation, and so on, can be extracted. The flow of information in such molecular communication networks is investigated in several prototype molecules. These illustrative (model) applications of the orbital communication theory of chemical bonds (CTCB) deal with several classical issues in the electronic structure theory: atom hybridization/promotion, single and multiple chemical bonds, bond conjugation, and so on. The localized bonds in hydrides and delocalized [pi]-bonds in simple hydrocarbons, as well as the multiple bonds in CO and CO2, are diagnosed using the entropy/information descriptors of CTCB. The atom promotion in hydrides and bond conjugation in [pi]-electron systems are investigated in more detail. A major drawback of the previous two-electron approach to molecular channels, namely, two weak bond differentiation in aromatic systems, has been shown to be remedied in the one-electron approach.

Assessment of ten density functionals through the use of local hyper-softness to get insights about the catalytic activity : Iron-based organometallic compounds for ethylene polymerization as testing molecules.

PubMed

Martínez-Araya, Jorge I; Glossman-Mitnik, Daniel

2018-01-18

Ten functionals were used to assess their capability to compute a local reactivity descriptor coming from the Conceptual Density Functional Theory on a group of iron-based organometallic compounds that have been synthesized by Zohuri, G.H. et al. in 2010; these compounds bear the following substituent groups: H-, O 2 N- and CH 3 O- at the para position of the pyridine ring and their catalytic activities were experimentally measured by these authors. The present work involved a theoretical analysis applied on the aforementioned iron-based compounds thus leading to suggest a new 2,6-bis(imino)pyridine catalyst based on iron(II) bearing a fluorine atom whose possible catalytic activity is suggested to be near the catalytic activity of the complex bearing a hydrogen atom as a substituent group by means of the so called local hyper-softness (LHS) thus opening a chance to estimate a possible value of catalytic activity for a new catalyst that has not been synthesized yet without simulating the entire process of ethylene polymerization. Since Conceptual DFT is not a predictive theory, but rather interpretative, an analysis of the used reactivity descriptor and its dependence upon the level of theory was carried in the present work, thus revealing that care should be taken when DFT calculations are used for these purposes.

Influence of Interpretation Aids on Attentional Capture, Visual Processing, and Understanding of Front-of-Package Nutrition Labels.

PubMed

Antúnez, Lucía; Giménez, Ana; Maiche, Alejandro; Ares, Gastón

2015-01-01

To study the influence of 2 interpretational aids of front-of-package (FOP) nutrition labels (color code and text descriptors) on attentional capture and consumers' understanding of nutritional information. A full factorial design was used to assess the influence of color code and text descriptors using visual search and eye tracking. Ten trained assessors participated in the visual search study and 54 consumers completed the eye-tracking study. In the visual search study, assessors were asked to indicate whether there was a label high in fat within sets of mayonnaise labels with different FOP labels. In the eye-tracking study, assessors answered a set of questions about the nutritional content of labels. The researchers used logistic regression to evaluate the influence of interpretational aids of FOP nutrition labels on the percentage of correct answers. Analyses of variance were used to evaluate the influence of the studied variables on attentional measures and participants' response times. Response times were significantly higher for monochromatic FOP labels compared with color-coded ones (3,225 vs 964 ms; P < .001), which suggests that color codes increase attentional capture. The highest number and duration of fixations and visits were recorded on labels that did not include color codes or text descriptors (P < .05). The lowest percentage of incorrect answers was observed when the nutrient level was indicated using color code and text descriptors (P < .05). The combination of color codes and text descriptors seems to be the most effective alternative to increase attentional capture and understanding of nutritional information. Copyright © 2015 Society for Nutrition Education and Behavior. Published by Elsevier Inc. All rights reserved.

Trends in Children's Video Game Play: Practical but Not Creative Thinking

ERIC Educational Resources Information Center

Hamlen, Karla R.

2013-01-01

Prior research has found common trends among children's video game play as related to gender, age, interests, creativity, and other descriptors. This study re-examined the previously reported trends by utilizing principal components analysis with variables such as creativity, general characteristics, and problem-solving methods to determine…

Computational study of frontier orbitals, moments, chemical reactivity and thermodynamic parameters of sildenafil

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sachdeva, Ritika, E-mail: ritika.sachdeva21@gmail.com; Kaur, Prabhjot; Singh, V. P.

2016-05-06

Analysis of frontier orbitals of sildenafil has been carried using Density Functional Theory. On the basis of HOMO-LUMO energy, values of global chemical reactivity descriptors such as electronegativity, chemical hardness, softness, chemical potential, electrophilicity index have been calculated. Calculated values of dipole moment, polarizability, hyperpolarizability have also been reported for sildenafil along with its thermodynamic parameters.

Species associations structured by environment and land-use history promote beta-diversity in a temperate forest.

PubMed

Murphy, Stephen J; Audino, Livia D; Whitacre, James; Eck, Jenalle L; Wenzel, John W; Queenborough, Simon A; Comita, Liza S

2015-03-01

Patterns of diversity and community composition in forests are controlled by a combination of environmental factors, historical events, and stochastic or neutral mechanisms. Each of these processes has been linked to forest community assembly, but their combined contributions to alpha and beta-diversity in forests has not been well explored. Here we use variance partitioning to analyze approximately 40,000 individual trees of 49 species, collected within 137 ha of sampling area spread across a 900-ha temperate deciduous forest reserve in Pennsylvania to ask (1) To what extent is site-to-site variation in species richness and community composition of a temperate forest explained by measured environmental gradients and by spatial descriptors (used here to estimate dispersal-assembly or unmeasured, spatially structured processes)? (2) How does the incorporation of land-use history information increase the importance attributed to deterministic community assembly? and (3) How do the distributions and abundances of individual species within the community correlate with these factors? Environmental variables (i.e., topography, soils, and distance to stream), spatial descriptors (i.e., spatial eigenvectors derived from Cartesian coordinates), and land-use history variables (i.e., land-use type and intensity, forest age, and distance to road), explained about half of the variation in both species richness and community composition. Spatial descriptors explained the most variation, followed by measured environmental variables and then by land- use history. Individual species revealed variable responses to each of these sets of predictor variables. Several species were associated with stream habitats, and others were strictly delimited across opposing north- and south-facing slopes. Several species were also associated with areas that experienced recent (i.e., <100 years) human land-use impacts. These results indicate that deterministic factors, including environmental and land-use history variables, are important drivers of community response. The large amount of "unexplained" variation seen here (about 50%) is commonly observed in other such studies attempting to explain distribution and abundance patterns of plant communities. Determining whether such large fractions of unaccounted for variation are caused by a lack of sufficient data, or are an indication of stochastic features of forest communities globally, will remain an important challenge for ecologists in the future.

Ionic and Covalent Stabilization of Intermediates and Transition States in Catalysis by Solid Acids

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deshlahra, Prashant; Carr, Robert T.; Iglesia, Enrique

Reactivity descriptors describe catalyst properties that determine the stability of kinetically relevant transition states and adsorbed intermediates. Theoretical descriptors, such as deprotonation energies (DPE), rigorously account for Brønsted acid strength for catalytic solids with known structure. Here, mechanistic interpretations of methanol dehydration turnover rates are used to assess how charge reorganization (covalency) and electrostatic interactions determine DPE and how such interactions are recovered when intermediates and transition states interact with the conjugate anion in W and Mo polyoxometalate (POM) clusters and gaseous mineral acids. Turnover rates are lower and kinetically relevant species are less stable on Mo than W POMmore » clusters with similar acid strength, and such species are more stable on mineral acids than that predicted from W-POM DPE–reactivity trends, indicating that DPE and acid strength are essential but incomplete reactivity descriptors. Born–Haber thermochemical cycles indicate that these differences reflect more effective charge reorganization upon deprotonation of Mo than W POM clusters and the much weaker reorganization in mineral acids. Such covalency is disrupted upon deprotonation but cannot be recovered fully upon formation of ion pairs at transition states. Predictive descriptors of reactivity for general classes of acids thus require separate assessments of the covalent and ionic DPE components. Here, we describe methods to estimate electrostatic interactions, which, taken together with energies derived from density functional theory, give the covalent and ionic energy components of protons, intermediates, and transition states. In doing so, we provide a framework to predict the reactive properties of protons for chemical reactions mediated by ion-pair transition states.« less

Textural kinetics: a novel dynamic contrast-enhanced (DCE)-MRI feature for breast lesion classification.

PubMed

Agner, Shannon C; Soman, Salil; Libfeld, Edward; McDonald, Margie; Thomas, Kathleen; Englander, Sarah; Rosen, Mark A; Chin, Deanna; Nosher, John; Madabhushi, Anant

2011-06-01

Dynamic contrast-enhanced (DCE)-magnetic resonance imaging (MRI) of the breast has emerged as an adjunct imaging tool to conventional X-ray mammography due to its high detection sensitivity. Despite the increasing use of breast DCE-MRI, specificity in distinguishing malignant from benign breast lesions is low, and interobserver variability in lesion classification is high. The novel contribution of this paper is in the definition of a new DCE-MRI descriptor that we call textural kinetics, which attempts to capture spatiotemporal changes in breast lesion texture in order to distinguish malignant from benign lesions. We qualitatively and quantitatively demonstrated on 41 breast DCE-MRI studies that textural kinetic features outperform signal intensity kinetics and lesion morphology features in distinguishing benign from malignant lesions. A probabilistic boosting tree (PBT) classifier in conjunction with textural kinetic descriptors yielded an accuracy of 90%, sensitivity of 95%, specificity of 82%, and an area under the curve (AUC) of 0.92. Graph embedding, used for qualitative visualization of a low-dimensional representation of the data, showed the best separation between benign and malignant lesions when using textural kinetic features. The PBT classifier results and trends were also corroborated via a support vector machine classifier which showed that textural kinetic features outperformed the morphological, static texture, and signal intensity kinetics descriptors. When textural kinetic attributes were combined with morphologic descriptors, the resulting PBT classifier yielded 89% accuracy, 99% sensitivity, 76% specificity, and an AUC of 0.91.

Selection of molecular descriptors with artificial intelligence for the understanding of HIV-1 protease peptidomimetic inhibitors-activity.

PubMed

Sirois, S; Tsoukas, C M; Chou, Kuo-Chen; Wei, Dongqing; Boucher, C; Hatzakis, G E

2005-03-01

Quantitative Structure Activity Relationship (QSAR) techniques are used routinely by computational chemists in drug discovery and development to analyze datasets of compounds. Quantitative numerical methods like Partial Least Squares (PLS) and Artificial Neural Networks (ANN) have been used on QSAR to establish correlations between molecular properties and bioactivity. However, ANN may be advantageous over PLS because it considers the interrelations of the modeled variables. This study focused on the HIV-1 Protease (HIV-1 Pr) inhibitors belonging to the peptidomimetic class of compounds. The main objective was to select molecular descriptors with the best predictive value for antiviral potency (Ki). PLS and ANN were used to predict Ki activity of HIV-1 Pr inhibitors and the results were compared. To address the issue of dimensionality reduction, Genetic Algorithms (GA) were used for variable selection and their performance was compared against that of ANN. Finally, the structure of the optimum ANN achieving the highest Pearson's-R coefficient was determined. On the basis of Pearson's-R, PLS and ANN were compared to determine which exhibits maximum performance. Training and validation of models was performed on 15 random split sets of the master dataset consisted of 231 compounds. For each compound 192 molecular descriptors were considered. The molecular structure and constant of inhibition (Ki) were selected from the NIAID database. Study findings suggested that non-covalent interactions such as hydrophobicity, shape and hydrogen bonding describe well the antiviral activity of the HIV-1 Pr compounds. The significance of lipophilicity and relationship to HIV-1 associated hyperlipidemia and lipodystrophy syndrome warrant further investigation.

A generalized operational formula based on total electronic densities to obtain 3D pictures of the dual descriptor to reveal nucleophilic and electrophilic sites accurately on closed-shell molecules.

PubMed

Martínez-Araya, Jorge I

2016-09-30

By means of the conceptual density functional theory, the so-called dual descriptor (DD) has been adapted to be used in any closed-shell molecule that presents degeneracy in its frontier molecular orbitals. The latter is of paramount importance because a correct description of local reactivity will allow to predict the most favorable sites on a molecule to undergo nucleophilic or electrophilic attacks; on the contrary, an incomplete description of local reactivity might have serio us consequences, particularly for those experimental chemists that have the need of getting an insight about reactivity of chemical reagents before using them in synthesis to obtain a new compound. In the present work, the old approach based only on electronic densities of frontier molecular orbitals is replaced by the most accurate procedure that implies the use of total electronic densities thus keeping consistency with the essential principle of the DFT in which the electronic density is the fundamental variable and not the molecular orbitals. As a result of the present work, the DD will be able to properly describe local reactivities only in terms of total electronic densities. To test the proposed operational formula, 12 very common molecules were selected as the original definition of the DD was not able to describe their local reactivities properly. The ethylene molecule was additionally used to test the capability of the proposed operational formula to reveal a correct local reactivity even in absence of degeneracy in frontier molecular orbitals. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

New molecular descriptors based on local properties at the molecular surface and a boiling-point model derived from them.

PubMed

Ehresmann, Bernd; de Groot, Marcel J; Alex, Alexander; Clark, Timothy

2004-01-01

New molecular descriptors based on statistical descriptions of the local ionization potential, local electron affinity, and the local polarizability at the surface of the molecule are proposed. The significance of these descriptors has been tested by calculating them for the Maybridge database in addition to our set of 26 descriptors reported previously. The new descriptors show little correlation with those already in use. Furthermore, the principal components of the extended set of descriptors for the Maybridge data show that especially the descriptors based on the local electron affinity extend the variance in our set of descriptors, which we have previously shown to be relevant to physical properties. The first nine principal components are shown to be most significant. As an example of the usefulness of the new descriptors, we have set up a QSPR model for boiling points using both the old and new descriptors.

Chaining direct memory access data transfer operations for compute nodes in a parallel computer

DOEpatents

Archer, Charles J.; Blocksome, Michael A.

2010-09-28

Methods, systems, and products are disclosed for chaining DMA data transfer operations for compute nodes in a parallel computer that include: receiving, by an origin DMA engine on an origin node in an origin injection FIFO buffer for the origin DMA engine, a RGET data descriptor specifying a DMA transfer operation data descriptor on the origin node and a second RGET data descriptor on the origin node, the second RGET data descriptor specifying a target RGET data descriptor on the target node, the target RGET data descriptor specifying an additional DMA transfer operation data descriptor on the origin node; creating, by the origin DMA engine, an RGET packet in dependence upon the RGET data descriptor, the RGET packet containing the DMA transfer operation data descriptor and the second RGET data descriptor; and transferring, by the origin DMA engine to a target DMA engine on the target node, the RGET packet.

Replenishing data descriptors in a DMA injection FIFO buffer

DOEpatents

Archer, Charles J [Rochester, MN; Blocksome, Michael A [Rochester, MN; Cernohous, Bob R [Rochester, MN; Heidelberger, Philip [Cortlandt Manor, NY; Kumar, Sameer [White Plains, NY; Parker, Jeffrey J [Rochester, MN

2011-10-11

Methods, apparatus, and products are disclosed for replenishing data descriptors in a Direct Memory Access (`DMA`) injection first-in-first-out (`FIFO`) buffer that include: determining, by a messaging module on an origin compute node, whether a number of data descriptors in a DMA injection FIFO buffer exceeds a predetermined threshold, each data descriptor specifying an application message for transmission to a target compute node; queuing, by the messaging module, a plurality of new data descriptors in a pending descriptor queue if the number of the data descriptors in the DMA injection FIFO buffer exceeds the predetermined threshold; establishing, by the messaging module, interrupt criteria that specify when to replenish the injection FIFO buffer with the plurality of new data descriptors in the pending descriptor queue; and injecting, by the messaging module, the plurality of new data descriptors into the injection FIFO buffer in dependence upon the interrupt criteria.

Prediction of solvation enthalpy of gaseous organic compounds in propanol

NASA Astrophysics Data System (ADS)

Golmohammadi, Hassan; Dashtbozorgi, Zahra

2016-09-01

The purpose of this paper is to present a novel way for developing quantitative structure-property relationship (QSPR) models to predict the gas-to-propanol solvation enthalpy (Δ H solv) of 95 organic compounds. Different kinds of descriptors were calculated for each compound using the Dragon software package. The variable selection technique of replacement method (RM) was employed to select the optimal subset of solute descriptors. Our investigation reveals that the dependence of physical chemistry properties of solution on solvation enthalpy is nonlinear and that the RM method is unable to model the solvation enthalpy accurately. The results established that the calculated Δ H solv values by SVM were in good agreement with the experimental ones, and the performances of the SVM models were superior to those obtained by RM model.

A chirality-based metrics for free-energy calculations in biomolecular systems.

PubMed

Pietropaolo, Adriana; Branduardi, Davide; Bonomi, Massimiliano; Parrinello, Michele

2011-09-01

In this work, we exploit the chirality index introduced in (Pietropaolo et al., Proteins 2008, 70, 667) as an effective descriptor of the secondary structure of proteins to explore their complex free-energy landscape. We use the chirality index as an alternative metrics in the path collective variables (PCVs) framework and we show in the prototypical case of the C-terminal domain of immunoglobulin binding protein GB1 that relevant configurations can be efficiently sampled in combination with well-tempered metadynamics. While the projections of the configurations found onto a variety of different descriptors are fully consistent with previously reported calculations, this approach provides a unifying perspective of the folding mechanism which was not possible using metadynamics with the previous formulation of PCVs. Copyright © 2011 Wiley Periodicals, Inc.

Bulk-surface relationship of an electronic structure for high-throughput screening of metal oxide catalysts

NASA Astrophysics Data System (ADS)

Kweun, Joshua Minwoo; Li, Chenzhe; Zheng, Yongping; Cho, Maenghyo; Kim, Yoon Young; Cho, Kyeongjae

2016-05-01

Designing metal-oxides consisting of earth-abundant elements has been a crucial issue to replace precious metal catalysts. To achieve efficient screening of metal-oxide catalysts via bulk descriptors rather than surface descriptors, we investigated the relationship between the electronic structure of bulk and that of the surface for lanthanum-based perovskite oxides, LaMO3 (M = Ti, V, Cr, Mn, Fe, Co, Ni, Cu). Through density functional theory calculations, we examined the d-band occupancy of the bulk and surface transition-metal atoms (nBulk and nSurf) and the adsorption energy of an oxygen atom (Eads) on (001), (110), and (111) surfaces. For the (001) surface, we observed strong correlation between the nBulk and nSurf with an R-squared value over 94%, and the result was interpreted in terms of ligand field splitting and antibonding/bonding level splitting. Moreover, the Eads on the surfaces was highly correlated with the nBulk with an R-squared value of more than 94%, and different surface relaxations could be explained by the bulk electronic structure (e.g., LaMnO3 vs. LaTiO3). These results suggest that a bulk-derived descriptor such as nBulk can be used to screen metal-oxide catalysts.

Reduced density gradient as a novel approach for estimating QSAR descriptors, and its application to 1, 4-dihydropyridine derivatives with potential antihypertensive effects.

PubMed

Jardínez, Christiaan; Vela, Alberto; Cruz-Borbolla, Julián; Alvarez-Mendez, Rodrigo J; Alvarado-Rodríguez, José G

2016-12-01

The relationship between the chemical structure and biological activity (log IC 50 ) of 40 derivatives of 1,4-dihydropyridines (DHPs) was studied using density functional theory (DFT) and multiple linear regression analysis methods. With the aim of improving the quantitative structure-activity relationship (QSAR) model, the reduced density gradient s( r) of the optimized equilibrium geometries was used as a descriptor to include weak non-covalent interactions. The QSAR model highlights the correlation between the log IC 50 with highest molecular orbital energy (E HOMO ), molecular volume (V), partition coefficient (log P), non-covalent interactions NCI(H4-G) and the dual descriptor [Δf(r)]. The model yielded values of R 2 =79.57 and Q 2 =69.67 that were validated with the next four internal analytical validations DK=0.076, DQ=-0.006, R P =0.056, and R N =0.000, and the external validation Q 2 boot =64.26. The QSAR model found can be used to estimate biological activity with high reliability in new compounds based on a DHP series. Graphical abstract The good correlation between the log IC 50 with the NCI (H4-G) estimated by the reduced density gradient approach of the DHP derivatives.

Multi-resolutional shape features via non-Euclidean wavelets: Applications to statistical analysis of cortical thickness

PubMed Central

Kim, Won Hwa; Singh, Vikas; Chung, Moo K.; Hinrichs, Chris; Pachauri, Deepti; Okonkwo, Ozioma C.; Johnson, Sterling C.

2014-01-01

Statistical analysis on arbitrary surface meshes such as the cortical surface is an important approach to understanding brain diseases such as Alzheimer’s disease (AD). Surface analysis may be able to identify specific cortical patterns that relate to certain disease characteristics or exhibit differences between groups. Our goal in this paper is to make group analysis of signals on surfaces more sensitive. To do this, we derive multi-scale shape descriptors that characterize the signal around each mesh vertex, i.e., its local context, at varying levels of resolution. In order to define such a shape descriptor, we make use of recent results from harmonic analysis that extend traditional continuous wavelet theory from the Euclidean to a non-Euclidean setting (i.e., a graph, mesh or network). Using this descriptor, we conduct experiments on two different datasets, the Alzheimer’s Disease NeuroImaging Initiative (ADNI) data and images acquired at the Wisconsin Alzheimer’s Disease Research Center (W-ADRC), focusing on individuals labeled as having Alzheimer’s disease (AD), mild cognitive impairment (MCI) and healthy controls. In particular, we contrast traditional univariate methods with our multi-resolution approach which show increased sensitivity and improved statistical power to detect a group-level effects. We also provide an open source implementation. PMID:24614060

A novel knowledge-based system for interpreting complex engineering drawings: theory, representation, and implementation.

PubMed

Lu, Tong; Tai, Chiew-Lan; Yang, Huafei; Cai, Shijie

2009-08-01

We present a novel knowledge-based system to automatically convert real-life engineering drawings to content-oriented high-level descriptions. The proposed method essentially turns the complex interpretation process into two parts: knowledge representation and knowledge-based interpretation. We propose a new hierarchical descriptor-based knowledge representation method to organize the various types of engineering objects and their complex high-level relations. The descriptors are defined using an Extended Backus Naur Form (EBNF), facilitating modification and maintenance. When interpreting a set of related engineering drawings, the knowledge-based interpretation system first constructs an EBNF-tree from the knowledge representation file, then searches for potential engineering objects guided by a depth-first order of the nodes in the EBNF-tree. Experimental results and comparisons with other interpretation systems demonstrate that our knowledge-based system is accurate and robust for high-level interpretation of complex real-life engineering projects.

On the behavior of Bronsted-Evans-Polanyi Relations for Transition Metal Oxides

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vojvodic, Aleksandra

2011-08-22

Versatile Broensted-Evans-Polanyi (BEP) relations are found from density functional theory for a wide range of transition metal oxides including rutiles and perovskites. For oxides, the relation depends on the type of oxide, the active site and the dissociating molecule. The slope of the BEP relation is strongly coupled to the adsorbate geometry in the transition state. If it is final state-like the dissociative chemisorption energy can be considered as a descriptor for the dissociation. If it is initial state-like, on the other hand, the dissociative chemisorption energy is not suitable as descriptor for the dissociation. Dissociation of molecules with strongmore » intramolecular bonds belong to the former and molecules with weak intramolecular bonds to the latter group. We show, for the prototype system La-perovskites, that there is a 'cyclic' behavior in the transition state characteristics upon change of the active transition metal of the oxide.« less

A method for real-time implementation of HOG feature extraction

NASA Astrophysics Data System (ADS)

Luo, Hai-bo; Yu, Xin-rong; Liu, Hong-mei; Ding, Qing-hai

2011-08-01

Histogram of oriented gradient (HOG) is an efficient feature extraction scheme, and HOG descriptors are feature descriptors which is widely used in computer vision and image processing for the purpose of biometrics, target tracking, automatic target detection(ATD) and automatic target recognition(ATR) etc. However, computation of HOG feature extraction is unsuitable for hardware implementation since it includes complicated operations. In this paper, the optimal design method and theory frame for real-time HOG feature extraction based on FPGA were proposed. The main principle is as follows: firstly, the parallel gradient computing unit circuit based on parallel pipeline structure was designed. Secondly, the calculation of arctangent and square root operation was simplified. Finally, a histogram generator based on parallel pipeline structure was designed to calculate the histogram of each sub-region. Experimental results showed that the HOG extraction can be implemented in a pixel period by these computing units.

High throughput heuristics for prioritizing human exposure to environmental chemicals.

PubMed

Wambaugh, John F; Wang, Anran; Dionisio, Kathie L; Frame, Alicia; Egeghy, Peter; Judson, Richard; Setzer, R Woodrow

2014-11-04

The risk posed to human health by any of the thousands of untested anthropogenic chemicals in our environment is a function of both the hazard presented by the chemical and the extent of exposure. However, many chemicals lack estimates of exposure intake, limiting the understanding of health risks. We aim to develop a rapid heuristic method to determine potential human exposure to chemicals for application to the thousands of chemicals with little or no exposure data. We used Bayesian methodology to infer ranges of exposure consistent with biomarkers identified in urine samples from the U.S. population by the National Health and Nutrition Examination Survey (NHANES). We performed linear regression on inferred exposure for demographic subsets of NHANES demarked by age, gender, and weight using chemical descriptors and use information from multiple databases and structure-based calculators. Five descriptors are capable of explaining roughly 50% of the variability in geometric means across 106 NHANES chemicals for all the demographic groups, including children aged 6-11. We use these descriptors to estimate human exposure to 7968 chemicals, the majority of which have no other quantitative exposure prediction. For thousands of chemicals with no other information, this approach allows forecasting of average exposure intake of environmental chemicals.

Using network analysis to study behavioural phenotypes: an example using domestic dogs.

PubMed

Goold, Conor; Vas, Judit; Olsen, Christine; Newberry, Ruth C

2016-10-01

Phenotypic integration describes the complex interrelationships between organismal traits, traditionally focusing on morphology. Recently, research has sought to represent behavioural phenotypes as composed of quasi-independent latent traits. Concurrently, psychologists have opposed latent variable interpretations of human behaviour, proposing instead a network perspective envisaging interrelationships between behaviours as emerging from causal dependencies. Network analysis could also be applied to understand integrated behavioural phenotypes in animals. Here, we assimilate this cross-disciplinary progression of ideas by demonstrating the use of network analysis on survey data collected on behavioural and motivational characteristics of police patrol and detection dogs ( Canis lupus familiaris ). Networks of conditional independence relationships illustrated a number of functional connections between descriptors, which varied between dog types. The most central descriptors denoted desirable characteristics in both patrol and detection dog networks, with 'Playful' being widely correlated and possessing mediating relationships between descriptors. Bootstrap analyses revealed the stability of network results. We discuss the results in relation to previous research on dog personality, and benefits of using network analysis to study behavioural phenotypes. We conclude that a network perspective offers widespread opportunities for advancing the understanding of phenotypic integration in animal behaviour.

Phenotyping of Eggplant Wild Relatives and Interspecific Hybrids with Conventional and Phenomics Descriptors Provides Insight for Their Potential Utilization in Breeding

PubMed Central

Kaushik, Prashant; Prohens, Jaime; Vilanova, Santiago; Gramazio, Pietro; Plazas, Mariola

2016-01-01

Eggplant (Solanum melongena) is related to a large number of wild species that are a source of variation for breeding programmes, in particular for traits related to adaptation to climate change. However, wild species remain largely unexploited for eggplant breeding. Detailed phenotypic characterization of wild species and their hybrids with eggplant may allow identifying promising wild species and information on the genetic control and heterosis of relevant traits. We characterizated six eggplant accessions, 21 accessions of 12 wild species (the only primary genepool species S. insanum and 11 secondary genepool species) and 45 interspecific hybrids of eggplant with wild species (18 with S. insanum and 27 with secondary genepool species) using 27 conventional morphological descriptors and 20 fruit morphometric descriptors obtained with the phenomics tool Tomato Analyzer. Significant differences were observed among cultivated, wild and interspecific hybrid groups for 18 conventional and 18 Tomato Analyzer descriptors, with hybrids generally having intermediate values. Wild species were generally more variable than cultivated accessions and interspecific hybrids displayed intermediate ranges of variation and coefficient of variation (CV) values, except for fruit shape traits in which the latter were the most variable. The multivariate principal components analysis (PCA) reveals a clear separation of wild species and cultivated accessions. Interspecific hybrids with S. insanum plotted closer to cultivated eggplant, while hybrids with secondary genepool species generally clustered together with wild species. Many differences were observed among wild species for traits of agronomic interest, which allowed identifying species of greatest potential interest for eggplant breeding. Heterosis values were positive for most vigor-related traits, while for fruit size values were close to zero for hybrids with S. incanum and highly negative for hybrids with secondary genepool species. Our results allowed the identification of potentially interesting wild species and interspecific hybrids for introgression breeding in eggplant. This is an important step for broadening the genetic base of eggplant and for breeding for adaptation to climate change in this crop. PMID:27242876

Simulating Timber and Deer Food Potential In Loblolly Pine Plantations

Treesearch

Clifford A. Myers

1977-01-01

This computer program analyzes both timber and deer food production on managed forests, providing estimates of the number of acres required per deer for each week or month, yearly timber cuts, and current timber growing stock, as well as a cost and return analysis of the timber operation. Input variables include stand descriptors, controls on management, stumpage...

Temperature sensitivity of organic compound destruction in SCWO process.

PubMed

Tan, Yaqin; Shen, Zhemin; Guo, Weimin; Ouyang, Chuang; Jia, Jinping; Jiang, Weili; Zhou, Haiyun

2014-03-01

To study the temperature sensitivity of the destruction of organic compounds in supercritical water oxidation process (SCWO), oxidation effects of twelve chemicals in supercritical water were investigated. The SCWO reaction rates of different compounds improved to varying degrees with the increase of temperature, so the highest slope of the temperature-effect curve (imax) was defined as the maximum ratio of removal ratio to working temperature. It is an important index to stand for the temperature sensitivity effect in SCWO. It was proven that the higher imax is, the more significant the effect of temperature on the SCWO effect is. Since the high-temperature area of SCWO equipment is subject to considerable damage from fatigue, the temperature is of great significance in SCWO equipment operation. Generally, most compounds (imax > 0.25) can be completely oxidized when the reactor temperature reaches 500°C. However, some compounds (imax > 0.25) need a higher temperature for complete oxidation, up to 560°C. To analyze the correlation coefficients between imax and various molecular descriptors, a quantum chemical method was used in this study. The structures of the twelve organic compounds were optimized by the Density Functional Theory B3LYP/6-311G method, as well as their quantum properties. It was shown that six molecular descriptors were negatively correlated to imax while other three descriptors were positively correlated to imax. Among them, dipole moment had the greatest effect on the oxidation thermodynamics of the twelve organic compounds. Once a correlation between molecular descriptors and imax is established, SCWO can be run at an appropriate temperature according to molecular structure. Copyright © 2014 The Research Centre for Eco-Environmental Sciences, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.

Anomaly Detection Based on Local Nearest Neighbor Distance Descriptor in Crowded Scenes

PubMed Central

Hu, Shiqiang; Zhang, Huanlong; Luo, Lingkun

2014-01-01

We propose a novel local nearest neighbor distance (LNND) descriptor for anomaly detection in crowded scenes. Comparing with the commonly used low-level feature descriptors in previous works, LNND descriptor has two major advantages. First, LNND descriptor efficiently incorporates spatial and temporal contextual information around the video event that is important for detecting anomalous interaction among multiple events, while most existing feature descriptors only contain the information of single event. Second, LNND descriptor is a compact representation and its dimensionality is typically much lower than the low-level feature descriptor. Therefore, not only the computation time and storage requirement can be accordingly saved by using LNND descriptor for the anomaly detection method with offline training fashion, but also the negative aspects caused by using high-dimensional feature descriptor can be avoided. We validate the effectiveness of LNND descriptor by conducting extensive experiments on different benchmark datasets. Experimental results show the promising performance of LNND-based method against the state-of-the-art methods. It is worthwhile to notice that the LNND-based approach requires less intermediate processing steps without any subsequent processing such as smoothing but achieves comparable event better performance. PMID:25105164

Information origins of the chemical bond: Bond descriptors from molecular communication channels in orbital resolution

NASA Astrophysics Data System (ADS)

Nalewajski, Roman F.

The flow of information in the molecular communication networks in the (condensed) atomic orbital (AO) resolution is investigated and the plane-wave (momentum-space) interpretation of the average Fisher information in the molecular information system is given. It is argued using the quantum-mechanical superposition principle that, in the LCAO MO theory the squares of corresponding elements of the Charge and Bond-Order (CBO) matrix determine the conditional probabilities between AO, which generate the molecular communication system of the Orbital Communication Theory (OCT) of the chemical bond. The conditional-entropy ("noise," information-theoretic "covalency") and the mutual-information (information flow, information-theoretic "ionicity") descriptors of these molecular channels are related to Wiberg's covalency indices of chemical bonds. The illustrative application of OCT to the three-orbital model of the chemical bond X-Y, which is capable of describing the forward- and back-donations as well as the atom promotion accompanying the bond formation, is reported. It is demonstrated that the entropy/information characteristics of these separate bond-effects can be extracted by an appropriate reduction of the output of the molecular information channel, carried out by combining several exits into a single (condensed) one. The molecular channels in both the AO and hybrid orbital representations are examined for both the molecular and representative promolecular input probabilities.

Self-consistent self-interaction corrected density functional theory calculations for atoms using Fermi-Löwdin orbitals: Optimized Fermi-orbital descriptors for Li-Kr

NASA Astrophysics Data System (ADS)

Kao, Der-you; Withanage, Kushantha; Hahn, Torsten; Batool, Javaria; Kortus, Jens; Jackson, Koblar

2017-10-01

In the Fermi-Löwdin orbital method for implementing self-interaction corrections (FLO-SIC) in density functional theory (DFT), the local orbitals used to make the corrections are generated in a unitary-invariant scheme via the choice of the Fermi orbital descriptors (FODs). These are M positions in 3-d space (for an M-electron system) that can be loosely thought of as classical electron positions. The orbitals that minimize the DFT energy including the SIC are obtained by finding optimal positions for the FODs. In this paper, we present optimized FODs for the atoms from Li-Kr obtained using an unbiased search method and self-consistent FLO-SIC calculations. The FOD arrangements display a clear shell structure that reflects the principal quantum numbers of the orbitals. We describe trends in the FOD arrangements as a function of atomic number. FLO-SIC total energies for the atoms are presented and are shown to be in close agreement with the results of previous SIC calculations that imposed explicit constraints to determine the optimal local orbitals, suggesting that FLO-SIC yields the same solutions for atoms as these computationally demanding earlier methods, without invoking the constraints.

Assessment of Beer Quality Based on a Robotic Pourer, Computer Vision, and Machine Learning Algorithms Using Commercial Beers.

PubMed

Gonzalez Viejo, Claudia; Fuentes, Sigfredo; Torrico, Damir D; Howell, Kate; Dunshea, Frank R

2018-05-01

Sensory attributes of beer are directly linked to perceived foam-related parameters and beer color. The aim of this study was to develop an objective predictive model using machine learning modeling to assess the intensity levels of sensory descriptors in beer using the physical measurements of color and foam-related parameters. A robotic pourer (RoboBEER), was used to obtain 15 color and foam-related parameters from 22 different commercial beer samples. A sensory session using quantitative descriptive analysis (QDA ® ) with trained panelists was conducted to assess the intensity of 10 beer descriptors. Results showed that the principal component analysis explained 64% of data variability with correlations found between foam-related descriptors from sensory and RoboBEER such as the positive and significant correlation between carbon dioxide and carbonation mouthfeel (R = 0.62), correlation of viscosity to sensory, and maximum volume of foam and total lifetime of foam (R = 0.75, R = 0.77, respectively). Using the RoboBEER parameters as inputs, an artificial neural network (ANN) regression model showed high correlation (R = 0.91) to predict the intensity levels of 10 related sensory descriptors such as yeast, grains and hops aromas, hops flavor, bitter, sour and sweet tastes, viscosity, carbonation, and astringency. This paper is a novel approach for food science using machine modeling techniques that could contribute significantly to rapid screenings of food and brewage products for the food industry and the implementation of Artificial Intelligence (AI). The use of RoboBEER to assess beer quality showed to be a reliable, objective, accurate, and less time-consuming method to predict sensory descriptors compared to trained sensory panels. Hence, this method could be useful as a rapid screening procedure to evaluate beer quality at the end of the production line for industry applications. © 2018 Institute of Food Technologists®.

The application of feature selection to the development of Gaussian process models for percutaneous absorption.

PubMed

Lam, Lun Tak; Sun, Yi; Davey, Neil; Adams, Rod; Prapopoulou, Maria; Brown, Marc B; Moss, Gary P

2010-06-01

The aim was to employ Gaussian processes to assess mathematically the nature of a skin permeability dataset and to employ these methods, particularly feature selection, to determine the key physicochemical descriptors which exert the most significant influence on percutaneous absorption, and to compare such models with established existing models. Gaussian processes, including automatic relevance detection (GPRARD) methods, were employed to develop models of percutaneous absorption that identified key physicochemical descriptors of percutaneous absorption. Using MatLab software, the statistical performance of these models was compared with single linear networks (SLN) and quantitative structure-permeability relationships (QSPRs). Feature selection methods were used to examine in more detail the physicochemical parameters used in this study. A range of statistical measures to determine model quality were used. The inherently nonlinear nature of the skin data set was confirmed. The Gaussian process regression (GPR) methods yielded predictive models that offered statistically significant improvements over SLN and QSPR models with regard to predictivity (where the rank order was: GPR > SLN > QSPR). Feature selection analysis determined that the best GPR models were those that contained log P, melting point and the number of hydrogen bond donor groups as significant descriptors. Further statistical analysis also found that great synergy existed between certain parameters. It suggested that a number of the descriptors employed were effectively interchangeable, thus questioning the use of models where discrete variables are output, usually in the form of an equation. The use of a nonlinear GPR method produced models with significantly improved predictivity, compared with SLN or QSPR models. Feature selection methods were able to provide important mechanistic information. However, it was also shown that significant synergy existed between certain parameters, and as such it was possible to interchange certain descriptors (i.e. molecular weight and melting point) without incurring a loss of model quality. Such synergy suggested that a model constructed from discrete terms in an equation may not be the most appropriate way of representing mechanistic understandings of skin absorption.

[The use of Cantonese pain descriptors among healthy young adults in Hong Kong].

PubMed

Chung, W Y; Wong, C H; Yang, J C; Tan, P P

1998-12-01

The interpretation and expression of pain are closely related to an individual's social and cultural background. To convey messages on pain, language and words (pain descriptors) is particularly significant in assessment and evaluation of pain severity and its management. Therefore, the study of pain descriptors is crucial in clinical practice. It was of exploratory-descriptive design. Samples were recruited by convenience. Data were collected by structured self-administered questionnaire. Data obtained included demographic information and pain descriptors used by the subjects in various pain conditions. Data were analyzed by descriptive statistics. Pain descriptors were categorized according to nature, process, intensity, aggravating factors, accompanying symptoms and behavioral manifestation. Total number of pain descriptors (in Cantonese) based on real pain experience was 3017, mean was 3 (n = 986). The commonest used descriptors was the nature of pain (41%). The intensity of pain constituted 20%. There was no significant difference in the number of pain descriptors between male and female. However, there was a significant difference between the type of pain descriptors used (Mfemale = 526, Mmale = 453, Z = -2.9729, p = 0.0029). There were also significant differences in the use of pain descriptors among the various age groups (X2 = 15.0157, df = 4, P = 0.0047) and educational levels (X2 = 11.2443, df = 4, P = 0.0240). The types of descriptors used increased with an increase in age and education levels. This exploratory-descriptive study explores the use of pain descriptors among Chinese young adults in Hong Kong. The result shows that female use more pain descriptors than male. The pain descriptors that female used are mostly of nature type. The similarities and differences in findings with those of the Ho's (1991) are compared.

A 3D QSAR CoMFA study of non-peptide angiotensin II receptor antagonists

NASA Astrophysics Data System (ADS)

Belvisi, Laura; Bravi, Gianpaolo; Catalano, Giovanna; Mabilia, Massimo; Salimbeni, Aldo; Scolastico, Carlo

1996-12-01

A series of non-peptide angiotensin II receptor antagonists was investigated with the aim of developing a 3D QSAR model using comparative molecular field analysis descriptors and approaches. The main goals of the study were dictated by an interest in methodologies and an understanding of the binding requirements to the AT1 receptor. Consistency with the previously derived activity models was always checked to contemporarily test the validity of the various hypotheses. The specific conformations chosen for the study, the procedures invoked to superimpose all structures, the conditions employed to generate steric and electrostatic field values and the various PCA/PLS runs are discussed in detail. The effect of experimental design techniques to select objects (molecules) and variables (descriptors) with respect to the predictive power of the QSAR models derived was especially analysed.

Mathematical biodescriptors of proteomics maps: background and applications.

PubMed

Basak, Subhash C; Gute, Brian D

2008-05-01

This article reviews recent developments in the formulation and application of biodescriptors to characterize proteomics maps. Such biodescriptors can be derived by applying techniques from discrete mathematics (graph theory, linear algebra and information theory). This review focuses on the development of biodescriptors for proteomics maps derived from 2D gel electrophoresis. Preliminary results demonstrated that such descriptors have a reasonable ability to differentiate between proteomics patterns that result from exposure to closely related individual chemicals and complex mixtures, such as the jet fuel JP-8. Further research is required to evaluate the utility of these proteomics-based biodescriptors for drug discovery and predictive toxicology.

Free energy force field (FEFF) 3D-QSAR analysis of a set of Plasmodium falciparum dihydrofolate reductase inhibitors

NASA Astrophysics Data System (ADS)

Santos-Filho, Osvaldo A.; Mishra, Rama K.; Hopfinger, A. J.

2001-09-01

Free energy force field (FEFF) 3D-QSAR analysis was used to construct ligand-receptor binding models for a set of 18 structurally diverse antifolates including pyrimethamine, cycloguanil, methotrexate, aminopterin and trimethoprim, and 13 pyrrolo[2,3-d]pyrimidines. The molecular target (`receptor') used was a 3D-homology model of a specific mutant type of Plasmodium falciparum (Pf) dihydrofolate reductase (DHFR). The dependent variable of the 3D-QSAR models is the IC50 inhibition constant for the specific mutant type of PfDHFR. The independent variables of the 3D-QSAR models (the descriptors) are scaled energy terms of a modified first-generation AMBER force field combined with a hydration shell aqueous solvation model and a collection of 2D-QSAR descriptors often used in QSAR studies. Multiple temperature molecular dynamics simulation (MDS) and the genetic function approximation (GFA) were employed using partial least square (PLS) and multidimensional linear regressions as the fitting functions to develop FEFF 3D-QSAR models for the binding process. The significant FEFF energy terms in the best 3D-QSAR models include energy contributions of the direct ligand-receptor interaction. Some changes in conformational energy terms of the ligand due to binding to the enzyme are also found to be important descriptors. The FEFF 3D-QSAR models indicate some structural features perhaps relevant to the mechanism of resistance of the PfDHFR to current antimalarials. The FEFF 3D-QSAR models are also compared to receptor-independent (RI) 4D-QSAR models developed in an earlier study and subsequently refined using recently developed generalized alignment rules.

How diverse are diversity assessment methods? A comparative analysis and benchmarking of molecular descriptor space.

PubMed

Koutsoukas, Alexios; Paricharak, Shardul; Galloway, Warren R J D; Spring, David R; Ijzerman, Adriaan P; Glen, Robert C; Marcus, David; Bender, Andreas

2014-01-27

Chemical diversity is a widely applied approach to select structurally diverse subsets of molecules, often with the objective of maximizing the number of hits in biological screening. While many methods exist in the area, few systematic comparisons using current descriptors in particular with the objective of assessing diversity in bioactivity space have been published, and this shortage is what the current study is aiming to address. In this work, 13 widely used molecular descriptors were compared, including fingerprint-based descriptors (ECFP4, FCFP4, MACCS keys), pharmacophore-based descriptors (TAT, TAD, TGT, TGD, GpiDAPH3), shape-based descriptors (rapid overlay of chemical structures (ROCS) and principal moments of inertia (PMI)), a connectivity-matrix-based descriptor (BCUT), physicochemical-property-based descriptors (prop2D), and a more recently introduced molecular descriptor type (namely, "Bayes Affinity Fingerprints"). We assessed both the similar behavior of the descriptors in assessing the diversity of chemical libraries, and their ability to select compounds from libraries that are diverse in bioactivity space, which is a property of much practical relevance in screening library design. This is particularly evident, given that many future targets to be screened are not known in advance, but that the library should still maximize the likelihood of containing bioactive matter also for future screening campaigns. Overall, our results showed that descriptors based on atom topology (i.e., fingerprint-based descriptors and pharmacophore-based descriptors) correlate well in rank-ordering compounds, both within and between descriptor types. On the other hand, shape-based descriptors such as ROCS and PMI showed weak correlation with the other descriptors utilized in this study, demonstrating significantly different behavior. We then applied eight of the molecular descriptors compared in this study to sample a diverse subset of sample compounds (4%) from an initial population of 2587 compounds, covering the 25 largest human activity classes from ChEMBL and measured the coverage of activity classes by the subsets. Here, it was found that "Bayes Affinity Fingerprints" achieved an average coverage of 92% of activity classes. Using the descriptors ECFP4, GpiDAPH3, TGT, and random sampling, 91%, 84%, 84%, and 84% of the activity classes were represented in the selected compounds respectively, followed by BCUT, prop2D, MACCS, and PMI (in order of decreasing performance). In addition, we were able to show that there is no visible correlation between compound diversity in PMI space and in bioactivity space, despite frequent utilization of PMI plots to this end. To summarize, in this work, we assessed which descriptors select compounds with high coverage of bioactivity space, and can hence be used for diverse compound selection for biological screening. In cases where multiple descriptors are to be used for diversity selection, this work describes which descriptors behave complementarily, and can hence be used jointly to focus on different aspects of diversity in chemical space.

Evaluation of vegetation post-fire resilience in the Alpine region using descriptors derived from MODIS spectral index time series

NASA Astrophysics Data System (ADS)

Di Mauro, Biagio; Fava, Francesco; Busetto, Lorenzo; Crosta, Giovanni Franco; Colombo, Roberto

2013-04-01

In this study a method based on the analysis of MODerate-resolution Imaging Spectroradiometer (MODIS) time series is proposed to estimate the post-fire resilience of mountain vegetation (broadleaf forest and prairies) in the Italian Alps. Resilience is defined herewith as the ability of a dynamical system to counteract disturbances. It can be quantified by the amount of time the disturbed system takes to resume, in statistical terms, an ecological functionality comparable with its undisturbed behavior. Satellite images of the Normalized Difference Vegetation Index (NDVI) and of the Enhanced Vegetation Index (EVI) with spatial resolution of 250m and temporal resolution of 16 days in the 2000-2012 time period were used. Wildfire affected areas in the Lombardy region between the years 2000 and 2010 were analysed. Only large fires (affected area >40ha) were selected. For each burned area, an undisturbed adjacent control site was located. Data pre-processing consisted in the smoothing of MODIS time series for noise removal and then a double logistic function was fitted. Land surface phenology descriptors (proxies for growing season start/end/length and green biomass) were extracted in order to characterize the time evolution of the vegetation. Descriptors from a burned area were compared to those extracted from the respective control site by means of the one-way analysis of variance. According to the number of subsequent years which exhibit statistically meaningful difference between burned and control site, five classes of resilience were identified and a set of thematic maps was created for each descriptor. The same method was applied to all 84 aggregated events and to events aggregated by main land cover. EVI index results more sensitive to fire impact than NDVI index. Analysis shows that fire causes both a reduction of the biomass and a variation in the phenology of the Alpine vegetation. Results suggest an average ecosystem resilience of 6-7 years. Moreover, broadleaf forest and prairies show different post-fire behavior in terms of land surface phenology descriptors. In addition to the above analysis, another method is proposed, which derives from the qualitative theory of dynamical systems. The (time dependent) spectral index of a burned area over the period of one year was plotted against its counterpart from the control site. Yearly plots (or scattergrams) before and after the fire were obtained. Each plot is a sequence of points on the plane, which are the vertices of a generally self-intersecting polygonal chain. Some geometrical descriptors were obtained from the yearly chains of each fire. Principal Components Analysis (PCA) of geometrical descriptors was applied to a set of case studies and the obtained results provide a system dynamics interpretation of the natural process.

Correlation between length and tilt of lipid tails

NASA Astrophysics Data System (ADS)

Kopelevich, Dmitry I.; Nagle, John F.

2015-10-01

It is becoming recognized from simulations, and to a lesser extent from experiment, that the classical Helfrich-Canham membrane continuum mechanics model can be fruitfully enriched by the inclusion of molecular tilt, even in the fluid, chain disordered, biologically relevant phase of lipid bilayers. Enriched continuum theories then add a tilt modulus κθ to accompany the well recognized bending modulus κ. Different enrichment theories largely agree for many properties, but it has been noticed that there is considerable disagreement in one prediction; one theory postulates that the average length of the hydrocarbon chain tails increases strongly with increasing tilt and another predicts no increase. Our analysis of an all-atom simulation favors the latter theory, but it also shows that the overall tail length decreases slightly with increasing tilt. We show that this deviation from continuum theory can be reconciled by consideration of the average shape of the tails, which is a descriptor not obviously includable in continuum theory.

A Global Covariance Descriptor for Nuclear Atypia Scoring in Breast Histopathology Images.

PubMed

Khan, Adnan Mujahid; Sirinukunwattana, Korsuk; Rajpoot, Nasir

2015-09-01

Nuclear atypia scoring is a diagnostic measure commonly used to assess tumor grade of various cancers, including breast cancer. It provides a quantitative measure of deviation in visual appearance of cell nuclei from those in normal epithelial cells. In this paper, we present a novel image-level descriptor for nuclear atypia scoring in breast cancer histopathology images. The method is based on the region covariance descriptor that has recently become a popular method in various computer vision applications. The descriptor in its original form is not suitable for classification of histopathology images as cancerous histopathology images tend to possess diversely heterogeneous regions in a single field of view. Our proposed image-level descriptor, which we term as the geodesic mean of region covariance descriptors, possesses all the attractive properties of covariance descriptors lending itself to tractable geodesic-distance-based k-nearest neighbor classification using efficient kernels. The experimental results suggest that the proposed image descriptor yields high classification accuracy compared to a variety of widely used image-level descriptors.

Bond additive modeling 10. Upper and lower bounds of bond incident degree indices of catacondensed fluoranthenes

NASA Astrophysics Data System (ADS)

Vukičević, Damir; Đurđević, Jelena

2011-10-01

Bond incident degree index is a descriptor that is calculated as the sum of the bond contributions such that each bond contribution depends solely on the degrees of its incident vertices (e.g. Randić index, Zagreb index, modified Zagreb index, variable Randić index, atom-bond connectivity index, augmented Zagreb index, sum-connectivity index, many Adriatic indices, and many variable Adriatic indices). In this Letter we find tight upper and lower bounds for bond incident degree index for catacondensed fluoranthenes with given number of hexagons.

Modelling by partial least squares the relationship between the HPLC mobile phases and analytes on phenyl column.

PubMed

Markopoulou, Catherine K; Kouskoura, Maria G; Koundourellis, John E

2011-06-01

Twenty-five descriptors and 61 structurally different analytes have been used on a partial least squares (PLS) to latent structure technique in order to study chromatographically their interaction mechanism on a phenyl column. According to the model, 240 different retention times of the analytes, expressed as Y variable (log k), at different % MeOH mobile-phase concentrations have been correlated with their theoretical most important structural or molecular descriptors. The goodness-of-fit was estimated by the coefficient of multiple determinations r(2) (0.919), and the root mean square error of estimation (RMSEE=0.1283) values with a predictive ability (Q(2)) of 0.901. The model was further validated using cross-validation (CV), validated by 20 response permutations r(2) (0.0, 0.0146), Q(2) (0.0, -0.136) and validated by external prediction. The contribution of certain mechanism interactions between the analytes, the mobile phase and the column, proportional or counterbalancing is also studied. Trying to evaluate the influence on Y of every variable in a PLS model, VIP (variables importance in the projection) plot provides evidence that lipophilicity (expressed as Log D, Log P), polarizability, refractivity and the eluting power of the mobile phase are dominant in the retention mechanism on a phenyl column. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Morphological diversity of cassava accessions of the south-central mesoregion of the State of Mato Grosso, Brazil.

PubMed

Zago, B W; Barelli, M A A; Hoogerheide, E S S; Corrêa, C L; Delforno, G I S; da Silva, C J

2017-08-17

Genetic variability of cassava (Manihot esculenta Crantz) in Brazil is wide, being this the result of natural and cultural selection during pre- and post-domestication of the species in different environments. Given the number of species of the genus found in the region (38 of a total of 98 species), the central region of Brazil was defined as the primary center of cassava diversity. Therefore, genetic diversity characterization of cassava accessions is fundamental, both for farmers and for plant breeders, because it allows the organization of genetic resources and better utilization of available genetic diversity. This research aims to assess genetic divergence of cassava accessions from the south-central region of the State of Mato Grosso, based on multi-categorical morphological traits. For this purpose, 38 qualitative and quantitative morphological descriptors were used. Genetic diversity was expressed by the genetic similarity index, with subsequent clustering of accessions by the modified Tocher's procedure and UPGMA. Of 38 descriptors, only growth habit of stem showed no variability. Tocher and UPGMA methods were efficient and corroborated on group composition. Both methods were able to group accessions of different localities in distinct group consistency.

Self-pacing direct memory access data transfer operations for compute nodes in a parallel computer

DOEpatents

Blocksome, Michael A

2015-02-17

Methods, apparatus, and products are disclosed for self-pacing DMA data transfer operations for nodes in a parallel computer that include: transferring, by an origin DMA on an origin node, a RTS message to a target node, the RTS message specifying an message on the origin node for transfer to the target node; receiving, in an origin injection FIFO for the origin DMA from a target DMA on the target node in response to transferring the RTS message, a target RGET descriptor followed by a DMA transfer operation descriptor, the DMA descriptor for transmitting a message portion to the target node, the target RGET descriptor specifying an origin RGET descriptor on the origin node that specifies an additional DMA descriptor for transmitting an additional message portion to the target node; processing, by the origin DMA, the target RGET descriptor; and processing, by the origin DMA, the DMA transfer operation descriptor.

A comparison between space-time video descriptors

NASA Astrophysics Data System (ADS)

Costantini, Luca; Capodiferro, Licia; Neri, Alessandro

2013-02-01

The description of space-time patches is a fundamental task in many applications such as video retrieval or classification. Each space-time patch can be described by using a set of orthogonal functions that represent a subspace, for example a sphere or a cylinder, within the patch. In this work, our aim is to investigate the differences between the spherical descriptors and the cylindrical descriptors. In order to compute the descriptors, the 3D spherical and cylindrical Zernike polynomials are employed. This is important because both the functions are based on the same family of polynomials, and only the symmetry is different. Our experimental results show that the cylindrical descriptor outperforms the spherical descriptor. However, the performances of the two descriptors are similar.

Physicochemical descriptors of aromatic character and their use in drug discovery.

PubMed

Ritchie, Timothy J; Macdonald, Simon J F

2014-09-11

Published physicochemical descriptors of molecules that convey aromaticity-related character are reviewed in the context of drug design and discovery. Studies that have employed aromatic descriptors are discussed, and several descriptors are compared and contrasted.

A comparison of logistic regression analysis and an artificial neural network using the BI-RADS lexicon for ultrasonography in conjunction with introbserver variability.

PubMed

Kim, Sun Mi; Han, Heon; Park, Jeong Mi; Choi, Yoon Jung; Yoon, Hoi Soo; Sohn, Jung Hee; Baek, Moon Hee; Kim, Yoon Nam; Chae, Young Moon; June, Jeon Jong; Lee, Jiwon; Jeon, Yong Hwan

2012-10-01

To determine which Breast Imaging Reporting and Data System (BI-RADS) descriptors for ultrasound are predictors for breast cancer using logistic regression (LR) analysis in conjunction with interobserver variability between breast radiologists, and to compare the performance of artificial neural network (ANN) and LR models in differentiation of benign and malignant breast masses. Five breast radiologists retrospectively reviewed 140 breast masses and described each lesion using BI-RADS lexicon and categorized final assessments. Interobserver agreements between the observers were measured by kappa statistics. The radiologists' responses for BI-RADS were pooled. The data were divided randomly into train (n = 70) and test sets (n = 70). Using train set, optimal independent variables were determined by using LR analysis with forward stepwise selection. The LR and ANN models were constructed with the optimal independent variables and the biopsy results as dependent variable. Performances of the models and radiologists were evaluated on the test set using receiver-operating characteristic (ROC) analysis. Among BI-RADS descriptors, margin and boundary were determined as the predictors according to stepwise LR showing moderate interobserver agreement. Area under the ROC curves (AUC) for both of LR and ANN were 0.87 (95% CI, 0.77-0.94). AUCs for the five radiologists ranged 0.79-0.91. There was no significant difference in AUC values among the LR, ANN, and radiologists (p > 0.05). Margin and boundary were found as statistically significant predictors with good interobserver agreement. Use of the LR and ANN showed similar performance to that of the radiologists for differentiation of benign and malignant breast masses.

Application of a Cloud Model-Set Pair Analysis in Hazard Assessment for Biomass Gasification Stations.

PubMed

Yan, Fang; Xu, Kaili

2017-01-01

Because a biomass gasification station includes various hazard factors, hazard assessment is needed and significant. In this article, the cloud model (CM) is employed to improve set pair analysis (SPA), and a novel hazard assessment method for a biomass gasification station is proposed based on the cloud model-set pair analysis (CM-SPA). In this method, cloud weight is proposed to be the weight of index. In contrast to the index weight of other methods, cloud weight is shown by cloud descriptors; hence, the randomness and fuzziness of cloud weight will make it effective to reflect the linguistic variables of experts. Then, the cloud connection degree (CCD) is proposed to replace the connection degree (CD); the calculation algorithm of CCD is also worked out. By utilizing the CCD, the hazard assessment results are shown by some normal clouds, and the normal clouds are reflected by cloud descriptors; meanwhile, the hazard grade is confirmed by analyzing the cloud descriptors. After that, two biomass gasification stations undergo hazard assessment via CM-SPA and AHP based SPA, respectively. The comparison of assessment results illustrates that the CM-SPA is suitable and effective for the hazard assessment of a biomass gasification station and that CM-SPA will make the assessment results more reasonable and scientific.

Quantitative structure-retention relationship studies for taxanes including epimers and isomeric metabolites in ultra fast liquid chromatography.

PubMed

Dong, Pei-Pei; Ge, Guang-Bo; Zhang, Yan-Yan; Ai, Chun-Zhi; Li, Guo-Hui; Zhu, Liang-Liang; Luan, Hong-Wei; Liu, Xing-Bao; Yang, Ling

2009-10-16

Seven pairs of epimers and one pair of isomeric metabolites of taxanes, each pair of which have similar structures but different retention behaviors, together with additional 13 taxanes with different substitutions were chosen to investigate the quantitative structure-retention relationship (QSRR) of taxanes in ultra fast liquid chromatography (UFLC). Monte Carlo variable selection (MCVS) method was adopted to choose descriptors. The selected four descriptors were used to build QSRR model with multi-linear regression (MLR) and artificial neural network (ANN) modeling techniques. Both linear and nonlinear models show good predictive ability, of which ANN model was better with the determination coefficient R(2) for training, validation and test set being 0.9892, 0.9747 and 0.9840, respectively. The results of 100 times' leave-12-out cross validation showed the robustness of this model. All the isomers can be correctly differentiated by this model. According to the selected descriptors, the three dimensional structural information was critical for recognition of epimers. Hydrophobic interaction was the uppermost factor for retention in UFLC. Molecules' polarizability and polarity properties were also closely correlated with retention behaviors. This QSRR model will be useful for separation and identification of taxanes including epimers and metabolites from botanical or biological samples.

Application of a Cloud Model-Set Pair Analysis in Hazard Assessment for Biomass Gasification Stations

PubMed Central

Yan, Fang; Xu, Kaili

2017-01-01

Because a biomass gasification station includes various hazard factors, hazard assessment is needed and significant. In this article, the cloud model (CM) is employed to improve set pair analysis (SPA), and a novel hazard assessment method for a biomass gasification station is proposed based on the cloud model-set pair analysis (CM-SPA). In this method, cloud weight is proposed to be the weight of index. In contrast to the index weight of other methods, cloud weight is shown by cloud descriptors; hence, the randomness and fuzziness of cloud weight will make it effective to reflect the linguistic variables of experts. Then, the cloud connection degree (CCD) is proposed to replace the connection degree (CD); the calculation algorithm of CCD is also worked out. By utilizing the CCD, the hazard assessment results are shown by some normal clouds, and the normal clouds are reflected by cloud descriptors; meanwhile, the hazard grade is confirmed by analyzing the cloud descriptors. After that, two biomass gasification stations undergo hazard assessment via CM-SPA and AHP based SPA, respectively. The comparison of assessment results illustrates that the CM-SPA is suitable and effective for the hazard assessment of a biomass gasification station and that CM-SPA will make the assessment results more reasonable and scientific. PMID:28076440

Material identification of real impact sounds: effects of size variation in steel, glass, wood, and plexiglass plates.

PubMed

Giordano, Bruno L; McAdams, Stephen

2006-02-01

Identification of the material of struck objects of variable size was investigated. Previous studies on this issue assumed recognition to be based on acoustical measures of damping. This assumption was tested, comparing the power of a damping measure in explaining identification data with that of several other acoustical descriptors. Listeners' performance was perfect with respect to gross material categories (steel-glass and wood-plexiglass) comprising materials of vastly different mechanical properties. Impaired performance was observed for materials within the same gross category, identification being based on the size of the objects alone. The damping descriptor accounted for the identification of the gross categories. However other descriptors such as signal duration explained the results equally well. Materials within the same gross category were identified mainly on the basis of signal frequency. Overall poor support for the relevance of damping to material perception was found. An analysis of the acoustical support for perfect material identification was carried out. Sufficient acoustical information for perfect performance was found. Thus, procedural biases for the origin of the effects of size could be discarded, pointing toward their cognitive, rather than methodological nature. Identification performance was explained in terms of the regularities of the everyday acoustical environment.

Descriptors of sensation confirm the multidimensional nature of desire to void.

PubMed

Das, Rebekah; Buckley, Jonathan D; Williams, Marie T

2015-02-01

To collect and categorize descriptors of "desire to void" sensation, determine the reliability of descriptor categories and assess whether descriptor categories discriminate between people with and without symptoms of overactive bladder. This observational, repeated measures study involved 64 Australian volunteers (47 female), aged 50 years or more, with and without symptoms of overactive bladder. Descriptors of desire to void sensation were derived from a structured interview (conducted on two occasions, 1 week apart). Descriptors were recorded verbatim and categorized in a three-stage process. Overactive bladder status was determined by the Overactive Bladder Awareness Tool and the Overactive Bladder Symptom Score. McNemar's test assessed the reliability of descriptors volunteered between two occasions and Partial Least Squares Regression determined whether language categories discriminated according to overactive bladder status. Post hoc Chi squared analysis and relative risk calculation determined the size and direction of overactive bladder prediction. Thirteen language categories (Urgency, Fullness, Pressure, Tickle/tingle, Pain/ache, Heavy, Normal, Intense, Sudden, Annoying, Uncomfortable, Anxiety, and Unique somatic) encapsulated 344 descriptors of sensation. Descriptor categories were stable between two interviews. The categories "Urgency" and "Fullness" predicted overactive bladder status. Participants who volunteered "Urgency" descriptors were twice as likely to have overactive bladder and participants who volunteered "Fullness" descriptors were almost three times as likely not to have overactive bladder. The sensation of desire to void is reliably described over sessions separated by a week, the language used reflects multiple dimensions of sensation, and can predict overactive bladder status. © 2013 Wiley Periodicals, Inc.

The great descriptor melting pot: mixing descriptors for the common good of QSAR models.

PubMed

Tseng, Yufeng J; Hopfinger, Anton J; Esposito, Emilio Xavier

2012-01-01

The usefulness and utility of QSAR modeling depends heavily on the ability to estimate the values of molecular descriptors relevant to the endpoints of interest followed by an optimized selection of descriptors to form the best QSAR models from a representative set of the endpoints of interest. The performance of a QSAR model is directly related to its molecular descriptors. QSAR modeling, specifically model construction and optimization, has benefited from its ability to borrow from other unrelated fields, yet the molecular descriptors that form QSAR models have remained basically unchanged in both form and preferred usage. There are many types of endpoints that require multiple classes of descriptors (descriptors that encode 1D through multi-dimensional, 4D and above, content) needed to most fully capture the molecular features and interactions that contribute to the endpoint. The advantages of QSAR models constructed from multiple, and different, descriptor classes have been demonstrated in the exploration of markedly different, and principally biological systems and endpoints. Multiple examples of such QSAR applications using different descriptor sets are described and that examined. The take-home-message is that a major part of the future of QSAR analysis, and its application to modeling biological potency, ADME-Tox properties, general use in virtual screening applications, as well as its expanding use into new fields for building QSPR models, lies in developing strategies that combine and use 1D through nD molecular descriptors.

A Fully Automated Method to Detect and Segment a Manufactured Object in an Underwater Color Image

NASA Astrophysics Data System (ADS)

Barat, Christian; Phlypo, Ronald

2010-12-01

We propose a fully automated active contours-based method for the detection and the segmentation of a moored manufactured object in an underwater image. Detection of objects in underwater images is difficult due to the variable lighting conditions and shadows on the object. The proposed technique is based on the information contained in the color maps and uses the visual attention method, combined with a statistical approach for the detection and an active contour for the segmentation of the object to overcome the above problems. In the classical active contour method the region descriptor is fixed and the convergence of the method depends on the initialization. With our approach, this dependence is overcome with an initialization using the visual attention results and a criterion to select the best region descriptor. This approach improves the convergence and the processing time while providing the advantages of a fully automated method.

Vocalization characteristics of North Atlantic right whale surface active groups in the calving habitat, southeastern United States.

PubMed

Trygonis, Vasilis; Gerstein, Edmund; Moir, Jim; McCulloch, Stephen

2013-12-01

Passive acoustic surveys were conducted to assess the vocal behavior of North Atlantic right whales (Eubalaena glacialis) in the designated critical calving habitat along the shallow coastal waters of southeastern United States. Underwater vocalizations were recorded using autonomous buoys deployed in close proximity to surface active groups (SAGs). Nine main vocalization types were identified with manual inspection of spectrograms, and standard acoustic descriptors were extracted. Classification trees were used to examine the distinguishing characteristics of calls and quantify their variability within the SAG vocal repertoire. The results show that descriptors of frequency, bandwidth, and spectral disorder are the most important parameters for partitioning the SAG repertoire, contrary to duration-related measures. The reported source levels and vocalization statistics provide sound production data vital to inform regional passive acoustic monitoring and conservation for this endangered species.

Evaluation of bovine hemimandible morphology by means of elliptic Fourier descriptors.

PubMed

Parés-Casanova, Pere M

2014-01-01

The aim of this research was to compare size (area) and shape variations of bovine hemimandibles according to age. Digital photographs were obtained for 34 hemimandibles belonging to different European breeds of cattle. The specimens were classified according to age, as determined by molar eruption: b months ("young", M1 erupting, n = 8), 10 months ("immature", M2 erupting, n = 9) and over 24 months ("adult", M3 fully erupted, n = 17). Captured images were then digitally analysed based on elliptic Fourier descriptors, which mathematically characterise the area and shape. Hemimandibular areas only showed significant differences between the adults (2752.3 cm2 +/- 250.4) and young subjects (2373.8 cm2 +/- 300.2). The areas for each age group were not linked to linear shape modifications, which was the same for all age groups. So, bovine hemimandibular form change is mainly related to size changes. Shape variability is centred on the condylar ramus.

Convenient QSAR model for predicting the complexation of structurally diverse compounds with beta-cyclodextrins.

PubMed

Pérez-Garrido, Alfonso; Morales Helguera, Aliuska; Abellán Guillén, Adela; Cordeiro, M Natália D S; Garrido Escudero, Amalio

2009-01-15

This paper reports a QSAR study for predicting the complexation of a large and heterogeneous variety of substances (233 organic compounds) with beta-cyclodextrins (beta-CDs). Several different theoretical molecular descriptors, calculated solely from the molecular structure of the compounds under investigation, and an efficient variable selection procedure, like the Genetic Algorithm, led to models with satisfactory global accuracy and predictivity. But the best-final QSAR model is based on Topological descriptors meanwhile offering a reasonable interpretation. This QSAR model was able to explain ca. 84% of the variance in the experimental activity, and displayed very good internal cross-validation statistics and predictivity on external data. It shows that the driving forces for CD complexation are mainly hydrophobic and steric (van der Waals) interactions. Thus, the results of our study provide a valuable tool for future screening and priority testing of beta-CDs guest molecules.

Spatiotemporal attention operator using isotropic contrast and regional homogeneity

NASA Astrophysics Data System (ADS)

Palenichka, Roman; Lakhssassi, Ahmed; Zaremba, Marek

2011-04-01

A multiscale operator for spatiotemporal isotropic attention is proposed to reliably extract attention points during image sequence analysis. Its consecutive local maxima indicate attention points as the centers of image fragments of variable size with high intensity contrast, region homogeneity, regional shape saliency, and temporal change presence. The scale-adaptive estimation of temporal change (motion) and its aggregation with the regional shape saliency contribute to the accurate determination of attention points in image sequences. Multilocation descriptors of an image sequence are extracted at the attention points in the form of a set of multidimensional descriptor vectors. A fast recursive implementation is also proposed to make the operator's computational complexity independent from the spatial scale size, which is the window size in the spatial averaging filter. Experiments on the accuracy of attention-point detection have proved the operator consistency and its high potential for multiscale feature extraction from image sequences.

On the Preferred Active Sites of Promoted MoS 2 for Hydrodesulfurization with Minimal Organonitrogen Inhibition

DOE PAGES

Rangarajan, Srinivas; Mavrikakis, Manos

2016-12-14

Hydrodesulfurization is a process to produce ultralow-sulfur diesel fuel. Although promoted molybdenum sulfide (MoS 2) catalysts have been used industrially for several decades, the active site requirements for selective hydrodesulfurization of organosulfur compounds with minimal inhibition by organonitrogen constituents of a real gasoil feed has not been resolved. By using molecular binding energy descriptors derived from plane wave density functional theory calculations for comparative adsorption of organosulfur and organonitrogen compounds, we analyzed more than 20 potential sites on unpromoted and Ni- and Co-promoted MoS 2. We also found that hydrogen sulfide and ammonia are simple descriptors of adsorption of stericallymore » unhindered organosulfur and organonitrogen compounds such as dibenzothiophene and acridine, respectively. Further, organonitrogen compounds in gasoil bind more strongly than organosulfur compounds on all sites except on sites with exposed metal atoms on the corner and sulfur edges of promoted MoS 2. Consequently, these sites are proposed as required for maximum-hydrodesulfurization minimum-inhibition catalysis.« less

Blue M2: an intermediate melanoidin studied via conceptual DFT.

PubMed

Frau, Juan; Glossman-Mitnik, Daniel

2018-05-31

In this computational study, ten density functionals, viz. CAM-B3LYP, LC-ω PBE, M11, M11L, MN12L, MN12SX, N12, N12SX, ω B97X, and ω B97XD, related to the Def2TZVP basis sets, are assessed together with the SMD solvation model for calculation of the molecular properties and structure of blue-M2 intermediate melanoidin pigment. All the chemical reactivity descriptors for the system are calculated via conceptual density functional theory (DFT). The active sites suitable for nucleophilic, electrophilic, and radical attacks are selected by linking them with the Fukui function indices, electrophilic Parr functions, and condensed dual descriptors Δf(r), respectively. The prediction of the maximum absorption wavelength is considerably accurate relative to its experimental value. The study reveals that the MN12SX and N12SX density functionals are the most appropriate density functionals for predicting the chemical reactivity of the molecule under study.

Simple idea to generate fragment and pharmacophore descriptors and their implications in chemical informatics.

PubMed

Catana, Cornel

2009-03-01

Using a well-defined set of fragments/pharmacophores, a new methodology to calculate fragment/ pharmacophore descriptors for any molecule onto which at least one fragment/pharmacophore can be mapped is presented. To each fragment/pharmacophore present in a molecule, we attach a descriptor that is calculated by identifying the molecule's atoms onto which it maps and summing over its constituent atomic descriptors. The attached descriptors are named C-fragment/pharmacophore descriptors, and this methodology can be applied to any descriptors defined at the atomic level, such as the partition coefficient, molar refractivity, electrotopological state, etc. By using this methodology, the same fragment/pharmacophore can be shown to have different values in different molecules resulting in better discrimination power. As we know, fragment and pharmacophore fingerprints have a lot of applications in chemical informatics. This study has attempted to find the impact of replacing the traditional value of "1" in a fingerprint with real numbers derived form C-fragment/pharmacophore descriptors. One way to do this is to assess the utility of C-fragment/ pharmacophore descriptors in modeling different end points. Here, we exemplify with data from CYP and hERG. The fact that, in many cases, the obtained models were fairly successful and C-fragment descriptors were ranked among the top ones supports the idea that they play an important role in correlation. When we modeled hERG with C-pharmacophore descriptors, however, the model performances decreased slightly, and we attribute this, mainly to the fact that there is no technique capable of handling multiple instances (states). We hope this will open new research, especially in the emerging field of machine learning. Further research is needed to see the impact of C-fragment/pharmacophore descriptors in similarity/dissimilarity applications.

Systems Biological Approach of Molecular Descriptors Connectivity: Optimal Descriptors for Oral Bioavailability Prediction

PubMed Central

Ahmed, Shiek S. S. J.; Ramakrishnan, V.

2012-01-01

Background Poor oral bioavailability is an important parameter accounting for the failure of the drug candidates. Approximately, 50% of developing drugs fail because of unfavorable oral bioavailability. In silico prediction of oral bioavailability (%F) based on physiochemical properties are highly needed. Although many computational models have been developed to predict oral bioavailability, their accuracy remains low with a significant number of false positives. In this study, we present an oral bioavailability model based on systems biological approach, using a machine learning algorithm coupled with an optimal discriminative set of physiochemical properties. Results The models were developed based on computationally derived 247 physicochemical descriptors from 2279 molecules, among which 969, 605 and 705 molecules were corresponds to oral bioavailability, intestinal absorption (HIA) and caco-2 permeability data set, respectively. The partial least squares discriminate analysis showed 49 descriptors of HIA and 50 descriptors of caco-2 are the major contributing descriptors in classifying into groups. Of these descriptors, 47 descriptors were commonly associated to HIA and caco-2, which suggests to play a vital role in classifying oral bioavailability. To determine the best machine learning algorithm, 21 classifiers were compared using a bioavailability data set of 969 molecules with 47 descriptors. Each molecule in the data set was represented by a set of 47 physiochemical properties with the functional relevance labeled as (+bioavailability/−bioavailability) to indicate good-bioavailability/poor-bioavailability molecules. The best-performing algorithm was the logistic algorithm. The correlation based feature selection (CFS) algorithm was implemented, which confirms that these 47 descriptors are the fundamental descriptors for oral bioavailability prediction. Conclusion The logistic algorithm with 47 selected descriptors correctly predicted the oral bioavailability, with a predictive accuracy of more than 71%. Overall, the method captures the fundamental molecular descriptors, that can be used as an entity to facilitate prediction of oral bioavailability. PMID:22815781

Systems biological approach of molecular descriptors connectivity: optimal descriptors for oral bioavailability prediction.

PubMed

Ahmed, Shiek S S J; Ramakrishnan, V

2012-01-01

Poor oral bioavailability is an important parameter accounting for the failure of the drug candidates. Approximately, 50% of developing drugs fail because of unfavorable oral bioavailability. In silico prediction of oral bioavailability (%F) based on physiochemical properties are highly needed. Although many computational models have been developed to predict oral bioavailability, their accuracy remains low with a significant number of false positives. In this study, we present an oral bioavailability model based on systems biological approach, using a machine learning algorithm coupled with an optimal discriminative set of physiochemical properties. The models were developed based on computationally derived 247 physicochemical descriptors from 2279 molecules, among which 969, 605 and 705 molecules were corresponds to oral bioavailability, intestinal absorption (HIA) and caco-2 permeability data set, respectively. The partial least squares discriminate analysis showed 49 descriptors of HIA and 50 descriptors of caco-2 are the major contributing descriptors in classifying into groups. Of these descriptors, 47 descriptors were commonly associated to HIA and caco-2, which suggests to play a vital role in classifying oral bioavailability. To determine the best machine learning algorithm, 21 classifiers were compared using a bioavailability data set of 969 molecules with 47 descriptors. Each molecule in the data set was represented by a set of 47 physiochemical properties with the functional relevance labeled as (+bioavailability/-bioavailability) to indicate good-bioavailability/poor-bioavailability molecules. The best-performing algorithm was the logistic algorithm. The correlation based feature selection (CFS) algorithm was implemented, which confirms that these 47 descriptors are the fundamental descriptors for oral bioavailability prediction. The logistic algorithm with 47 selected descriptors correctly predicted the oral bioavailability, with a predictive accuracy of more than 71%. Overall, the method captures the fundamental molecular descriptors, that can be used as an entity to facilitate prediction of oral bioavailability.

A contour-based shape descriptor for biomedical image classification and retrieval

NASA Astrophysics Data System (ADS)

You, Daekeun; Antani, Sameer; Demner-Fushman, Dina; Thoma, George R.

2013-12-01

Contours, object blobs, and specific feature points are utilized to represent object shapes and extract shape descriptors that can then be used for object detection or image classification. In this research we develop a shape descriptor for biomedical image type (or, modality) classification. We adapt a feature extraction method used in optical character recognition (OCR) for character shape representation, and apply various image preprocessing methods to successfully adapt the method to our application. The proposed shape descriptor is applied to radiology images (e.g., MRI, CT, ultrasound, X-ray, etc.) to assess its usefulness for modality classification. In our experiment we compare our method with other visual descriptors such as CEDD, CLD, Tamura, and PHOG that extract color, texture, or shape information from images. The proposed method achieved the highest classification accuracy of 74.1% among all other individual descriptors in the test, and when combined with CSD (color structure descriptor) showed better performance (78.9%) than using the shape descriptor alone.

Age-dependent radial increases in wood specific gravity of tropical pioneers in Costa Rica

Treesearch

Bruce G. Williamson; Michael C. Wiemann

2010-01-01

Wood specific gravity is the single best descriptor of wood functional properties and tree life-history traits, and it is the most important variable in estimating carbon stocks in forests. Tropical pioneer trees produce wood of increasing specific gravity across the trunk radius as they grow in stature. Here, we tested whether radial increases in wood specific gravity...

Visual Information Theory and Visual Representation for Achieving Provable Bounds in Vision-Based Control and Decision

DTIC Science & Technology

2014-07-30

of the IEEE Intl. Conf. on Comp. Vis. and Patt . Recog. (CVPR). 07-JAN-14, . : , B. Taylor, A. Ayvaci, A. Ravichandran, and S. Soatto.. Semantic video...detection, localization and tracking, Intl. Conf. on Comp. Vis. Patt . Recog.. 06-JAN-11, . : , Michalis Raptis, Iasonas Kokkinos, Stefano Soatto...of the IEEE Intl. Conf. on Comp. Vis. and Patt . Recog., 2012. [12] M. Raptis and S. Soatto. Tracklet descriptors for action modeling and video

Direct water decomposition on transition metal surfaces: Structural dependence and catalytic screening

DOE PAGES

Tsai, Charlie; Lee, Kyoungjin; Yoo, Jong Suk; ...

2016-02-16

Density functional theory calculations are used to investigate thermal water decomposition over the close-packed (111), stepped (211), and open (100) facets of transition metal surfaces. A descriptor-based approach is used to determine that the (211) facet leads to the highest possible rates. As a result, a range of 96 binary alloys were screened for their potential activity and a rate control analysis was performed to assess how the overall rate could be improved.

Electronic structure and physicochemical properties of selected penicillins

NASA Astrophysics Data System (ADS)

Soriano-Correa, Catalina; Ruiz, Juan F. Sánchez; Raya, A.; Esquivel, Rodolfo O.

Traditionally, penicillins have been used as antibacterial agents due to their characteristics and widespread applications with few collateral effects, which have motivated several theoretical and experimental studies. Despite the latter, their mechanism of biological action has not been completely elucidated. We present a theoretical study at the Hartree-Fock and density functional theory (DFT) levels of theory of a selected group of penicillins such as the penicillin-G, amoxicillin, ampicillin, dicloxacillin, and carbenicillin molecules, to systematically determine the electron structure of full ?-lactam antibiotics. Our results allow us to analyze the electronic properties of the pharmacophore group, the aminoacyl side-chain, and the influence of the substituents (R and X) attached to the aminoacyl side-chain at 6? (in contrast with previous studies focused at the 3? substituents), and to corroborate the results of previous studies performed at the semiempirical level, solely on the ?-lactam ring of penicillins. Besides, several density descriptors are determined with the purpose of analyzing their link to the antibacterial activity of these penicillin compounds. Our results for the atomic charges (fitted to the electrostatic potential), the bond orders, and several global reactivity descriptors, such as the dipole moments, ionization potential, hardness, and the electrophilicity index, led us to characterize: the active sites, the effect of the electron-attracting substituent properties and their physicochemical features, which altogether, might be important to understand the biological activity of these type of molecules.

An effective content-based image retrieval technique for image visuals representation based on the bag-of-visual-words model

PubMed Central

Jabeen, Safia; Mehmood, Zahid; Mahmood, Toqeer; Saba, Tanzila; Rehman, Amjad; Mahmood, Muhammad Tariq

2018-01-01

For the last three decades, content-based image retrieval (CBIR) has been an active research area, representing a viable solution for retrieving similar images from an image repository. In this article, we propose a novel CBIR technique based on the visual words fusion of speeded-up robust features (SURF) and fast retina keypoint (FREAK) feature descriptors. SURF is a sparse descriptor whereas FREAK is a dense descriptor. Moreover, SURF is a scale and rotation-invariant descriptor that performs better in the case of repeatability, distinctiveness, and robustness. It is robust to noise, detection errors, geometric, and photometric deformations. It also performs better at low illumination within an image as compared to the FREAK descriptor. In contrast, FREAK is a retina-inspired speedy descriptor that performs better for classification-based problems as compared to the SURF descriptor. Experimental results show that the proposed technique based on the visual words fusion of SURF-FREAK descriptors combines the features of both descriptors and resolves the aforementioned issues. The qualitative and quantitative analysis performed on three image collections, namely Corel-1000, Corel-1500, and Caltech-256, shows that proposed technique based on visual words fusion significantly improved the performance of the CBIR as compared to the feature fusion of both descriptors and state-of-the-art image retrieval techniques. PMID:29694429

An effective content-based image retrieval technique for image visuals representation based on the bag-of-visual-words model.

PubMed

Jabeen, Safia; Mehmood, Zahid; Mahmood, Toqeer; Saba, Tanzila; Rehman, Amjad; Mahmood, Muhammad Tariq

2018-01-01

For the last three decades, content-based image retrieval (CBIR) has been an active research area, representing a viable solution for retrieving similar images from an image repository. In this article, we propose a novel CBIR technique based on the visual words fusion of speeded-up robust features (SURF) and fast retina keypoint (FREAK) feature descriptors. SURF is a sparse descriptor whereas FREAK is a dense descriptor. Moreover, SURF is a scale and rotation-invariant descriptor that performs better in the case of repeatability, distinctiveness, and robustness. It is robust to noise, detection errors, geometric, and photometric deformations. It also performs better at low illumination within an image as compared to the FREAK descriptor. In contrast, FREAK is a retina-inspired speedy descriptor that performs better for classification-based problems as compared to the SURF descriptor. Experimental results show that the proposed technique based on the visual words fusion of SURF-FREAK descriptors combines the features of both descriptors and resolves the aforementioned issues. The qualitative and quantitative analysis performed on three image collections, namely Corel-1000, Corel-1500, and Caltech-256, shows that proposed technique based on visual words fusion significantly improved the performance of the CBIR as compared to the feature fusion of both descriptors and state-of-the-art image retrieval techniques.

External validation of a publicly available computer assisted diagnostic tool for mammographic mass lesions with two high prevalence research datasets.

PubMed

Benndorf, Matthias; Burnside, Elizabeth S; Herda, Christoph; Langer, Mathias; Kotter, Elmar

2015-08-01

Lesions detected at mammography are described with a highly standardized terminology: the breast imaging-reporting and data system (BI-RADS) lexicon. Up to now, no validated semantic computer assisted classification algorithm exists to interactively link combinations of morphological descriptors from the lexicon to a probabilistic risk estimate of malignancy. The authors therefore aim at the external validation of the mammographic mass diagnosis (MMassDx) algorithm. A classification algorithm like MMassDx must perform well in a variety of clinical circumstances and in datasets that were not used to generate the algorithm in order to ultimately become accepted in clinical routine. The MMassDx algorithm uses a naïve Bayes network and calculates post-test probabilities of malignancy based on two distinct sets of variables, (a) BI-RADS descriptors and age ("descriptor model") and (b) BI-RADS descriptors, age, and BI-RADS assessment categories ("inclusive model"). The authors evaluate both the MMassDx (descriptor) and MMassDx (inclusive) models using two large publicly available datasets of mammographic mass lesions: the digital database for screening mammography (DDSM) dataset, which contains two subsets from the same examinations-a medio-lateral oblique (MLO) view and cranio-caudal (CC) view dataset-and the mammographic mass (MM) dataset. The DDSM contains 1220 mass lesions and the MM dataset contains 961 mass lesions. The authors evaluate discriminative performance using area under the receiver-operating-characteristic curve (AUC) and compare this to the BI-RADS assessment categories alone (i.e., the clinical performance) using the DeLong method. The authors also evaluate whether assigned probabilistic risk estimates reflect the lesions' true risk of malignancy using calibration curves. The authors demonstrate that the MMassDx algorithms show good discriminatory performance. AUC for the MMassDx (descriptor) model in the DDSM data is 0.876/0.895 (MLO/CC view) and AUC for the MMassDx (inclusive) model in the DDSM data is 0.891/0.900 (MLO/CC view). AUC for the MMassDx (descriptor) model in the MM data is 0.862 and AUC for the MMassDx (inclusive) model in the MM data is 0.900. In all scenarios, MMassDx performs significantly better than clinical performance, P < 0.05 each. The authors furthermore demonstrate that the MMassDx algorithm systematically underestimates the risk of malignancy in the DDSM and MM datasets, especially when low probabilities of malignancy are assigned. The authors' results reveal that the MMassDx algorithms have good discriminatory performance but less accurate calibration when tested on two independent validation datasets. Improvement in calibration and testing in a prospective clinical population will be important steps in the pursuit of translation of these algorithms to the clinic.

Quantitative structure-retention relationships for gas chromatographic retention indices of alkylbenzenes with molecular graph descriptors.

PubMed

Ivanciuc, O; Ivanciuc, T; Klein, D J; Seitz, W A; Balaban, A T

2001-02-01

Quantitative structure-retention relationships (QSRR) represent statistical models that quantify the connection between the molecular structure and the chromatographic retention indices of organic compounds, allowing the prediction of retention indices of novel, not yet synthesized compounds, solely from their structural descriptors. Using multiple linear regression, QSRR models for the gas chromatographic Kováts retention indices of 129 alkylbenzenes are generated using molecular graph descriptors. The correlational ability of structural descriptors computed from 10 molecular matrices is investigated, showing that the novel reciprocal matrices give numerical indices with improved correlational ability. A QSRR equation with 5 graph descriptors gives the best calibration and prediction results, demonstrating the usefulness of the molecular graph descriptors in modeling chromatographic retention parameters. The sequential orthogonalization of descriptors suggests simpler QSRR models by eliminating redundant structural information.

Influence of thermodynamic parameter in Lanosterol 14alpha-demethylase inhibitory activity as antifungal agents: a QSAR approach.

PubMed

Vasanthanathan, Poongavanam; Lakshmi, Manickavasagam; Arockia Babu, Marianesan; Kaskhedikar, Sathish Gopalrao

2006-06-01

A quantitative structure activity relationship, Hansch approach was applied on twenty compounds of chromene derivatives as Lanosterol 14alpha-demethylase inhibitory activity against eight fungal organisms. Various physicochemical descriptors and reported minimum inhibitory concentration values of different fungal organisms were used as independent variables and dependent variable respectively. The best models for eight different fungal organisms were first validated by leave-one-out cross validation procedure. It was revealed that thermodynamic parameters were found to have overall significant correlationship with anti fungal activity and these studies provide an insight to design new molecules.

Signaling completion of a message transfer from an origin compute node to a target compute node

DOEpatents

Blocksome, Michael A [Rochester, MN; Parker, Jeffrey J [Rochester, MN

2011-05-24

Signaling completion of a message transfer from an origin node to a target node includes: sending, by an origin DMA engine, an RTS message, the RTS message specifying an application message for transfer to the target node from the origin node; receiving, by the origin DMA engine, a remote get message containing a data descriptor for the message and a completion notification descriptor, the completion notification descriptor specifying a local direct put transfer operation for transferring data locally on the origin node; inserting, by the origin DMA engine in an injection FIFO buffer, the data descriptor followed by the completion notification descriptor; transferring, by the origin DMA engine to the target node, the message in dependence upon the data descriptor; and notifying, by the origin DMA engine, the application that transfer of the message is complete in dependence upon the completion notification descriptor.

Direct memory access transfer completion notification

DOEpatents

Archer, Charles J. , Blocksome; Michael A. , Parker; Jeffrey, J [Rochester, MN

2011-02-15

Methods, systems, and products are disclosed for DMA transfer completion notification that include: inserting, by an origin DMA on an origin node in an origin injection FIFO, a data descriptor for an application message; inserting, by the origin DMA, a reflection descriptor in the origin injection FIFO, the reflection descriptor specifying a remote get operation for injecting a completion notification descriptor in a reflection injection FIFO on a reflection node; transferring, by the origin DMA to a target node, the message in dependence upon the data descriptor; in response to completing the message transfer, transferring, by the origin DMA to the reflection node, the completion notification descriptor in dependence upon the reflection descriptor; receiving, by the origin DMA from the reflection node, a completion packet; and notifying, by the origin DMA in response to receiving the completion packet, the origin node's processing core that the message transfer is complete.

Signaling completion of a message transfer from an origin compute node to a target compute node

DOEpatents

Blocksome, Michael A [Rochester, MN

2011-02-15

Signaling completion of a message transfer from an origin node to a target node includes: sending, by an origin DMA engine, an RTS message, the RTS message specifying an application message for transfer to the target node from the origin node; receiving, by the origin DMA engine, a remote get message containing a data descriptor for the message and a completion notification descriptor, the completion notification descriptor specifying a local memory FIFO data transfer operation for transferring data locally on the origin node; inserting, by the origin DMA engine in an injection FIFO buffer, the data descriptor followed by the completion notification descriptor; transferring, by the origin DMA engine to the target node, the message in dependence upon the data descriptor; and notifying, by the origin DMA engine, the application that transfer of the message is complete in dependence upon the completion notification descriptor.

Multi-Scale Surface Descriptors

PubMed Central

Cipriano, Gregory; Phillips, George N.; Gleicher, Michael

2010-01-01

Local shape descriptors compactly characterize regions of a surface, and have been applied to tasks in visualization, shape matching, and analysis. Classically, curvature has be used as a shape descriptor; however, this differential property characterizes only an infinitesimal neighborhood. In this paper, we provide shape descriptors for surface meshes designed to be multi-scale, that is, capable of characterizing regions of varying size. These descriptors capture statistically the shape of a neighborhood around a central point by fitting a quadratic surface. They therefore mimic differential curvature, are efficient to compute, and encode anisotropy. We show how simple variants of mesh operations can be used to compute the descriptors without resorting to expensive parameterizations, and additionally provide a statistical approximation for reduced computational cost. We show how these descriptors apply to a number of uses in visualization, analysis, and matching of surfaces, particularly to tasks in protein surface analysis. PMID:19834190

Respiratory complaints in Chinese: cultural and diagnostic specificities.

PubMed

Han, Jiangna; Zhu, Yuanjue; Li, Shunwei; Chen, Xiansheng; Put, Claudia; Van de Woestijne, Karel P; Van den Bergh, Omer

2005-06-01

We investigated the qualitative components of a wide range of Chinese descriptors of dyspnea and associated symptoms, and their relevance for clinical diagnosis. Sixty-one spontaneously reported descriptors were elicited in Chinese patients to make a symptom checklist, which was administered to new groups of patients with different cardiopulmonary diseases, to patients with medically unexplained dyspnea and to healthy subjects. Test-retest reliability was satisfactory for most of the descriptors. A principal component analysis on 61 descriptors yielded the following eight factors: dyspnea-effort of breathing; dyspnea-affective aspect; wheezing; anxiety; tingling; palpitation; coughing and sputum; and dying experience. Although the descriptors of dyspnea-effort of breathing resembled Western wordings and were shared by patients with a variety of diseases, the descriptors of dyspnea-affective aspect appeared to be more culturally specific and were primarily linked to the diagnosis of medically unexplained dyspnea, whereas wheezing was specifically linked to asthma. Three factors of breathlessness were found in Chinese. The descriptors of dyspnea-effort of breathing and wheezing appear to be similar to Western descriptors, whereas the dyspnea-affective aspect seems to bear cultural specificity.

Receptive fields selection for binary feature description.

PubMed

Fan, Bin; Kong, Qingqun; Trzcinski, Tomasz; Wang, Zhiheng; Pan, Chunhong; Fua, Pascal

2014-06-01

Feature description for local image patch is widely used in computer vision. While the conventional way to design local descriptor is based on expert experience and knowledge, learning-based methods for designing local descriptor become more and more popular because of their good performance and data-driven property. This paper proposes a novel data-driven method for designing binary feature descriptor, which we call receptive fields descriptor (RFD). Technically, RFD is constructed by thresholding responses of a set of receptive fields, which are selected from a large number of candidates according to their distinctiveness and correlations in a greedy way. Using two different kinds of receptive fields (namely rectangular pooling area and Gaussian pooling area) for selection, we obtain two binary descriptors RFDR and RFDG .accordingly. Image matching experiments on the well-known patch data set and Oxford data set demonstrate that RFD significantly outperforms the state-of-the-art binary descriptors, and is comparable with the best float-valued descriptors at a fraction of processing time. Finally, experiments on object recognition tasks confirm that both RFDR and RFDG successfully bridge the performance gap between binary descriptors and their floating-point competitors.

Alignment-independent comparison of binding sites based on DrugScore potential fields encoded by 3D Zernike descriptors.

PubMed

Nisius, Britta; Gohlke, Holger

2012-09-24

Analyzing protein binding sites provides detailed insights into the biological processes proteins are involved in, e.g., into drug-target interactions, and so is of crucial importance in drug discovery. Herein, we present novel alignment-independent binding site descriptors based on DrugScore potential fields. The potential fields are transformed to a set of information-rich descriptors using a series expansion in 3D Zernike polynomials. The resulting Zernike descriptors show a promising performance in detecting similarities among proteins with low pairwise sequence identities that bind identical ligands, as well as within subfamilies of one target class. Furthermore, the Zernike descriptors are robust against structural variations among protein binding sites. Finally, the Zernike descriptors show a high data compression power, and computing similarities between binding sites based on these descriptors is highly efficient. Consequently, the Zernike descriptors are a useful tool for computational binding site analysis, e.g., to predict the function of novel proteins, off-targets for drug candidates, or novel targets for known drugs.

PROFEAT Update: A Protein Features Web Server with Added Facility to Compute Network Descriptors for Studying Omics-Derived Networks.

PubMed

Zhang, P; Tao, L; Zeng, X; Qin, C; Chen, S Y; Zhu, F; Yang, S Y; Li, Z R; Chen, W P; Chen, Y Z

2017-02-03

The studies of biological, disease, and pharmacological networks are facilitated by the systems-level investigations using computational tools. In particular, the network descriptors developed in other disciplines have found increasing applications in the study of the protein, gene regulatory, metabolic, disease, and drug-targeted networks. Facilities are provided by the public web servers for computing network descriptors, but many descriptors are not covered, including those used or useful for biological studies. We upgraded the PROFEAT web server http://bidd2.nus.edu.sg/cgi-bin/profeat2016/main.cgi for computing up to 329 network descriptors and protein-protein interaction descriptors. PROFEAT network descriptors comprehensively describe the topological and connectivity characteristics of unweighted (uniform binding constants and molecular levels), edge-weighted (varying binding constants), node-weighted (varying molecular levels), edge-node-weighted (varying binding constants and molecular levels), and directed (oriented processes) networks. The usefulness of the network descriptors is illustrated by the literature-reported studies of the biological networks derived from the genome, interactome, transcriptome, metabolome, and diseasome profiles. Copyright © 2016 Elsevier Ltd. All rights reserved.

Clustering-based Feature Learning on Variable Stars

NASA Astrophysics Data System (ADS)

Mackenzie, Cristóbal; Pichara, Karim; Protopapas, Pavlos

2016-04-01

The success of automatic classification of variable stars depends strongly on the lightcurve representation. Usually, lightcurves are represented as a vector of many descriptors designed by astronomers called features. These descriptors are expensive in terms of computing, require substantial research effort to develop, and do not guarantee a good classification. Today, lightcurve representation is not entirely automatic; algorithms must be designed and manually tuned up for every survey. The amounts of data that will be generated in the future mean astronomers must develop scalable and automated analysis pipelines. In this work we present a feature learning algorithm designed for variable objects. Our method works by extracting a large number of lightcurve subsequences from a given set, which are then clustered to find common local patterns in the time series. Representatives of these common patterns are then used to transform lightcurves of a labeled set into a new representation that can be used to train a classifier. The proposed algorithm learns the features from both labeled and unlabeled lightcurves, overcoming the bias using only labeled data. We test our method on data sets from the Massive Compact Halo Object survey and the Optical Gravitational Lensing Experiment; the results show that our classification performance is as good as and in some cases better than the performance achieved using traditional statistical features, while the computational cost is significantly lower. With these promising results, we believe that our method constitutes a significant step toward the automation of the lightcurve classification pipeline.

Unravelling connections between river flow and large-scale climate: experiences from Europe

NASA Astrophysics Data System (ADS)

Hannah, D. M.; Kingston, D. G.; Lavers, D.; Stagge, J. H.; Tallaksen, L. M.

2016-12-01

The United Nations has identified better knowledge of large-scale water cycle processes as essential for socio-economic development and global water-food-energy security. In this context, and given the ever-growing concerns about climate change/ variability and human impacts on hydrology, there is an urgent research need: (a) to quantify space-time variability in regional river flow, and (b) to improve hydroclimatological understanding of climate-flow connections as a basis for identifying current and future water-related issues. In this paper, we draw together studies undertaken at the pan-European scale: (1) to evaluate current methods for assessing space-time dynamics for different streamflow metrics (annual regimes, low flows and high flows) and for linking flow variability to atmospheric drivers (circulation indices, air-masses, gridded climate fields and vapour flux); and (2) to propose a plan for future research connecting streamflow and the atmospheric conditions in Europe and elsewhere. We believe this research makes a useful, unique contribution to the literature through a systematic inter-comparison of different streamflow metrics and atmospheric descriptors. In our findings, we highlight the need to consider appropriate atmospheric descriptors (dependent on the target flow metric and region of interest) and to develop analytical techniques that best characterise connections in the ocean-atmosphere-land surface process chain. We call for the need to consider not only atmospheric interactions, but also the role of the river basin-scale terrestrial hydrological processes in modifying the climate signal response of river flows.

Dyspnea descriptors developed in Brazil: application in obese patients and in patients with cardiorespiratory diseases.

PubMed

Teixeira, Christiane Aires; Rodrigues Júnior, Antonio Luiz; Straccia, Luciana Cristina; Vianna, Elcio Dos Santos Oliveira; Silva, Geruza Alves da; Martinez, José Antônio Baddini

2011-01-01

To develop a set of descriptive terms applied to the sensation of dyspnea (dyspnea descriptors) for use in Brazil and to investigate the usefulness of these descriptors in four distinct clinical conditions that can be accompanied by dyspnea. We collected 111 dyspnea descriptors from 67 patients and 10 health professionals. These descriptors were analyzed and reduced to 15 based on their frequency of use, similarity of meaning, and potential pathophysiological value. Those 15 descriptors were applied in 50 asthma patients, 50 COPD patients, 30 patients with heart failure, and 50 patients with class II or III obesity. The three best descriptors, as selected by the patients, were studied by cluster analysis. Potential associations between the identified clusters and the four clinical conditions were also investigated. The use of this set of descriptors led to a solution with seven clusters, designated sufoco (suffocating), aperto (tight), rápido (rapid), fadiga (fatigue), abafado (stuffy), trabalho/inspiração (work/inhalation), and falta de ar (shortness of breath). Overlapping of descriptors was quite common among the patients, regardless of their clinical condition. Asthma was significantly associated with the sufoco and trabalho/inspiração clusters, whereas COPD and heart failure were associated with the sufoco, trabalho/inspiração, and falta de ar clusters. Obesity was associated only with the falta de ar cluster. In Brazil, patients who are accustomed to perceiving dyspnea employ various descriptors in order to describe the symptom, and these descriptors can be grouped into similar clusters. In our study sample, such clusters showed no usefulness in differentiating among the four clinical conditions evaluated.

Reproducibility of the NEPTUNE descriptor-based scoring system on whole-slide images and histologic and ultrastructural digital images.

PubMed

Barisoni, Laura; Troost, Jonathan P; Nast, Cynthia; Bagnasco, Serena; Avila-Casado, Carmen; Hodgin, Jeffrey; Palmer, Matthew; Rosenberg, Avi; Gasim, Adil; Liensziewski, Chrysta; Merlino, Lino; Chien, Hui-Ping; Chang, Anthony; Meehan, Shane M; Gaut, Joseph; Song, Peter; Holzman, Lawrence; Gibson, Debbie; Kretzler, Matthias; Gillespie, Brenda W; Hewitt, Stephen M

2016-07-01

The multicenter Nephrotic Syndrome Study Network (NEPTUNE) digital pathology scoring system employs a novel and comprehensive methodology to document pathologic features from whole-slide images, immunofluorescence and ultrastructural digital images. To estimate inter- and intra-reader concordance of this descriptor-based approach, data from 12 pathologists (eight NEPTUNE and four non-NEPTUNE) with experience from training to 30 years were collected. A descriptor reference manual was generated and a webinar-based protocol for consensus/cross-training implemented. Intra-reader concordance for 51 glomerular descriptors was evaluated on jpeg images by seven NEPTUNE pathologists scoring 131 glomeruli three times (Tests I, II, and III), each test following a consensus webinar review. Inter-reader concordance of glomerular descriptors was evaluated in 315 glomeruli by all pathologists; interstitial fibrosis and tubular atrophy (244 cases, whole-slide images) and four ultrastructural podocyte descriptors (178 cases, jpeg images) were evaluated once by six and five pathologists, respectively. Cohen's kappa for inter-reader concordance for 48/51 glomerular descriptors with sufficient observations was moderate (0.40

Benchmarking of protein descriptor sets in proteochemometric modeling (part 2): modeling performance of 13 amino acid descriptor sets

PubMed Central

2013-01-01

Background While a large body of work exists on comparing and benchmarking descriptors of molecular structures, a similar comparison of protein descriptor sets is lacking. Hence, in the current work a total of 13 amino acid descriptor sets have been benchmarked with respect to their ability of establishing bioactivity models. The descriptor sets included in the study are Z-scales (3 variants), VHSE, T-scales, ST-scales, MS-WHIM, FASGAI, BLOSUM, a novel protein descriptor set (termed ProtFP (4 variants)), and in addition we created and benchmarked three pairs of descriptor combinations. Prediction performance was evaluated in seven structure-activity benchmarks which comprise Angiotensin Converting Enzyme (ACE) dipeptidic inhibitor data, and three proteochemometric data sets, namely (1) GPCR ligands modeled against a GPCR panel, (2) enzyme inhibitors (NNRTIs) with associated bioactivities against a set of HIV enzyme mutants, and (3) enzyme inhibitors (PIs) with associated bioactivities on a large set of HIV enzyme mutants. Results The amino acid descriptor sets compared here show similar performance (<0.1 log units RMSE difference and <0.1 difference in MCC), while errors for individual proteins were in some cases found to be larger than those resulting from descriptor set differences ( > 0.3 log units RMSE difference and >0.7 difference in MCC). Combining different descriptor sets generally leads to better modeling performance than utilizing individual sets. The best performers were Z-scales (3) combined with ProtFP (Feature), or Z-Scales (3) combined with an average Z-Scale value for each target, while ProtFP (PCA8), ST-Scales, and ProtFP (Feature) rank last. Conclusions While amino acid descriptor sets capture different aspects of amino acids their ability to be used for bioactivity modeling is still – on average – surprisingly similar. Still, combining sets describing complementary information consistently leads to small but consistent improvement in modeling performance (average MCC 0.01 better, average RMSE 0.01 log units lower). Finally, performance differences exist between the targets compared thereby underlining that choosing an appropriate descriptor set is of fundamental for bioactivity modeling, both from the ligand- as well as the protein side. PMID:24059743

Theoretical insights on flavanones as antioxidants and UV filters: A TDDFT and NLMO study.

PubMed

Ajmala Shireen, P; Abdul Mujeeb, V M; Muraleedharan, K

2017-05-01

UV radiations can cause several irritations to the skin like sunburn, photo aging and even skin cancer. Sunscreens are widely used to protect the skin against these harmful radiations. One of the ingredients present in these sunscreens are organic molecules capable of absorbing these harmful radiations. Recently, the search is on for antioxidant molecules which can act as UV filters as they can facilitate photo protection. In this study, a computational investigation based on density functional theory (DFT) is attempted on flavanones namely pinocembrin, pinostrobin and alpinetin found in Boesenbergia pandurata. Several quantum chemical descriptors are computed to understand the antioxidant potentiality of these molecules. Quantum chemical descriptors of these flavanone molecules are found to be comparable to that of well-known anti-oxidant quercetin. UV response of these molecules are studied using time dependent density functional theory (TD-DFT) formalism and by means of natural bond orbital (NBO) theory. It could be seen that these molecules exhibit a broad absorption in the UV region 270-390nm. This falls exactly in the region of harmful UVB and UVA radiation. Thus, these molecules have the potential to absorb the harmful UV radiation. From NLMO cluster studies, the orbital contribution to absorption is explained. In flavanones, unlike other classes of flavonoids, there is a discontinuity in the electron conjugation due to the absence of C2C3 double bond. This might be the key structural feature that leads to the absorption of these molecules to be centered around the UV region. These molecules can thus be treated as promising candidates for antioxidant UV filters in sunscreens. Copyright © 2017 Elsevier B.V. All rights reserved.

Toxicity prediction of PHDDs and phenols in the light of nucleic acid bases and DNA base pair interaction.

PubMed

Mondal Roy, Sutapa; Roy, Debesh R; Sahoo, Suban K

2015-11-01

The applicability of Density Functional Theory (DFT) based descriptors for the development of quantitative structure-toxicity relationships (QSTR) is assessed for two different series of toxic aromatic compounds, viz., polyhalogenated dibenzo-p-dioxins (PHDDs) and phenols (PHs). A series of 20 compounds each for PHDDs and PHs with their experimental toxicities (IC50 and IGC50) is chosen in the present study to develop DFT based efficient quantum chemical parameters (QCPs) for explaining the toxin potential of the considered compounds. A systematic analysis to find out the electron donation/acceptance nature of these selected compounds with the considered model biosystems, viz., nucleic acid (NA) bases and DNA base pairs, is performed to identify potential QCPs. Accordingly, PHDDs is found to be electron acceptors whereas phenols as donors, during their interaction with biosystems. Two parameter regression model is carried out comprising global charge transfer (ΔN), and local Fukui Function's for nucleophilic attack (fk(+)) for PHDDs and the same for electrophilic attack (fk(-)) in case of PHs. It is heartening to note that our chosen descriptors, viz, charge transfer (ΔN) and Fukui Function (fk(±)) plays a crucial role by explaining more than 90% of the observed toxic behavior (in terms of correlation-coefficient, R) of PHDDs and PHs. The developed QCPs, viz., ΔN and fk(±) can be added as the new descriptors in the QSTR parlance. Copyright © 2015 Elsevier Inc. All rights reserved.

The two faces of hydrogen-bond strength on triple AAA-DDD arrays.

PubMed

Lopez, Alfredo Henrique Duarte; Caramori, Giovanni Finoto; Coimbra, Daniel Fernando; Parreira, Renato Luis Tame; da Silva, Éder Henrique

2013-12-02

Systems that are connected through multiple hydrogen bonds are the cornerstone of molecular recognition processes in biology, and they are increasingly being employed in supramolecular chemistry, specifically in molecular self-assembly processes. For this reason, the effects of different substituents (NO2, CN, F, Cl, Br, OCH3 and NH2) on the electronic structure, and consequently on the magnitude of hydrogen bonds in triple AAA-DDD arrays (A=acceptor, D=donor) were evaluated in the light of topological [electron localization function (ELF) and quantum theory of atoms in molecules (QTAIM)], energetic [Su-Li energy-decomposition analysis (EDA) and natural bond orbital analysis (NBO)], and geometrical analysis. The results based on local H-bond descriptors (geometries, QTAIM, ELF, and NBO) indicate that substitutions with electron-withdrawing groups on the AAA module tend to strengthen, whereas electron-donating substituents tend to weaken the covalent character of the AAA-DDD intermolecular H-bonds, and also indicate that the magnitude of the effect is dependent on the position of substitution. In contrast, Su-Li EDA results show an opposite behavior when compared to local H-bond descriptors, indicating that electron-donating substituents tend to increase the magnitude of H-bonds in AAA-DDD arrays, and thus suggesting that the use of local H-bond descriptors describes the nature of H bonds only partially, not providing enough insight about the strength of such H bonds. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The association between negative self-descriptions and depressive symptomology: does culture make a difference?

PubMed

Saint Arnault, Denise; Sakamoto, Shinji; Moriwaki, Aiko

2005-04-01

Research findings that depressed Americans endorse more negative self-related adjectives than controls may be related to a shared self-enhancement cultural frame. This study examines the relationship between negative core self-descriptors and depressive symptoms in 79 Japanese and 50 American women. Americans had more positive self-descriptions and core self-descriptors; however, there were no cultural group differences in number of negative self-descriptors or core self-descriptors. There was a significant correlation between negative core self-descriptor and Beck Depression Inventory (BDI) for Americans only, explaining 10.6% of the BDI variance. Analysis of variance revealed that there was significant BDI group differences for American negative core self-descriptor only. Theoretical possibilities are discussed.

The Association Between Negative Self-Descriptions and Depressive Symptomology: Does Culture Make a Difference?

PubMed Central

Sakamoto, Shinji; Moriwaki, Aiko

2007-01-01

Research findings that depressed Americans endorse more negative self-related adjectives than controls may be related to a shared self-enhancement cultural frame. This study examines the relationship between negative core self-descriptors and depressive symptoms in 79 Japanese and 50 American women. Americans had more positive self-descriptions and core self-descriptors; however, there were no cultural group differences in number of negative self-descriptors or core self-descriptors. There was a significant correlation between negative core self-descriptor and Beck Depression Inventory (BDI) for Americans only, explaining 10.6% of the BDI variance. Analysis of variance revealed that there was significant BDI group differences for American negative core self-descriptor only. Theoretical possibilities are discussed. PMID:15902678

Partition Coefficients of Organics between Water and Carbon Dioxide Revisited: Correlation with Solute Molecular Descriptors and Solvent Cohesive Properties.

PubMed

Roth, Michal

2016-12-06

High-pressure phase behavior of systems containing water, carbon dioxide and organics has been important in several environment- and energy-related fields including carbon capture and storage, CO 2 sequestration and CO 2 -assisted enhanced oil recovery. Here, partition coefficients (K-factors) of organic solutes between water and supercritical carbon dioxide have been correlated with extended linear solvation energy relationships (LSERs). In addition to the Abraham molecular descriptors of the solutes, the explanatory variables also include the logarithm of solute vapor pressure, the solubility parameters of carbon dioxide and water, and the internal pressure of water. This is the first attempt to include also the properties of water as explanatory variables in LSER correlations of K-factor data in CO 2 -water-organic systems. Increasing values of the solute hydrogen bond acidity, the solute hydrogen bond basicity, the solute dipolarity/polarizability, the internal pressure of water and the solubility parameter of water all tend to reduce the K-factor, that is, to favor the solute partitioning to the water-rich phase. On the contrary, increasing values of the solute characteristic volume, the solute vapor pressure and the solubility parameter of CO 2 tend to raise the K-factor, that is, to favor the solute partitioning to the CO 2 -rich phase.

The effect of video interviews with STEM professionals on STEM-subject attitude and STEM-career interest of middle school students in conservative Protestant Christian schools

NASA Astrophysics Data System (ADS)

Alsup, Philip R.

Inspiring learners toward career options available in STEM fields (Science, Technology, Engineering, and Mathematics) is important not only for economic development but also for maintaining creative thinking and innovation. Limited amounts of research in STEM education have focused on the population of students enrolled in religious and parochial schools, and given the historic conflict between religion and science, this sector of American education is worthy of examination. The purpose of this quantitative study is to extend Gottfredson's (1981) Theory of Circumscription and Compromise as it relates to occupational aspirations. Bem's (1981) Gender Schema Theory is examined as it relates to the role of gender in career expectations, and Crenshaw's (1989) Intersectionality Theory is included as it pertains to religion as a group identifier. Six professionals in STEM career fields were video recorded while being interviewed about their skills and education as well as positive and negative aspects of their jobs. The interviews were compiled into a 25-minute video for the purpose of increasing understanding of STEM careers among middle school viewers. The research questions asked whether middle school students from conservative, Protestant Christian schools in a Midwest region increased in STEM-subject attitude and STEM-career interest as a result of viewing the video and whether gender interacted with exposure to the video. A quasi-experimental, nonequivalent control groups, pretest/posttest factorial design was employed to evaluate data collected from the STEM Semantic Survey. A Two-Way ANCOVA revealed no significant differences in dependent variables from pretest to posttest. Implications of the findings are examined and recommendations for future research are made. Descriptors: STEM career interest, STEM attitude, STEM gender disparity, Occupational aspirations, Conservative Protestant education.

Quantification of intra-tumour cell proliferation heterogeneity using imaging descriptors of 18F fluorothymidine-positron emission tomography

NASA Astrophysics Data System (ADS)

Willaime, J. M. Y.; Turkheimer, F. E.; Kenny, L. M.; Aboagye, E. O.

2013-01-01

Intra-tumour heterogeneity is a characteristic shared by all cancers. We explored the use of texture variables derived from images of [18F]fluorothymidine-positron emission tomography (FLT-PET), thus notionally assessing the heterogeneity of proliferation in individual tumours. Our aims were to study the range of textural feature values across tissue types, verify the repeatability of these image descriptors and further, to explore associations with clinical response to chemotherapy in breast cancer patients. The repeatability of 28 textural descriptors was assessed in patients who had two FLT-PET scans prior to therapy using relative differences and the intra-class correlation coefficient (ICC). We tested associations between features at baseline and clinical response measured in 11 patients after three cycles of chemotherapy, and explored changes in FLT-PET at one week after the start of therapy. A subset of eight features was characterized by low variations at baseline (<±30%) and high repeatability (0.7 ≤ ICC ≤ 1). The intensity distribution profile suggested fewer highly proliferating cells in lesions of non-responders compared to responders at baseline. A true increase in CV and homogeneity was measured in four out of six responders one week after the start of therapy. A number of textural features derived from FLT-PET are altered following chemotherapy in breast cancer, and should be evaluated in larger clinical trials for clinical relevance.

Assessing the statistical relationships among water-derived climate variables, rainfall, and remotely sensed features of vegetation: implications for evaluating the habitat of ticks.

PubMed

Alonso-Carné, J; García-Martín, A; Estrada-Peña, A

2015-01-01

Ticks are sensitive to changes in relative humidity and saturation deficit at the microclimate scale. Trends and changes in rainfall are commonly used as descriptors of field observations of tick populations, to capture the climate niche of ticks or to predict the climate suitability for ticks under future climate scenarios. We evaluated daily and monthly relationships between rainfall, relative humidity and saturation deficit over different ecosystems in Europe using daily climate values from 177 stations over a period of 10 years. We demonstrate that rainfall is poorly correlated with both relative humidity and saturation deficit in any of the ecological domains studied. We conclude that the amount of rainfall recorded in 1 day does not correlate with the values of humidity or saturation deficit recorded 24 h later: rainfall is not an adequate surrogate for evaluating the physiological processes of ticks at regional scales. We compared the Normalized Difference Vegetation Index (NDVI), a descriptor of photosynthetic activity, at a spatial resolution of 0.05°, with monthly averages of relative humidity and saturation deficit and also determined a lack of significant correlation. With the limitations of spatial scale and habitat coverage of this study, we suggest that the rainfall or NDVI cannot replace relative humidity or saturation deficit as descriptors of tick processes.

Quantification of ultrasonic texture intra-heterogeneity via volumetric stochastic modeling for tissue characterization.

PubMed

Al-Kadi, Omar S; Chung, Daniel Y F; Carlisle, Robert C; Coussios, Constantin C; Noble, J Alison

2015-04-01

Intensity variations in image texture can provide powerful quantitative information about physical properties of biological tissue. However, tissue patterns can vary according to the utilized imaging system and are intrinsically correlated to the scale of analysis. In the case of ultrasound, the Nakagami distribution is a general model of the ultrasonic backscattering envelope under various scattering conditions and densities where it can be employed for characterizing image texture, but the subtle intra-heterogeneities within a given mass are difficult to capture via this model as it works at a single spatial scale. This paper proposes a locally adaptive 3D multi-resolution Nakagami-based fractal feature descriptor that extends Nakagami-based texture analysis to accommodate subtle speckle spatial frequency tissue intensity variability in volumetric scans. Local textural fractal descriptors - which are invariant to affine intensity changes - are extracted from volumetric patches at different spatial resolutions from voxel lattice-based generated shape and scale Nakagami parameters. Using ultrasound radio-frequency datasets we found that after applying an adaptive fractal decomposition label transfer approach on top of the generated Nakagami voxels, tissue characterization results were superior to the state of art. Experimental results on real 3D ultrasonic pre-clinical and clinical datasets suggest that describing tumor intra-heterogeneity via this descriptor may facilitate improved prediction of therapy response and disease characterization. Copyright © 2014 The Authors. Published by Elsevier B.V. All rights reserved.

Important clinical descriptors to include in the examination and assessment of patients with femoroacetabular impingement syndrome: an international and multi-disciplinary Delphi survey.

PubMed

Reiman, M P; Thorborg, K; Covington, K; Cook, C E; Hölmich, P

2017-06-01

Determine which examination findings are key clinical descriptors of femoroacetabular impingement syndrome (FAIS) through use of an international, multi-disciplinary expert panel. A three-round Delphi survey utilizing an international, multi-disciplinary expert panel operationally defined from international publications and presentations was utilized. All six domains (subjective examination, patient-reported outcome measures, physical examination, special tests, physical performance measures, and diagnostic imaging) had at least one descriptor with 75% consensus agreement for diagnosis and assessment of FAIS. Diagnostic imaging was the domain with the highest level of agreement. Domains such as patient-reported outcome measures (PRO's) and physical examination were identified as non-diagnostic measures (rather as assessments of disease impact). Although it also had the greatest level of variability in description of examination domains, diagnostic imaging continues to be the preeminent diagnostic measure for FAIS. No single domain should be utilized as the sole diagnostic or assessment parameter for FAIS. While not all investigated domains provide diagnostic capability for FAIS, those that do not are able to serve purpose as a measure of disease impact (e.g., impairments and activity limitations). The clinical relevance of this Delphi survey is the understanding that a comprehensive assessment measuring both diagnostic capability and disease impact most accurately reflects the patient with FAIS. V.

Automated selection of BI-RADS lesion descriptors for reporting calcifications in mammograms

NASA Astrophysics Data System (ADS)

Paquerault, Sophie; Jiang, Yulei; Nishikawa, Robert M.; Schmidt, Robert A.; D'Orsi, Carl J.; Vyborny, Carl J.; Newstead, Gillian M.

2003-05-01

We are developing an automated computer technique to describe calcifications in mammograms according to the BI-RADS lexicon. We evaluated this technique by its agreement with radiologists' description of the same lesions. Three expert mammographers reviewed our database of 90 cases of digitized mammograms containing clustered microcalcifications and described the calcifications according to BI-RADS. In our study, the radiologists used only 4 of the 5 calcification distribution descriptors and 5 of the 14 calcification morphology descriptors contained in BI-RADS. Our computer technique was therefore designed specifically for these 4 calcification distribution descriptors and 5 calcification morphology descriptors. For calcification distribution, 4 linear discriminant analysis (LDA) classifiers were developed using 5 computer-extracted features to produce scores of how well each descriptor describes a cluster. Similarly, for calcification morphology, 5 LDAs were designed using 10 computer-extracted features. We trained the LDAs using only the BI-RADS data reported by the first radiologist and compared the computer output to the descriptor data reported by all 3 radiologists (for the first radiologist, the leave-one-out method was used). The computer output consisted of the best calcification distribution descriptor and the best 2 calcification morphology descriptors. The results of the comparison with the data from each radiologist, respectively, were: for calcification distribution, percent agreement, 74%, 66%, and 73%, kappa value, 0.44, 0.36, and 0.46; for calcification morphology, percent agreement, 83%, 77%, and 57%, kappa value, 0.78, 0.70, and 0.44. These results indicate that the proposed computer technique can select BI-RADS descriptors in good agreement with radiologists.

Reduction in spread of excitation from current focusing at multiple cochlear locations in cochlear implant users.

PubMed

Padilla, Monica; Landsberger, David M

2016-03-01

Channel interaction from a broad spread of excitation is likely to be a limiting factor in performance by cochlear implant users. Although partial tripolar stimulation has been shown to reduce spread of excitation, the magnitude of the reduction is highly variable across subjects. Because the reduction in spread of excitation is typically only measured at one electrode for a given subject, the degree of variability across cochlear locations is unknown. The first goal of the present study was to determine if the reduction in spread of excitation observed from partial tripolar current focusing systematically varies across the cochlea. The second goal was to measure the variability in reduction of spread of excitation relative to monopolar stimulation across the cochlea. The third goal was to expand upon previous results that suggest that scaling of verbal descriptors can be used to predict the reduction in spread of excitation, by increasing the limited number of sites previously evaluated and verify the relationships remain with the larger dataset. The spread of excitation for monopolar and partial tripolar stimulation was measured at 5 cochlear locations using a psychophysical forward masking task. Results of the present study suggest that although partial tripolar stimulation typically reduces spread of excitation, the degree of reduction in spread of excitation was found to be highly variable and no effect of cochlear location was found. Additionally, subjective scaling of certain verbal descriptors (Clean/Dirty, Pure/Noisy) correlated with the reduction in spread of excitation suggesting sound quality scaling might be used as a quick clinical estimate of channels providing a reduction in spread of excitation. This quick scaling technique might help clinicians determine which patients would be most likely to benefit from a focused strategy. Copyright © 2016 Elsevier B.V. All rights reserved.

Reduction in Spread of Excitation from Current Focusing at Multiple Cochlear Locations in Cochlear Implant Users

PubMed Central

Padilla, Monica; Landsberger, David M.

2016-01-01

Channel interaction from a broad spread of excitation is likely to be a limiting factor in performance by cochlear implant users. Although partial tripolar stimulation has been shown to reduce spread of excitation, the magnitude of the reduction is highly variable across subjects. Because the reduction in spread of excitation is typically only measured at one electrode for a given subject, the degree of variability across cochlear locations is unknown. The first goal of the present study was to determine if the reduction in spread of excitation observed from partial tripolar current focusing systematically varies across the cochlea. The second goal was to measure the variability in reduction of spread of excitation relative to monopolar stimulation across the cochlea. The third goal was to expand upon previous results that suggest that scaling of verbal descriptors can be used to predict the reduction in spread of excitation, by increasing the limited number of sites previously evaluated and verify the relationships remain with the larger dataset. The spread of excitation for monopolar and partial tripolar stimulation was measured at 5 cochlear locations using a psychophysical forward masking task. Results of the present study suggest that although partial tripolar stimulation typically reduces spread of excitation, the degree of reduction in spread of excitation was found to be highly variable and no effect of cochlear location was found. Additionally, subjective scaling of certain verbal descriptors (Clean/Dirty, Pure/Noisy) correlated with the reduction in spread of excitation suggesting sound quality scaling might be used as a quick clinical estimate of channels providing a reduction in spread of excitation. This quick scaling technique might help clinicians determine which patients would be most likely to benefit from a focused strategy. PMID:26778546

Visual feature discrimination versus compression ratio for polygonal shape descriptors

NASA Astrophysics Data System (ADS)

Heuer, Joerg; Sanahuja, Francesc; Kaup, Andre

2000-10-01

In the last decade several methods for low level indexing of visual features appeared. Most often these were evaluated with respect to their discrimination power using measures like precision and recall. Accordingly, the targeted application was indexing of visual data within databases. During the standardization process of MPEG-7 the view on indexing of visual data changed, taking also communication aspects into account where coding efficiency is important. Even if the descriptors used for indexing are small compared to the size of images, it is recognized that there can be several descriptors linked to an image, characterizing different features and regions. Beside the importance of a small memory footprint for the transmission of the descriptor and the memory footprint in a database, eventually the search and filtering can be sped up by reducing the dimensionality of the descriptor if the metric of the matching can be adjusted. Based on a polygon shape descriptor presented for MPEG-7 this paper compares the discrimination power versus memory consumption of the descriptor. Different methods based on quantization are presented and their effect on the retrieval performance are measured. Finally an optimized computation of the descriptor is presented.

Determination of descriptors for polycyclic aromatic hydrocarbons and related compounds by chromatographic methods and liquid-liquid partition in totally organic biphasic systems.

PubMed

Ariyasena, Thiloka C; Poole, Colin F

2014-09-26

Retention factors on several columns and at various temperatures using gas chromatography and from reversed-phase liquid chromatography on a SunFire C18 column with various mobile phase compositions containing acetonitrile, methanol and tetrahydrofuran as strength adjusting solvents are combined with liquid-liquid partition coefficients in totally organic biphasic systems to calculate descriptors for 23 polycyclic aromatic hydrocarbons and eighteen related compounds of environmental interest. The use of a consistent protocol for the above measurements provides descriptors that are more self consistent for the estimation of physicochemical properties (octanol-water, air-octanol, air-water, aqueous solubility, and subcooled liquid vapor pressure). The descriptor in this report tend to have smaller values for the L and E descriptors and random differences in the B and S descriptors compared with literature sources. A simple atom fragment constant model is proposed for the estimation of descriptors from structure for polycyclic aromatic hydrocarbons. The new descriptors show no bias in the prediction of the air-water partition coefficient for polycyclic aromatic hydrocarbons unlike the literature values. Copyright © 2014 Elsevier B.V. All rights reserved.

Hybrid Histogram Descriptor: A Fusion Feature Representation for Image Retrieval.

PubMed

Feng, Qinghe; Hao, Qiaohong; Chen, Yuqi; Yi, Yugen; Wei, Ying; Dai, Jiangyan

2018-06-15

Currently, visual sensors are becoming increasingly affordable and fashionable, acceleratingly the increasing number of image data. Image retrieval has attracted increasing interest due to space exploration, industrial, and biomedical applications. Nevertheless, designing effective feature representation is acknowledged as a hard yet fundamental issue. This paper presents a fusion feature representation called a hybrid histogram descriptor (HHD) for image retrieval. The proposed descriptor comprises two histograms jointly: a perceptually uniform histogram which is extracted by exploiting the color and edge orientation information in perceptually uniform regions; and a motif co-occurrence histogram which is acquired by calculating the probability of a pair of motif patterns. To evaluate the performance, we benchmarked the proposed descriptor on RSSCN7, AID, Outex-00013, Outex-00014 and ETHZ-53 datasets. Experimental results suggest that the proposed descriptor is more effective and robust than ten recent fusion-based descriptors under the content-based image retrieval framework. The computational complexity was also analyzed to give an in-depth evaluation. Furthermore, compared with the state-of-the-art convolutional neural network (CNN)-based descriptors, the proposed descriptor also achieves comparable performance, but does not require any training process.

Real-time, resource-constrained object classification on a micro-air vehicle

NASA Astrophysics Data System (ADS)

Buck, Louis; Ray, Laura

2013-12-01

A real-time embedded object classification algorithm is developed through the novel combination of binary feature descriptors, a bag-of-visual-words object model and the cortico-striatal loop (CSL) learning algorithm. The BRIEF, ORB and FREAK binary descriptors are tested and compared to SIFT descriptors with regard to their respective classification accuracies, execution times, and memory requirements when used with CSL on a 12.6 g ARM Cortex embedded processor running at 800 MHz. Additionally, the effect of x2 feature mapping and opponent-color representations used with these descriptors is examined. These tests are performed on four data sets of varying sizes and difficulty, and the BRIEF descriptor is found to yield the best combination of speed and classification accuracy. Its use with CSL achieves accuracies between 67% and 95% of those achieved with SIFT descriptors and allows for the embedded classification of a 128x192 pixel image in 0.15 seconds, 60 times faster than classification with SIFT. X2 mapping is found to provide substantial improvements in classification accuracy for all of the descriptors at little cost, while opponent-color descriptors are offer accuracy improvements only on colorful datasets.

Local intensity area descriptor for facial recognition in ideal and noise conditions

NASA Astrophysics Data System (ADS)

Tran, Chi-Kien; Tseng, Chin-Dar; Chao, Pei-Ju; Ting, Hui-Min; Chang, Liyun; Huang, Yu-Jie; Lee, Tsair-Fwu

2017-03-01

We propose a local texture descriptor, local intensity area descriptor (LIAD), which is applied for human facial recognition in ideal and noisy conditions. Each facial image is divided into small regions from which LIAD histograms are extracted and concatenated into a single feature vector to represent the facial image. The recognition is performed using a nearest neighbor classifier with histogram intersection and chi-square statistics as dissimilarity measures. Experiments were conducted with LIAD using the ORL database of faces (Olivetti Research Laboratory, Cambridge), the Face94 face database, the Georgia Tech face database, and the FERET database. The results demonstrated the improvement in accuracy of our proposed descriptor compared to conventional descriptors [local binary pattern (LBP), uniform LBP, local ternary pattern, histogram of oriented gradients, and local directional pattern]. Moreover, the proposed descriptor was less sensitive to noise and had low histogram dimensionality. Thus, it is expected to be a powerful texture descriptor that can be used for various computer vision problems.

A 3D model retrieval approach based on Bayesian networks lightfield descriptor

NASA Astrophysics Data System (ADS)

Xiao, Qinhan; Li, Yanjun

2009-12-01

A new 3D model retrieval methodology is proposed by exploiting a novel Bayesian networks lightfield descriptor (BNLD). There are two key novelties in our approach: (1) a BN-based method for building lightfield descriptor; and (2) a 3D model retrieval scheme based on the proposed BNLD. To overcome the disadvantages of the existing 3D model retrieval methods, we explore BN for building a new lightfield descriptor. Firstly, 3D model is put into lightfield, about 300 binary-views can be obtained along a sphere, then Fourier descriptors and Zernike moments descriptors can be calculated out from binaryviews. Then shape feature sequence would be learned into a BN model based on BN learning algorithm; Secondly, we propose a new 3D model retrieval method by calculating Kullback-Leibler Divergence (KLD) between BNLDs. Beneficial from the statistical learning, our BNLD is noise robustness as compared to the existing methods. The comparison between our method and the lightfield descriptor-based approach is conducted to demonstrate the effectiveness of our proposed methodology.

Covariance descriptor fusion for target detection

NASA Astrophysics Data System (ADS)

Cukur, Huseyin; Binol, Hamidullah; Bal, Abdullah; Yavuz, Fatih

2016-05-01

Target detection is one of the most important topics for military or civilian applications. In order to address such detection tasks, hyperspectral imaging sensors provide useful images data containing both spatial and spectral information. Target detection has various challenging scenarios for hyperspectral images. To overcome these challenges, covariance descriptor presents many advantages. Detection capability of the conventional covariance descriptor technique can be improved by fusion methods. In this paper, hyperspectral bands are clustered according to inter-bands correlation. Target detection is then realized by fusion of covariance descriptor results based on the band clusters. The proposed combination technique is denoted Covariance Descriptor Fusion (CDF). The efficiency of the CDF is evaluated by applying to hyperspectral imagery to detect man-made objects. The obtained results show that the CDF presents better performance than the conventional covariance descriptor.

Multifractal evaluation of simulated precipitation intensities from the COSMO NWP model

NASA Astrophysics Data System (ADS)

Wolfensberger, Daniel; Gires, Auguste; Tchiguirinskaia, Ioulia; Schertzer, Daniel; Berne, Alexis

2017-12-01

The framework of universal multifractals (UM) characterizes the spatio-temporal variability in geophysical data over a wide range of scales with only a limited number of scale-invariant parameters. This work aims to clarify the link between multifractals (MFs) and more conventional weather descriptors and to show how they can be used to perform a multi-scale evaluation of model data. The first part of this work focuses on a MF analysis of the climatology of precipitation intensities simulated by the COSMO numerical weather prediction model. Analysis of the spatial structure of the MF parameters, and their correlations with external meteorological and topographical descriptors, reveals that simulated precipitation tends to be smoother at higher altitudes, and that the mean intermittency is mostly influenced by the latitude. A hierarchical clustering was performed on the external descriptors, yielding three different clusters, which correspond roughly to Alpine/continental, Mediterranean and temperate regions. Distributions of MF parameters within these three clusters are shown to be statistically significantly different, indicating that the MF signature of rain is indeed geographically dependent. The second part of this work is event-based and focuses on the smaller scales. The MF parameters of precipitation intensities at the ground are compared with those obtained from the Swiss radar composite during three events corresponding to typical synoptic conditions over Switzerland. The results of this analysis show that the COSMO simulations exhibit spatial scaling breaks that are not present in the radar data, indicating that the model is not able to simulate the observed variability at all scales. A comparison of the operational one-moment microphysical parameterization scheme of COSMO with a more advanced two-moment scheme reveals that, while no scheme systematically outperforms the other, the two-moment scheme tends to produce larger extreme values and more discontinuous precipitation fields, which agree better with the radar composite.

A new descriptor via bio-mimetic chromatography and modeling for the blood brain barrier (Part II).

PubMed

Kouskoura, Maria G; Piteni, Aikaterini I; Markopoulou, Catherine K

2018-05-25

Within the context of drug design methodology for the central nervous system (CNS), a predictive model which can shorten the process of finding new candidate drugs was developed. Therefore, the retention time of 51 molecules which are clinically established to enter the blood brain barrier (BBB), were recorded on two HPLC columns. For this purpose, a lipophilic butyl (C 4 ) stationary phase was used to simulate the behavior of a drug regarding BBB permeability and a zwitterionic-HILIC to simulate blood. The results were plotted as Y variables on two Partial Least Squares (PLS) models, while 25 specific physicochemical properties (significant for lipid bilayers BBB permeation or blood) were used as X descriptors. Both models can be utilized to predict the drugability of a new molecule avoiding needless animal experiments, as well as time and material consuming syntheses. The developed models were validated (R 2  ≥ 0.90, Q 2  ≥ 0.83), and based on the results specific variables were proved to be significant for the studied phenomenon. Additionally, a new factor symbolized as MT was introduced. MT incorporated the experimental results and it was calculated by the fraction of the sum of the retention time of the drug on the two columns (t r(butyl)  + t r(HILIC) ) divided by the molecular volume (V m ) of each analyte. This new descriptor was used as an equivalent to the logarithm of BBB permeability (logBB) and may indicate the ability of a new molecule to act as a candidate drug able to enter the BBB. Comprehending the extend of contribution of several molecular attributes to the in vivo distribution of a drug may enlighten the knowledge on pharmacokinetics and clinical variation, and enable scientists to design more efficient drug molecules. Copyright © 2018 Elsevier B.V. All rights reserved.

Quantitative structure activity relationship studies of sulfamide derivatives as carbonic anhydrase inhibitor: as antiglaucoma agents.

PubMed

Kumar, Surendra; Singh, Vineet; Tiwari, Meena

2007-07-01

Selective inhibition of ciliary process enzyme i.e. Carbonic Anhydrase-II is an excellent approach in reducing elevated intraocular pressure, thus treating glaucoma. Due to characteristic physicochemical properties of sulphonamide (Inhibition of Carbonic Anhydrase), they are clinically effective against glaucoma. But the non-specificity of sulphonamide derivatives to isozyme, leads to a range of side effects. Presently, the absence of comparative studies related to the binding of the sulphonamides as inhibitors to CA isozymes limits their use. In this paper we have represented "Three Dimensional Quantitative Structure Activity Relationship" study to characterize structural features of Sulfamide derivative [RR'NSO(2)NH(2)] as inhibitors, that are required for selective binding of carbonic anhydrase isozymes (CAI and CAII). In the analysis, stepwise multiple linear regression was performed using physiochemical parameters as independent variable and CA-I and CA-II inhibitory activity as dependent variable, respectively. The best multiparametric QSAR model obtained for CA-I inhibitory activity shows good statistical significance (r= 0.9714) and predictability (Q(2)=0.8921), involving the Electronic descriptors viz. Highest Occupied Molecular Orbital, Lowest Unoccupied Molecular Orbital and Steric descriptors viz. Principal moment of Inertia at X axis. Similarly, CA-II inhibitory activity also shows good statistical significance (r=0.9644) and predictability (Q(2)=0.8699) involving aforementioned descriptors. The predictive power of the model was successfully tested externally using a set of six compounds as test set for CA-I inhibitory activity and a set of seven compounds in case of CA-II inhibitory activity with good predictive squared correlation coefficient, r(2)(pred)=0.6016 and 0.7662, respectively. Overview of analysis favours substituents with high electronegativity and less bulk at R and R' positions of the parent nucleus, provides a basis to design new Sulfamide derivatives possessing potent and selective carbonic anhydrase-II inhibitory activity.

Classification and Verification of Handwritten Signatures with Time Causal Information Theory Quantifiers.

PubMed

Rosso, Osvaldo A; Ospina, Raydonal; Frery, Alejandro C

2016-01-01

We present a new approach for handwritten signature classification and verification based on descriptors stemming from time causal information theory. The proposal uses the Shannon entropy, the statistical complexity, and the Fisher information evaluated over the Bandt and Pompe symbolization of the horizontal and vertical coordinates of signatures. These six features are easy and fast to compute, and they are the input to an One-Class Support Vector Machine classifier. The results are better than state-of-the-art online techniques that employ higher-dimensional feature spaces which often require specialized software and hardware. We assess the consistency of our proposal with respect to the size of the training sample, and we also use it to classify the signatures into meaningful groups.

Description of Abnormal Breathing Is Associated With Improved Outcomes and Delayed Telephone Cardiopulmonary Resuscitation Instructions.

PubMed

Fukushima, Hidetada; Panczyk, Micah; Hu, Chengcheng; Dameff, Christian; Chikani, Vatsal; Vadeboncoeur, Tyler; Spaite, Daniel W; Bobrow, Bentley J

2017-08-29

Emergency 9-1-1 callers use a wide range of terms to describe abnormal breathing in persons with out-of-hospital cardiac arrest (OHCA). These breathing descriptors can obstruct the telephone cardiopulmonary resuscitation (CPR) process. We conducted an observational study of emergency call audio recordings linked to confirmed OHCAs in a statewide Utstein-style database. Breathing descriptors fell into 1 of 8 groups (eg, gasping, snoring). We divided the study population into groups with and without descriptors for abnormal breathing to investigate the impact of these descriptors on patient outcomes and telephone CPR process. Callers used descriptors in 459 of 2411 cases (19.0%) between October 1, 2010, and December 31, 2014. Survival outcome was better when the caller used a breathing descriptor (19.6% versus 8.8%, P <0.0001), with an odds ratio of 1.63 (95% confidence interval, 1.17-2.25). After exclusions, 379 of 459 cases were eligible for process analysis. When callers described abnormal breathing, the rates of telecommunicator OHCA recognition, CPR instruction, and telephone CPR were lower than when callers did not use a breathing descriptor (79.7% versus 93.0%, P <0.0001; 65.4% versus 72.5%, P =0.0078; and 60.2% versus 66.9%, P =0.0123, respectively). The time interval between call receipt and OHCA recognition was longer when the caller used a breathing descriptor (118.5 versus 73.5 seconds, P <0.0001). Descriptors of abnormal breathing are associated with improved outcomes but also with delays in the identification of OHCA. Familiarizing telecommunicators with these descriptors may improve the telephone CPR process including OHCA recognition for patients with increased probability of survival. © 2017 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.

Descriptions and identifications of strangers by youth and adult eyewitnesses.

PubMed

Pozzulo, Joanna D; Warren, Kelly L

2003-04-01

Two studies varying target gender and mode of target exposure were conducted to compare the quantity, nature, and accuracy of free recall person descriptions provided by youths and adults. In addition, the relation among age, identification accuracy, and number of descriptors reported was considered. Youths (10-14 years) reported fewer descriptors than adults. Exterior facial descriptors (e.g., hair items) were predominant and accurately reported by youths and adults. Accuracy was consistently problematic for youths when reporting body descriptors (e.g., height, weight) and interior facial features. Youths reported a similar number of descriptors when making accurate versus inaccurate identification decisions. This pattern also was consistent for adults. With target-absent lineups, the difference in the number of descriptors reported between adults and youths was greater when making a false positive versus correct rejection.

Human region segmentation and description methods for domiciliary healthcare monitoring using chromatic methodology

NASA Astrophysics Data System (ADS)

Al-Temeemy, Ali A.

2018-03-01

A descriptor is proposed for use in domiciliary healthcare monitoring systems. The descriptor is produced from chromatic methodology to extract robust features from the monitoring system's images. It has superior discrimination capabilities, is robust to events that normally disturb monitoring systems, and requires less computational time and storage space to achieve recognition. A method of human region segmentation is also used with this descriptor. The performance of the proposed descriptor was evaluated using experimental data sets, obtained through a series of experiments performed in the Centre for Intelligent Monitoring Systems, University of Liverpool. The evaluation results show high recognition performance for the proposed descriptor in comparison to traditional descriptors, such as moments invariant. The results also show the effectiveness of the proposed segmentation method regarding distortion effects associated with domiciliary healthcare systems.

The contribution of the hydrogen bond acidity on the lipophilicity of drugs estimated from chromatographic measurements.

PubMed

Pallicer, Juan M; Pascual, Rosalia; Port, Adriana; Rosés, Martí; Ràfols, Clara; Bosch, Elisabeth

2013-02-14

The influence of the hydrogen bond acidity when the 1-octanol/water partition coefficient (log P(o/w)) of drugs is determined from chromatographic measurements was studied in this work. This influence was firstly evaluated by means of the comparison between the Abraham solvation parameter model when it is applied to express the 1-octanol/water partitioning and the chromatographic retention, expressed as the solute polarity p. Then, several hydrogen bond acidity descriptors were compared in order to determine properly the log P(o/w) of drugs. These descriptors were obtained from different software and comprise two-dimensional parameters such as the calculated Abraham hydrogen bond acidity A and three-dimensional descriptors like HDCA-2 from CODESSA program or WO1 and DRDODO descriptors calculated from Volsurf+software. The additional HOMO-LUMO polarizability descriptor should be added when the three-dimensional descriptors are used to complement the chromatographic retention. The models generated using these descriptors were compared studying the correlations between the determined log P(o/w) values and the reference ones. The comparison showed that there was no significant difference between the tested models and any of them was able to determine the log P(o/w) of drugs from a single chromatographic measurement and the correspondent molecular descriptors terms. However, the model that involved the calculated A descriptor was simpler and it is thus recommended for practical uses. Copyright © 2012 Elsevier B.V. All rights reserved.

Correspondence of verbal descriptor and numeric rating scales for pain intensity: an item response theory calibration.

PubMed

Edelen, Maria Orlando; Saliba, Debra

2010-07-01

Assessing pain intensity in older adults is critical and challenging. There is debate about the most effective way to ask older adults to describe their pain severity, and clinicians vary in their preferred approaches, making comparison of pain intensity scores across settings difficult. A total of 3,676 residents from 71 community nursing homes across eight states were asked about pain presence. The 1,960 residents who reported pain within the past 5 days (53% of total, 70% female; age: M = 77.9, SD = 12.4) were included in analyses. Those who reported pain were also asked to provide a rating of pain intensity using either a verbal descriptor scale (VDS; mild, moderate, severe, and very severe and horrible), a numeric rating scale (NRS; 0 = no pain to 10 = worst pain imaginable), or both. We used item response theory (IRT) methods to identify the correspondence between the VDS and the NRS response options by estimating item parameters for these and five additional pain items. The sample reported moderate amounts of pain on average. Examination of the IRT location parameters for the pain intensity items indicated the following approximate correspondence: VDS mild approximately NRS 1-4, VDS moderate approximately NRS 5-7, VDS severe approximately NRS 8-9, and VDS very severe, horrible approximately NRS 10. This IRT calibration provides a crosswalk between the two response scales so that either can be used in practice depending on the preference of the clinician and respondent.

Laban movement analysis to classify emotions from motion

NASA Astrophysics Data System (ADS)

Dewan, Swati; Agarwal, Shubham; Singh, Navjyoti

2018-04-01

In this paper, we present the study of Laban Movement Analysis (LMA) to understand basic human emotions from nonverbal human behaviors. While there are a lot of studies on understanding behavioral patterns based on natural language processing and speech processing applications, understanding emotions or behavior from non-verbal human motion is still a very challenging and unexplored field. LMA provides a rich overview of the scope of movement possibilities. These basic elements can be used for generating movement or for describing movement. They provide an inroad to understanding movement and for developing movement efficiency and expressiveness. Each human being combines these movement factors in his/her own unique way and organizes them to create phrases and relationships which reveal personal, artistic, or cultural style. In this work, we build a motion descriptor based on a deep understanding of Laban theory. The proposed descriptor builds up on previous works and encodes experiential features by using temporal windows. We present a more conceptually elaborate formulation of Laban theory and test it in a relatively new domain of behavioral research with applications in human-machine interaction. The recognition of affective human communication may be used to provide developers with a rich source of information for creating systems that are capable of interacting well with humans. We test our algorithm on UCLIC dataset which consists of body motions of 13 non-professional actors portraying angry, fear, happy and sad emotions. We achieve an accuracy of 87.30% on this dataset.

Chemical dynamics between wells across a time-dependent barrier: Self-similarity in the Lagrangian descriptor and reactive basins.

PubMed

Junginger, Andrej; Duvenbeck, Lennart; Feldmaier, Matthias; Main, Jörg; Wunner, Günter; Hernandez, Rigoberto

2017-08-14

In chemical or physical reaction dynamics, it is essential to distinguish precisely between reactants and products for all times. This task is especially demanding in time-dependent or driven systems because therein the dividing surface (DS) between these states often exhibits a nontrivial time-dependence. The so-called transition state (TS) trajectory has been seen to define a DS which is free of recrossings in a large number of one-dimensional reactions across time-dependent barriers and thus, allows one to determine exact reaction rates. A fundamental challenge to applying this method is the construction of the TS trajectory itself. The minimization of Lagrangian descriptors (LDs) provides a general and powerful scheme to obtain that trajectory even when perturbation theory fails. Both approaches encounter possible breakdowns when the overall potential is bounded, admitting the possibility of returns to the barrier long after the trajectories have reached the product or reactant wells. Such global dynamics cannot be captured by perturbation theory. Meanwhile, in the LD-DS approach, it leads to the emergence of additional local minima which make it difficult to extract the optimal branch associated with the desired TS trajectory. In this work, we illustrate this behavior for a time-dependent double-well potential revealing a self-similar structure of the LD, and we demonstrate how the reflections and side-minima can be addressed by an appropriate modification of the LD associated with the direct rate across the barrier.

Intuitive Density Functional Theory-Based Energy Decomposition Analysis for Protein-Ligand Interactions.

PubMed

Phipps, M J S; Fox, T; Tautermann, C S; Skylaris, C-K

2017-04-11

First-principles quantum mechanical calculations with methods such as density functional theory (DFT) allow the accurate calculation of interaction energies between molecules. These interaction energies can be dissected into chemically relevant components such as electrostatics, polarization, and charge transfer using energy decomposition analysis (EDA) approaches. Typically EDA has been used to study interactions between small molecules; however, it has great potential to be applied to large biomolecular assemblies such as protein-protein and protein-ligand interactions. We present an application of EDA calculations to the study of ligands that bind to the thrombin protein, using the ONETEP program for linear-scaling DFT calculations. Our approach goes beyond simply providing the components of the interaction energy; we are also able to provide visual representations of the changes in density that happen as a result of polarization and charge transfer, thus pinpointing the functional groups between the ligand and protein that participate in each kind of interaction. We also demonstrate with this approach that we can focus on studying parts (fragments) of ligands. The method is relatively insensitive to the protocol that is used to prepare the structures, and the results obtained are therefore robust. This is an application to a real protein drug target of a whole new capability where accurate DFT calculations can produce both energetic and visual descriptors of interactions. These descriptors can be used to provide insights for tailoring interactions, as needed for example in drug design.

A novel binary shape context for 3D local surface description

NASA Astrophysics Data System (ADS)

Dong, Zhen; Yang, Bisheng; Liu, Yuan; Liang, Fuxun; Li, Bijun; Zang, Yufu

2017-08-01

3D local surface description is now at the core of many computer vision technologies, such as 3D object recognition, intelligent driving, and 3D model reconstruction. However, most of the existing 3D feature descriptors still suffer from low descriptiveness, weak robustness, and inefficiency in both time and memory. To overcome these challenges, this paper presents a robust and descriptive 3D Binary Shape Context (BSC) descriptor with high efficiency in both time and memory. First, a novel BSC descriptor is generated for 3D local surface description, and the performance of the BSC descriptor under different settings of its parameters is analyzed. Next, the descriptiveness, robustness, and efficiency in both time and memory of the BSC descriptor are evaluated and compared to those of several state-of-the-art 3D feature descriptors. Finally, the performance of the BSC descriptor for 3D object recognition is also evaluated on a number of popular benchmark datasets, and an urban-scene dataset is collected by a terrestrial laser scanner system. Comprehensive experiments demonstrate that the proposed BSC descriptor obtained high descriptiveness, strong robustness, and high efficiency in both time and memory and achieved high recognition rates of 94.8%, 94.1% and 82.1% on the considered UWA, Queen, and WHU datasets, respectively.

Robust image region descriptor using local derivative ordinal binary pattern

NASA Astrophysics Data System (ADS)

Shang, Jun; Chen, Chuanbo; Pei, Xiaobing; Liang, Hu; Tang, He; Sarem, Mudar

2015-05-01

Binary image descriptors have received a lot of attention in recent years, since they provide numerous advantages, such as low memory footprint and efficient matching strategy. However, they utilize intermediate representations and are generally less discriminative than floating-point descriptors. We propose an image region descriptor, namely local derivative ordinal binary pattern, for object recognition and image categorization. In order to preserve more local contrast and edge information, we quantize the intensity differences between the central pixels and their neighbors of the detected local affine covariant regions in an adaptive way. These differences are then sorted and mapped into binary codes and histogrammed with a weight of the sum of the absolute value of the differences. Furthermore, the gray level of the central pixel is quantized to further improve the discriminative ability. Finally, we combine them to form a joint histogram to represent the features of the image. We observe that our descriptor preserves more local brightness and edge information than traditional binary descriptors. Also, our descriptor is robust to rotation, illumination variations, and other geometric transformations. We conduct extensive experiments on the standard ETHZ and Kentucky datasets for object recognition and PASCAL for image classification. The experimental results show that our descriptor outperforms existing state-of-the-art methods.

Determination of solute descriptors by chromatographic methods.

PubMed

Poole, Colin F; Atapattu, Sanka N; Poole, Salwa K; Bell, Andrea K

2009-10-12

The solvation parameter model is now well established as a useful tool for obtaining quantitative structure-property relationships for chemical, biomedical and environmental processes. The model correlates a free-energy related property of a system to six free-energy derived descriptors describing molecular properties. These molecular descriptors are defined as L (gas-liquid partition coefficient on hexadecane at 298K), V (McGowan's characteristic volume), E (excess molar refraction), S (dipolarity/polarizability), A (hydrogen-bond acidity), and B (hydrogen-bond basicity). McGowan's characteristic volume is trivially calculated from structure and the excess molar refraction can be calculated for liquids from their refractive index and easily estimated for solids. The remaining four descriptors are derived by experiment using (largely) two-phase partitioning, chromatography, and solubility measurements. In this article, the use of gas chromatography, reversed-phase liquid chromatography, micellar electrokinetic chromatography, and two-phase partitioning for determining solute descriptors is described. A large database of experimental retention factors and partition coefficients is constructed after first applying selection tools to remove unreliable experimental values and an optimized collection of varied compounds with descriptor values suitable for calibrating chromatographic systems is presented. These optimized descriptors are demonstrated to be robust and more suitable than other groups of descriptors characterizing the separation properties of chromatographic systems.

SVM prediction of ligand-binding sites in bacterial lipoproteins employing shape and physio-chemical descriptors.

PubMed

Kadam, Kiran; Prabhakar, Prashant; Jayaraman, V K

2012-11-01

Bacterial lipoproteins play critical roles in various physiological processes including the maintenance of pathogenicity and numbers of them are being considered as potential candidates for generating novel vaccines. In this work, we put forth an algorithm to identify and predict ligand-binding sites in bacterial lipoproteins. The method uses three types of pocket descriptors, namely fpocket descriptors, 3D Zernike descriptors and shell descriptors, and combines them with Support Vector Machine (SVM) method for the classification. The three types of descriptors represent shape-based properties of the pocket as well as its local physio-chemical features. All three types of descriptors, along with their hybrid combinations are evaluated with SVM and to improve classification performance, WEKA-InfoGain feature selection is applied. Results obtained in the study show that the classifier successfully differentiates between ligand-binding and non-binding pockets. For the combination of three types of descriptors, 10 fold cross-validation accuracy of 86.83% is obtained for training while the selected model achieved test Matthews Correlation Coefficient (MCC) of 0.534. Individually or in combination with new and existing methods, our model can be a very useful tool for the prediction of potential ligand-binding sites in bacterial lipoproteins.

A sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, J. D.; Oberkampf, William Louis; Helton, Jon Craig

2006-10-01

Evidence theory provides an alternative to probability theory for the representation of epistemic uncertainty in model predictions that derives from epistemic uncertainty in model inputs, where the descriptor epistemic is used to indicate uncertainty that derives from a lack of knowledge with respect to the appropriate values to use for various inputs to the model. The potential benefit, and hence appeal, of evidence theory is that it allows a less restrictive specification of uncertainty than is possible within the axiomatic structure on which probability theory is based. Unfortunately, the propagation of an evidence theory representation for uncertainty through a modelmore » is more computationally demanding than the propagation of a probabilistic representation for uncertainty, with this difficulty constituting a serious obstacle to the use of evidence theory in the representation of uncertainty in predictions obtained from computationally intensive models. This presentation describes and illustrates a sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory. Preliminary trials indicate that the presented strategy can be used to propagate uncertainty representations based on evidence theory in analysis situations where naive sampling-based (i.e., unsophisticated Monte Carlo) procedures are impracticable due to computational cost.« less

Spherical harmonics based descriptor for neural network potentials: Structure and dynamics of Au147 nanocluster.

PubMed

Jindal, Shweta; Chiriki, Siva; Bulusu, Satya S

2017-05-28

We propose a highly efficient method for fitting the potential energy surface of a nanocluster using a spherical harmonics based descriptor integrated with an artificial neural network. Our method achieves the accuracy of quantum mechanics and speed of empirical potentials. For large sized gold clusters (Au 147 ), the computational time for accurate calculation of energy and forces is about 1.7 s, which is faster by several orders of magnitude compared to density functional theory (DFT). This method is used to perform the global minimum optimizations and molecular dynamics simulations for Au 147 , and it is found that its global minimum is not an icosahedron. The isomer that can be regarded as the global minimum is found to be 4 eV lower in energy than the icosahedron and is confirmed from DFT. The geometry of the obtained global minimum contains 105 atoms on the surface and 42 atoms in the core. A brief study on the fluxionality in Au 147 is performed, and it is concluded that Au 147 has a dynamic surface, thus opening a new window for studying its reaction dynamics.

Spherical harmonics based descriptor for neural network potentials: Structure and dynamics of Au147 nanocluster

NASA Astrophysics Data System (ADS)

Jindal, Shweta; Chiriki, Siva; Bulusu, Satya S.

2017-05-01

We propose a highly efficient method for fitting the potential energy surface of a nanocluster using a spherical harmonics based descriptor integrated with an artificial neural network. Our method achieves the accuracy of quantum mechanics and speed of empirical potentials. For large sized gold clusters (Au147), the computational time for accurate calculation of energy and forces is about 1.7 s, which is faster by several orders of magnitude compared to density functional theory (DFT). This method is used to perform the global minimum optimizations and molecular dynamics simulations for Au147, and it is found that its global minimum is not an icosahedron. The isomer that can be regarded as the global minimum is found to be 4 eV lower in energy than the icosahedron and is confirmed from DFT. The geometry of the obtained global minimum contains 105 atoms on the surface and 42 atoms in the core. A brief study on the fluxionality in Au147 is performed, and it is concluded that Au147 has a dynamic surface, thus opening a new window for studying its reaction dynamics.

Molecular Reactivity and Absorption Properties of Melanoidin Blue-G1 through Conceptual DFT.

PubMed

Frau, Juan; Glossman-Mitnik, Daniel

2018-03-02

This computational study presents the assessment of eleven density functionals that include CAM-B3LYP, LC-wPBE, M11, M11L, MN12L, MN12SX, N12, N12SX, wB97, wB97X and wB97XD related to the Def2TZVP basis sets together with the Solvation Model Density (SMD) solvation model in calculating the molecular properties and structure of the Blue-G1 intermediate melanoidin pigment. The chemical reactivity descriptors for the system are calculated via the conceptual Density Functional Theory (DFT). The choice of the active sites related to the nucleophilic, electrophilic, as well as radical attacks is made by linking them with the Fukui function indices, the electrophilic Parr functions and the condensed dual descriptor Δ f ( r ) . The prediction of the maximum absorption wavelength tends to be considerably accurate relative to its experimental value. The study found the MN12SX and N12SX density functionals to be the most appropriate density functionals in predicting the chemical reactivity of the studied molecule.

Time-cumulated visible and infrared radiance histograms used as descriptors of surface and cloud variations

NASA Technical Reports Server (NTRS)

Seze, Genevieve; Rossow, William B.

1991-01-01

The spatial and temporal stability of the distributions of satellite-measured visible and infrared radiances, caused by variations in clouds and surfaces, are investigated using bidimensional and monodimensional histograms and time-composite images. Similar analysis of the histograms of the original and time-composite images provides separation of the contributions of the space and time variations to the total variations. The variability of both the surfaces and clouds is found to be larger at scales much larger than the minimum resolved by satellite imagery. This study shows that the shapes of these histograms are distinctive characteristics of the different climate regimes and that particular attributes of these histograms can be related to several general, though not universal, properties of clouds and surface variations at regional and synoptic scales. There are also significant exceptions to these relationships in particular climate regimes. The characteristics of these radiance histograms provide a stable well defined descriptor of the cloud and surface properties.

Exploring factors that influence work analysis data: A meta-analysis of design choices, purposes, and organizational context.

PubMed

DuVernet, Amy M; Dierdorff, Erich C; Wilson, Mark A

2015-09-01

Work analysis is fundamental to designing effective human resource systems. The current investigation extends previous research by identifying the differential effects of common design decisions, purposes, and organizational contexts on the data generated by work analyses. The effects of 19 distinct factors that span choices of descriptor, collection method, rating scale, and data source, as well as project purpose and organizational features, are explored. Meta-analytic results cumulated from 205 articles indicate that many of these variables hold significant consequences for work analysis data. Factors pertaining to descriptor choice, collection method, rating scale, and the purpose for conducting the work analysis each showed strong associations with work analysis data. The source of the work analysis information and organizational context in which it was conducted displayed fewer relationships. Findings can be used to inform choices work analysts make about methodology and postcollection evaluations of work analysis information. (c) 2015 APA, all rights reserved).

A computational framework to characterize and compare the geometry of coronary networks.

PubMed

Bulant, C A; Blanco, P J; Lima, T P; Assunção, A N; Liberato, G; Parga, J R; Ávila, L F R; Pereira, A C; Feijóo, R A; Lemos, P A

2017-03-01

This work presents a computational framework to perform a systematic and comprehensive assessment of the morphometry of coronary arteries from in vivo medical images. The methodology embraces image segmentation, arterial vessel representation, characterization and comparison, data storage, and finally analysis. Validation is performed using a sample of 48 patients. Data mining of morphometric information of several coronary arteries is presented. Results agree to medical reports in terms of basic geometric and anatomical variables. Concerning geometric descriptors, inter-artery and intra-artery correlations are studied. Data reported here can be useful for the construction and setup of blood flow models of the coronary circulation. Finally, as an application example, similarity criterion to assess vasculature likelihood based on geometric features is presented and used to test geometric similarity among sibling patients. Results indicate that likelihood, measured through geometric descriptors, is stronger between siblings compared with non-relative patients. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Revealing chemophoric sites in organophosphorus insecticides through the MIA-QSPR modeling of soil sorption data.

PubMed

Daré, Joyce K; Silva, Cristina F; Freitas, Matheus P

2017-10-01

Soil sorption of insecticides employed in agriculture is an important parameter to probe the environmental fate of organic chemicals. Therefore, methods for the prediction of soil sorption of new agrochemical candidates, as well as for the rationalization of the molecular characteristics responsible for a given sorption profile, are extremely beneficial for the environment. A quantitative structure-property relationship method based on chemical structure images as molecular descriptors provided a reliable model for the soil sorption prediction of 24 widely used organophosphorus insecticides. By means of contour maps obtained from the partial least squares regression coefficients and the variable importance in projection scores, key molecular moieties were targeted for possible structural modification, in order to obtain novel and more environmentally friendly insecticide candidates. The image-based descriptors applied encode molecular arrangement, atoms connectivity, groups size, and polarity; consequently, the findings in this work cannot be achieved by a simple relationship with hydrophobicity, usually described by the octanol-water partition coefficient. Copyright © 2017 Elsevier Inc. All rights reserved.

Prediction of Partition Coefficients of Organic Compounds between SPME/PDMS and Aqueous Solution

PubMed Central

Chao, Keh-Ping; Lu, Yu-Ting; Yang, Hsiu-Wen

2014-01-01

Polydimethylsiloxane (PDMS) is commonly used as the coated polymer in the solid phase microextraction (SPME) technique. In this study, the partition coefficients of organic compounds between SPME/PDMS and the aqueous solution were compiled from the literature sources. The correlation analysis for partition coefficients was conducted to interpret the effect of their physicochemical properties and descriptors on the partitioning process. The PDMS-water partition coefficients were significantly correlated to the polarizability of organic compounds (r = 0.977, p < 0.05). An empirical model, consisting of the polarizability, the molecular connectivity index, and an indicator variable, was developed to appropriately predict the partition coefficients of 61 organic compounds for the training set. The predictive ability of the empirical model was demonstrated by using it on a test set of 26 chemicals not included in the training set. The empirical model, applying the straightforward calculated molecular descriptors, for estimating the PDMS-water partition coefficient will contribute to the practical applications of the SPME technique. PMID:24534804

Users guide for the Water Resources Division bibliographic retrieval and report generation system

USGS Publications Warehouse

Tamberg, Nora

1983-01-01

The WRDBIB Retrieval and Report-generation system has been developed by applying Multitrieve (CSD 1980, Reston) software to bibliographic data files. The WRDBIB data base includes some 9 ,000 records containing bibliographic citations and descriptors of WRD reports released for publication during 1968-1982. The data base is resident in the Reston Multics computer and may be accessed by registered Multics users in the field. The WRDBIB Users Guide provides detailed procedures on how to run retrieval programs using WRDBIB library files, and how to prepare custom bibliographic reports and author indexes. Users may search the WRDBIB data base on the following variable fields as described in the Data Dictionary: Authors, organizational source, title, citation, publication year, descriptors, and the WRSIC (accession) number. The Users Guide provides ample examples of program runs illustrating various retrieval and report generation aspects. Appendices include Multics access and file manipulation procedures; a ' Glossary of Selected Terms'; and a complete ' Retrieval Session ' with step-by-step outlines. (USGS)

Human access and landscape structure effects on Andean forest bird richness

NASA Astrophysics Data System (ADS)

Aubad, Jorge; Aragón, Pedro; Rodríguez, Miguel Á.

2010-07-01

We analyzed the influence of human access and landscape structure on forest bird species richness in a fragmented landscape of the Colombian Andes. In Latin America, habitat loss and fragmentation are considered as the greatest threats to biodiversity because a large number of countryside villagers complement their food and incomes with the extraction of forest resources. Anthropogenic actions may also affect forest species by bird hunting or indirectly through modifying the structure of forest habitats. We surveyed 14 secondary cloud forest remnants to generate bird species richness data for each of them. We also quantified six landscape structure descriptors of forest patch size (patch area and core area), shape (perimeter of each fragment and the Patton's shape index) and isolation (nearest neighbor distance and edge contrast), and generated (using principal components analysis) a synthetic human influence variable based on the distance of each fragment to roads and villages, as well as the total slope of the fragments. Species richness was related to these variables using generalized linear models (GLMs) complemented with model selection techniques based on information theory and partial regression analysis. We found that forest patch size and accessibility were key drivers of bird richness, which increased toward largest patches, but decreased in those more accessible to humans and their potential disturbances. Both patch area and human access effects on forest bird species richness were complementary and similar in magnitude. Our results provide a basis for biodiversity conservation plans and initiatives of Andean forest diversity.

Circular blurred shape model for multiclass symbol recognition.

PubMed

Escalera, Sergio; Fornés, Alicia; Pujol, Oriol; Lladós, Josep; Radeva, Petia

2011-04-01

In this paper, we propose a circular blurred shape model descriptor to deal with the problem of symbol detection and classification as a particular case of object recognition. The feature extraction is performed by capturing the spatial arrangement of significant object characteristics in a correlogram structure. The shape information from objects is shared among correlogram regions, where a prior blurring degree defines the level of distortion allowed in the symbol, making the descriptor tolerant to irregular deformations. Moreover, the descriptor is rotation invariant by definition. We validate the effectiveness of the proposed descriptor in both the multiclass symbol recognition and symbol detection domains. In order to perform the symbol detection, the descriptors are learned using a cascade of classifiers. In the case of multiclass categorization, the new feature space is learned using a set of binary classifiers which are embedded in an error-correcting output code design. The results over four symbol data sets show the significant improvements of the proposed descriptor compared to the state-of-the-art descriptors. In particular, the results are even more significant in those cases where the symbols suffer from elastic deformations.

Photoperiodic controls on ecosystem-level photosynthetic capacity

NASA Astrophysics Data System (ADS)

Stoy, P. C.; Trowbridge, A. M.; Bauerle, W.

2012-12-01

Most models of photosynthesis at the leaf or canopy level assume that temperature is the dominant control on the variability of photosynthetic parameters. Recent studies, however, have found that photoperiod is a better descriptor of the seasonal variability of photosynthetic function at the leaf and plant scale, and that spectral indices of leaf functionality are poor descriptors of this seasonality. We explored the variability of photosynthesic parameters at the ecosystem scale using over 100 site-years of air temperature and gross primary productivity (GPP) data from non-tropical forested sites in the Free/Fair Use LaThuille FLUXNET database (www.fluxdata.org), excluding sites that were classified as dry and/or with savanna vegetation, where we expected GPP to be driven by moisture availability. Both GPP and GPP normalized by daily photosynthetic photon flux density (GPPn) were considered, and photoperiod was calculated from eddy covariance tower coordinates. We performed a Granger causality analysis, a method based on the understanding that causes precede effects, on both the GPP and GPPn. Photoperiod Granger-caused GPP (GPPn) in 95% (87%) of all site-years. While temperature Granger-caused GPP in a mere 23% of site years, it Granger-caused GPPn 73% of the time. Both temperature values are significantly less than the percent of cases in which day length Granger-caused GPP (p<0.05, Student's t-test). An inverse analysis was performed for completeness, and it was found that GPP Granger-caused photoperiod (temperature) in 39% (78%) of all site years. Results demonstrate that incorporating simple photoperiod controls may be a logical step in improving ecosystem and global model output.

Descriptors for ions and ion-pairs for use in linear free energy relationships.

PubMed

Abraham, Michael H; Acree, William E

2016-01-22

The determination of Abraham descriptors for single ions is reviewed, and equations are given for the partition of single ions from water to a number of solvents. These ions include permanent anions and cations and ionic species such as carboxylic acid anions, phenoxide anions and protonated base cations. Descriptors for a large number of ions and ionic species are listed, and equations for the prediction of Abraham descriptors for ionic species are given. The application of descriptors for ions and ionic species to physicochemical processes is given; these are to water-solvent partitions, HPLC retention data, immobilised artificial membranes, the Finkelstein reaction and diffusion in water. Applications to biological processes include brain permeation, microsomal degradation of drugs, skin permeation and human intestinal absorption. The review concludes with a section on the determination of descriptors for ion-pairs. Copyright © 2015 Elsevier B.V. All rights reserved.

Identifying factors of comfort in using hand tools.

PubMed

Kuijt-Evers, L F M; Groenesteijn, L; de Looze, M P; Vink, P

2004-09-01

To design comfortable hand tools, knowledge about comfort/discomfort in using hand tools is required. We investigated which factors determine comfort/discomfort in using hand tools according to users. Therefore, descriptors of comfort/discomfort in using hand tools were collected from literature and interviews. After that, the relatedness of a selection of the descriptors to comfort in using hand tools was investigated. Six comfort factors could be distinguished (functionality, posture and muscles, irritation and pain of hand and fingers, irritation of hand surface, handle characteristics, aesthetics). These six factors can be classified into three meaningful groups: functionality, physical interaction and appearance. The main conclusions were that (1) the same descriptors were related to comfort and discomfort in using hand tools, (2) descriptors of functionality are most related to comfort in using hand tools followed by descriptors of physical interaction and (3) descriptors of appearance become secondary in comfort in using hand tools.

Impact of low alcohol verbal descriptors on perceived strength: An experimental study.

PubMed

Vasiljevic, Milica; Couturier, Dominique-Laurent; Marteau, Theresa M

2018-02-01

Low alcohol labels are a set of labels that carry descriptors such as 'low' or 'lighter' to denote alcohol content in beverages. There is growing interest from policymakers and producers in lower strength alcohol products. However, there is a lack of evidence on how the general population perceives verbal descriptors of strength. The present research examines consumers' perceptions of strength (% ABV) and appeal of alcohol products using low or high alcohol verbal descriptors. A within-subjects experimental study in which participants rated the strength and appeal of 18 terms denoting low (nine terms), high (eight terms) and regular (one term) strengths for either (1) wine or (2) beer according to drinking preference. Thousand six hundred adults (796 wine and 804 beer drinkers) sampled from a nationally representative UK panel. Low, Lower, Light, Lighter, and Reduced formed a cluster and were rated as denoting lower strength products than Regular, but higher strength than the cluster with intensifiers consisting of Extra Low, Super Low, Extra Light, and Super Light. Similar clustering in perceived strength was observed amongst the high verbal descriptors. Regular was the most appealing strength descriptor, with the low and high verbal descriptors using intensifiers rated least appealing. The perceived strength and appeal of alcohol products diminished the more the verbal descriptors implied a deviation from Regular. The implications of these findings are discussed in terms of policy implications for lower strength alcohol labelling and associated public health outcomes. Statement of contribution What is already known about this subject? Current UK and EU legislation limits the number of low strength verbal descriptors and the associated alcohol by volume (ABV) to 1.2% ABV and lower. There is growing interest from policymakers and producers to extend the range of lower strength alcohol products above the current cap of 1.2% ABV set out in national legislation. There is a lack of evidence on how the general population perceives verbal descriptors of alcohol product strength (both low and high). What does this study add? Verbal descriptors of lower strength wine and beer form two clusters and effectively communicate reduced alcohol content. Low, Lower, Light, Lighter, and Reduced were considered lower in strength than Regular (average % ABV). Descriptors using intensifiers (Extra Low, Super Low, Extra Light, and Super Light) were considered lowest in strength. Similar clustering in perceived strength was observed amongst the high verbal descriptors. The appeal of alcohol products reduced the more the verbal descriptors implied a deviation from Regular. © 2017 The Authors. British Journal of Health Psychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.

Mobile visual object identification: from SIFT-BoF-RANSAC to Sketchprint

NASA Astrophysics Data System (ADS)

Voloshynovskiy, Sviatoslav; Diephuis, Maurits; Holotyak, Taras

2015-03-01

Mobile object identification based on its visual features find many applications in the interaction with physical objects and security. Discriminative and robust content representation plays a central role in object and content identification. Complex post-processing methods are used to compress descriptors and their geometrical information, aggregate them into more compact and discriminative representations and finally re-rank the results based on the similarity geometries of descriptors. Unfortunately, most of the existing descriptors are not very robust and discriminative once applied to the various contend such as real images, text or noise-like microstructures next to requiring at least 500-1'000 descriptors per image for reliable identification. At the same time, the geometric re-ranking procedures are still too complex to be applied to the numerous candidates obtained from the feature similarity based search only. This restricts that list of candidates to be less than 1'000 which obviously causes a higher probability of miss. In addition, the security and privacy of content representation has become a hot research topic in multimedia and security communities. In this paper, we introduce a new framework for non- local content representation based on SketchPrint descriptors. It extends the properties of local descriptors to a more informative and discriminative, yet geometrically invariant content representation. In particular it allows images to be compactly represented by 100 SketchPrint descriptors without being fully dependent on re-ranking methods. We consider several use cases, applying SketchPrint descriptors to natural images, text documents, packages and micro-structures and compare them with the traditional local descriptors.

Insights into geometries, stabilities, electronic structures, reactivity descriptors, and magnetic properties of bimetallic Nim Cun-m (m = 1, 2; n = 3-13) clusters: Comparison with pure copper clusters.

PubMed

Singh, Raman K; Iwasa, Takeshi; Taketsugu, Tetsuya

2018-05-25

A long-range corrected density functional theory (LC-DFT) was applied to study the geometric structures, relative stabilities, electronic structures, reactivity descriptors and magnetic properties of the bimetallic NiCu n -1 and Ni 2 Cu n -2 (n = 3-13) clusters, obtained by doping one or two Ni atoms to the lowest energy structures of Cu n , followed by geometry optimizations. The optimized geometries revealed that the lowest energy structures of the NiCu n -1 and Ni 2 Cu n -2 clusters favor the Ni atom(s) situated at the most highly coordinated position of the host copper clusters. The averaged binding energy, the fragmentation energies and the second-order energy differences signified that the Ni doped clusters can continue to gain an energy during the growth process. The electronic structures revealed that the highest occupied molecular orbital and the lowest unoccupied molecular orbital energies of the LC-DFT are reliable and can be used to predict the vertical ionization potential and the vertical electron affinity of the systems. The reactivity descriptors such as the chemical potential, chemical hardness and electrophilic power, and the reactivity principle such as the minimum polarizability principle are operative for characterizing and rationalizing the electronic structures of these clusters. Moreover, doping of Ni atoms into the copper clusters carry most of the total spin magnetic moment. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.

QSAR, DFT and molecular modeling studies of peptides from HIV-1 to describe their recognition properties by MHC-I.

PubMed

Andrade-Ochoa, S; García-Machorro, J; Bello, Martiniano; Rodríguez-Valdez, L M; Flores-Sandoval, C A; Correa-Basurto, J

2017-08-03

Human immunodeficiency virus type-1 (HIV-1) has infected more than 40 million people around the world. HIV-1 treatment still has several side effects, and the development of a vaccine, which is another potential option for decreasing human infections, has faced challenges. This work presents a computational study that includes a quantitative structure activity relationship(QSAR) using density functional theory(DFT) for reported peptides to identify the principal quantum mechanics descriptors related to peptide activity. In addition, the molecular recognition properties of these peptides are explored on major histocompatibility complex I (MHC-I) through docking and molecular dynamics (MD) simulations accompanied by the Molecular Mechanics Generalized Born Surface Area (MMGBSA) approach for correlating peptide activity reported elsewhere vs. theoretical peptide affinity. The results show that the carboxylic acid and hydroxyl groups are chemical moieties that have an inverse relationship with biological activity. The number of sulfides, pyrroles and imidazoles from the peptide structure are directly related to biological activity. In addition, the HOMO orbital energy values of the total absolute charge and the Ghose-Crippen molar refractivity of peptides are descriptors directly related to the activity and affinity on MHC-I. Docking and MD simulation studies accompanied by an MMGBSA analysis show that the binding free energy without considering the entropic contribution is energetically favorable for all the complexes. Furthermore, good peptide interaction with the most affinity is evaluated experimentally for three proteins. Overall, this study shows that the combination of quantum mechanics descriptors and molecular modeling studies could help describe the immunogenic properties of peptides from HIV-1.

A PROMIS Measure of Neuropathic Pain Quality

PubMed Central

Askew, Robert L.; Cook, Karon F.; Keefe, Francis J.; Nowinski, Cindy J; Cella, David; Revicki, Dennis A.; DeWitt, Esi M. Morgan; Michaud, Kaleb; Trence, Dace L.; Amtmann, Dagmar

2016-01-01

Objectives Neuropathic pain is a consequence of many chronic conditions. This study aimed to develop a unidimensional neuropathic pain scale whose scores represent levels of neuropathic pain and distinguish between individuals with neuropathic and non-neuropathic pain conditions. Methods A candidate item pool of 42 pain quality descriptors was administered to participants with osteoarthritis, rheumatoid arthritis, diabetic neuropathy, and cancer chemotherapy-induced peripheral neuropathy. A subset of pain quality descriptors (items) that best distinguished between participants with and those without neuropathic pain conditions were identified. Dimensionality of pain descriptors was evaluated in a development sample and cross-validated in a hold-out sample. Item responses were calibrated using an item response theory model, and scores were generated on a T-score metric. Neuropathic pain scale scores were evaluated in terms of reliability, validity, and the ability to distinguish between participants with and without conditions typically associated with neuropathic pain. Results Of the 42 initial items, 5 were identified for the Patient Reported Outcome Measurement Information System (PROMIS) Neuropathic Pain Quality scale (PROMIS-PQ-Neuro). The IRT-generated T-scores exhibited good discriminatory ability based on receiver operator characteristic analysis. Score thresholds were identified that optimize sensitivity and specificity. Construct, criterion, and discriminant validity, and reliability of scale scores were supported. Conclusions The 5-item PROMIS PQ-Neuro is a short and practical measure that can be used to identify patients more likely to have neuropathic pain and to distinguish levels of neuropathic pain. The data collected will support future research that targets other unidimensional pain quality domains (e.g., nociceptive pain). PMID:27565279

Automated morphometry provides accurate and reproducible virtual staging of liver fibrosis in chronic hepatitis C

PubMed Central

Calès, Paul; Chaigneau, Julien; Hunault, Gilles; Michalak, Sophie; Cavaro-Menard, Christine; Fasquel, Jean-Baptiste; Bertrais, Sandrine; Rousselet, Marie-Christine

2015-01-01

Background: Liver fibrosis staging provides prognostic value, although hampered by observer variability. We used digital analysis to develop diagnostic morphometric scores for significant fibrosis, cirrhosis and fibrosis staging in chronic hepatitis C. Materials and Methods: We automated the measurement of 44 classical and new morphometric descriptors. The reference was histological METAVIR fibrosis (F) staging (F0 to F4) on liver biopsies. The derivation population included 416 patients and liver biopsies ≥20 mm-length. Two validation population included 438 patients. Results: In the derivation population, the area under the receiver operating characteristic (AUROC) for clinically significant fibrosis (F stage ≥2) of a logistic score combining 5 new descriptors (stellar fibrosis area, edge linearity, bridge thickness, bridge number, nodularity) was 0.957. The AUROC for cirrhosis of 6 new descriptors (edge linearity, nodularity, portal stellar fibrosis area, portal distance, granularity, fragmentation) was 0.994. Predicted METAVIR F staging combining 8 morphometric descriptors agreed well with METAVIR F staging by pathologists: κ = 0.868. Morphometric score of clinically significant fibrosis had a higher correlation with porto-septal fibrosis area (rs = 0.835) than METAVIR F staging (rs = 0.756, P < 0.001) and the same correlations with fibrosis biomarkers, e.g., serum hyaluronate: rs = 0.484 versus rs = 0.476 for METAVIR F (P = 0.862). In the validation population, the AUROCs of clinically significant fibrosis and cirrhosis scores were, respectively: 0.893 and 0.993 in 153 patients (biopsy < 20 mm); 0.955 and 0.994 in 285 patients (biopsy ≥ 20 mm). The three morphometric diagnoses agreed with consensus expert reference as well as or better than diagnoses by first-line pathologists in 285 patients, respectively: significant fibrosis: 0.733 versus 0.733 (κ), cirrhosis: 0.900 versus 0.827, METAVIR F: 0.881 versus 0.865. Conclusion: The new automated morphometric scores provide reproducible and accurate diagnoses of fibrosis stages via “virtual expert pathologist.” PMID:26110088

Words that describe chronic musculoskeletal pain: implications for assessing pain quality across cultures.

PubMed

Sharma, Saurab; Pathak, Anupa; Jensen, Mark P

2016-01-01

People from different cultures who speak different languages may experience pain differently. This possible variability has important implications for evaluating the validity of pain quality measures that are directly translated into different languages without cultural adaptations. The aim of this study was to evaluate the impact of language and culture on the validity of pain quality measures by comparing the words that individuals with chronic pain from Nepal use to describe their pain with those used by patients from the USA. A total of 101 individuals with chronic musculoskeletal pain in Nepal were asked to describe their pain. The rates of the different pain descriptor domains and phrases used by the Nepali sample were then compared to the published rates of descriptors used by patients from the USA. The content validity of commonly used measures for assessing pain quality was then evaluated. While there was some similarity between patients from Nepal and the USA in how they describe pain, there were also important differences, especially in how pain quality was described. For example, many patients from Nepal used metaphors to describe their pain. Also, the patients from Nepal often used a category of pain descriptor - which describes a physical state - not used by patients from the USA. Only the original McGill Pain Questionnaire was found to have content validity for assessing pain quality in patients from Nepal, although other existing pain quality measures could be adapted to be content valid by adding one or two additional descriptors, depending on the measure in question. The findings indicate that direct translations of measures that are developed using samples of patients from one country or culture are not necessarily content valid for use in other countries or cultures; some adaptations may be required in order for such measures to be most useful in new language and culture.

Mobile Visual Search Based on Histogram Matching and Zone Weight Learning

NASA Astrophysics Data System (ADS)

Zhu, Chuang; Tao, Li; Yang, Fan; Lu, Tao; Jia, Huizhu; Xie, Xiaodong

2018-01-01

In this paper, we propose a novel image retrieval algorithm for mobile visual search. At first, a short visual codebook is generated based on the descriptor database to represent the statistical information of the dataset. Then, an accurate local descriptor similarity score is computed by merging the tf-idf weighted histogram matching and the weighting strategy in compact descriptors for visual search (CDVS). At last, both the global descriptor matching score and the local descriptor similarity score are summed up to rerank the retrieval results according to the learned zone weights. The results show that the proposed approach outperforms the state-of-the-art image retrieval method in CDVS.

Genotype by environment (climate) interaction improves genomic prediction for production traits in US Holstein cattle.

PubMed

Tiezzi, F; de Los Campos, G; Parker Gaddis, K L; Maltecca, C

2017-03-01

Genotype by environment interaction (G × E) in dairy cattle productive traits has been shown to exist, but current genetic evaluation methods do not take this component into account. As several environmental descriptors (e.g., climate, farming system) are known to vary within the United States, not accounting for the G × E could lead to reranking of bulls and loss in genetic gain. Using test-day records on milk yield, somatic cell score, fat, and protein percentage from all over the United States, we computed within herd-year-season daughter yield deviations for 1,087 Holstein bulls and regressed them on genetic and environmental information to estimate variance components and to assess prediction accuracy. Genomic information was obtained from a 50k SNP marker panel. Environmental effect inputs included herd (160 levels), geographical region (7 levels), geographical location (2 variables), climate information (7 variables), and management conditions of the herds (16 total variables divided in 4 subgroups). For each set of environmental descriptors, environmental, genomic, and G × E components were sequentially fitted. Variance components estimates confirmed the presence of G × E on milk yield, with its effect being larger than main genetic effect and the environmental effect for some models. Conversely, G × E was moderate for somatic cell score and small for milk composition. Genotype by environment interaction, when included, partially eroded the genomic effect (as compared with the models where G × E was not included), suggesting that the genomic variance could at least in part be attributed to G × E not appropriately accounted for. Model predictive ability was assessed using 3 cross-validation schemes (new bulls, incomplete progeny test, and new environmental conditions), and performance was compared with a reference model including only the main genomic effect. In each scenario, at least 1 of the models including G × E was able to perform better than the reference model, although it was not possible to find the overall best-performing model that included the same set of environmental descriptors. In general, the methodology used is promising in accounting for G × E in genomic predictions, but challenges exist in identifying a unique set of covariates capable of describing the entire variety of environments. Copyright © 2017 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Learning Rotation-Invariant Local Binary Descriptor.

PubMed

Duan, Yueqi; Lu, Jiwen; Feng, Jianjiang; Zhou, Jie

2017-08-01

In this paper, we propose a rotation-invariant local binary descriptor (RI-LBD) learning method for visual recognition. Compared with hand-crafted local binary descriptors, such as local binary pattern and its variants, which require strong prior knowledge, local binary feature learning methods are more efficient and data-adaptive. Unlike existing learning-based local binary descriptors, such as compact binary face descriptor and simultaneous local binary feature learning and encoding, which are susceptible to rotations, our RI-LBD first categorizes each local patch into a rotational binary pattern (RBP), and then jointly learns the orientation for each pattern and the projection matrix to obtain RI-LBDs. As all the rotation variants of a patch belong to the same RBP, they are rotated into the same orientation and projected into the same binary descriptor. Then, we construct a codebook by a clustering method on the learned binary codes, and obtain a histogram feature for each image as the final representation. In order to exploit higher order statistical information, we extend our RI-LBD to the triple rotation-invariant co-occurrence local binary descriptor (TRICo-LBD) learning method, which learns a triple co-occurrence binary code for each local patch. Extensive experimental results on four different visual recognition tasks, including image patch matching, texture classification, face recognition, and scene classification, show that our RI-LBD and TRICo-LBD outperform most existing local descriptors.

Determination of Abraham model solute descriptors for the monomeric and dimeric forms of trans-cinnamic acid using measured solubilities from the Open Notebook Science Challenge.

PubMed

Bradley, Jean-Claude; Abraham, Michael H; Acree, William E; Lang, Andrew Sid; Beck, Samantha N; Bulger, David A; Clark, Elizabeth A; Condron, Lacey N; Costa, Stephanie T; Curtin, Evan M; Kurtu, Sozit B; Mangir, Mark I; McBride, Matthew J

2015-01-01

Calculating Abraham descriptors from solubility values requires that the solute have the same form when dissolved in all solvents. However, carboxylic acids can form dimers when dissolved in non-polar solvents. For such compounds Abraham descriptors can be calculated for both the monomeric and dimeric forms by treating the polar and non-polar systems separately. We illustrate the method of how this can be done by calculating the Abraham descriptors for both the monomeric and dimeric forms of trans-cinnamic acid, the first time that descriptors for a carboxylic acid dimer have been obtained. Abraham descriptors were calculated for the monomeric form of trans-cinnamic acid using experimental solubility measurements in polar solvents from the Open Notebook Science Challenge together with a number of water-solvent partition coefficients from the literature. Similarly, experimental solubility measurements in non-polar solvents were used to determine Abraham descriptors for the trans-cinnamic acid dimer. Abraham descriptors were calculated for both the monomeric and dimeric forms of trans-cinnamic acid. This allows for the prediction of further solubilities of trans-cinnamic acid in both polar and non-polar solvents with an error of about 0.10 log units. Graphical abstractMolar concentration of trans-cinnamic acid in various polar and non-polar solvents.

On the Relationship between Molecular Hit Rates in High-Throughput Screening and Molecular Descriptors.

PubMed

Hansson, Mari; Pemberton, John; Engkvist, Ola; Feierberg, Isabella; Brive, Lars; Jarvis, Philip; Zander-Balderud, Linda; Chen, Hongming

2014-06-01

High-throughput screening (HTS) is widely used in the pharmaceutical industry to identify novel chemical starting points for drug discovery projects. The current study focuses on the relationship between molecular hit rate in recent in-house HTS and four common molecular descriptors: lipophilicity (ClogP), size (heavy atom count, HEV), fraction of sp(3)-hybridized carbons (Fsp3), and fraction of molecular framework (f(MF)). The molecular hit rate is defined as the fraction of times the molecule has been assigned as active in the HTS campaigns where it has been screened. Beta-binomial statistical models were built to model the molecular hit rate as a function of these descriptors. The advantage of the beta-binomial statistical models is that the correlation between the descriptors is taken into account. Higher degree polynomial terms of the descriptors were also added into the beta-binomial statistic model to improve the model quality. The relative influence of different molecular descriptors on molecular hit rate has been estimated, taking into account that the descriptors are correlated to each other through applying beta-binomial statistical modeling. The results show that ClogP has the largest influence on the molecular hit rate, followed by Fsp3 and HEV. f(MF) has only a minor influence besides its correlation with the other molecular descriptors. © 2013 Society for Laboratory Automation and Screening.

Predicting in vivo effect levels for repeat-dose systemic toxicity using chemical, biological, kinetic and study covariates.

PubMed

Truong, Lisa; Ouedraogo, Gladys; Pham, LyLy; Clouzeau, Jacques; Loisel-Joubert, Sophie; Blanchet, Delphine; Noçairi, Hicham; Setzer, Woodrow; Judson, Richard; Grulke, Chris; Mansouri, Kamel; Martin, Matthew

2018-02-01

In an effort to address a major challenge in chemical safety assessment, alternative approaches for characterizing systemic effect levels, a predictive model was developed. Systemic effect levels were curated from ToxRefDB, HESS-DB and COSMOS-DB from numerous study types totaling 4379 in vivo studies for 1247 chemicals. Observed systemic effects in mammalian models are a complex function of chemical dynamics, kinetics, and inter- and intra-individual variability. To address this complex problem, systemic effect levels were modeled at the study-level by leveraging study covariates (e.g., study type, strain, administration route) in addition to multiple descriptor sets, including chemical (ToxPrint, PaDEL, and Physchem), biological (ToxCast), and kinetic descriptors. Using random forest modeling with cross-validation and external validation procedures, study-level covariates alone accounted for approximately 15% of the variance reducing the root mean squared error (RMSE) from 0.96 log 10 to 0.85 log 10  mg/kg/day, providing a baseline performance metric (lower expectation of model performance). A consensus model developed using a combination of study-level covariates, chemical, biological, and kinetic descriptors explained a total of 43% of the variance with an RMSE of 0.69 log 10  mg/kg/day. A benchmark model (upper expectation of model performance) was also developed with an RMSE of 0.5 log 10  mg/kg/day by incorporating study-level covariates and the mean effect level per chemical. To achieve a representative chemical-level prediction, the minimum study-level predicted and observed effect level per chemical were compared reducing the RMSE from 1.0 to 0.73 log 10  mg/kg/day, equivalent to 87% of predictions falling within an order-of-magnitude of the observed value. Although biological descriptors did not improve model performance, the final model was enriched for biological descriptors that indicated xenobiotic metabolism gene expression, oxidative stress, and cytotoxicity, demonstrating the importance of accounting for kinetics and non-specific bioactivity in predicting systemic effect levels. Herein, we generated an externally predictive model of systemic effect levels for use as a safety assessment tool and have generated forward predictions for over 30,000 chemicals.

Chemodiversity and molecular plasticity: recognition processes as explored by property spaces.

PubMed

Vistoli, Giulio; Pedretti, Alessandro; Testa, Bernard

2011-06-01

In the last few years, a need to account for molecular flexibility in drug-design methodologies has emerged, even if the dynamic behavior of molecular properties is seldom made explicit. For a flexible molecule, it is indeed possible to compute different values for a given conformation-dependent property and the ensemble of such values defines a property space that can be used to describe its molecular variability; a most representative case is the lipophilicity space. In this review, a number of applications of lipophilicity space and other property spaces are presented, showing that this concept can be fruitfully exploited: to investigate the constraints exerted by media of different levels of structural organization, to examine processes of molecular recognition and binding at an atomic level, to derive informative descriptors to be included in quantitative structure--activity relationships and to analyze protein simulations extracting the relevant information. Much molecular information is neglected in the descriptors used by medicinal chemists, while the concept of property space can fill this gap by accounting for the often-disregarded dynamic behavior of both small ligands and biomacromolecules. Property space also introduces some innovative concepts such as molecular sensitivity and plasticity, which appear best suited to explore the ability of a molecule to adapt itself to the environment variously modulating its property and conformational profiles. Globally, such concepts can enhance our understanding of biological phenomena providing fruitful descriptors in drug-design and pharmaceutical sciences.

Artificial neural networks and the study of the psychoactivity of cannabinoid compounds.

PubMed

Honório, Káthia M; de Lima, Emmanuela F; Quiles, Marcos G; Romero, Roseli A F; Molfetta, Fábio A; da Silva, Albérico B F

2010-06-01

Cannabinoid compounds have widely been employed because of its medicinal and psychotropic properties. These compounds are isolated from Cannabis sativa (or marijuana) and are used in several medical treatments, such as glaucoma, nausea associated to chemotherapy, pain and many other situations. More recently, its use as appetite stimulant has been indicated in patients with cachexia or AIDS. In this work, the influence of several molecular descriptors on the psychoactivity of 50 cannabinoid compounds is analyzed aiming one obtain a model able to predict the psychoactivity of new cannabinoids. For this purpose, initially, the selection of descriptors was carried out using the Fisher's weight, the correlation matrix among the calculated variables and principal component analysis. From these analyses, the following descriptors have been considered more relevant: E(LUMO) (energy of the lowest unoccupied molecular orbital), Log P (logarithm of the partition coefficient), VC4 (volume of the substituent at the C4 position) and LP1 (Lovasz-Pelikan index, a molecular branching index). To follow, two neural network models were used to construct a more adequate model for classifying new cannabinoid compounds. The first model employed was multi-layer perceptrons, with algorithm back-propagation, and the second model used was the Kohonen network. The results obtained from both networks were compared and showed that both techniques presented a high percentage of correctness to discriminate psychoactive and psychoinactive compounds. However, the Kohonen network was superior to multi-layer perceptrons.

Human impacts affect tree community features of 20 forest fragments of a vanishing neotropical hotspot.

PubMed

Pereira, José Aldo Alves; de Oliveira-Filho, Ary Teixeira; Eisenlohr, Pedro V; Miranda, Pedro L S; de Lemos Filho, José Pires

2015-02-01

The loss in forest area due to human occupancy is not the only threat to the remaining biodiversity: forest fragments are susceptible to additional human impact. Our aim was to investigate the effect of human impact on tree community features (species composition and abundance, and structural descriptors) and check if there was a decrease in the number of slender trees, an increase in the amount of large trees, and also a reduction in the number of tree species that occur in 20 fragments of Atlantic montane semideciduous forest in southeastern Brazil. We produced digital maps of each forest fragment using Landsat 7 satellite images and processed the maps to obtain morphometric variables. We used investigative questionnaires and field observations to survey the history of human impact. We then converted the information into scores given to the extent, severity, and duration of each impact, including proportional border area, fire, trails, coppicing, logging, and cattle, and converted these scores into categorical levels. We used linear models to assess the effect of impacts on tree species abundance distribution and stand structural descriptors. Part of the variation in floristic patterns was significantly correlated to the impacts of fire, logging, and proportional border area. Structural descriptors were influenced by cattle and outer roads. Our results provided, for the first time, strong evidence that tree species occurrence and abundance, and forest structure of Atlantic seasonal forest fragments respond differently to various modes of disturbance by humans.

Convolution Comparison Pattern: An Efficient Local Image Descriptor for Fingerprint Liveness Detection

PubMed Central

Gottschlich, Carsten

2016-01-01

We present a new type of local image descriptor which yields binary patterns from small image patches. For the application to fingerprint liveness detection, we achieve rotation invariant image patches by taking the fingerprint segmentation and orientation field into account. We compute the discrete cosine transform (DCT) for these rotation invariant patches and attain binary patterns by comparing pairs of two DCT coefficients. These patterns are summarized into one or more histograms per image. Each histogram comprises the relative frequencies of pattern occurrences. Multiple histograms are concatenated and the resulting feature vector is used for image classification. We name this novel type of descriptor convolution comparison pattern (CCP). Experimental results show the usefulness of the proposed CCP descriptor for fingerprint liveness detection. CCP outperforms other local image descriptors such as LBP, LPQ and WLD on the LivDet 2013 benchmark. The CCP descriptor is a general type of local image descriptor which we expect to prove useful in areas beyond fingerprint liveness detection such as biological and medical image processing, texture recognition, face recognition and iris recognition, liveness detection for face and iris images, and machine vision for surface inspection and material classification. PMID:26844544

Improved Prediction of Blood-Brain Barrier Permeability Through Machine Learning with Combined Use of Molecular Property-Based Descriptors and Fingerprints.

PubMed

Yuan, Yaxia; Zheng, Fang; Zhan, Chang-Guo

2018-03-21

Blood-brain barrier (BBB) permeability of a compound determines whether the compound can effectively enter the brain. It is an essential property which must be accounted for in drug discovery with a target in the brain. Several computational methods have been used to predict the BBB permeability. In particular, support vector machine (SVM), which is a kernel-based machine learning method, has been used popularly in this field. For SVM training and prediction, the compounds are characterized by molecular descriptors. Some SVM models were based on the use of molecular property-based descriptors (including 1D, 2D, and 3D descriptors) or fragment-based descriptors (known as the fingerprints of a molecule). The selection of descriptors is critical for the performance of a SVM model. In this study, we aimed to develop a generally applicable new SVM model by combining all of the features of the molecular property-based descriptors and fingerprints to improve the accuracy for the BBB permeability prediction. The results indicate that our SVM model has improved accuracy compared to the currently available models of the BBB permeability prediction.

Discriminative latent models for recognizing contextual group activities.

PubMed

Lan, Tian; Wang, Yang; Yang, Weilong; Robinovitch, Stephen N; Mori, Greg

2012-08-01

In this paper, we go beyond recognizing the actions of individuals and focus on group activities. This is motivated from the observation that human actions are rarely performed in isolation; the contextual information of what other people in the scene are doing provides a useful cue for understanding high-level activities. We propose a novel framework for recognizing group activities which jointly captures the group activity, the individual person actions, and the interactions among them. Two types of contextual information, group-person interaction and person-person interaction, are explored in a latent variable framework. In particular, we propose three different approaches to model the person-person interaction. One approach is to explore the structures of person-person interaction. Differently from most of the previous latent structured models, which assume a predefined structure for the hidden layer, e.g., a tree structure, we treat the structure of the hidden layer as a latent variable and implicitly infer it during learning and inference. The second approach explores person-person interaction in the feature level. We introduce a new feature representation called the action context (AC) descriptor. The AC descriptor encodes information about not only the action of an individual person in the video, but also the behavior of other people nearby. The third approach combines the above two. Our experimental results demonstrate the benefit of using contextual information for disambiguating group activities.

Discriminative Latent Models for Recognizing Contextual Group Activities

PubMed Central

Lan, Tian; Wang, Yang; Yang, Weilong; Robinovitch, Stephen N.; Mori, Greg

2012-01-01

In this paper, we go beyond recognizing the actions of individuals and focus on group activities. This is motivated from the observation that human actions are rarely performed in isolation; the contextual information of what other people in the scene are doing provides a useful cue for understanding high-level activities. We propose a novel framework for recognizing group activities which jointly captures the group activity, the individual person actions, and the interactions among them. Two types of contextual information, group-person interaction and person-person interaction, are explored in a latent variable framework. In particular, we propose three different approaches to model the person-person interaction. One approach is to explore the structures of person-person interaction. Differently from most of the previous latent structured models, which assume a predefined structure for the hidden layer, e.g., a tree structure, we treat the structure of the hidden layer as a latent variable and implicitly infer it during learning and inference. The second approach explores person-person interaction in the feature level. We introduce a new feature representation called the action context (AC) descriptor. The AC descriptor encodes information about not only the action of an individual person in the video, but also the behavior of other people nearby. The third approach combines the above two. Our experimental results demonstrate the benefit of using contextual information for disambiguating group activities. PMID:22144516

Discriminatory power of common genetic variants in personalized breast cancer diagnosis

NASA Astrophysics Data System (ADS)

Wu, Yirong; Abbey, Craig K.; Liu, Jie; Ong, Irene; Peissig, Peggy; Onitilo, Adedayo A.; Fan, Jun; Yuan, Ming; Burnside, Elizabeth S.

2016-03-01

Technology advances in genome-wide association studies (GWAS) has engendered optimism that we have entered a new age of precision medicine, in which the risk of breast cancer can be predicted on the basis of a person's genetic variants. The goal of this study is to evaluate the discriminatory power of common genetic variants in breast cancer risk estimation. We conducted a retrospective case-control study drawing from an existing personalized medicine data repository. We collected variables that predict breast cancer risk: 153 high-frequency/low-penetrance genetic variants, reflecting the state-of-the-art GWAS on breast cancer, mammography descriptors and BI-RADS assessment categories in the Breast Imaging Reporting and Data System (BI-RADS) lexicon. We trained and tested naïve Bayes models by using these predictive variables. We generated ROC curves and used the area under the ROC curve (AUC) to quantify predictive performance. We found that genetic variants achieved comparable predictive performance to BI-RADS assessment categories in terms of AUC (0.650 vs. 0.659, p-value = 0.742), but significantly lower predictive performance than the combination of BI-RADS assessment categories and mammography descriptors (0.650 vs. 0.751, p-value < 0.001). A better understanding of relative predictive capability of genetic variants and mammography data may benefit clinicians and patients to make appropriate decisions about breast cancer screening, prevention, and treatment in the era of precision medicine.

Application of a symbolic motion structure representation algorithm to identify upper extremity kinematic changes during a repetitive task.

PubMed

Whittaker, Rachel L; Park, Woojin; Dickerson, Clark R

2018-04-27

Efficient and holistic identification of fatigue-induced movement strategies can be limited by large between-subject variability in descriptors of joint angle data. One promising alternative to traditional, or computationally intensive methods is the symbolic motion structure representation algorithm (SMSR), which identifies the basic spatial-temporal structure of joint angle data using string descriptors of temporal joint angle trajectories. This study attempted to use the SMSR to identify changes in upper extremity time series joint angle data during a repetitive goal directed task causing muscle fatigue. Twenty-eight participants (15 M, 13 F) performed a seated repetitive task until fatigued. Upper extremity joint angles were extracted from motion capture for representative task cycles. SMSRs, averages and ranges of several joint angles were compared at the start and end of the repetitive task to identify kinematic changes with fatigue. At the group level, significant increases in the range of all joint angle data existed with large between-subject variability that posed a challenge to the interpretation of these fatigue-related changes. However, changes in the SMSRs across participants effectively summarized the adoption of adaptive movement strategies. This establishes SMSR as a viable, logical, and sensitive method of fatigue identification via kinematic changes, with novel application and pragmatism for visual assessment of fatigue development. Copyright © 2018 Elsevier Ltd. All rights reserved.

Predicting the Oxygen-Binding Properties of Platinum Nanoparticle Ensembles by Combining High-Precision Electron Microscopy and Density Functional Theory.

PubMed

Aarons, Jolyon; Jones, Lewys; Varambhia, Aakash; MacArthur, Katherine E; Ozkaya, Dogan; Sarwar, Misbah; Skylaris, Chris-Kriton; Nellist, Peter D

2017-07-12

Many studies of heterogeneous catalysis, both experimental and computational, make use of idealized structures such as extended surfaces or regular polyhedral nanoparticles. This simplification neglects the morphological diversity in real commercial oxygen reduction reaction (ORR) catalysts used in fuel-cell cathodes. Here we introduce an approach that combines 3D nanoparticle structures obtained from high-throughput high-precision electron microscopy with density functional theory. Discrepancies between experimental observations and cuboctahedral/truncated-octahedral particles are revealed and discussed using a range of widely used descriptors, such as electron-density, d-band centers, and generalized coordination numbers. We use this new approach to determine the optimum particle size for which both detrimental surface roughness and particle shape effects are minimized.

Classification and Verification of Handwritten Signatures with Time Causal Information Theory Quantifiers

PubMed Central

Ospina, Raydonal; Frery, Alejandro C.

2016-01-01

We present a new approach for handwritten signature classification and verification based on descriptors stemming from time causal information theory. The proposal uses the Shannon entropy, the statistical complexity, and the Fisher information evaluated over the Bandt and Pompe symbolization of the horizontal and vertical coordinates of signatures. These six features are easy and fast to compute, and they are the input to an One-Class Support Vector Machine classifier. The results are better than state-of-the-art online techniques that employ higher-dimensional feature spaces which often require specialized software and hardware. We assess the consistency of our proposal with respect to the size of the training sample, and we also use it to classify the signatures into meaningful groups. PMID:27907014

Dyspnea descriptors translated from English to Portuguese: application in obese patients and in patients with cardiorespiratory diseases.

PubMed

Teixeira, Christiane Aires; Rodrigues Júnior, Antonio Luiz; Straccia, Luciana Cristina; Vianna, Elcio Dos Santos Oliveira; Silva, Geruza Alves da; Martinez, José Antônio Baddini

2011-01-01

To investigate the usefulness of descriptive terms applied to the sensation of dyspnea (dyspnea descriptors) that were developed in English and translated to Brazilian Portuguese in patients with four distinct clinical conditions that can be accompanied by dyspnea. We translated, from English to Brazilian Portuguese, a list of 15 dyspnea descriptors reported in a study conducted in the USA. Those 15 descriptors were applied in 50 asthma patients, 50 COPD patients, 30 patients with heart failure, and 50 patients with class II or III obesity. The three best descriptors, as selected by the patients, were studied by cluster analysis. Potential associations between the identified clusters and the four clinical conditions were also investigated. The use of this set of descriptors led to a solution with nine clusters, designated expiração (exhalation), fome de ar (air hunger), sufoco (suffocating), superficial (shallow), rápido (rapid), aperto (tight), falta de ar (shortness of breath), trabalho (work), and inspiração (inhalation). Overlapping of the descriptors was quite common among the patients, regardless of their clinical condition. Asthma, COPD, and heart failure were significantly associated with the inspiração cluster. Heart failure was also associated with the trabalho cluster, whereas obesity was not associated with any of the clusters. In our study sample, the application of dyspnea descriptors translated from English to Portuguese led to the identification of distinct clusters, some of which were similar to those identified in a study conducted in the USA. The translated descriptors were less useful than were those developed in Brazil regarding their ability to generate significant associations among the clinical conditions investigated here.

An object-based approach for areal rainfall estimation and validation of atmospheric models

NASA Astrophysics Data System (ADS)

Troemel, Silke; Simmer, Clemens

2010-05-01

An object-based approach for areal rainfall estimation is applied to pseudo-radar data simulated of a weatherforecast model as well as to real radar volume data. The method aims at an as fully as possible exploitation of three-dimensional radar signals produced by precipitation generating systems during their lifetime to enhance areal rainfall estimation. Therefore tracking of radar-detected precipitation-centroids is performed and rain events are investigated using so-called Integral Radar Volume Descriptors (IRVD) containing relevant information of the underlying precipitation process. Some investigated descriptors are statistical quantities from the radar reflectivities within the boundary of a tracked rain cell like the area mean reflectivity or the compactness of a cell; others evaluate the mean vertical structure during the tracking period at the near surface reflectivity-weighted center of the cell like the mean effective efficiency or the mean echo top height. The stage of evolution of a system is given by the trend in the brightband fraction or related quantities. Furthermore, two descriptors not directly derived from radar data are considered: the mean wind shear and an orographic rainfall amplifier. While in case of pseudo-radar data a model based on a small set of IRVDs alone provides rainfall estimates of high accuracy, the application of such a model to the real world remains within the accuracies achievable with a constant Z-R-relationship. However, a combined model based on single IRVDs and the Marshall-Palmer Z-R-estimator already provides considerable enhancements even though the resolution of the data base used has room for improvement. The mean echo top height, the mean effective efficiency, the empirical standard deviation and the Marshall-Palmer estimator are detected for the final rainfall estimator. High correlations between storm height and rain rates, a shift of the probability distribution to higher values with increasing effective efficiency, and the possibility to classify continental and maritime systems using the effective efficiency confirm the informative value of the qualified descriptors. The IRVDs especially correct for the underestimation in case of intense rain events, and the information content of descriptors is most likely higher than demonstrated so far. We used quite sparse information about meteorological variables needed for the calculation of some IRVDs from single radiosoundings, and several descriptors suffered from the range-dependent vertical resolution of the reflectivity profile. Inclusion of neighbouring radars and assimilation runs of weather forecasting models will further enhance the accuracy of rainfall estimates. Finally, the clear difference between the IRVD selection from the pseudo-radar data and from the real world data hint to a new object-based avenue for the validation of higher resolution atmospheric models and for evaluating their potential to digest radar observations in data assimilation schemes.

Direct memory access transfer completion notification

DOEpatents

Chen, Dong; Giampapa, Mark E.; Heidelberger, Philip; Kumar, Sameer; Parker, Jeffrey J.; Steinmacher-Burow, Burkhard D.; Vranas, Pavlos

2010-07-27

Methods, compute nodes, and computer program products are provided for direct memory access (`DMA`) transfer completion notification. Embodiments include determining, by an origin DMA engine on an origin compute node, whether a data descriptor for an application message to be sent to a target compute node is currently in an injection first-in-first-out (`FIFO`) buffer in dependence upon a sequence number previously associated with the data descriptor, the total number of descriptors currently in the injection FIFO buffer, and the current sequence number for the newest data descriptor stored in the injection FIFO buffer; and notifying a processor core on the origin DMA engine that the message has been sent if the data descriptor for the message is not currently in the injection FIFO buffer.

Character context: a shape descriptor for Arabic handwriting recognition

NASA Astrophysics Data System (ADS)

Mudhsh, Mohammed; Almodfer, Rolla; Duan, Pengfei; Xiong, Shengwu

2017-11-01

In the handwriting recognition field, designing good descriptors are substantial to obtain rich information of the data. However, the handwriting recognition research of a good descriptor is still an open issue due to unlimited variation in human handwriting. We introduce a "character context descriptor" that efficiently dealt with the structural characteristics of Arabic handwritten characters. First, the character image is smoothed and normalized, then the character context descriptor of 32 feature bins is built based on the proposed "distance function." Finally, a multilayer perceptron with regularization is used as a classifier. On experimentation with a handwritten Arabic characters database, the proposed method achieved a state-of-the-art performance with recognition rate equal to 98.93% and 99.06% for the 66 and 24 classes, respectively.

Adjacent bin stability evaluating for feature description

NASA Astrophysics Data System (ADS)

Nie, Dongdong; Ma, Qinyong

2018-04-01

Recent study improves descriptor performance by accumulating stability votes for all scale pairs to compose the local descriptor. We argue that the stability of a bin depends on the differences across adjacent pairs more than the differences across all scale pairs, and a new local descriptor is composed based on the hypothesis. A series of SIFT descriptors are extracted from multiple scales firstly. Then the difference value of the bin across adjacent scales is calculated, and the stability value of a bin is calculated based on it and accumulated to compose the final descriptor. The performance of the proposed method is evaluated with two popular matching datasets, and compared with other state-of-the-art works. Experimental results show that the proposed method performs satisfactorily.

RPBS: Rotational Projected Binary Structure for point cloud representation

NASA Astrophysics Data System (ADS)

Fang, Bin; Zhou, Zhiwei; Ma, Tao; Hu, Fangyu; Quan, Siwen; Ma, Jie

2018-03-01

In this paper, we proposed a novel three-dimension local surface descriptor named RPBS for point cloud representation. First, points cropped form the query point within a predefined radius is regard as a local surface patch. Then pose normalization is done to the local surface to equip our descriptor with the invariance to rotation transformation. To obtain more information about the cropped surface, multi-view representation is formed by successively rotating it along the coordinate axis. Further, orthogonal projections to the three coordinate plane are adopted to construct two-dimension distribution matrixes, and binarization is applied to each matrix by following the rule that whether the grid is occupied, if yes, set the grid one, otherwise zero. We calculate the binary maps from all the viewpoints and concatenate them together as the final descriptor. Comparative experiments for evaluating our proposed descriptor is conducted on the standard dataset named Bologna with several state-of-the-art 3D descriptors, and results show that our descriptor achieves the best performance on feature matching experiments.

Branch length similarity entropy-based descriptors for shape representation

NASA Astrophysics Data System (ADS)

Kwon, Ohsung; Lee, Sang-Hee

2017-11-01

In previous studies, we showed that the branch length similarity (BLS) entropy profile could be successfully used for the shape recognition such as battle tanks, facial expressions, and butterflies. In the present study, we proposed new descriptors, roundness, symmetry, and surface roughness, for the recognition, which are more accurate and fast in the computation than the previous descriptors. The roundness represents how closely a shape resembles to a circle, the symmetry characterizes how much one shape is similar with another when the shape is moved in flip, and the surface roughness quantifies the degree of vertical deviations of a shape boundary. To evaluate the performance of the descriptors, we used the database of leaf images with 12 species. Each species consisted of 10 - 20 leaf images and the total number of images were 160. The evaluation showed that the new descriptors successfully discriminated the leaf species. We believe that the descriptors can be a useful tool in the field of pattern recognition.

Gun bore flaw image matching based on improved SIFT descriptor

NASA Astrophysics Data System (ADS)

Zeng, Luan; Xiong, Wei; Zhai, You

2013-01-01

In order to increase the operation speed and matching ability of SIFT algorithm, the SIFT descriptor and matching strategy are improved. First, a method of constructing feature descriptor based on sector area is proposed. By computing the gradients histogram of location bins which are parted into 6 sector areas, a descriptor with 48 dimensions is constituted. It can reduce the dimension of feature vector and decrease the complexity of structuring descriptor. Second, it introduce a strategy that partitions the circular region into 6 identical sector areas starting from the dominate orientation. Consequently, the computational complexity is reduced due to cancellation of rotation operation for the area. The experimental results indicate that comparing with the OpenCV SIFT arithmetic, the average matching speed of the new method increase by about 55.86%. The matching veracity can be increased even under some variation of view point, illumination, rotation, scale and out of focus. The new method got satisfied results in gun bore flaw image matching. Keywords: Metrology, Flaw image matching, Gun bore, Feature descriptor

Rapid prediction of chemical metabolism by human UDP-glucuronosyltransferase isoforms using quantum chemical descriptors derived with the electronegativity equalization method.

PubMed

Sorich, Michael J; McKinnon, Ross A; Miners, John O; Winkler, David A; Smith, Paul A

2004-10-07

This study aimed to evaluate in silico models based on quantum chemical (QC) descriptors derived using the electronegativity equalization method (EEM) and to assess the use of QC properties to predict chemical metabolism by human UDP-glucuronosyltransferase (UGT) isoforms. Various EEM-derived QC molecular descriptors were calculated for known UGT substrates and nonsubstrates. Classification models were developed using support vector machine and partial least squares discriminant analysis. In general, the most predictive models were generated with the support vector machine. Combining QC and 2D descriptors (from previous work) using a consensus approach resulted in a statistically significant improvement in predictivity (to 84%) over both the QC and 2D models and the other methods of combining the descriptors. EEM-derived QC descriptors were shown to be both highly predictive and computationally efficient. It is likely that EEM-derived QC properties will be generally useful for predicting ADMET and physicochemical properties during drug discovery.

Temperature and relative humidity influence the ripening descriptors of Camembert-type cheeses throughout ripening.

PubMed

Leclercq-Perlat, M-N; Sicard, M; Perrot, N; Trelea, I C; Picque, D; Corrieu, G

2015-02-01

Ripening descriptors are the main factors that determine consumers' preferences of soft cheeses. Six descriptors were defined to represent the sensory changes in Camembert cheeses: Penicillium camemberti appearance, cheese odor and rind color, creamy underrind thickness and consistency, and core hardness. To evaluate the effects of the main process parameters on these descriptors, Camembert cheeses were ripened under different temperatures (8, 12, and 16°C) and relative humidity (RH; 88, 92, and 98%). The sensory descriptors were highly dependent on the temperature and RH used throughout ripening in a ripening chamber. All sensory descriptor changes could be explained by microorganism growth, pH, carbon substrate metabolism, and cheese moisture, as well as by microbial enzymatic activities. On d 40, at 8°C and 88% RH, all sensory descriptors scored the worst: the cheese was too dry, its odor and its color were similar to those of the unripe cheese, the underrind was driest, and the core was hardest. At 16°C and 98% RH, the odor was strongly ammonia and the color was dark brown, and the creamy underrind represented the entire thickness of the cheese but was completely runny, descriptors indicative of an over ripened cheese. Statistical analysis showed that the best ripening conditions to achieve an optimum balance between cheese sensory qualities and marketability were 13±1°C and 94±1% RH. Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

The effects of variant descriptors on the potential effectiveness of plain packaging.

PubMed

Borland, Ron; Savvas, Steven

2014-01-01

To examine the effects that variant descriptor labels on cigarette packs have on smokers' perceptions of those packs and the cigarettes contained within. As part of two larger web-based studies (each involved 160 young adult ever-smokers 18-29 years old), respondents were shown a computer image of a plain cigarette pack and sets of related variant descriptors. The sets included terms that varied in terms of descriptors of colours as names, flavour strength, degrees of filter venting, filter types, quality, type of cigarette and numbers. For each set, respondents rated the highest and lowest of two or three of the following four characteristics: quality, strongest or weakest in taste, delivers most or least tar/nicotine, and most or least level of harm. There were significant differences on all four ratings. Quality ratings were the least differentiated. Except for colour descriptors, where 'Gold' rated high in quality but medium in other ratings, ratings of quality, harm, strength and delivery were all positively associated when rated on the same descriptors. Descriptor labels on cigarette packs, can affect smokers' perceptions of the characteristics of the cigarettes contained within. Therefore, they are a potential means by which product differentiation can occur. In particular, having variants differing in perceived strength while not differing in deliveries of harmful ingredients is particularly problematic. Any packaging policy should take into account the possibility that variant descriptors can mislead smokers into making inappropriate product attributions.

ADME evaluation in drug discovery. 1. Applications of genetic algorithms to the prediction of blood-brain partitioning of a large set of drugs.

PubMed

Hou, Tingjun; Xu, Xiaojie

2002-12-01

In this study, the relationships between the brain-blood concentration ratio of 96 structurally diverse compounds with a large number of structurally derived descriptors were investigated. The linear models were based on molecular descriptors that can be calculated for any compound simply from a knowledge of its molecular structure. The linear correlation coefficients of the models were optimized by genetic algorithms (GAs), and the descriptors used in the linear models were automatically selected from 27 structurally derived descriptors. The GA optimizations resulted in a group of linear models with three or four molecular descriptors with good statistical significance. The change of descriptor use as the evolution proceeds demonstrates that the octane/water partition coefficient and the partial negative solvent-accessible surface area multiplied by the negative charge are crucial to brain-blood barrier permeability. Moreover, we found that the predictions using multiple QSPR models from GA optimization gave quite good results in spite of the diversity of structures, which was better than the predictions using the best single model. The predictions for the two external sets with 37 diverse compounds using multiple QSPR models indicate that the best linear models with four descriptors are sufficiently effective for predictive use. Considering the ease of computation of the descriptors, the linear models may be used as general utilities to screen the blood-brain barrier partitioning of drugs in a high-throughput fashion.

Vocabulary Development and Maintenance--Descriptors. ERIC Processing Manual, Section VIII (Part 1).

ERIC Educational Resources Information Center

Houston, Jim, Ed.

Comprehensive rules, guidelines, and examples are provided for use by ERIC indexers and lexicographers in developing and maintaining the "Thesaurus of ERIC Descriptors." Evaluation and decision criteria, research procedures, and inputting details for adding new Descriptors are documented. Instructions for modifying existing Thesaurus…

Three dimensional Lagrangian structures in the Antarctic Polar Vortex.

NASA Astrophysics Data System (ADS)

Mancho, Ana M.; Garcia-Garrido, Victor J.; Curbelo, Jezabel; Niang, Coumba; Mechoso, Carlos R.; Wiggins, Stephen

2017-04-01

Dynamical systems theory has supported the description of transport processes in fluid dynamics. For understanding trajectory patterns in chaotic advection the geometrical approach by Poincaré seeks for spatial structures that separate regions corresponding to qualitatively different types of trajectories. These structures have been referred to as Lagrangian Coherent Structures (LCS), which typically in geophysical flows are well described under the approach of incompressible 2D flows. Different tools have been used to visualize LCS. In this presentation we use Lagrangian Descriptors [1,2,3,4] (function M) for visualizing 3D Lagrangian structures in the atmosphere, in particular in the Antarctic Polar Vortex. The function M is computed in a fully 3D incompressible flow obtained from data provided by the European Centre for Medium-Range Weather Forecast and it is represented in 2D surfaces. We discuss the findings during the final warming that took place in the spring of 1979 [5]. This research is supported by MINECO grant MTM2014-56392-R. Support is acknowledged also from CSIC grant COOPB20265, U.S. NSF grant AGS-1245069 and ONR grant No. N00014- 01-1-0769. C. Niang acknowledges Fundacion Mujeres por Africa and ICMAT Severo Ochoa project SEV-2011-0087 for financial support. [1] C. Mendoza, A. M. Mancho. The hidden geometry of ocean flows. Physical Review Letters 105 (2010), 3, 038501-1-038501-4. [2] A. M. Mancho, S. Wiggins, J. Curbelo, C. Mendoza. Lagrangian Descriptors: A Method for Revealing Phase Space Structures of General Time Dependent Dynamical Systems. Communications in Nonlinear Science and Numerical Simulation. 18 (2013) 3530-3557. [3] C. Lopesino, F. Balibrea-Iniesta, S. Wiggins and A. M. Mancho. Lagrangian descriptors for two dimensional, area preserving autonomous and nonautonomous maps. Communications in Nonlinear Science and Numerical Simulations, 27 (2015) (1-3), 40-51. [4] C. Lopesino, F. Balibrea-Iniesta, V. J. García-Garrido, S. Wiggins, and A. M. Mancho, A. M. A theoretical framework for lagrangian descriptors. International Journal of Bifurcation and Chaos (2017) to appear. [5] The three-dimensional Lagrangian geometry of the Antarctic Polar Vortex circulation. Preprint.

Vibration mode shape recognition using image processing

NASA Astrophysics Data System (ADS)

Wang, Weizhuo; Mottershead, John E.; Mares, Cristinel

2009-10-01

Currently the most widely used method for comparing mode shapes from finite elements and experimental measurements is the modal assurance criterion (MAC), which can be interpreted as the cosine of the angle between the numerical and measured eigenvectors. However, the eigenvectors only contain the displacement of discrete coordinates, so that the MAC index carries no explicit information on shape features. New techniques, based upon the well-developed philosophies of image processing (IP) and pattern recognition (PR) are considered in this paper. The Zernike moment descriptor (ZMD), Fourier descriptor (FD), and wavelet descriptor (WD) are the most popular shape descriptors due to their outstanding properties in IP and PR. These include (1) for the ZMD-rotational invariance, expression and computing efficiency, ease of reconstruction and robustness to noise; (2) for the FD—separation of the global shape and shape-details by low and high frequency components, respectively, invariance under geometric transformation; (3) for the WD—multi-scale representation and local feature detection. Once a shape descriptor has been adopted, the comparison of mode shapes is transformed to a comparison of multidimensional shape feature vectors. Deterministic and statistical methods are presented. The deterministic problem of measuring the degree of similarity between two mode shapes (possibly one from a vibration test and the other from a finite element model) may be carried out using Pearson's correlation. Similar shape feature vectors may be arranged in clusters separated by Euclidian distances in the feature space. In the statistical analysis we are typically concerned with the classification of a test mode shape according to clusters of shape feature vectors obtained from a randomised finite element model. The dimension of the statistical problem may often be reduced by principal component analysis. Then, in addition to the Euclidian distance, the Mahalanobis distance, defining the separation of the test point from the cluster in terms of its standard deviation, becomes an important measure. Bayesian decision theory may be applied to formally minimise the risk of misclassification of the test shape feature vector. In this paper the ZMD is applied to the problem of mode shape recognition for a circular plate. Results show that the ZMD has considerable advantages over the traditional MAC index when identifying the cyclically symmetric mode shapes that occur in axisymmetric structures at identical frequencies. Mode shape recognition of rectangular plates is carried out by the FD. Also, the WD is applied to the problem of recognising the mode shapes in the thin and thick regions of a plate with different thicknesses. It shows the benefit of using the WD to identify mode-shapes having both local and global components. The comparison and classification of mode shapes using IP and PR provides a 'toolkit' to complement the conventional MAC approach. The selection of a particular shape descriptor and classification method will depend upon the problem in hand and the experience of the analyst.

DDC Descriptor Frequencies.

ERIC Educational Resources Information Center

Klingbiel, Paul H.; Jacobs, Charles R.

This report summarizes the frequency of use of the 7144 descriptors used for indexing technical reports in the Defense Documentation Center (DDC) collection. The descriptors are arranged alphabetically in the first section and by frequency in the second section. The frequency data cover about 427,000 AD documents spanning the interval from March…

MultiDK: A Multiple Descriptor Multiple Kernel Approach for Molecular Discovery and Its Application to Organic Flow Battery Electrolytes.

PubMed

Kim, Sungjin; Jinich, Adrián; Aspuru-Guzik, Alán

2017-04-24

We propose a multiple descriptor multiple kernel (MultiDK) method for efficient molecular discovery using machine learning. We show that the MultiDK method improves both the speed and accuracy of molecular property prediction. We apply the method to the discovery of electrolyte molecules for aqueous redox flow batteries. Using multiple-type-as opposed to single-type-descriptors, we obtain more relevant features for machine learning. Following the principle of "wisdom of the crowds", the combination of multiple-type descriptors significantly boosts prediction performance. Moreover, by employing multiple kernels-more than one kernel function for a set of the input descriptors-MultiDK exploits nonlinear relations between molecular structure and properties better than a linear regression approach. The multiple kernels consist of a Tanimoto similarity kernel and a linear kernel for a set of binary descriptors and a set of nonbinary descriptors, respectively. Using MultiDK, we achieve an average performance of r 2 = 0.92 with a test set of molecules for solubility prediction. We also extend MultiDK to predict pH-dependent solubility and apply it to a set of quinone molecules with different ionizable functional groups to assess their performance as flow battery electrolytes.

Deep Learning for Lowtextured Image Matching

NASA Astrophysics Data System (ADS)

Kniaz, V. V.; Fedorenko, V. V.; Fomin, N. A.

2018-05-01

Low-textured objects pose challenges for an automatic 3D model reconstruction. Such objects are common in archeological applications of photogrammetry. Most of the common feature point descriptors fail to match local patches in featureless regions of an object. Hence, automatic documentation of the archeological process using Structure from Motion (SfM) methods is challenging. Nevertheless, such documentation is possible with the aid of a human operator. Deep learning-based descriptors have outperformed most of common feature point descriptors recently. This paper is focused on the development of a new Wide Image Zone Adaptive Robust feature Descriptor (WIZARD) based on the deep learning. We use a convolutional auto-encoder to compress discriminative features of a local path into a descriptor code. We build a codebook to perform point matching on multiple images. The matching is performed using the nearest neighbor search and a modified voting algorithm. We present a new "Multi-view Amphora" (Amphora) dataset for evaluation of point matching algorithms. The dataset includes images of an Ancient Greek vase found at Taman Peninsula in Southern Russia. The dataset provides color images, a ground truth 3D model, and a ground truth optical flow. We evaluated the WIZARD descriptor on the "Amphora" dataset to show that it outperforms the SIFT and SURF descriptors on the complex patch pairs.

Application of the artificial neural network in quantitative structure-gradient elution retention relationship of phenylthiocarbamyl amino acids derivatives.

PubMed

Tham, S Y; Agatonovic-Kustrin, S

2002-05-15

Quantitative structure-retention relationship(QSRR) method was used to model reversed-phase high-performance liquid chromatography (RP-HPLC) separation of 18 selected amino acids. Retention data for phenylthiocarbamyl (PTC) amino acids derivatives were obtained using gradient elution on ODS column with mobile phase of varying acetonitrile, acetate buffer and containing 0.5 ml/l of triethylamine (TEA). Molecular structure of each amino acid was encoded with 36 calculated molecular descriptors. The correlation between the molecular descriptors and the retention time of the compounds in the calibration set was established using the genetic neural network method. A genetic algorithm (GA) was used to select important molecular descriptors and supervised artificial neural network (ANN) was used to correlate mobile phase composition and selected descriptors with the experimentally derived retention times. Retention time values were used as the network's output and calculated molecular descriptors and mobile phase composition as the inputs. The best model with five input descriptors was chosen, and the significance of the selected descriptors for amino acid separation was examined. Results confirmed the dominant role of the organic modifier in such chromatographic systems in addition to lipophilicity (log P) and molecular size and shape (topological indices) of investigated solutes.

Effective structural descriptors for natural and engineered radioactive waste confinement barriers

NASA Astrophysics Data System (ADS)

Lemmens, Laurent; Rogiers, Bart; De Craen, Mieke; Laloy, Eric; Jacques, Diederik; Huysmans, Marijke; Swennen, Rudy; Urai, Janos L.; Desbois, Guillaume

2017-04-01

The microstructure of a radioactive waste confinement barrier strongly influences its flow and transport properties. Numerical flow and transport simulations for these porous media at the pore scale therefore require input data that describe the microstructure as accurately as possible. To date, no imaging method can resolve all heterogeneities within important radioactive waste confinement barrier materials as hardened cement paste and natural clays at the micro scale (nm-cm). Therefore, it is necessary to merge information from different 2D and 3D imaging methods using porous media reconstruction techniques. To qualitatively compare the results of different reconstruction techniques, visual inspection might suffice. To quantitatively compare training-image based algorithms, Tan et al. (2014) proposed an algorithm using an analysis of distance. However, the ranking of the algorithm depends on the choice of the structural descriptor, in their case multiple-point or cluster-based histograms. We present here preliminary work in which we will review different structural descriptors and test their effectiveness, for capturing the main structural characteristics of radioactive waste confinement barrier materials, to determine the descriptors to use in the analysis of distance. The investigated descriptors are particle size distributions, surface area distributions, two point probability functions, multiple point histograms, linear functions and two point cluster functions. The descriptor testing consists of stochastically generating realizations from a reference image using the simulated annealing optimization procedure introduced by Karsanina et al. (2015). This procedure basically minimizes the differences between pre-specified descriptor values associated with the training image and the image being produced. The most efficient descriptor set can therefore be identified by comparing the image generation quality among the tested descriptor combinations. The assessment of the quality of the simulations will be made by combining all considered descriptors. Once the set of the most efficient descriptors is determined, they can be used in the analysis of distance, to rank different reconstruction algorithms in a more objective way in future work. Karsanina MV, Gerke KM, Skvortsova EB, Mallants D (2015) Universal Spatial Correlation Functions for Describing and Reconstructing Soil Microstructure. PLoS ONE 10(5): e0126515. doi:10.1371/journal.pone.0126515 Tan, Xiaojin, Pejman Tahmasebi, and Jef Caers. "Comparing training-image based algorithms using an analysis of distance." Mathematical Geosciences 46.2 (2014): 149-169.

Holo-analysis.

PubMed

Rosen, G D

2006-06-01

Meta-analysis is a vague descriptor used to encompass very diverse methods of data collection analysis, ranging from simple averages to more complex statistical methods. Holo-analysis is a fully comprehensive statistical analysis of all available data and all available variables in a specified topic, with results expressed in a holistic factual empirical model. The objectives and applications of holo-analysis include software production for prediction of responses with confidence limits, translation of research conditions to praxis (field) circumstances, exposure of key missing variables, discovery of theoretically unpredictable variables and interactions, and planning future research. Holo-analyses are cited as examples of the effects on broiler feed intake and live weight gain of exogenous phytases, which account for 70% of variation in responses in terms of 20 highly significant chronological, dietary, environmental, genetic, managemental, and nutrient variables. Even better future accountancy of variation will be facilitated if and when authors of papers routinely provide key data for currently neglected variables, such as temperatures, complete feed formulations, and mortalities.

Estimation of Monthly Near Surface Air Temperature Using Geographically Weighted Regression in China

NASA Astrophysics Data System (ADS)

Wang, M. M.; He, G. J.; Zhang, Z. M.; Zhang, Z. J.; Liu, X. G.

2018-04-01

Near surface air temperature (NSAT) is a primary descriptor of terrestrial environment conditions. The availability of NSAT with high spatial resolution is deemed necessary for several applications such as hydrology, meteorology and ecology. In this study, a regression-based NSAT mapping method is proposed. This method is combined remote sensing variables with geographical variables, and uses geographically weighted regression to estimate NSAT. The altitude was selected as geographical variable; and the remote sensing variables include land surface temperature (LST) and Normalized Difference vegetation index (NDVI). The performance of the proposed method was assessed by predict monthly minimum, mean, and maximum NSAT from point station measurements in China, a domain with a large area, complex topography, and highly variable station density, and the NSAT maps were validated against the meteorology observations. Validation results with meteorological data show the proposed method achieved an accuracy of 1.58 °C. It is concluded that the proposed method for mapping NSAT is very operational and has good precision.

How good is good? Students and assessors' perceptions of qualitative markers of performance.

PubMed

Ma, Heung Kan; Min, Cynthia; Neville, Alan; Eva, Kevin

2013-01-01

Qualitative markers of performance are routinely used for medical student assessment, though the extent to which such markers can be readily translated to actionable pieces of information remains uncertain. To explore (a) the perceived value to be indicated by descriptor phrases commonly used for describing student performance, (b) the perceived weight of the different performance domains (e.g. communication skills, work ethic, knowledge base, etc), and (c) whether or not the perceived value of the descriptors changes as a function of the performance domains. Five domains of performance were identified from the thematic coding of past medical student transcripts (N = 156). From the transcripts, 91 distinct descriptors indicating the language commonly used by assessors were also identified. From the list of 91 descriptors, Thurstone's method of equal-appearing intervals was used to extract 10 descriptors that were representative of the continuum of student performance. A modified paired comparisons method was then used to enable the relative ranking of each of 10 descriptors combined with each of 5 different domains of performance. A web-based survey was used to collect responses from participants (N = 209), which consisted of medical students and faculty members who were previously involved in student assessment. Results demonstrated that respondents did not simply sum positive and negative descriptors in a uniform manner. Rather, comments on some domains (e.g., "ability to apply patient centred medicine") were seen as particularly positive when associated with positive descriptors but not particularly negative when associated with negative descriptors. For others (e.g., "receptivity and responsiveness to feedback") the reverse was true. Comments on "knowledge-base" elicited a relatively muted perception at both ends of the scale. Finally, the results also revealed moderate misalignment in the perceptions of assessors and students. The findings from this study suggest that the use of any given descriptor conveys slightly different meaning dependent on the context in which it is used. This helps to address some key issues surrounding the application of qualitative markers to performance assessment in medical education.

Diagnostic performance and reproducibility of T2w based and diffusion weighted imaging (DWI) based PI-RADSv2 lexicon descriptors for prostate MRI.

PubMed

Benndorf, Matthias; Hahn, Felix; Krönig, Malte; Jilg, Cordula Annette; Krauss, Tobias; Langer, Mathias; Dovi-Akué, Philippe

2017-08-01

To examine the diagnostic performance of PI-RADSv2 T2w and diffusion weighted imaging (DWI) based lexicon descriptors, inter-observer agreement for descriptor assignment and diagnostic accuracy of the PI-RADSv2 assessment categories for multiparametric prostate MRI. 176 lesions in 79 consecutive patients are analyzed, lesions are histopathologically verified by MRI-ultrasound fusion biopsy. All lesions are rated according to the PI-RADSv2 lexicon, descriptors for T2w and DWI sequences and resulting assessment categories are assigned by two independent blinded radiologists. We perform receiver-operating-characteristic analysis using the assessment categories. To analyze inter-observer agreement, we calculate weighted kappa values for assessment category assignment and unweighted kappa values for descriptor assignment. PI-RADSv2 assessment categories yield an area under the curve of 0.76/0.74 (radiologist 1/radiologist 2), P >0.05. Weighted kappa for agreement is 0.601 in the peripheral zone and 0.580 in the transition zone. We detect a difference in the cancer rate for PI-RADSv2 category 3 between peripheral zone (32%) and transition zone (12%), P <0.05. We obtain moderate agreement at most for descriptor assignment with kappa values ranging from 0.082 (T2w shape in the transition zone) to 0.407 (T2w signal intensity in the peripheral zone) and 0.493 (ADC pattern in the peripheral zone). Our analysis corroborates typical descriptors for benign/malignant lesions, but also reveals insights into potential pitfalls - T2w wedge shaped lesions in the peripheral zone have a considerable cancer rate, despite being labelled category 2 in the lexicon. Agreement for descriptor assignment in the PI-RADSv2 lexicon is at most moderate in our study. Typical descriptors for benign and malignant lesions are validated, whereas the discriminatory power of some descriptors is challenged. The difference in the cancer rate for PI-RADSv2 category 3 between peripheral zone and transition zone should be considered when management recommendations are linked to assessment categories in the future. Copyright © 2017 Elsevier B.V. All rights reserved.

A Modern Update and Usage of Historical Variable Star Catalogs

NASA Astrophysics Data System (ADS)

Pagnotta, Ashley; Graur, Or; Murray, Zachary; Kruk, Julia; Christie-Dervaux, Lucien; Chen, Dong Yi

2015-01-01

One of the earliest modern variable star catalogs was constructed by Henrietta Swan Leavitt during her tenure at the Harvard College Observatory (HCO) in the early 1900s. Originally published in 1908, Leavitt's catalog listed 1777 variables in the Magellanic Clouds (MCs). The construction and analysis of this catalog allowed her to subsequently discover the Cepheid period-luminosity relationship, now known as the Leavitt Law. The MC variable star catalogs were updated and expanded by Cecilia Payne-Gaposchkin in 1966 and 1971. Although newer studies of the MC variables have been performed since then, the new information has not always been correlated with the old due to a lack of modern descriptors of the stars listed in the Harvard MC catalogs. We will discuss the history of MC variable star catalogs, especially those compiled using the HCO plates, as well as our modernized version of the Leavitt and Payne-Gaposchkin catalogs. Our modern catalog can be used in conjunction with the archival plates (primarily via the Digital Access to a Sky Century @ Harvard scanning project) to study the secular behavior of the MC variable stars over the past century.

Polar Lipids Analysis of Cultured Phytoplankton Reveals Significant Inter-taxa Changes, Low Influence of Growth Stage, and Usefulness in Chemotaxonomy.

PubMed

Cañavate, José Pedro; Armada, Isabel; Hachero-Cruzado, Ismael

2017-05-01

The high lipid diversity of microalgae has been used to taxonomically differentiate phytoplankton taxa at the class level. However, important lipids such as phospholipids (PL) and betaine lipids (BL) with potential chemotaxonomy application in phytoplankton ecology have been scarcely studied. The chemotaxonomy value of PL and BL depends on their intraspecific extent of variation as microalgae respond to external changing factors. To determine such effects, lipid class changes occurring at different growth stages in 15 microalgae from ten different classes were analyzed. BL occurred in 14 species and were the less affected lipids by growth stage with diacylglyceryl-hydroxymethyl-N,N,N-trimethyl-b-alanine (DGTA) showing the highest stability. PL were more influenced by growth stage with phosphatidylcholine (PC), phosphatidylglycerol (PG), and phosphatidyletanolamine (PE) declining towards older culture stages in some species. Glycolipids were the more common lipids, and no evident age-related variability pattern could be associated to taxonomic diversity. Selecting BL and PL as descriptor variables optimally distinguished microalgae taxonomic variability at all growth stages. Principal coordinate analysis arranged species through a main tendency from diacylglyceryl-hydroxymethyl-N,N,N-trimethyl-b-alanine (DGCC) containing species (mainly dinoflagellates and haptophytes) to DGTA or PC containing species (mainly cryptophytes). Two diatom classes with similar fatty acid profiles could be distinguished from their respective content in DGTA (Bacillariophyceae) or DGCC (Mediophyceae). In green lineage classes (Trebouxiophyceae, Porphyridophyceae, and Chlorodendrophyceae), PC was a better descriptor than BL. BL and PL explained a higher proportion of microalgae taxonomic variation than did fatty acids and played a complementary role as lipid markers.

Local Multi-Grouped Binary Descriptor With Ring-Based Pooling Configuration and Optimization.

PubMed

Gao, Yongqiang; Huang, Weilin; Qiao, Yu

2015-12-01

Local binary descriptors are attracting increasingly attention due to their great advantages in computational speed, which are able to achieve real-time performance in numerous image/vision applications. Various methods have been proposed to learn data-dependent binary descriptors. However, most existing binary descriptors aim overly at computational simplicity at the expense of significant information loss which causes ambiguity in similarity measure using Hamming distance. In this paper, by considering multiple features might share complementary information, we present a novel local binary descriptor, referred as ring-based multi-grouped descriptor (RMGD), to successfully bridge the performance gap between current binary and floated-point descriptors. Our contributions are twofold. First, we introduce a new pooling configuration based on spatial ring-region sampling, allowing for involving binary tests on the full set of pairwise regions with different shapes, scales, and distances. This leads to a more meaningful description than the existing methods which normally apply a limited set of pooling configurations. Then, an extended Adaboost is proposed for an efficient bit selection by emphasizing high variance and low correlation, achieving a highly compact representation. Second, the RMGD is computed from multiple image properties where binary strings are extracted. We cast multi-grouped features integration as rankSVM or sparse support vector machine learning problem, so that different features can compensate strongly for each other, which is the key to discriminativeness and robustness. The performance of the RMGD was evaluated on a number of publicly available benchmarks, where the RMGD outperforms the state-of-the-art binary descriptors significantly.

RANZCR Body Systems Framework of diagnostic imaging examination descriptors.

PubMed

Pitman, Alexander G; Penlington, Lisa; Doromal, Darren; Slater, Gregory; Vukolova, Natalia

2014-08-01

A unified and logical system of descriptors for diagnostic imaging examinations and procedures is a desirable resource for radiology in Australia and New Zealand and is needed to support core activities of RANZCR. Existing descriptor systems available in Australia and New Zealand (including the Medicare DIST and the ACC Schedule) have significant limitations and are inappropriate for broader clinical application. An anatomically based grid was constructed, with anatomical structures arranged in rows and diagnostic imaging modalities arranged in columns (including nuclear medicine and positron emission tomography). The grid was segregated into five body systems. The cells at the intersection of an anatomical structure row and an imaging modality column were populated with short, formulaic descriptors of the applicable diagnostic imaging examinations. Clinically illogical or physically impossible combinations were 'greyed out'. Where the same examination applied to different anatomical structures, the descriptor was kept identical for the purposes of streamlining. The resulting Body Systems Framework of diagnostic imaging examination descriptors lists all the reasonably common diagnostic imaging examinations currently performed in Australia and New Zealand using a unified grid structure allowing navigation by both referrers and radiologists. The Framework has been placed on the RANZCR website and is available for access free of charge by registered users. The Body Systems Framework of diagnostic imaging examination descriptors is a system of descriptors based on relationships between anatomical structures and imaging modalities. The Framework is now available as a resource and reference point for the radiology profession and to support core College activities. © 2014 The Royal Australian and New Zealand College of Radiologists.

Improving virtual screening predictive accuracy of Human kallikrein 5 inhibitors using machine learning models.

PubMed

Fang, Xingang; Bagui, Sikha; Bagui, Subhash

2017-08-01

The readily available high throughput screening (HTS) data from the PubChem database provides an opportunity for mining of small molecules in a variety of biological systems using machine learning techniques. From the thousands of available molecular descriptors developed to encode useful chemical information representing the characteristics of molecules, descriptor selection is an essential step in building an optimal quantitative structural-activity relationship (QSAR) model. For the development of a systematic descriptor selection strategy, we need the understanding of the relationship between: (i) the descriptor selection; (ii) the choice of the machine learning model; and (iii) the characteristics of the target bio-molecule. In this work, we employed the Signature descriptor to generate a dataset on the Human kallikrein 5 (hK 5) inhibition confirmatory assay data and compared multiple classification models including logistic regression, support vector machine, random forest and k-nearest neighbor. Under optimal conditions, the logistic regression model provided extremely high overall accuracy (98%) and precision (90%), with good sensitivity (65%) in the cross validation test. In testing the primary HTS screening data with more than 200K molecular structures, the logistic regression model exhibited the capability of eliminating more than 99.9% of the inactive structures. As part of our exploration of the descriptor-model-target relationship, the excellent predictive performance of the combination of the Signature descriptor and the logistic regression model on the assay data of the Human kallikrein 5 (hK 5) target suggested a feasible descriptor/model selection strategy on similar targets. Copyright © 2017 Elsevier Ltd. All rights reserved.

Expanding Preschoolers' Use of Object Descriptions and Comparisons by Teaching "Category-Descriptor" Statements.

ERIC Educational Resources Information Center

Weisberg, Paul

2003-01-01

Six preschool children, mostly from poverty-level backgrounds, were taught to make descriptive statements about objects. The category-descriptor statements were organized and sequenced into four clusters. As sets of new statements were successively taught and evaluated, the number and diversity of probed category and descriptor terms steadily and…

Texture classification using non-Euclidean Minkowski dilation

NASA Astrophysics Data System (ADS)

Florindo, Joao B.; Bruno, Odemir M.

2018-03-01

This study presents a new method to extract meaningful descriptors of gray-scale texture images using Minkowski morphological dilation based on the Lp metric. The proposed approach is motivated by the success previously achieved by Bouligand-Minkowski fractal descriptors on texture classification. In essence, such descriptors are directly derived from the morphological dilation of a three-dimensional representation of the gray-level pixels using the classical Euclidean metric. In this way, we generalize the dilation for different values of p in the Lp metric (Euclidean is a particular case when p = 2) and obtain the descriptors from the cumulated distribution of the distance transform computed over the texture image. The proposed method is compared to other state-of-the-art approaches (such as local binary patterns and textons for example) in the classification of two benchmark data sets (UIUC and Outex). The proposed descriptors outperformed all the other approaches in terms of rate of images correctly classified. The interesting results suggest the potential of these descriptors in this type of task, with a wide range of possible applications to real-world problems.

Flavoured cigarettes, sensation seeking and adolescents' perceptions of cigarette brands.

PubMed

Manning, K C; Kelly, K J; Comello, M L

2009-12-01

This study examined the interactive effects of cigarette package flavour descriptors and sensation seeking on adolescents' brand perceptions. High school students (n = 253) were randomly assigned to one of two experimental conditions and sequentially exposed to cigarette package illustrations for three different brands. In the flavour descriptor condition, the packages included a description of the cigarettes as "cherry", while in the traditional descriptor condition the cigarette brands were described with common phrases found on tobacco packages such as "domestic blend." Following exposure to each package participants' hedonic beliefs, brand attitudes and trial intentions were assessed. Sensation seeking was also measured, and participants were categorised as lower or higher sensation seekers. Across hedonic belief, brand attitude and trial intention measures, there were interactions between package descriptor condition and sensation seeking. These interactions revealed that among high (but not low) sensation seekers, exposure to cigarette packages including sweet flavour descriptors led to more favourable brand impressions than did exposure to packages with traditional descriptors. Among high sensation seeking youths, the appeal of cigarette brands is enhanced through the use of flavours and associated descriptions on product packaging.

An Effective 3D Shape Descriptor for Object Recognition with RGB-D Sensors

PubMed Central

Liu, Zhong; Zhao, Changchen; Wu, Xingming; Chen, Weihai

2017-01-01

RGB-D sensors have been widely used in various areas of computer vision and graphics. A good descriptor will effectively improve the performance of operation. This article further analyzes the recognition performance of shape features extracted from multi-modality source data using RGB-D sensors. A hybrid shape descriptor is proposed as a representation of objects for recognition. We first extracted five 2D shape features from contour-based images and five 3D shape features over point cloud data to capture the global and local shape characteristics of an object. The recognition performance was tested for category recognition and instance recognition. Experimental results show that the proposed shape descriptor outperforms several common global-to-global shape descriptors and is comparable to some partial-to-global shape descriptors that achieved the best accuracies in category and instance recognition. Contribution of partial features and computational complexity were also analyzed. The results indicate that the proposed shape features are strong cues for object recognition and can be combined with other features to boost accuracy. PMID:28245553

Bayesian screening for active compounds in high-dimensional chemical spaces combining property descriptors and molecular fingerprints.

PubMed

Vogt, Martin; Bajorath, Jürgen

2008-01-01

Bayesian classifiers are increasingly being used to distinguish active from inactive compounds and search large databases for novel active molecules. We introduce an approach to directly combine the contributions of property descriptors and molecular fingerprints in the search for active compounds that is based on a Bayesian framework. Conventionally, property descriptors and fingerprints are used as alternative features for virtual screening methods. Following the approach introduced here, probability distributions of descriptor values and fingerprint bit settings are calculated for active and database molecules and the divergence between the resulting combined distributions is determined as a measure of biological activity. In test calculations on a large number of compound activity classes, this methodology was found to consistently perform better than similarity searching using fingerprints and multiple reference compounds or Bayesian screening calculations using probability distributions calculated only from property descriptors. These findings demonstrate that there is considerable synergy between different types of property descriptors and fingerprints in recognizing diverse structure-activity relationships, at least in the context of Bayesian modeling.

Prediction of olive oil sensory descriptors using instrumental data fusion and partial least squares (PLS) regression.

PubMed

Borràs, Eva; Ferré, Joan; Boqué, Ricard; Mestres, Montserrat; Aceña, Laura; Calvo, Angels; Busto, Olga

2016-08-01

Headspace-Mass Spectrometry (HS-MS), Fourier Transform Mid-Infrared spectroscopy (FT-MIR) and UV-Visible spectrophotometry (UV-vis) instrumental responses have been combined to predict virgin olive oil sensory descriptors. 343 olive oil samples analyzed during four consecutive harvests (2010-2014) were used to build multivariate calibration models using partial least squares (PLS) regression. The reference values of the sensory attributes were provided by expert assessors from an official taste panel. The instrumental data were modeled individually and also using data fusion approaches. The use of fused data with both low- and mid-level of abstraction improved PLS predictions for all the olive oil descriptors. The best PLS models were obtained for two positive attributes (fruity and bitter) and two defective descriptors (fusty and musty), all of them using data fusion of MS and MIR spectral fingerprints. Although good predictions were not obtained for some sensory descriptors, the results are encouraging, specially considering that the legal categorization of virgin olive oils only requires the determination of fruity and defective descriptors. Copyright © 2016 Elsevier B.V. All rights reserved.

Unifying Exchange Sensitivity in Transition-Metal Spin-State Ordering and Catalysis through Bond Valence Metrics.

PubMed

Gani, Terry Z H; Kulik, Heather J

2017-11-14

Accurate predictions of spin-state ordering, reaction energetics, and barrier heights are critical for the computational discovery of open-shell transition-metal (TM) catalysts. Semilocal approximations in density functional theory, such as the generalized gradient approximation (GGA), suffer from delocalization error that causes them to overstabilize strongly bonded states. Descriptions of energetics and bonding are often improved by introducing a fraction of exact exchange (e.g., erroneous low-spin GGA ground states are instead correctly predicted as high-spin with a hybrid functional). The degree of spin-splitting sensitivity to exchange can be understood based on the chemical composition of the complex, but the effect of exchange on reaction energetics within a single spin state is less well-established. Across a number of model iron complexes, we observe strong exchange sensitivities of reaction barriers and energies that are of the same magnitude as those for spin splitting energies. We rationalize trends in both reaction and spin energetics by introducing a measure of delocalization, the bond valence of the metal-ligand bonds in each complex. The bond valence thus represents a simple-to-compute property that unifies understanding of exchange sensitivity for catalytic properties and spin-state ordering in TM complexes. Close agreement of the resulting per-metal-organic-bond sensitivity estimates, together with failure of alternative descriptors demonstrates the utility of the bond valence as a robust descriptor of how differences in metal-ligand delocalization produce differing relative energetics with exchange tuning. Our unified description explains the overall effect of exact exchange tuning on the paradigmatic two-state FeO + /CH 4 reaction that combines challenges of spin-state and reactivity predictions. This new descriptor-sensitivity relationship provides a path to quantifying how predictions in transition-metal complex screening are sensitive to the method used.

Characterizing region of interest in image using MPEG-7 visual descriptors

NASA Astrophysics Data System (ADS)

Ryu, Min-Sung; Park, Soo-Jun; Won, Chee Sun

2005-08-01

In this paper, we propose a region-based image retrieval system using EHD (Edge Histogram Descriptor) and CLD (Color Layout Descriptor) of MPEG-7 descriptors. The combined descriptor can efficiently describe edge and color features in terms of sub-image regions. That is, the basic unit for the selection of the region-of-interest (ROI) in the image is the sub-image block of the EHD, which corresponds to 16 (i.e., 4x4) non-overlapping image blocks in the image space. This implies that, to have a one-to-one region correspondence between EHD and CLD, we need to take an 8x8 inverse DCT (IDCT) for the CLD. Experimental results show that the proposed retrieval scheme can be used for image retrieval with the ROI based image retrieval for MPEG-7 indexed images.

Learning to assign binary weights to binary descriptor

NASA Astrophysics Data System (ADS)

Huang, Zhoudi; Wei, Zhenzhong; Zhang, Guangjun

2016-10-01

Constructing robust binary local feature descriptors are receiving increasing interest due to their binary nature, which can enable fast processing while requiring significantly less memory than their floating-point competitors. To bridge the performance gap between the binary and floating-point descriptors without increasing the computational cost of computing and matching, optimal binary weights are learning to assign to binary descriptor for considering each bit might contribute differently to the distinctiveness and robustness. Technically, a large-scale regularized optimization method is applied to learn float weights for each bit of the binary descriptor. Furthermore, binary approximation for the float weights is performed by utilizing an efficient alternatively greedy strategy, which can significantly improve the discriminative power while preserve fast matching advantage. Extensive experimental results on two challenging datasets (Brown dataset and Oxford dataset) demonstrate the effectiveness and efficiency of the proposed method.

Effect of Loss of Heart Rate Variability on T-Wave Heterogeneity and QT Variability in Heart Failure Patients: Implications in Ventricular Arrhythmogenesis.

PubMed

Nayyar, Sachin; Hasan, Muhammad A; Roberts-Thomson, Kurt C; Sullivan, Thomas; Baumert, Mathias

2017-06-01

Heart rate variability (HRV) modulates dynamics of ventricular repolarization. A diminishing value of HRV is associated with increased vulnerability to life-threatening ventricular arrhythmias, however the causal relationship is not well-defined. We evaluated if fixed-rate atrial pacing that abolishes the effect of physiological HRV, will alter ventricular repolarization wavefronts and is relevant to ventricular arrhythmogenesis. The study was performed in 16 subjects: 8 heart failure patients with spontaneous ventricular tachycardia [HFVT], and 8 subjects with structurally normal hearts (H Norm ). The T-wave heterogeneity descriptors [total cosine angle between QRS and T-wave loop vectors (TCRT, negative value corresponds to large difference in the 2 loops), T-wave morphology dispersion, T-wave loop dispersion] and QT intervals were analyzed in a beat-to-beat manner on 3-min records of 12-lead surface ECG at baseline and during atrial pacing at 80 and 100 bpm. The global T-wave heterogeneity was expressed as mean values of each of the T-wave morphology descriptors and variability in QT intervals (QTV) as standard deviation of QT intervals. Baseline T-wave morphology dispersion and QTV were higher in HFVT compared to H Norm subjects (p ≤ 0.02). While group differences in T-wave morphology dispersion and T-wave loop dispersion remained unaltered with atrial pacing, TCRT tended to fall more in HFVT patients compared to H Norm subjects (interaction p value = 0.086). Atrial pacing failed to reduce QTV in both groups, however group differences were augmented (p < 0.0001). Atrial pacing and consequent loss of HRV appears to introduce unfavorable changes in ventricular repolarization in HFVT subjects. It widens the spatial relationship between wavefronts of ventricular depolarization and repolarization. This may partly explain the concerning relation between poorer HRV and the risk of ventricular arrhythmias.

Quantitative structure-retention relationship models for the prediction of the reversed-phase HPLC gradient retention based on the heuristic method and support vector machine.

PubMed

Du, Hongying; Wang, Jie; Yao, Xiaojun; Hu, Zhide

2009-01-01

The heuristic method (HM) and support vector machine (SVM) were used to construct quantitative structure-retention relationship models by a series of compounds to predict the gradient retention times of reversed-phase high-performance liquid chromatography (HPLC) in three different columns. The aims of this investigation were to predict the retention times of multifarious compounds, to find the main properties of the three columns, and to indicate the theory of separation procedures. In our method, we correlated the retention times of many diverse structural analytes in three columns (Symmetry C18, Chromolith, and SG-MIX) with their representative molecular descriptors, calculated from the molecular structures alone. HM was used to select the most important molecular descriptors and build linear regression models. Furthermore, non-linear regression models were built using the SVM method; the performance of the SVM models were better than that of the HM models, and the prediction results were in good agreement with the experimental values. This paper could give some insights into the factors that were likely to govern the gradient retention process of the three investigated HPLC columns, which could theoretically supervise the practical experiment.

Revisiting caffeate's capabilities as a complexation agent to silver cation in mining processes by means of the dual descriptor--a conceptual DFT approach.

PubMed

Martínez-Araya, Jorge Ignacio

2012-09-01

Caffeic acid (C(9)H(8)O(4)) and its conjugate base C(9)H(7)O(4) (-) (anionic form-known as caffeate) were analyzed computationally through the use of quantum chemistry to assess their intrinsic global and local reactivity using the tools of conceptual density functional theory. The anionic form was found to be better at coordinating the silver cation than caffeic acid thus suggesting the use of caffeate as a complexation agent. The complexation capability of caffeate was compared with that of some of the most common ligand agents used to coordinate silver cations. Local reactivity descriptors allowed identification of the preferred sites on caffeate for silver cation coordination thus generating a plausible silver complex. All silver complexes were analyzed thermodynamically considering interaction energies in both gas and aqueous phases; the complexation free energy in aqueous phase was also determined. These results suggest that more attention be paid to the caffeate anion and its derivatives because this work has shed new light on the behavior of this anion in the recovery of silver cations that could be exploited in silver mining processes in a environmentally friendly way.

Comparison of efficiency of distance measurement methodologies in mango (Mangifera indica) progenies based on physicochemical descriptors.

PubMed

Alves, E O S; Cerqueira-Silva, C B M; Souza, A M; Santos, C A F; Lima Neto, F P; Corrêa, R X

2012-03-14

We investigated seven distance measures in a set of observations of physicochemical variables of mango (Mangifera indica) submitted to multivariate analyses (distance, projection and grouping). To estimate the distance measurements, five mango progeny (total of 25 genotypes) were analyzed, using six fruit physicochemical descriptors (fruit weight, equatorial diameter, longitudinal diameter, total soluble solids in °Brix, total titratable acidity, and pH). The distance measurements were compared by the Spearman correlation test, projection in two-dimensional space and grouping efficiency. The Spearman correlation coefficients between the seven distance measurements were, except for the Mahalanobis' generalized distance (0.41 ≤ rs ≤ 0.63), high and significant (rs ≥ 0.91; P < 0.001). Regardless of the origin of the distance matrix, the unweighted pair group method with arithmetic mean grouping method proved to be the most adequate. The various distance measurements and grouping methods gave different values for distortion (-116.5 ≤ D ≤ 74.5), cophenetic correlation (0.26 ≤ rc ≤ 0.76) and stress (-1.9 ≤ S ≤ 58.9). Choice of distance measurement and analysis methods influence the.

Chemometric studies on potential larvicidal compounds against Aedes aegypti.

PubMed

Scotti, Luciana; Scotti, Marcus Tullius; Silva, Viviane Barros; Santos, Sandra Regina Lima; Cavalcanti, Sócrates C H; Mendonça, Francisco J B

2014-03-01

The mosquito Aedes aegypti (Diptera, Culicidae) is the vector of yellow and dengue fever. In this study, chemometric tools, such as, Principal Component Analysis (PCA), Consensus PCA (CPCA), and Partial Least Squares Regression (PLS), were applied to a set of fifty five active compounds against Ae. aegypti larvae, which includes terpenes, cyclic alcohols, phenolic compounds, and their synthetic derivatives. The calculations were performed using the VolSurf+ program. CPCA analysis suggests that the higher weight blocks of descriptors were SIZE/SHAPE, DRY, and H2O. The PCA was generated with 48 descriptors selected from the previous blocks. The scores plot showed good separation between more and less potent compounds. The first two PCs accounted for over 60% of the data variance. The best model obtained in PLS, after validation leave-one-out, exhibited q(2) = 0.679 and r(2) = 0.714. External prediction model was R(2) = 0.623. The independent variables having a hydrophobic profile were strongly correlated to the biological data. The interaction maps generated with the GRID force field showed that the most active compounds exhibit more interaction with the DRY probe.

Two-dimensional habitat modeling in the Yellowstone/Upper Missouri River system

USGS Publications Warehouse

Waddle, T. J.; Bovee, K.D.; Bowen, Z.H.

1997-01-01

This study is being conducted to provide the aquatic biology component of a decision support system being developed by the U.S. Bureau of Reclamation. In an attempt to capture the habitat needs of Great Plains fish communities we are looking beyond previous habitat modeling methods. Traditional habitat modeling approaches have relied on one-dimensional hydraulic models and lumped compositional habitat metrics to describe aquatic habitat. A broader range of habitat descriptors is available when both composition and configuration of habitats is considered. Habitat metrics that consider both composition and configuration can be adapted from terrestrial biology. These metrics are most conveniently accessed with spatially explicit descriptors of the physical variables driving habitat composition. Two-dimensional hydrodynamic models have advanced to the point that they may provide the spatially explicit description of physical parameters needed to address this problem. This paper reports progress to date on applying two-dimensional hydraulic and habitat models on the Yellowstone and Missouri Rivers and uses examples from the Yellowstone River to illustrate the configurational metrics as a new tool for assessing riverine habitats.

Prediction of octanol-water partition coefficients of organic compounds by multiple linear regression, partial least squares, and artificial neural network.

PubMed

Golmohammadi, Hassan

2009-11-30

A quantitative structure-property relationship (QSPR) study was performed to develop models those relate the structure of 141 organic compounds to their octanol-water partition coefficients (log P(o/w)). A genetic algorithm was applied as a variable selection tool. Modeling of log P(o/w) of these compounds as a function of theoretically derived descriptors was established by multiple linear regression (MLR), partial least squares (PLS), and artificial neural network (ANN). The best selected descriptors that appear in the models are: atomic charge weighted partial positively charged surface area (PPSA-3), fractional atomic charge weighted partial positive surface area (FPSA-3), minimum atomic partial charge (Qmin), molecular volume (MV), total dipole moment of molecule (mu), maximum antibonding contribution of a molecule orbital in the molecule (MAC), and maximum free valency of a C atom in the molecule (MFV). The result obtained showed the ability of developed artificial neural network to prediction of partition coefficients of organic compounds. Also, the results revealed the superiority of ANN over the MLR and PLS models. Copyright 2009 Wiley Periodicals, Inc.

Static friction between rigid fractal surfaces

NASA Astrophysics Data System (ADS)

Alonso-Marroquin, Fernando; Huang, Pengyu; Hanaor, Dorian A. H.; Flores-Johnson, E. A.; Proust, Gwénaëlle; Gan, Yixiang; Shen, Luming

2015-09-01

Using spheropolygon-based simulations and contact slope analysis, we investigate the effects of surface topography and atomic scale friction on the macroscopically observed friction between rigid blocks with fractal surface structures. From our mathematical derivation, the angle of macroscopic friction is the result of the sum of the angle of atomic friction and the slope angle between the contact surfaces. The latter is obtained from the determination of all possible contact slopes between the two surface profiles through an alternative signature function. Our theory is validated through numerical simulations of spheropolygons with fractal Koch surfaces and is applied to the description of frictional properties of Weierstrass-Mandelbrot surfaces. The agreement between simulations and theory suggests that for interpreting macroscopic frictional behavior, the descriptors of surface morphology should be defined from the signature function rather than from the slopes of the contacting surfaces.

Shuffling cross-validation-bee algorithm as a new descriptor selection method for retention studies of pesticides in biopartitioning micellar chromatography.

PubMed

Zarei, Kobra; Atabati, Morteza; Ahmadi, Monire

2017-05-04

Bee algorithm (BA) is an optimization algorithm inspired by the natural foraging behaviour of honey bees to find the optimal solution which can be proposed to feature selection. In this paper, shuffling cross-validation-BA (CV-BA) was applied to select the best descriptors that could describe the retention factor (log k) in the biopartitioning micellar chromatography (BMC) of 79 heterogeneous pesticides. Six descriptors were obtained using BA and then the selected descriptors were applied for model development using multiple linear regression (MLR). The descriptor selection was also performed using stepwise, genetic algorithm and simulated annealing methods and MLR was applied to model development and then the results were compared with those obtained from shuffling CV-BA. The results showed that shuffling CV-BA can be applied as a powerful descriptor selection method. Support vector machine (SVM) was also applied for model development using six selected descriptors by BA. The obtained statistical results using SVM were better than those obtained using MLR, as the root mean square error (RMSE) and correlation coefficient (R) for whole data set (training and test), using shuffling CV-BA-MLR, were obtained as 0.1863 and 0.9426, respectively, while these amounts for the shuffling CV-BA-SVM method were obtained as 0.0704 and 0.9922, respectively.

Using probabilistic model as feature descriptor on a smartphone device for autonomous navigation of unmanned ground vehicles

NASA Astrophysics Data System (ADS)

Desai, Alok; Lee, Dah-Jye

2013-12-01

There has been significant research on the development of feature descriptors in the past few years. Most of them do not emphasize real-time applications. This paper presents the development of an affine invariant feature descriptor for low resource applications such as UAV and UGV that are equipped with an embedded system with a small microprocessor, a field programmable gate array (FPGA), or a smart phone device. UAV and UGV have proven suitable for many promising applications such as unknown environment exploration, search and rescue operations. These applications required on board image processing for obstacle detection, avoidance and navigation. All these real-time vision applications require a camera to grab images and match features using a feature descriptor. A good feature descriptor will uniquely describe a feature point thus allowing it to be correctly identified and matched with its corresponding feature point in another image. A few feature description algorithms are available for a resource limited system. They either require too much of the device's resource or too much simplification on the algorithm, which results in reduction in performance. This research is aimed at meeting the needs of these systems without sacrificing accuracy. This paper introduces a new feature descriptor called PRObabilistic model (PRO) for UGV navigation applications. It is a compact and efficient binary descriptor that is hardware-friendly and easy for implementation.

Quantitative structure-retention relationships of flavonoids unraveled by immobilized artificial membrane chromatography.

PubMed

Santoro, Adriana Leandra; Carrilho, Emanuel; Lanças, Fernando Mauro; Montanari, Carlos Alberto

2016-06-10

The pharmacokinetic properties of flavonoids with differing degrees of lipophilicity were investigated using immobilized artificial membranes (IAMs) as the stationary phase in high performance liquid chromatography (HPLC). For each flavonoid compound, we investigated whether the type of column used affected the correlation between the retention factors and the calculated octanol/water partition (log Poct). Three-dimensional (3D) molecular descriptors were calculated from the molecular structure of each compound using i) VolSurf software, ii) the GRID method (computational procedure for determining energetically favorable binding sites in molecules of known structure using a probe for calculating the 3D molecular interaction fields, between the probe and the molecule), and iii) the relationship between partition and molecular structure, analyzed in terms of physicochemical descriptors. The VolSurf built-in Caco-2 model was used to estimate compound permeability. The extent to which the datasets obtained from different columns differ both from each other and from both the calculated log Poct and the predicted permeability in Caco-2 cells was examined by principal component analysis (PCA). The immobilized membrane partition coefficients (kIAM) were analyzed using molecular descriptors in partial least square regression (PLS) and a quantitative structure-retention relationship was generated for the chromatographic retention in the cholesterol column. The cholesterol column provided the best correlation with the permeability predicted by the Caco-2 cell model and a good fit model with great prediction power was obtained for its retention data (R(2)=0.96 and Q(2)=0.85 with four latent variables). Copyright © 2015 Elsevier B.V. All rights reserved.

Resemblance profiles as clustering decision criteria: Estimating statistical power, error, and correspondence for a hypothesis test for multivariate structure.

PubMed

Kilborn, Joshua P; Jones, David L; Peebles, Ernst B; Naar, David F

2017-04-01

Clustering data continues to be a highly active area of data analysis, and resemblance profiles are being incorporated into ecological methodologies as a hypothesis testing-based approach to clustering multivariate data. However, these new clustering techniques have not been rigorously tested to determine the performance variability based on the algorithm's assumptions or any underlying data structures. Here, we use simulation studies to estimate the statistical error rates for the hypothesis test for multivariate structure based on dissimilarity profiles (DISPROF). We concurrently tested a widely used algorithm that employs the unweighted pair group method with arithmetic mean (UPGMA) to estimate the proficiency of clustering with DISPROF as a decision criterion. We simulated unstructured multivariate data from different probability distributions with increasing numbers of objects and descriptors, and grouped data with increasing overlap, overdispersion for ecological data, and correlation among descriptors within groups. Using simulated data, we measured the resolution and correspondence of clustering solutions achieved by DISPROF with UPGMA against the reference grouping partitions used to simulate the structured test datasets. Our results highlight the dynamic interactions between dataset dimensionality, group overlap, and the properties of the descriptors within a group (i.e., overdispersion or correlation structure) that are relevant to resemblance profiles as a clustering criterion for multivariate data. These methods are particularly useful for multivariate ecological datasets that benefit from distance-based statistical analyses. We propose guidelines for using DISPROF as a clustering decision tool that will help future users avoid potential pitfalls during the application of methods and the interpretation of results.

Evaluation of estimation methods for organic carbon normalized sorption coefficients

USGS Publications Warehouse

Baker, James R.; Mihelcic, James R.; Luehrs, Dean C.; Hickey, James P.

1997-01-01

A critically evaluated set of 94 soil water partition coefficients normalized to soil organic carbon content (Koc) is presented for 11 classes of organic chemicals. This data set is used to develop and evaluate Koc estimation methods using three different descriptors. The three types of descriptors used in predicting Koc were octanol/water partition coefficient (Kow), molecular connectivity (mXt) and linear solvation energy relationships (LSERs). The best results were obtained estimating Koc from Kow, though a slight improvement in the correlation coefficient was obtained by using a two-parameter regression with Kow and the third order difference term from mXt. Molecular connectivity correlations seemed to be best suited for use with specific chemical classes. The LSER provided a better fit than mXt but not as good as the correlation with Koc. The correlation to predict Koc from Kow was developed for 72 chemicals; log Koc = 0.903* log Kow + 0.094. This correlation accounts for 91% of the variability in the data for chemicals with log Kow ranging from 1.7 to 7.0. The expression to determine the 95% confidence interval on the estimated Koc is provided along with an example for two chemicals of different hydrophobicity showing the confidence interval of the retardation factor determined from the estimated Koc. The data showed that Koc is not likely to be applicable for chemicals with log Kow < 1.7. Finally, the Koc correlation developed using Kow as a descriptor was compared with three nonclass-specific correlations and two 'commonly used' class-specific correlations to determine which method(s) are most suitable.

Environmental variables measured at multiple spatial scales exert uneven influence on fish assemblages of floodplain lakes

USGS Publications Warehouse

Dembkowski, Daniel J.; Miranda, Leandro E.

2014-01-01

We examined the interaction between environmental variables measured at three different scales (i.e., landscape, lake, and in-lake) and fish assemblage descriptors across a range of over 50 floodplain lakes in the Mississippi Alluvial Valley of Mississippi and Arkansas. Our goal was to identify important local- and landscape-level determinants of fish assemblage structure. Relationships between fish assemblage structure and variables measured at broader scales (i.e., landscape-level and lake-level) were hypothesized to be stronger than relationships with variables measured at finer scales (i.e., in-lake variables). Results suggest that fish assemblage structure in floodplain lakes was influenced by variables operating on three different scales. However, and contrary to expectations, canonical correlations between in-lake environmental characteristics and fish assemblage structure were generally stronger than correlations between landscape-level and lake-level variables and fish assemblage structure, suggesting a hierarchy of influence. From a resource management perspective, our study suggests that landscape-level and lake-level variables may be manipulated for conservation or restoration purposes, and in-lake variables and fish assemblage structure may be used to monitor the success of such efforts.

On the Development and Use of Large Chemical Similarity Networks, Informatics Best Practices and Novel Chemical Descriptors Towards Materials Quantitative Structure Property Relationships

NASA Astrophysics Data System (ADS)

Krein, Michael

After decades of development and use in a variety of application areas, Quantitative Structure Property Relationships (QSPRs) and related descriptor-based statistical learning methods have achieved a level of infamy due to their misuse. The field is rife with past examples of overtrained models, overoptimistic performance assessment, and outright cheating in the form of explicitly removing data to fit models. These actions do not serve the community well, nor are they beneficial to future predictions based on established models. In practice, in order to select combinations of descriptors and machine learning methods that might work best, one must consider the nature and size of the training and test datasets, be aware of existing hypotheses about the data, and resist the temptation to bias structure representation and modeling to explicitly fit the hypotheses. The definition and application of these best practices is important for obtaining actionable modeling outcomes, and for setting user expectations of modeling accuracy when predicting the endpoint values of unknowns. A wide variety of statistical learning approaches, descriptor types, and model validation strategies are explored herein, with the goals of helping end users understand the factors involved in creating and using QSPR models effectively, and to better understand relationships within the data, especially by looking at the problem space from multiple perspectives. Molecular relationships are commonly envisioned in a continuous high-dimensional space of numerical descriptors, referred to as chemistry space. Descriptor and similarity metric choice influence the partitioning of this space into regions corresponding to local structural similarity. These regions, known as domains of applicability, are most likely to be successfully modeled by a QSPR. In Chapter 2, the network topology and scaling relationships of several chemistry spaces are thoroughly investigated. Chemistry spaces studied include the ZINC data set, a qHTS PubChem bioassay, as well as the protein binding sites from the PDB. The characteristics of these networks are compared and contrasted with those of the bioassay Structure Activity Landscape Index (SALI) subnetwork, which maps discontinuities or cliffs in the structure activity landscape. Mapping this newly generated information over underlying chemistry space networks generated using different descriptors demonstrates local modeling capacity and can guide the choice of better local representations of chemistry space. Chapter 2 introduces and demonstrates this novel concept, which also enables future work in visualization and interpretation of chemical spaces. Initially, it was discovered that there were no community-available tools to leverage best-practice ideas to comprehensively build, compare, and interpret QSPRs. The Yet Another Modeling System (YAMS) tool performs a series of balanced, rational decisions in dataset preprocessing and parameter/feature selection over a choice of modeling methods. To date, YAMS is the only community-available informatics tool that performs such decisions consistently between methods while also providing multiple model performance comparisons and detailed descriptor importance information. The focus of the tool is thus to convey rich information about model quality and predictions that help to "close the loop" between modeling and experimental efforts, for example, in tailoring nanocomposite properties. Polymer nanocomposites (PNC) are complex material systems encompassing many potential structures, chemistries, and self assembled morphologies that could significantly impact commercial and military applications. There is a strong desire to characterize and understand the tradespace of nanocomposites, to identify the important factors relating nanostructure to materials properties and determine an effective way to control materials properties at the manufacturing scale. Due to the complexity of the systems, existing design approaches rely heavily on trial-and-error learning. By leveraging existing experimental data, Materials Quantitative Structure-Property Relationships (MQSPRs) relate molecular structures to the polar and dispersive components of corresponding surface tensions. In turn, existing theories relate polymer and nanofiller polar and dispersive surface tension components to the dispersion state and interfacial polymer relaxation times. These quantities may, in the future, be used as input to continuum mechanics approaches shown able to predict the thermomechanical response of nanocomposites. For a polymer dataset and a particle dataset, multiple structural representations and descriptor sets are benchmarked, including a set of high performance surface-property descriptors developed as part of this work. The systematic variation of structural representations as part of the informatics approach reveals important insight in modeling polymers, and should become common practice when defining new problem spaces.

Discovering collectively informative descriptors from high-throughput experiments

PubMed Central

2009-01-01

Background Improvements in high-throughput technology and its increasing use have led to the generation of many highly complex datasets that often address similar biological questions. Combining information from these studies can increase the reliability and generalizability of results and also yield new insights that guide future research. Results This paper describes a novel algorithm called BLANKET for symmetric analysis of two experiments that assess informativeness of descriptors. The experiments are required to be related only in that their descriptor sets intersect substantially and their definitions of case and control are consistent. From resulting lists of n descriptors ranked by informativeness, BLANKET determines shortlists of descriptors from each experiment, generally of different lengths p and q. For any pair of shortlists, four numbers are evident: the number of descriptors appearing in both shortlists, in exactly one shortlist, or in neither shortlist. From the associated contingency table, BLANKET computes Right Fisher Exact Test (RFET) values used as scores over a plane of possible pairs of shortlist lengths [1,2]. BLANKET then chooses a pair or pairs with RFET score less than a threshold; the threshold depends upon n and shortlist length limits and represents a quality of intersection achieved by less than 5% of random lists. Conclusions Researchers seek within a universe of descriptors some minimal subset that collectively and efficiently predicts experimental outcomes. Ideally, any smaller subset should be insufficient for reliable prediction and any larger subset should have little additional accuracy. As a method, BLANKET is easy to conceptualize and presents only moderate computational complexity. Many existing databases could be mined using BLANKET to suggest optimal sets of predictive descriptors. PMID:20021653

Health sciences descriptors in the brazilian speech-language and hearing science.

PubMed

Campanatti-Ostiz, Heliane; Andrade, Claudia Regina Furquim de

2010-01-01

Terminology in Speech-Language and Hearing Science. To propose a specific thesaurus about the Speech-Language and Hearing Science, for the English, Portuguese and Spanish languages, based on the existing keywords available on the Health Sciences Descriptors (DeCS). Methodology was based on the pilot study developed by Campanatti-Ostiz and Andrade; that had as a purpose to verify the methodological viability for the creation of a Speech-Language and Hearing Science category in the DeCS. The scientific journals selected for analyses of the titles, abstracts and keywords of all scientific articles were those in the field of the Speech-Language and Hearing Science, indexed on the SciELO. 1. Recovery of the Descriptors in the English language (Medical Subject Headings--MeSH); 2. Recovery and hierarchic organization of the descriptors in the Portuguese language was done (DeCS). The obtained data was analyzed as follows: descriptive analyses and relative relevance analyses of the DeCS areas. Based on the first analyses, we decided to select all 761 descriptors, with all the hierarchic numbers, independently of their occurrence (occurrence number--ON), and based on the second analyses, we decided to propose to exclude the less relevant areas and the exclusive DeCS areas. The proposal was finished with a total of 1676 occurrences of DeCS descriptors, distributed in the following areas: Anatomy; Diseases; Analytical, Diagnostic and Therapeutic Techniques and Equipments; Psychiatry and Psychology; Phenomena and Processes; Health Care. The presented proposal of a thesaurus contains the specific terminology of the Brazilian Speech-Language and Hearing Sciences and reflects the descriptors of the published scientific production. Being the DeCS a trilingual vocabulary (Portuguese, English and Spanish), the present descriptors organization proposition can be used in these three languages, allowing greater cultural interchange between different nations.

Direct memory access transfer completion notification

DOEpatents

Archer, Charles J [Rochester, MN; Blocksome, Michael A [Rochester, MN; Parker, Jeffrey J [Rochester, MN

2011-02-15

DMA transfer completion notification includes: inserting, by an origin DMA engine on an origin node in an injection first-in-first-out (`FIFO`) buffer, a data descriptor for an application message to be transferred to a target node on behalf of an application on the origin node; inserting, by the origin DMA engine, a completion notification descriptor in the injection FIFO buffer after the data descriptor for the message, the completion notification descriptor specifying a packet header for a completion notification packet; transferring, by the origin DMA engine to the target node, the message in dependence upon the data descriptor; sending, by the origin DMA engine, the completion notification packet to a local reception FIFO buffer using a local memory FIFO transfer operation; and notifying, by the origin DMA engine, the application that transfer of the message is complete in response to receiving the completion notification packet in the local reception FIFO buffer.

Direct memory access transfer completion notification

DOEpatents

Archer, Charles J.; Blocksome, Michael A.; Parker, Jeffrey J.

2010-08-17

Methods, apparatus, and products are disclosed for DMA transfer completion notification that include: inserting, by an origin DMA engine on an origin compute node in an injection FIFO buffer, a data descriptor for an application message to be transferred to a target compute node on behalf of an application on the origin compute node; inserting, by the origin DMA engine, a completion notification descriptor in the injection FIFO buffer after the data descriptor for the message, the completion notification descriptor specifying an address of a completion notification field in application storage for the application; transferring, by the origin DMA engine to the target compute node, the message in dependence upon the data descriptor; and notifying, by the origin DMA engine, the application that the transfer of the message is complete, including performing a local direct put operation to store predesignated notification data at the address of the completion notification field.

Segmentation of anatomical branching structures based on texture features and conditional random field

NASA Astrophysics Data System (ADS)

Nuzhnaya, Tatyana; Bakic, Predrag; Kontos, Despina; Megalooikonomou, Vasileios; Ling, Haibin

2012-02-01

This work is a part of our ongoing study aimed at understanding a relation between the topology of anatomical branching structures with the underlying image texture. Morphological variability of the breast ductal network is associated with subsequent development of abnormalities in patients with nipple discharge such as papilloma, breast cancer and atypia. In this work, we investigate complex dependence among ductal components to perform segmentation, the first step for analyzing topology of ductal lobes. Our automated framework is based on incorporating a conditional random field with texture descriptors of skewness, coarseness, contrast, energy and fractal dimension. These features are selected to capture the architectural variability of the enhanced ducts by encoding spatial variations between pixel patches in galactographic image. The segmentation algorithm was applied to a dataset of 20 x-ray galactograms obtained at the Hospital of the University of Pennsylvania. We compared the performance of the proposed approach with fully and semi automated segmentation algorithms based on neural network classification, fuzzy-connectedness, vesselness filter and graph cuts. Global consistency error and confusion matrix analysis were used as accuracy measurements. For the proposed approach, the true positive rate was higher and the false negative rate was significantly lower compared to other fully automated methods. This indicates that segmentation based on CRF incorporated with texture descriptors has potential to efficiently support the analysis of complex topology of the ducts and aid in development of realistic breast anatomy phantoms.

The recursive combination filter approach of pre-processing for the estimation of standard deviation of RR series.

PubMed

Mishra, Alok; Swati, D

2015-09-01

Variation in the interval between the R-R peaks of the electrocardiogram represents the modulation of the cardiac oscillations by the autonomic nervous system. This variation is contaminated by anomalous signals called ectopic beats, artefacts or noise which mask the true behaviour of heart rate variability. In this paper, we have proposed a combination filter of recursive impulse rejection filter and recursive 20% filter, with recursive application and preference of replacement over removal of abnormal beats to improve the pre-processing of the inter-beat intervals. We have tested this novel recursive combinational method with median method replacement to estimate the standard deviation of normal to normal (SDNN) beat intervals of congestive heart failure (CHF) and normal sinus rhythm subjects. This work discusses the improvement in pre-processing over single use of impulse rejection filter and removal of abnormal beats for heart rate variability for the estimation of SDNN and Poncaré plot descriptors (SD1, SD2, and SD1/SD2) in detail. We have found the 22 ms value of SDNN and 36 ms value of SD2 descriptor of Poincaré plot as clinical indicators in discriminating the normal cases from CHF cases. The pre-processing is also useful in calculation of Lyapunov exponent which is a nonlinear index as Lyapunov exponents calculated after proposed pre-processing modified in a way that it start following the notion of less complex behaviour of diseased states.

Using the product threshold model for estimating separately the effect of temperature on male and female fertility.

PubMed

Tusell, L; David, I; Bodin, L; Legarra, A; Rafel, O; López-Bejar, M; Piles, M

2011-12-01

Animals under environmental thermal stress conditions have reduced fertility due to impairment of some mechanisms involved in their reproductive performance that are different in males and females. As a consequence, the most sensitive periods of time and the magnitude of effect of temperature on fertility can differ between sexes. The objective of this study was to estimate separately the effect of temperature in different periods around the insemination time on male and on female fertility by using the product threshold model. This model assumes that an observed reproduction outcome is the result of the product of 2 unobserved variables corresponding to the unobserved fertilities of the 2 individuals involved in the mating. A total of 7,625 AI records from rabbits belonging to a line selected for growth rate and indoor daily temperature records were used. The average maximum daily temperature and the proportion of days in which the maximum temperature was greater than 25°C were used as temperature descriptors. These descriptors were calculated for several periods around the day of AI. In the case of males, 4 periods of time covered different stages of the spermatogenesis, the transit through the epididymus of the sperm, and the day of AI. For females, 5 periods of time covered the phases of preovulatory follicular maturation including day of AI and ovulation, fertilization and peri-implantational stage of the embryos, embryonic and early fetal periods of gestation, and finally, late gestation until birth. The effect of the different temperature descriptors was estimated in the corresponding male and female liabilities in a set of threshold product models. The temperature of the day of AI seems to be the most relevant temperature descriptor affecting male fertility because greater temperature records on the day of AI caused a decrease in male fertility (-6% in male fertility rate with respect to thermoneutrality). Departures from the thermal zone in temperature descriptors covering several periods before AI until early gestation had a negative effect on female fertility, with the pre- and peri-implantational period of the embryos being especially sensitive (from -5 to -6% in female fertility rate with respect to thermoneutrality). The latest period of gestation was unaffected by the temperature. Overall, magnitude and persistency of the temperatures reached in the conditions of this study do not seem to be great enough to have a large effect on male and female rabbit fertility.

OPDOT: A computer program for the optimum preliminary design of a transport airplane

NASA Technical Reports Server (NTRS)

Sliwa, S. M.; Arbuckle, P. D.

1980-01-01

A description of a computer program, OPDOT, for the optimal preliminary design of transport aircraft is given. OPDOT utilizes constrained parameter optimization to minimize a performance index (e.g., direct operating cost per block hour) while satisfying operating constraints. The approach in OPDOT uses geometric descriptors as independent design variables. The independent design variables are systematically iterated to find the optimum design. The technical development of the program is provided and a program listing with sample input and output are utilized to illustrate its use in preliminary design. It is not meant to be a user's guide, but rather a description of a useful design tool developed for studying the application of new technologies to transport airplanes.

Predicted Hematologic and Plasma Volume Responses Following Rapid Ascent to Progressive Altitudes

DTIC Science & Technology

2014-06-01

of these changes, and define baseline demographics and physiologic descriptors that are important in predicting these changes. The overall impact of... physiologic descriptors that are important in predicting these changes. Using general linear mixed models and a comprehensive relational database...accomplished using a comprehensive relational database containing individual ascent profiles, demographics, and physiologic subject descriptors as well as

CINAHL and MEDLINE: a comparison of indexing practices.

PubMed

Brenner, S H; McKinin, E J

1989-10-01

A random sample of fifty nursing articles indexed in both MEDLINE and CINAHL (NURSING & ALLIED HEALTH) during 1986 was used for comparing indexing practices. Indexing was analyzed by counting the number of major descriptors, the number of major and minor descriptors, the number of indexing access points, the number of common indexing access points, and the number and type of unique indexing access points. The study results indicate: there are few differences in the number of major descriptors used, MEDLINE uses almost twice as many descriptors, MEDLINE has almost twice as many indexing access points, and MEDLINE and CINAHL provide few common access points.

CINAHL and MEDLINE: a comparison of indexing practices.

PubMed Central

Brenner, S H; McKinin, E J

1989-01-01

A random sample of fifty nursing articles indexed in both MEDLINE and CINAHL (NURSING & ALLIED HEALTH) during 1986 was used for comparing indexing practices. Indexing was analyzed by counting the number of major descriptors, the number of major and minor descriptors, the number of indexing access points, the number of common indexing access points, and the number and type of unique indexing access points. The study results indicate: there are few differences in the number of major descriptors used, MEDLINE uses almost twice as many descriptors, MEDLINE has almost twice as many indexing access points, and MEDLINE and CINAHL provide few common access points. PMID:2676049

Spherical harmonics coefficients for ligand-based virtual screening of cyclooxygenase inhibitors.

PubMed

Wang, Quan; Birod, Kerstin; Angioni, Carlo; Grösch, Sabine; Geppert, Tim; Schneider, Petra; Rupp, Matthias; Schneider, Gisbert

2011-01-01

Molecular descriptors are essential for many applications in computational chemistry, such as ligand-based similarity searching. Spherical harmonics have previously been suggested as comprehensive descriptors of molecular structure and properties. We investigate a spherical harmonics descriptor for shape-based virtual screening. We introduce and validate a partially rotation-invariant three-dimensional molecular shape descriptor based on the norm of spherical harmonics expansion coefficients. Using this molecular representation, we parameterize molecular surfaces, i.e., isosurfaces of spatial molecular property distributions. We validate the shape descriptor in a comprehensive retrospective virtual screening experiment. In a prospective study, we virtually screen a large compound library for cyclooxygenase inhibitors, using a self-organizing map as a pre-filter and the shape descriptor for candidate prioritization. 12 compounds were tested in vitro for direct enzyme inhibition and in a whole blood assay. Active compounds containing a triazole scaffold were identified as direct cyclooxygenase-1 inhibitors. This outcome corroborates the usefulness of spherical harmonics for representation of molecular shape in virtual screening of large compound collections. The combination of pharmacophore and shape-based filtering of screening candidates proved to be a straightforward approach to finding novel bioactive chemotypes with minimal experimental effort.

Fingerprint Identification Using SIFT-Based Minutia Descriptors and Improved All Descriptor-Pair Matching

PubMed Central

Zhou, Ru; Zhong, Dexing; Han, Jiuqiang

2013-01-01

The performance of conventional minutiae-based fingerprint authentication algorithms degrades significantly when dealing with low quality fingerprints with lots of cuts or scratches. A similar degradation of the minutiae-based algorithms is observed when small overlapping areas appear because of the quite narrow width of the sensors. Based on the detection of minutiae, Scale Invariant Feature Transformation (SIFT) descriptors are employed to fulfill verification tasks in the above difficult scenarios. However, the original SIFT algorithm is not suitable for fingerprint because of: (1) the similar patterns of parallel ridges; and (2) high computational resource consumption. To enhance the efficiency and effectiveness of the algorithm for fingerprint verification, we propose a SIFT-based Minutia Descriptor (SMD) to improve the SIFT algorithm through image processing, descriptor extraction and matcher. A two-step fast matcher, named improved All Descriptor-Pair Matching (iADM), is also proposed to implement the 1:N verifications in real-time. Fingerprint Identification using SMD and iADM (FISiA) achieved a significant improvement with respect to accuracy in representative databases compared with the conventional minutiae-based method. The speed of FISiA also can meet real-time requirements. PMID:23467056

A Concise Guide to Feature Histograms with Applications to LIDAR-Based Spacecraft Relative Navigation

NASA Astrophysics Data System (ADS)

Rhodes, Andrew P.; Christian, John A.; Evans, Thomas

2017-12-01

With the availability and popularity of 3D sensors, it is advantageous to re-examine the use of point cloud descriptors for the purpose of pose estimation and spacecraft relative navigation. One popular descriptor is the oriented unique repeatable clustered viewpoint feature histogram (OUR-CVFH), which is most often utilized in personal and industrial robotics to simultaneously recognize and navigate relative to an object. Recent research into using the OUR-CVFH descriptor for spacecraft navigation has produced favorable results. Since OUR-CVFH is the most recent innovation in a large family of feature histogram point cloud descriptors, discussions of parameter settings and insights into its functionality are spread among various publications and online resources. This paper organizes the history of feature histogram point cloud descriptors for a straightforward explanation of their evolution. This article compiles all the requisite information needed to implement OUR-CVFH into one location, as well as providing useful suggestions on how to tune the generation parameters. This work is beneficial for anyone interested in using this histogram descriptor for object recognition or navigation - may it be personal robotics or spacecraft navigation.

Retro-regression--another important multivariate regression improvement.

PubMed

Randić, M

2001-01-01

We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.

Can we apply the MRI BI-RADS lexicon morphology descriptors on contrast-enhanced spectral mammography?

PubMed

Kamal, Rasha M; Helal, Maha H; Mansour, Sahar M; Haggag, Marwa A; Nada, Omniya M; Farahat, Iman G; Alieldin, Nelly H

2016-07-12

To assess the feasibility of using the MRI breast imaging reporting and data system (BI-RADS) lexicon morphology descriptors to characterize enhancing breast lesions identified on contrast-enhanced spectral mammography (CESM). The study is a retrospective analysis of the morphology descriptors of 261 enhancing breast lesions identified on CESM in 239 patients. We presented the morphological categorization of the included lesions into focus, mass and non-mass. Further classifications included (1) the multiplicity for "focus" category, (2) the shape, margin and internal enhancement for "mass" category and (3) the distribution and internal enhancement for "non-mass" category. Each morphology descriptor was evaluated individually (irrespective of all other descriptors) by calculating its sensitivity, specificity, positive-predictive value (PPV) and negative-predictive value (NPV) and likelihood ratios (LRs). The study included 68/261 (26.1%) benign lesions and 193/261 (73.9%) malignant lesions. Intensely enhancing foci, whether single (7/12, 58.3%) or multiple (2/12, 16.7%), were malignant. Descriptors of "irregular"-shape (PPV: 92.4%) and "non-circumscribed" margin (odds ratio: 55.2, LR positive: 4.77; p-value: <0.001) were more compatible with malignancy. Internal mass enhancement patterns showed a very low specificity (58.0%) and NPV (40.0%). Non-mass enhancement (NME) was detected in 81/261 lesions. Asymmetrical NME in 81% (n = 52/81) lesions was malignant lesions and internal enhancement patterns indicative of malignancy were the heterogeneous and clumped ones. We can apply the MRI morphology descriptors to characterize lesions on CESM, but with few expectations. In many situations, irregular-shaped, non-circumscribed masses and NME with focal, ductal or segmental distribution and heterogeneous or clumped enhancement are the most suggestive descriptors of malignant pathologies. (1) The MRI BI-RADS lexicon morphology descriptors can be applied in the characterization of enhancing lesions on CESM with a few exceptions. (2) Multiple bilateral intensely enhancing foci should not be included under the normal background parenchymal enhancement unless they are proved to be benign by biopsy. (3) Mass lesion features that indicated malignancy were irregular-shaped, spiculated and irregular margins and heterogeneous internal enhancement patterns. The rim enhancement pattern should not be considered as a descriptor of malignant lesions unless CESM is coupled with an ultrasound examination.

Can we apply the MRI BI-RADS lexicon morphology descriptors on contrast-enhanced spectral mammography?

PubMed Central

Kamal, Rasha M; Helal, Maha H; Haggag, Marwa A; Nada, Omniya M; Farahat, Iman G; Alieldin, Nelly H

2016-01-01

Objective: To assess the feasibility of using the MRI breast imaging reporting and data system (BI-RADS) lexicon morphology descriptors to characterize enhancing breast lesions identified on contrast-enhanced spectral mammography (CESM). Methods: The study is a retrospective analysis of the morphology descriptors of 261 enhancing breast lesions identified on CESM in 239 patients. We presented the morphological categorization of the included lesions into focus, mass and non-mass. Further classifications included (1) the multiplicity for “focus” category, (2) the shape, margin and internal enhancement for “mass” category and (3) the distribution and internal enhancement for “non-mass” category. Each morphology descriptor was evaluated individually (irrespective of all other descriptors) by calculating its sensitivity, specificity, positive-predictive value (PPV) and negative-predictive value (NPV) and likelihood ratios (LRs). Results: The study included 68/261 (26.1%) benign lesions and 193/261 (73.9%) malignant lesions. Intensely enhancing foci, whether single (7/12, 58.3%) or multiple (2/12, 16.7%), were malignant. Descriptors of “irregular”-shape (PPV: 92.4%) and “non-circumscribed” margin (odds ratio: 55.2, LR positive: 4.77; p-value: <0.001) were more compatible with malignancy. Internal mass enhancement patterns showed a very low specificity (58.0%) and NPV (40.0%). Non-mass enhancement (NME) was detected in 81/261 lesions. Asymmetrical NME in 81% (n = 52/81) lesions was malignant lesions and internal enhancement patterns indicative of malignancy were the heterogeneous and clumped ones. Conclusion: We can apply the MRI morphology descriptors to characterize lesions on CESM, but with few expectations. In many situations, irregular-shaped, non-circumscribed masses and NME with focal, ductal or segmental distribution and heterogeneous or clumped enhancement are the most suggestive descriptors of malignant pathologies. Advances in knowledge: (1) The MRI BI-RADS lexicon morphology descriptors can be applied in the characterization of enhancing lesions on CESM with a few exceptions. (2) Multiple bilateral intensely enhancing foci should not be included under the normal background parenchymal enhancement unless they are proved to be benign by biopsy. (3) Mass lesion features that indicated malignancy were irregular-shaped, spiculated and irregular margins and heterogeneous internal enhancement patterns. The rim enhancement pattern should not be considered as a descriptor of malignant lesions unless CESM is coupled with an ultrasound examination. PMID:27327403

Heritabilities and genetic correlations in the same traits across different strata of herds created according to continuous genomic, genetic, and phenotypic descriptors.

PubMed

Yin, Tong; König, Sven

2018-03-01

The most common approach in dairy cattle to prove genotype by environment interactions is a multiple-trait model application, and considering the same traits in different environments as different traits. We enhanced such concepts by defining continuous phenotypic, genetic, and genomic herd descriptors, and applying random regression sire models. Traits of interest were test-day traits for milk yield, fat percentage, protein percentage, and somatic cell score, considering 267,393 records from 32,707 first-lactation Holstein cows. Cows were born in the years 2010 to 2013, and kept in 52 large-scale herds from 2 federal states of north-east Germany. The average number of genotyped cows per herd (45,613 single nucleotide polymorphism markers per cow) was 133.5 (range: 45 to 415 genotyped cows). Genomic herd descriptors were (1) the level of linkage disequilibrium (r 2 ) within specific chromosome segments, and (2) the average allele frequency for single nucleotide polymorphisms in close distance to a functional mutation. Genetic herd descriptors were the (1) intra-herd inbreeding coefficient, and (2) the percentage of daughters from foreign sires. Phenotypic herd descriptors were (1) herd size, and (2) the herd mean for nonreturn rate. Most correlations among herd descriptors were close to 0, indicating independence of genomic, genetic, and phenotypic characteristics. Heritabilities for milk yield increased with increasing intra-herd linkage disequilibrium, inbreeding, and herd size. Genetic correlations in same traits between adjacent levels of herd descriptors were close to 1, but declined for descriptor levels in greater distance. Genetic correlation declines were more obvious for somatic cell score, compared with test-day traits with larger heritabilities (fat percentage and protein percentage). Also, for milk yield, alterations of herd descriptor levels had an obvious effect on heritabilities and genetic correlations. By trend, multiple trait model results (based on created discrete herd classes) confirmed the random regression estimates. Identified alterations of breeding values in dependency of herd descriptors suggest utilization of specific sires for specific herd structures, offering new possibilities to improve sire selection strategies. Regarding genomic selection designs and genetic gain transfer into commercial herds, cow herds for the utilization in cow training sets should reflect the genomic, genetic, and phenotypic pattern of the broad population. Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Classifying Measures of Biological Variation

PubMed Central

Gregorius, Hans-Rolf; Gillet, Elizabeth M.

2015-01-01

Biological variation is commonly measured at two basic levels: variation within individual communities, and the distribution of variation over communities or within a metacommunity. We develop a classification for the measurement of biological variation on both levels: Within communities into the categories of dispersion and diversity, and within metacommunities into the categories of compositional differentiation and partitioning of variation. There are essentially two approaches to characterizing the distribution of trait variation over communities in that individuals with the same trait state or type tend to occur in the same community (describes differentiation tendencies), and individuals with different types tend to occur in different communities (describes apportionment tendencies). Both approaches can be viewed from the dual perspectives of trait variation distributed over communities (CT perspective) and community membership distributed over trait states (TC perspective). This classification covers most of the relevant descriptors (qualified measures) of biological variation, as is demonstrated with the help of major families of descriptors. Moreover, the classification is shown to open ways to develop new descriptors that meet current needs. Yet the classification also reveals the misclassification of some prominent and widely applied descriptors: Dispersion is often misclassified as diversity, particularly in cases where dispersion descriptor allow for the computation of effective numbers; the descriptor GST of population genetics is commonly misclassified as compositional differentiation and confused with partitioning-oriented differentiation, whereas it actually measures partitioning-oriented apportionment; descriptors of β-diversity are ambiguous about the differentiation effects they are supposed to represent and therefore require conceptual reconsideration. PMID:25807558

Advances in structural damage assessment using strain measurements and invariant shape descriptors

NASA Astrophysics Data System (ADS)

Patki, Amol Suhas

Energy conservation has become one of the most important topic of engineering research over the last couple of decades all around the world and implies reduced energy consumption in order to preserve rapidly depleting natural resources. Along with development of fuel-efficient power plants and technology utilizing alternate fuel to traditional fossil fuels, the design and manufacturing of light-weight energy-efficient structures plays a major role in energy conservation. However this reduction in material and/or weight cannot be achieved at the expense of safety. Thus it is essential to either increase the confidence in the analysis of mechanics of traditional isotropic materials to reduce safety factors or develop new structural materials, such as fiber-reinforced (FRP) polymer matrix composites, which tend to have a higher strength to weight ratio. This doctoral research work will focus on two problems faced by the structural mechanics community viz. effects of closure and overloads on fatigue cracks and structural health monitoring of composites. Fatigue life prediction is largely empirical which in recent years has been shown to be a conservative design model. Investigation of crack growth mechanisms, such as crack closure can lead to design optimization. However, the lack of understanding and accepted theories introduces a degree of uncertainty in such models. Many of the complexity and uncertainty arise from the lack of an experimental technique to quantify crack closure. In this context, this research work offers the most compelling evidence to date of the effects of overload retardation and a confirmation of the Wheeler model using direct experimental observations of the stress field and crack tip plastic zone with the aid of thermoelastic stress analysis. On the other hand, the uncertainties in the post-damage behavior of energy saving FRP-composite materials increase their capital cost and maintenance cost. Damage in isotropic materials tends to be local to the area surrounding the damage, while damage in orthotropic materials tends to have more global repercussions. This calls for analysis of full-field strain distributions adding to the complexity of post-damage life estimation. This study explores shape descriptors used in the field of medical imagery, military targeting and biometric recognition for obtaining a qualitative and quantitative comparison between full-field strain data recorded from damaged composite panels using sophisticated experimental techniques. These descriptors are capable of decomposing images with 103 to 106 pixels into a feature vector with only a few hundred elements. This ability of shape descriptors to achieve enormous reduction in strain data, while providing unique representation, makes them a practical choice for the purpose of structural damage assessment. Consequently, it is relatively easy to statistically compare the shape descriptors of the full-field strain maps using similarity measures rather than the strain maps themselves. However, the wide range of geometric and design features in engineering components pose difficulties in the application of traditional shape description techniques. Thus a new shape descriptor is developed which is applicable to a wide range of specimen geometries. This work also illustrates how shape description techniques can be applied to full-field finite element model validations and updating.

Computer-Aided Diagnosis of Solid Breast Lesions Using an Ultrasonic Multi-Feature Analysis Procedure

DTIC Science & Technology

2011-01-01

areas. We quantified morphometric features by geometric and fractal analysis of traced lesion boundaries. Although no single parameter can reliably...These include acoustic descriptors (“echogenicity,” “heterogeneity,” “shadowing”) and morphometric descriptors (“area,” “aspect ratio,” “border...quantitative descriptors; some morphometric features (such as border irregularity) also were particularly effective in lesion classification. Our

Spatio-temporal variability of ichthyophagous bird assemblage around western Mediterranean open-sea cage fish farms.

PubMed

Aguado-Giménez, Felipe; Eguía-Martínez, Sergio; Cerezo-Valverde, Jesús; García-García, Benjamín

2018-06-14

Ichthyophagous birds aggregate at cage fish farms attracted by caged and associated wild fish. Spatio-temporal variability of such birds was studied for a year through seasonal visual counts at eight farms in the western Mediterranean. Correlation with farm and location descriptors was assessed. Considerable spatio-temporal variability in fish-eating bird density and assemblage structure was observed among farms and seasons. Bird density increased from autumn to winter, with the great cormorant being the most abundant species, also accounting largely for differences among farms. Grey heron and little egret were also numerous at certain farms during the coldest seasons. Cattle egret was only observed at one farm. No shags were observed during winter. During spring and summer, bird density decreased markedly and only shags and little egrets were observed at only a few farms. Season and distance from farms to bird breeding/wintering grounds helped to explain some of the spatio-temporal variability. Copyright © 2018 Elsevier Ltd. All rights reserved.

Cutting Materials in Half: A Graph Theory Approach for Generating Crystal Surfaces and Its Prediction of 2D Zeolites

DOE Office of Scientific and Technical Information (OSTI.GOV)

Witman, Matthew; Ling, Sanliang; Boyd, Peter

Scientific interest in two-dimensional (2D) materials, ranging from graphene and other single layer materials to atomically thin crystals, is quickly increasing for a large variety of technological applications. While in silico design approaches have made a large impact in the study of 3D crystals, algorithms designed to discover atomically thin 2D materials from their parent 3D materials are by comparison more sparse. Here, we hypothesize that determining how to cut a 3D material in half (i.e., which Miller surface is formed) by severing a minimal number of bonds or a minimal amount of total bond energy per unit area canmore » yield insight into preferred crystal faces. We answer this question by implementing a graph theory technique to mathematically formalize the enumeration of minimum cut surfaces of crystals. While the algorithm is generally applicable to different classes of materials, we focus on zeolitic materials due to their diverse structural topology and because 2D zeolites have promising catalytic and separation performance compared to their 3D counterparts. We report here a simple descriptor based only on structural information that predicts whether a zeolite is likely to be synthesizable in the 2D form and correctly identifies the expressed surface in known layered 2D zeolites. The discovery of this descriptor allows us to highlight other zeolites that may also be synthesized in the 2D form that have not been experimentally realized yet. Finally, our method is general since the mathematical formalism can be applied to find the minimum cut surfaces of other crystallographic materials such as metal-organic frameworks, covalent-organic frameworks, zeolitic-imidazolate frameworks, metal oxides, etc.« less

Cutting Materials in Half: A Graph Theory Approach for Generating Crystal Surfaces and Its Prediction of 2D Zeolites.

PubMed

Witman, Matthew; Ling, Sanliang; Boyd, Peter; Barthel, Senja; Haranczyk, Maciej; Slater, Ben; Smit, Berend

2018-02-28

Scientific interest in two-dimensional (2D) materials, ranging from graphene and other single layer materials to atomically thin crystals, is quickly increasing for a large variety of technological applications. While in silico design approaches have made a large impact in the study of 3D crystals, algorithms designed to discover atomically thin 2D materials from their parent 3D materials are by comparison more sparse. We hypothesize that determining how to cut a 3D material in half (i.e., which Miller surface is formed) by severing a minimal number of bonds or a minimal amount of total bond energy per unit area can yield insight into preferred crystal faces. We answer this question by implementing a graph theory technique to mathematically formalize the enumeration of minimum cut surfaces of crystals. While the algorithm is generally applicable to different classes of materials, we focus on zeolitic materials due to their diverse structural topology and because 2D zeolites have promising catalytic and separation performance compared to their 3D counterparts. We report here a simple descriptor based only on structural information that predicts whether a zeolite is likely to be synthesizable in the 2D form and correctly identifies the expressed surface in known layered 2D zeolites. The discovery of this descriptor allows us to highlight other zeolites that may also be synthesized in the 2D form that have not been experimentally realized yet. Finally, our method is general since the mathematical formalism can be applied to find the minimum cut surfaces of other crystallographic materials such as metal-organic frameworks, covalent-organic frameworks, zeolitic-imidazolate frameworks, metal oxides, etc.

Cutting Materials in Half: A Graph Theory Approach for Generating Crystal Surfaces and Its Prediction of 2D Zeolites

PubMed Central

2018-01-01

Scientific interest in two-dimensional (2D) materials, ranging from graphene and other single layer materials to atomically thin crystals, is quickly increasing for a large variety of technological applications. While in silico design approaches have made a large impact in the study of 3D crystals, algorithms designed to discover atomically thin 2D materials from their parent 3D materials are by comparison more sparse. We hypothesize that determining how to cut a 3D material in half (i.e., which Miller surface is formed) by severing a minimal number of bonds or a minimal amount of total bond energy per unit area can yield insight into preferred crystal faces. We answer this question by implementing a graph theory technique to mathematically formalize the enumeration of minimum cut surfaces of crystals. While the algorithm is generally applicable to different classes of materials, we focus on zeolitic materials due to their diverse structural topology and because 2D zeolites have promising catalytic and separation performance compared to their 3D counterparts. We report here a simple descriptor based only on structural information that predicts whether a zeolite is likely to be synthesizable in the 2D form and correctly identifies the expressed surface in known layered 2D zeolites. The discovery of this descriptor allows us to highlight other zeolites that may also be synthesized in the 2D form that have not been experimentally realized yet. Finally, our method is general since the mathematical formalism can be applied to find the minimum cut surfaces of other crystallographic materials such as metal–organic frameworks, covalent-organic frameworks, zeolitic-imidazolate frameworks, metal oxides, etc. PMID:29532024

Cutting Materials in Half: A Graph Theory Approach for Generating Crystal Surfaces and Its Prediction of 2D Zeolites

DOE PAGES

Witman, Matthew; Ling, Sanliang; Boyd, Peter; ...

2018-02-06

Scientific interest in two-dimensional (2D) materials, ranging from graphene and other single layer materials to atomically thin crystals, is quickly increasing for a large variety of technological applications. While in silico design approaches have made a large impact in the study of 3D crystals, algorithms designed to discover atomically thin 2D materials from their parent 3D materials are by comparison more sparse. Here, we hypothesize that determining how to cut a 3D material in half (i.e., which Miller surface is formed) by severing a minimal number of bonds or a minimal amount of total bond energy per unit area canmore » yield insight into preferred crystal faces. We answer this question by implementing a graph theory technique to mathematically formalize the enumeration of minimum cut surfaces of crystals. While the algorithm is generally applicable to different classes of materials, we focus on zeolitic materials due to their diverse structural topology and because 2D zeolites have promising catalytic and separation performance compared to their 3D counterparts. We report here a simple descriptor based only on structural information that predicts whether a zeolite is likely to be synthesizable in the 2D form and correctly identifies the expressed surface in known layered 2D zeolites. The discovery of this descriptor allows us to highlight other zeolites that may also be synthesized in the 2D form that have not been experimentally realized yet. Finally, our method is general since the mathematical formalism can be applied to find the minimum cut surfaces of other crystallographic materials such as metal-organic frameworks, covalent-organic frameworks, zeolitic-imidazolate frameworks, metal oxides, etc.« less

Language and the pain experience.

PubMed

Wilson, Dianne; Williams, Marie; Butler, David

2009-03-01

People in persistent pain have been reported to pay increased attention to specific words or descriptors of pain. The amount of attention paid to pain or cues for pain (such as pain descriptors), has been shown to be a major factor in the modulation of persistent pain. This relationship suggests the possibility that language may have a role both in understanding and managing the persistent pain experience. The aim of this paper is to describe current models of neuromatrices for pain and language, consider the role of attention in persistent pain states and highlight discrepancies, in previous studies based on the McGill Pain Questionnaire (MPQ), of the role of attention on pain descriptors. The existence of a pain neuromatrix originally proposed by Melzack (1990) has been supported by emerging technologies. Similar technologies have recently allowed identification of multiple areas of involvement for the processing of auditory input and the construction of language. As with the construction of pain, this neuromatrix for speech and language may intersect with neural systems for broader cognitive functions such as attention, memory and emotion. A systematic search was undertaken to identify experimental or review studies, which specifically investigated the role of attention on pain descriptors (as cues for pain) in persistent pain patients. A total of 99 articles were retrieved from six databases, with 66 articles meeting the inclusion criteria. After duplicated articles were eliminated, the remaining 41 articles were reviewed in order to support a link between persistent pain, pain descriptors and attention. This review revealed a diverse range of specific pain descriptors, the majority of which were derived from the MPQ. Increased attention to pain descriptors was consistently reported to be associated with emotional state as well as being a significant factor in maintaining persistent pain. However, attempts to investigate the attentional bias of specific pain descriptors highlighted discrepancies between the studies. As well as the diversity of pain descriptors used in studies, they were inconsistently categorized into domains of pain. A lack of consistent bias towards certain pain descriptors was observed, and may be explained simply by the fact that the words provided are not those which subjects themselves would use. These findings suggest that the multidimensional and individual nature of the persistent pain experience may not be adequately explained by pain questionnaires such as the MPQ. Personalized pain descriptors may communicate the pain experience more appropriately, but may also contribute to an increased sensitivity of cortical pain processing areas by capturing increased attention for that individual. The language used as part of communication between therapists and people with persistent pain may provide an, as yet, unexplored adjunct strategy in management. Copyright (c) 2008 John Wiley & Sons, Ltd.

Staging Lung Cancer: Metastasis.

PubMed

Shroff, Girish S; Viswanathan, Chitra; Carter, Brett W; Benveniste, Marcelo F; Truong, Mylene T; Sabloff, Bradley S

2018-05-01

The updated eighth edition of the tumor, node, metastasis (TNM) classification for lung cancer includes revisions to T and M descriptors. In terms of the M descriptor, the classification of intrathoracic metastatic disease as M1a is unchanged from TNM-7. Extrathoracic metastatic disease, which was classified as M1b in TNM-7, is now subdivided into M1b (single metastasis, single organ) and M1c (multiple metastases in one or multiple organs) descriptors. In this article, the rationale for changes in the M descriptors, the utility of preoperative staging with PET/computed tomography, and the treatment options available for patients with oligometastatic disease are discussed. Copyright © 2018 Elsevier Inc. All rights reserved.

Extraction of information from major element chemical analyses of lunar basalts

NASA Technical Reports Server (NTRS)

Butler, J. C.

1985-01-01

Major element chemical analyses often form the framework within which similarities and differences of analyzed specimens are noted and used to propose or devise models. When percentages are formed the ratios of pairs of components are preserved whereas many familiar statistical and geometrical descriptors are likely to exhibit major changes. This ratio preserving aspect forms the basis for a proposed framework. An analysis of compositional variability within the data set of 42 major element analyses of lunar reference samples was selected to investigate this proposal.

Ligand Electron Density Shape Recognition Using 3D Zernike Descriptors

NASA Astrophysics Data System (ADS)

Gunasekaran, Prasad; Grandison, Scott; Cowtan, Kevin; Mak, Lora; Lawson, David M.; Morris, Richard J.

We present a novel approach to crystallographic ligand density interpretation based on Zernike shape descriptors. Electron density for a bound ligand is expanded in an orthogonal polynomial series (3D Zernike polynomials) and the coefficients from this expansion are employed to construct rotation-invariant descriptors. These descriptors can be compared highly efficiently against large databases of descriptors computed from other molecules. In this manuscript we describe this process and show initial results from an electron density interpretation study on a dataset containing over a hundred OMIT maps. We could identify the correct ligand as the first hit in about 30 % of the cases, within the top five in a further 30 % of the cases, and giving rise to an 80 % probability of getting the correct ligand within the top ten matches. In all but a few examples, the top hit was highly similar to the correct ligand in both shape and chemistry. Further extensions and intrinsic limitations of the method are discussed.

Towards Rational Design of Functional Fluoride and Oxyfluoride Materials from First Principles

NASA Astrophysics Data System (ADS)

Charles, Nenian

Complex transition metal compounds (TMCs) research has produced functional materials with a range of properties, including ferroelectricity, colossal magnetoresistance, nonlinear optical activity and high-temperature superconductivity. Conventional routes to tune properties in transition metal oxides, for example, have relied primarily on cation chemical substitution and interfacial effects in thin film heterostructures. In heteroanionic TMCs, exhibiting two chemically distinct anions coordinating the same or different cations, engineering of the anion sub-lattice for property control is a promising alternative approach. The presence of multiple anions provides additional design variables, such as anion order, that are absent in homoanionic counterparts. The more complex structural and chemical phase space of heteroanionic materials provides a unique opportunity to realize enhanced or unanticipated electronic, optical, and magnetic responses. Although there is growing interest in heteroanionic materials, and synthetic and characterization advances are occurring for these materials, the crystal-chemistry principles for realizing structural and property control are only slowing emerging. This dissertation employs anion engineering to investigate phenomena in transition metal fluorides and oxyfluorides compounds using first principles density functional theory calculations. Oxyfluorides are particularly intriguing owing their tendency to stabilize highly ordered anion sublattices as well as the potential to combine the advantageous properties of transition metal oxides and fluorides. This work 1) addresses the challenges of studying fluorides and oxyfluorides using first principles calculations; 2) evaluates the feasibility of using external stimuli, such as epitaxial strain and hydrostatic pressure, to control properties of fluorides and oxyfluorides; and 3) formulates a computational workflow based on multiple levels of theory and computation to elucidate structure-property relationships and anion-order descriptors. The insights gained in this work advance the understanding of oxide-fluoride anion engineered materials and we anticipate that it will motivate novel experimental efforts and materials by design in the future.

Describing a Strongly Correlated Model System with Density Functional Theory.

PubMed

Kong, Jing; Proynov, Emil; Yu, Jianguo; Pachter, Ruth

2017-07-06

The linear chain of hydrogen atoms, a basic prototype for the transition from a metal to Mott insulator, is studied with a recent density functional theory model functional for nondynamic and strong correlation. The computed cohesive energy curve for the transition agrees well with accurate literature results. The variation of the electronic structure in this transition is characterized with a density functional descriptor that yields the atomic population of effectively localized electrons. These new methods are also applied to the study of the Peierls dimerization of the stretched even-spaced Mott insulator to a chain of H 2 molecules, a different insulator. The transitions among the two insulating states and the metallic state of the hydrogen chain system are depicted in a semiquantitative phase diagram. Overall, we demonstrate the capability of studying strongly correlated materials with a mean-field model at the fundamental level, in contrast to the general pessimistic view on such a feasibility.

Using Theoretical Descriptions in Structure Activity Relations. 3. Electronic Descriptors

DTIC Science & Technology

1988-08-01

Activity Relationships (QSAR) have been used successfully in the past to develop predictive equations for several biological and physical properties...Linear Free Energy Relationships (,FF.3) and is based on work by Hammet in which he derived electronic descriptors for the dissociation of substituted...structure of a compound and its activity in a system. Several different structural descriptors have been used in QSAR equations . These range from

Building Scientific Confidence in the Development and ...

EPA Pesticide Factsheets

Read-across remains a popular data gap filling technique within category and analogue approaches for regulatory purposes. Acceptance of read-across is an ongoing challenge with several efforts underway for identifying and addressing uncertainties. Here we demonstrate an algorithmic approach to facilitate read-across using ToxCast in vitro bioactivity data in conjunction with chemical descriptor information to predict in vivo outcomes in guideline testing studies from ToxRefDB. Over 3400 different chemical structure descriptors were generated for a set of 976 chemicals and supplemented with the outcomes from 821 in vitro assays. The read-across prediction for a given chemical was based on the similarity weighted endpoint outcomes of its nearest neighbors calculated using in vitro bioactivity and chemical structure descriptors, called GenRA. GenRA is based on a computational approach for: (i) defining local validity domains using chemical and bioactivity descriptors, (ii) systematically deriving endpoint read-across predictions within these domains using similarity weighted activity of nearest neighbours, (iii) objectively evaluating predicted performance using tested chemicals, and (iv) assigning read-across predictions to untested chemicals along with estimates of uncertainty. We found in vitro bioactivity descriptors were often found to be more predictive of in vivo toxicity outcomes than chemical structure descriptors. We believe GenRA is an important first st

A Universal 3D Voxel Descriptor for Solid-State Material Informatics with Deep Convolutional Neural Networks.

PubMed

Kajita, Seiji; Ohba, Nobuko; Jinnouchi, Ryosuke; Asahi, Ryoji

2017-12-05

Material informatics (MI) is a promising approach to liberate us from the time-consuming Edisonian (trial and error) process for material discoveries, driven by machine-learning algorithms. Several descriptors, which are encoded material features to feed computers, were proposed in the last few decades. Especially to solid systems, however, their insufficient representations of three dimensionality of field quantities such as electron distributions and local potentials have critically hindered broad and practical successes of the solid-state MI. We develop a simple, generic 3D voxel descriptor that compacts any field quantities, in such a suitable way to implement convolutional neural networks (CNNs). We examine the 3D voxel descriptor encoded from the electron distribution by a regression test with 680 oxides data. The present scheme outperforms other existing descriptors in the prediction of Hartree energies that are significantly relevant to the long-wavelength distribution of the valence electrons. The results indicate that this scheme can forecast any functionals of field quantities just by learning sufficient amount of data, if there is an explicit correlation between the target properties and field quantities. This 3D descriptor opens a way to import prominent CNNs-based algorithms of supervised, semi-supervised and reinforcement learnings into the solid-state MI.

Developing descriptors to predict mechanical properties of nanotubes.

PubMed

Borders, Tammie L; Fonseca, Alexandre F; Zhang, Hengji; Cho, Kyeongjae; Rusinko, Andrew

2013-04-22

Descriptors and quantitative structure property relationships (QSPR) were investigated for mechanical property prediction of carbon nanotubes (CNTs). 78 molecular dynamics (MD) simulations were carried out, and 20 descriptors were calculated to build quantitative structure property relationships (QSPRs) for Young's modulus and Poisson's ratio in two separate analyses: vacancy only and vacancy plus methyl functionalization. In the first analysis, C(N2)/C(T) (number of non-sp2 hybridized carbons per the total carbons) and chiral angle were identified as critical descriptors for both Young's modulus and Poisson's ratio. Further analysis and literature findings indicate the effect of chiral angle is negligible at larger CNT radii for both properties. Raman spectroscopy can be used to measure C(N2)/C(T), providing a direct link between experimental and computational results. Poisson's ratio approaches two different limiting values as CNT radii increases: 0.23-0.25 for chiral and armchair CNTs and 0.10 for zigzag CNTs (surface defects <3%). In the second analysis, the critical descriptors were C(N2)/C(T), chiral angle, and M(N)/C(T) (number of methyl groups per total carbons). These results imply new types of defects can be represented as a new descriptor in QSPR models. Finally, results are qualified and quantified against experimental data.

The cell monolayer trajectory from the system state point of view.

PubMed

Stys, Dalibor; Vanek, Jan; Nahlik, Tomas; Urban, Jan; Cisar, Petr

2011-10-01

Time-lapse microscopic movies are being increasingly utilized for understanding the derivation of cell states and predicting cell future. Often, fluorescence and other types of labeling are not available or desirable, and cell state-definitions based on observable structures must be used. We present the methodology for cell behavior recognition and prediction based on the short term cell recurrent behavior analysis. This approach has theoretical justification in non-linear dynamics theory. The methodology is based on the general stochastic systems theory which allows us to define the cell states, trajectory and the system itself. We introduce the usage of a novel image content descriptor based on information contribution (gain) by each image point for the cell state characterization as the first step. The linkage between the method and the general system theory is presented as a general frame for cell behavior interpretation. We also discuss extended cell description, system theory and methodology for future development. This methodology may be used for many practical purposes, ranging from advanced, medically relevant, precise cell culture diagnostics to very utilitarian cell recognition in a noisy or uneven image background. In addition, the results are theoretically justified.

Establishment of an in silico phototoxicity prediction method by combining descriptors related to photo-absorption and photo-reaction.

PubMed

Haranosono, Yu; Kurata, Masaaki; Sakaki, Hideyuki

2014-08-01

One of the mechanisms of phototoxicity is photo-reaction, such as reactive oxygen species (ROS) generation following photo-absorption. We focused on ROS generation and photo-absorption as key-steps, because these key-steps are able to be described by photochemical properties, and these properties are dependent on chemical structure. Photo-reactivity of a compound is described by HOMO-LUMO Gap (HLG), generally. Herein, we showed that HLG can be used as a descriptor of the generation of reactive oxygen species. Moreover, the maximum-conjugated π electron number (PENMC), which we found as a descriptor of photo-absorption, could also predict in vitro phototoxicity. Each descriptor could predict in vitro phototoxicity with 70.0% concordance, but there was un-predicted area found (gray zone). Interestingly, some compounds in each gray zone were not common, indicating that the combination of two descriptors could improve prediction potential. We reset the cut-off lines to define positive zone, negative zone and gray zone for each descriptor. Thereby we overlapped HLG and PENMC in a graph, and divided the total area to nine zones with cut-off lines of each descriptor. The rules to prediction were decided to achieve the best concordance, and the concordances were improved up to 82.8% for self-validation, 81.6% for cross-validation. We found common properties among false positive or negative compounds, photo-reactive structure and photo-allergenic, respectively. In addition, our method could be adapted to compounds rich in structural diversity using only chemical structure without any statistical analysis and complicated calculation.

Towards interoperable and reproducible QSAR analyses: Exchange of datasets.

PubMed

Spjuth, Ola; Willighagen, Egon L; Guha, Rajarshi; Eklund, Martin; Wikberg, Jarl Es

2010-06-30

QSAR is a widely used method to relate chemical structures to responses or properties based on experimental observations. Much effort has been made to evaluate and validate the statistical modeling in QSAR, but these analyses treat the dataset as fixed. An overlooked but highly important issue is the validation of the setup of the dataset, which comprises addition of chemical structures as well as selection of descriptors and software implementations prior to calculations. This process is hampered by the lack of standards and exchange formats in the field, making it virtually impossible to reproduce and validate analyses and drastically constrain collaborations and re-use of data. We present a step towards standardizing QSAR analyses by defining interoperable and reproducible QSAR datasets, consisting of an open XML format (QSAR-ML) which builds on an open and extensible descriptor ontology. The ontology provides an extensible way of uniquely defining descriptors for use in QSAR experiments, and the exchange format supports multiple versioned implementations of these descriptors. Hence, a dataset described by QSAR-ML makes its setup completely reproducible. We also provide a reference implementation as a set of plugins for Bioclipse which simplifies setup of QSAR datasets, and allows for exporting in QSAR-ML as well as old-fashioned CSV formats. The implementation facilitates addition of new descriptor implementations from locally installed software and remote Web services; the latter is demonstrated with REST and XMPP Web services. Standardized QSAR datasets open up new ways to store, query, and exchange data for subsequent analyses. QSAR-ML supports completely reproducible creation of datasets, solving the problems of defining which software components were used and their versions, and the descriptor ontology eliminates confusions regarding descriptors by defining them crisply. This makes is easy to join, extend, combine datasets and hence work collectively, but also allows for analyzing the effect descriptors have on the statistical model's performance. The presented Bioclipse plugins equip scientists with graphical tools that make QSAR-ML easily accessible for the community.

Predicting Drug-induced Hepatotoxicity Using QSAR and Toxicogenomics Approaches

PubMed Central

Low, Yen; Uehara, Takeki; Minowa, Yohsuke; Yamada, Hiroshi; Ohno, Yasuo; Urushidani, Tetsuro; Sedykh, Alexander; Muratov, Eugene; Fourches, Denis; Zhu, Hao; Rusyn, Ivan; Tropsha, Alexander

2014-01-01

Quantitative Structure-Activity Relationship (QSAR) modeling and toxicogenomics are used independently as predictive tools in toxicology. In this study, we evaluated the power of several statistical models for predicting drug hepatotoxicity in rats using different descriptors of drug molecules, namely their chemical descriptors and toxicogenomic profiles. The records were taken from the Toxicogenomics Project rat liver microarray database containing information on 127 drugs (http://toxico.nibio.go.jp/datalist.html). The model endpoint was hepatotoxicity in the rat following 28 days of exposure, established by liver histopathology and serum chemistry. First, we developed multiple conventional QSAR classification models using a comprehensive set of chemical descriptors and several classification methods (k nearest neighbor, support vector machines, random forests, and distance weighted discrimination). With chemical descriptors alone, external predictivity (Correct Classification Rate, CCR) from 5-fold external cross-validation was 61%. Next, the same classification methods were employed to build models using only toxicogenomic data (24h after a single exposure) treated as biological descriptors. The optimized models used only 85 selected toxicogenomic descriptors and had CCR as high as 76%. Finally, hybrid models combining both chemical descriptors and transcripts were developed; their CCRs were between 68 and 77%. Although the accuracy of hybrid models did not exceed that of the models based on toxicogenomic data alone, the use of both chemical and biological descriptors enriched the interpretation of the models. In addition to finding 85 transcripts that were predictive and highly relevant to the mechanisms of drug-induced liver injury, chemical structural alerts for hepatotoxicity were also identified. These results suggest that concurrent exploration of the chemical features and acute treatment-induced changes in transcript levels will both enrich the mechanistic understanding of sub-chronic liver injury and afford models capable of accurate prediction of hepatotoxicity from chemical structure and short-term assay results. PMID:21699217

Relationship between Dyspnea Descriptors and Underlying Causes of the Symptom; a Cross-sectional Study

PubMed Central

Sajadi, Seyyed Mohammad Ali; Majidi, Alireza; Abdollahimajd, Fahimeh; jalali, Fatemeh

2017-01-01

Introduction: History taking and physical examination help clinicians identify the patient’s problem and effectively treat it. This study aimed to evaluate the descriptors of dyspnea in patients presenting to emergency department (ED) with asthma, congestive heart failure (CHF), and chronic obstructive pulmonary disease (COPD). Method: This cross-sectional study was conducted on all patients presenting to ED with chief complaint of dyspnea, during 2 years. The patients were asked to describe their dyspnea by choosing three items from the valid and reliable questionnaire or articulating their sensation. The relationship between dyspnea descriptors and underlying cause of symptom was evaluated using SPSS version 16. Results: 312 patients with the mean age of 60.96±17.01 years were evaluated (53.2% male). Most of the patients were > 65 years old (48.7%) and had basic level of education (76.9%). "My breath doesn’t go out all the way" with 83.1%, “My chest feels tight " with 45.8%, and "I feel that my airway is obstructed" with 40.7%, were the most frequent dyspnea descriptors in asthma patients. "My breathing requires work" with 46.3%, "I feel that I am suffocating" with 31.5%, and "My breath doesn’t go out all the way" with 29.6%, were the most frequent dyspnea descriptors in COPD patients. "My breathing is heavy" with 74.4%, "A hunger for more air” with 24.4%, and "I cannot get enough air" with 23.2%, were the most frequent dyspnea descriptors in CHF patients. Except for “My breath does not go in all the way”, there was significant correlation between studied dyspnea descriptors and underlying disease (p = 0.001 for all analyses). Conclusion: It seems that dyspnea descriptors along with other findings from history and physical examination could be helpful in differentiating the causes of the symptom in patients presenting to ED suffering from dyspnea. PMID:28894777

Relationship between Dyspnea Descriptors and Underlying Causes of the Symptom; a Cross-sectional Study.

PubMed

Sajadi, Seyyed Mohammad Ali; Majidi, Alireza; Abdollahimajd, Fahimeh; Jalali, Fatemeh

2017-01-01

History taking and physical examination help clinicians identify the patient's problem and effectively treat it. This study aimed to evaluate the descriptors of dyspnea in patients presenting to emergency department (ED) with asthma, congestive heart failure (CHF), and chronic obstructive pulmonary disease (COPD). This cross-sectional study was conducted on all patients presenting to ED with chief complaint of dyspnea, during 2 years. The patients were asked to describe their dyspnea by choosing three items from the valid and reliable questionnaire or articulating their sensation. The relationship between dyspnea descriptors and underlying cause of symptom was evaluated using SPSS version 16. 312 patients with the mean age of 60.96±17.01 years were evaluated (53.2% male). Most of the patients were > 65 years old (48.7%) and had basic level of education (76.9%). "My breath doesn't go out all the way" with 83.1%, "My chest feels tight " with 45.8%, and "I feel that my airway is obstructed" with 40.7%, were the most frequent dyspnea descriptors in asthma patients. "My breathing requires work" with 46.3%, "I feel that I am suffocating" with 31.5%, and "My breath doesn't go out all the way" with 29.6%, were the most frequent dyspnea descriptors in COPD patients. "My breathing is heavy" with 74.4%, "A hunger for more air" with 24.4%, and "I cannot get enough air" with 23.2%, were the most frequent dyspnea descriptors in CHF patients. Except for "My breath does not go in all the way", there was significant correlation between studied dyspnea descriptors and underlying disease (p = 0.001 for all analyses). It seems that dyspnea descriptors along with other findings from history and physical examination could be helpful in differentiating the causes of the symptom in patients presenting to ED suffering from dyspnea.

Towards interoperable and reproducible QSAR analyses: Exchange of datasets

PubMed Central

2010-01-01

Background QSAR is a widely used method to relate chemical structures to responses or properties based on experimental observations. Much effort has been made to evaluate and validate the statistical modeling in QSAR, but these analyses treat the dataset as fixed. An overlooked but highly important issue is the validation of the setup of the dataset, which comprises addition of chemical structures as well as selection of descriptors and software implementations prior to calculations. This process is hampered by the lack of standards and exchange formats in the field, making it virtually impossible to reproduce and validate analyses and drastically constrain collaborations and re-use of data. Results We present a step towards standardizing QSAR analyses by defining interoperable and reproducible QSAR datasets, consisting of an open XML format (QSAR-ML) which builds on an open and extensible descriptor ontology. The ontology provides an extensible way of uniquely defining descriptors for use in QSAR experiments, and the exchange format supports multiple versioned implementations of these descriptors. Hence, a dataset described by QSAR-ML makes its setup completely reproducible. We also provide a reference implementation as a set of plugins for Bioclipse which simplifies setup of QSAR datasets, and allows for exporting in QSAR-ML as well as old-fashioned CSV formats. The implementation facilitates addition of new descriptor implementations from locally installed software and remote Web services; the latter is demonstrated with REST and XMPP Web services. Conclusions Standardized QSAR datasets open up new ways to store, query, and exchange data for subsequent analyses. QSAR-ML supports completely reproducible creation of datasets, solving the problems of defining which software components were used and their versions, and the descriptor ontology eliminates confusions regarding descriptors by defining them crisply. This makes is easy to join, extend, combine datasets and hence work collectively, but also allows for analyzing the effect descriptors have on the statistical model's performance. The presented Bioclipse plugins equip scientists with graphical tools that make QSAR-ML easily accessible for the community. PMID:20591161

Microscopic structural descriptor of liquid water

NASA Astrophysics Data System (ADS)

Shi, Rui; Tanaka, Hajime

2018-03-01

The microscopic structure of liquid water has been believed to be the key to the understanding of the unique properties of this extremely important substance. Many structural descriptors have been developed for revealing local structural order in water, but their properties are still not well understood. The essential difficulty comes from structural fluctuations due to thermal noise, which are intrinsic to the liquid state. The most popular and widely used descriptors are the local structure index (LSI) and d5. Recently, Russo and Tanaka [Nat. Commun. 3, 3556 (2014)] introduced a new descriptor ζ which measures the translational order between the first and second shells considering hydrogen bonding (H-bonding) in the first shell. In this work, we compare the performance of these three structural descriptors for a popular water model known as TIP5P water. We show that local structural ordering can be properly captured only by the structural descriptor ζ, but not by the other two descriptors particularly at a high temperature, where thermal noise effects are severe. The key difference of ζ from LSI and d5 is that only ζ considers H-bonding which is crucial to detect high translational and tetrahedral order of not only oxygen but also hydrogen atoms. The importance of H-bonding is very natural, considering the fact that the locally favored structures are stabilized by energy gain due to the formation of four hydrogen bonds between the central water molecule and its neighboring ones in the first shell. Our analysis of the water structure by using ζ strongly supports the two-state model of water: water is a dynamic mixture of locally favored (ordered) and normal-liquid (disordered) structures. This work demonstrates the importance of H-bonding in the characterization of water's structures and provides a useful structural descriptor for water-type tetrahedral liquids to study their structure and dynamics.

Musicians, postural quality and musculoskeletal health: A literature's review.

PubMed

Blanco-Piñeiro, Patricia; Díaz-Pereira, M Pino; Martínez, Aurora

2017-01-01

An analysis of the salient characteristics of research papers published between 1989 and 2015 that evaluate the relationship between postural quality during musical performance and various performance quality and health factors, with emphasis on musculoskeletal health variables. Searches of Medline, Scopus and Google Scholar for papers that analysed the subject of the study objective. The following MeSH descriptors were used: posture; postural balance; muscle, skeletal; task performance and analysis; back; and spine and music. A descriptive statistical analysis of their methodology (sample types, temporal design, and postural, health and other variables analysed) and findings has been made. The inclusion criterion was that the body postural quality of the musicians during performance was included among the target study variables. Forty-one relevant empirical studies were found, written in English. Comparison and analysis of their results was hampered by great disparities in measuring instruments and operationalization of variables. Despite the growing interest in the relationships among these variables, the empirical knowledge base still has many limitations, making rigorous comparative analysis difficult. Copyright © 2016 Elsevier Ltd. All rights reserved.

Exploring objective climate classification for the Himalayan arc and adjacent regions using gridded data sources

NASA Astrophysics Data System (ADS)

Forsythe, N.; Blenkinsop, S.; Fowler, H. J.

2015-05-01

A three-step climate classification was applied to a spatial domain covering the Himalayan arc and adjacent plains regions using input data from four global meteorological reanalyses. Input variables were selected based on an understanding of the climatic drivers of regional water resource variability and crop yields. Principal component analysis (PCA) of those variables and k-means clustering on the PCA outputs revealed a reanalysis ensemble consensus for eight macro-climate zones. Spatial statistics of input variables for each zone revealed consistent, distinct climatologies. This climate classification approach has potential for enhancing assessment of climatic influences on water resources and food security as well as for characterising the skill and bias of gridded data sets, both meteorological reanalyses and climate models, for reproducing subregional climatologies. Through their spatial descriptors (area, geographic centroid, elevation mean range), climate classifications also provide metrics, beyond simple changes in individual variables, with which to assess the magnitude of projected climate change. Such sophisticated metrics are of particular interest for regions, including mountainous areas, where natural and anthropogenic systems are expected to be sensitive to incremental climate shifts.

2D-QSAR study of fullerene nanostructure derivatives as potent HIV-1 protease inhibitors

NASA Astrophysics Data System (ADS)

Barzegar, Abolfazl; Jafari Mousavi, Somaye; Hamidi, Hossein; Sadeghi, Mehdi

2017-09-01

The protease of human immunodeficiency virus1 (HIV-PR) is an essential enzyme for antiviral treatments. Carbon nanostructures of fullerene derivatives, have nanoscale dimension with a diameter comparable to the diameter of the active site of HIV-PR which would in turn inhibit HIV. In this research, two dimensional quantitative structure-activity relationships (2D-QSAR) of fullerene derivatives against HIV-PR activity were employed as a powerful tool for elucidation the relationships between structure and experimental observations. QSAR study of 49 fullerene derivatives was performed by employing stepwise-MLR, GAPLS-MLR, and PCA-MLR models for variable (descriptor) selection and model construction. QSAR models were obtained with higher ability to predict the activity of the fullerene derivatives against HIV-PR by a correlation coefficient (R2training) of 0.942, 0.89, and 0.87 as well as R2test values of 0.791, 0.67and 0.674 for stepwise-MLR, GAPLS-MLR, and PCA -MLR models, respectively. Leave-one-out cross-validated correlation coefficient (R2CV) and Y-randomization methods confirmed the models robustness. The descriptors indicated that the HIV-PR inhibition depends on the van der Waals volumes, polarizability, bond order between two atoms and electronegativities of fullerenes derivatives. 2D-QSAR simulation without needing receptor's active site geometry, resulted in useful descriptors mainly denoting ;C60 backbone-functional groups; and ;C60 functional groups; properties. Both properties in fullerene refer to the ligand fitness and improvement van der Waals interactions with HIV-PR active site. Therefore, the QSAR models can be used in the search for novel HIV-PR inhibitors based on fullerene derivatives.

Qualitative data analysis for an exploratory sensory study of Grechetto wine.

PubMed

Esti, Marco; González Airola, Ricardo L; Moneta, Elisabetta; Paperaio, Marina; Sinesio, Fiorella

2010-02-15

Grechetto is a traditional white-grape vine, widespread in Umbria and Lazio regions in central Italy. Despite the wine commercial diffusion, little literature on its sensory characteristics is available. The present study is an exploratory research conducted with the aim of identifying the sensory markers of Grechetto wine and of evaluating the effect of clone, geographical area, vintage and producer on sensory attributes. A qualitative sensory study was conducted on 16 wines, differing for vintage, Typical Geographic Indication, and clone, collected from 7 wineries, using a trained panel in isolation who referred to a glossary of 133 white wine descriptors. Sixty-five attributes identified by a minimum of 50% of the respondents were submitted to a correspondence analysis to link wine samples to the sensory attributes. Seventeen terms identified as common to all samples are considered as characteristics of Grechetto wine, 10 of which olfactory: fruity, apple, acacia flower, pineapple, banana, floral, herbaceous, honey, apricot and peach. In order to interpret the relationship between design variables and sensory attributes data on 2005 and 2006 wines, the 28 most discriminating descriptors were projected in a principal component analysis. The first principal component was best described by olfactory terms and the second by gustative attributes. Good reproducibility of results was obtained for the two vintages. For one winery, vintage effect (2002-2006) was described in a new principal component analysis model applied on 39 most discriminating descriptors, which globally explained about 84% of the variance. In the young wines the notes of sulphur, yeast, dried fruit, butter, combined with herbaceous fresh and tropical fruity notes (melon, grapefruit) were dominant. During wine aging, sweeter notes, like honey, caramel, jam, become more dominant as well as some mineral notes, such as tuff and flint. Copyright 2009 Elsevier B.V. All rights reserved.

New formulae for Zagreb indices

NASA Astrophysics Data System (ADS)

Cangul, Ismail Naci; Yurttas, Aysun; Togan, Muge; Cevik, Ahmet Sinan

2017-07-01

In this paper, we study with some graph descriptors also called topological indices. These descriptors are useful in determination of some properties of chemical structures and preferred to some earlier descriptors as they are more practical. Especially the first and second Zagreb indices together with the first and second multiplicative Zagreb indices are considered and they are calculated in terms of the smallest and largest vertex degrees and vertex number for some well-known classes of graphs.

Analysis of A Drug Target-based Classification System using Molecular Descriptors.

PubMed

Lu, Jing; Zhang, Pin; Bi, Yi; Luo, Xiaomin

2016-01-01

Drug-target interaction is an important topic in drug discovery and drug repositioning. KEGG database offers a drug annotation and classification using a target-based classification system. In this study, we gave an investigation on five target-based classes: (I) G protein-coupled receptors; (II) Nuclear receptors; (III) Ion channels; (IV) Enzymes; (V) Pathogens, using molecular descriptors to represent each drug compound. Two popular feature selection methods, maximum relevance minimum redundancy and incremental feature selection, were adopted to extract the important descriptors. Meanwhile, an optimal prediction model based on nearest neighbor algorithm was constructed, which got the best result in identifying drug target-based classes. Finally, some key descriptors were discussed to uncover their important roles in the identification of drug-target classes.

A blur-invariant local feature for motion blurred image matching

NASA Astrophysics Data System (ADS)

Tong, Qiang; Aoki, Terumasa

2017-07-01

Image matching between a blurred (caused by camera motion, out of focus, etc.) image and a non-blurred image is a critical task for many image/video applications. However, most of the existing local feature schemes fail to achieve this work. This paper presents a blur-invariant descriptor and a novel local feature scheme including the descriptor and the interest point detector based on moment symmetry - the authors' previous work. The descriptor is based on a new concept - center peak moment-like element (CPME) which is robust to blur and boundary effect. Then by constructing CPMEs, the descriptor is also distinctive and suitable for image matching. Experimental results show our scheme outperforms state of the art methods for blurred image matching

Lensing-induced morphology changes in CMB temperature maps in modified gravity theories

DOE Office of Scientific and Technical Information (OSTI.GOV)

Munshi, D.; Coles, P.; Hu, B.

2016-04-01

Lensing of the Cosmic Microwave Background (CMB) changes the morphology of pattern of temperature fluctuations, so topological descriptors such as Minkowski Functionals can probe the gravity model responsible for the lensing. We show how the recently introduced two-to-two and three-to-one kurt-spectra (and their associated correlation functions), which depend on the power spectrum of the lensing potential, can be used to probe modified gravity theories such as f ( R ) theories of gravity and quintessence models. We also investigate models based on effective field theory, which include the constant-Ω model, and low-energy Hořava theories. Estimates of the cumulative signal-to-noise formore » detection of lensing-induced morphology changes, reaches O(10{sup 3}) for the future planned CMB polarization mission COrE{sup +}. Assuming foreground removal is possible to ℓ{sub max}=3000, we show that many modified gravity theories can be rejected with a high level of significance, making this technique comparable in power to galaxy weak lensing or redshift surveys. These topological estimators are also useful in distinguishing lensing from other scattering secondaries at the level of the four-point function or trispectrum. Examples include the kinetic Sunyaev-Zel'dovich (kSZ) effect which shares, with lensing, a lack of spectral distortion. We also discuss the complication of foreground contamination from unsubtracted point sources.« less

Chemistry explained by topology: an alternative approach.

PubMed

Galvez, Jorge; Villar, Vincent M; Galvez-Llompart, Maria; Amigó, José M

2011-05-01

Molecular topology can be considered an application of graph theory in which the molecular structure is characterized through a set of graph-theoretical descriptors called topological indices. Molecular topology has found applications in many different fields, particularly in biology, chemistry, and pharmacology. The first topological index was introduced by H. Wiener in 1947 [1]. Although its very first application was the prediction of the boiling points of the alkanes, the Wiener index has demonstrated since then a predictive capability far beyond that. Along with the Wiener index, in this paper we focus on a few pioneering topological indices, just to illustrate the connection between physicochemical properties and molecular connectivity.

Molecular structure, spectroscopic and docking analysis of 1,3-diphenylpyrazole-4-propionic acid: A good prostaglandin reductase inhibitor

NASA Astrophysics Data System (ADS)

Kavitha, T.; Velraj, G.

2018-03-01

The molecule 1,3-diphenylpyrazole-4-propionic acid (DPPA) was optimized to its minimum energy level using density functional theory (DFT) calculations. The vibrational frequencies of DPPA were calculated along with their potential energy distribution (PED) and the obtained values are validated with the help of experimental calculations. The reactivity nature of the molecule was investigated with the aid of various DFT methods such as global reactivity descriptors, local reactivity descriptors, molecular electrostatic potential (MEP), natural bond orbitals (NBOs), etc. The prediction of activity spectra for substances (PASS) result forecast that, DPPA can be more active as a prostaglandin (PG) reductase inhibitor. The PGs are biologically synthesized by the cyclooxygenase (COX) enzyme which exists in COX1 and COX2 forms. The PGs produced by COX2 enzyme induces inflammation and fungal infections and hence the inhibition of COX2 enzyme is indispensable in anti-inflammation and anti-fungal activities. The docking analysis of DPPA with COX enzymes (both COX1 and COX2) were carried out and eventually, it was found that DPPA can selectively inhibit COX2 enzyme and can serve as a PG reductase inhibitor thereby acting as a lead compound for the treatment of inflammation and fungal diseases.

Conceptual DFT Study of the Local Chemical Reactivity of the Colored BISARG Melanoidin and Its Protonated Derivative

PubMed Central

Frau, Juan; Glossman-Mitnik, Daniel

2018-01-01

This computational study assessed eight fixed RSH (range-separated hybrid) density functionals that include CAM-B3LYP, LC-ωPBE, M11, MN12SX, N12SX, ωB97, ωB97X, and ωB97XD related to the Def2TZVP basis sets together with the SMD solvation model in the calculation the molecular structure and reactivity properties of the BISARG intermediate melanoidin pigment (5-(2-(E)-(Z)-5-[(2-furyl)methylidene]-3-(4-acetylamino-4-carboxybutyl)-2-imino-1,3-dihydroimidazol-4-ylideneamino(E)-4-[(2-furyl)methylidene]-5-oxo-1H-imidazol-1-yl)-2-acetylaminovaleric acid) and its protonated derivative, BISARG(p). The chemical reactivity descriptors for the systems were calculated via the Conceptual Density Functional Theory. The choice of active sites applicable to nucleophilic, electrophilic as well as radical attacks were made by linking them with Fukui functions indices, electrophilic and nucleophilic Parr functions, and the condensed Dual Descriptor Δf(r). The study found the MN12SX and N12SX density functionals to be the most appropriate in predicting the chemical reactivity of the molecular systems under study starting from the knowledge of the HOMO, LUMO, and HOMO-LUMO gap energies. PMID:29765937

Evaluating the Energetic Driving Force for Cocrystal Formation

PubMed Central

2017-01-01

We present a periodic density functional theory study of the stability of 350 organic cocrystals relative to their pure single-component structures, the largest study of cocrystals yet performed with high-level computational methods. Our calculations demonstrate that cocrystals are on average 8 kJ mol–1 more stable than their constituent single-component structures and are very rarely (<5% of cases) less stable; cocrystallization is almost always a thermodynamically favorable process. We consider the variation in stability between different categories of systems—hydrogen-bonded, halogen-bonded, and weakly bound cocrystals—finding that, contrary to chemical intuition, the presence of hydrogen or halogen bond interactions is not necessarily a good predictor of stability. Finally, we investigate the correlation of the relative stability with simple chemical descriptors: changes in packing efficiency and hydrogen bond strength. We find some broad qualitative agreement with chemical intuition—more densely packed cocrystals with stronger hydrogen bonding tend to be more stable—but the relationship is weak, suggesting that such simple descriptors do not capture the complex balance of interactions driving cocrystallization. Our conclusions suggest that while cocrystallization is often a thermodynamically favorable process, it remains difficult to formulate general rules to guide synthesis, highlighting the continued importance of high-level computation in predicting and rationalizing such systems. PMID:29445316

Conceptual DFT Study of the Local Chemical Reactivity of the Colored BISARG Melanoidin and Its Protonated Derivative.

PubMed

Frau, Juan; Glossman-Mitnik, Daniel

2018-01-01

This computational study assessed eight fixed RSH (range-separated hybrid) density functionals that include CAM-B3LYP, LC-ωPBE, M11, MN12SX, N12SX, ωB97, ωB97X, and ωB97XD related to the Def2TZVP basis sets together with the SMD solvation model in the calculation the molecular structure and reactivity properties of the BISARG intermediate melanoidin pigment (5-(2-(E)-(Z)-5-[(2-furyl)methylidene]-3-(4-acetylamino-4-carboxybutyl)-2-imino-1,3-dihydroimidazol-4-ylideneamino(E)-4-[(2-furyl)methylidene]-5-oxo-1H-imidazol-1-yl)-2-acetylaminovaleric acid) and its protonated derivative, BISARG(p). The chemical reactivity descriptors for the systems were calculated via the Conceptual Density Functional Theory. The choice of active sites applicable to nucleophilic, electrophilic as well as radical attacks were made by linking them with Fukui functions indices, electrophilic and nucleophilic Parr functions, and the condensed Dual Descriptor Δf( r ). The study found the MN12SX and N12SX density functionals to be the most appropriate in predicting the chemical reactivity of the molecular systems under study starting from the knowledge of the HOMO, LUMO, and HOMO-LUMO gap energies.

Local multifractal detrended fluctuation analysis for non-stationary image's texture segmentation

NASA Astrophysics Data System (ADS)

Wang, Fang; Li, Zong-shou; Li, Jin-wei

2014-12-01

Feature extraction plays a great important role in image processing and pattern recognition. As a power tool, multifractal theory is recently employed for this job. However, traditional multifractal methods are proposed to analyze the objects with stationary measure and cannot for non-stationary measure. The works of this paper is twofold. First, the definition of stationary image and 2D image feature detection methods are proposed. Second, a novel feature extraction scheme for non-stationary image is proposed by local multifractal detrended fluctuation analysis (Local MF-DFA), which is based on 2D MF-DFA. A set of new multifractal descriptors, called local generalized Hurst exponent (Lhq) is defined to characterize the local scaling properties of textures. To test the proposed method, both the novel texture descriptor and other two multifractal indicators, namely, local Hölder coefficients based on capacity measure and multifractal dimension Dq based on multifractal differential box-counting (MDBC) method, are compared in segmentation experiments. The first experiment indicates that the segmentation results obtained by the proposed Lhq are better than the MDBC-based Dq slightly and superior to the local Hölder coefficients significantly. The results in the second experiment demonstrate that the Lhq can distinguish the texture images more effectively and provide more robust segmentations than the MDBC-based Dq significantly.

Evaluating the Energetic Driving Force for Cocrystal Formation.

PubMed

Taylor, Christopher R; Day, Graeme M

2018-02-07

We present a periodic density functional theory study of the stability of 350 organic cocrystals relative to their pure single-component structures, the largest study of cocrystals yet performed with high-level computational methods. Our calculations demonstrate that cocrystals are on average 8 kJ mol -1 more stable than their constituent single-component structures and are very rarely (<5% of cases) less stable; cocrystallization is almost always a thermodynamically favorable process. We consider the variation in stability between different categories of systems-hydrogen-bonded, halogen-bonded, and weakly bound cocrystals-finding that, contrary to chemical intuition, the presence of hydrogen or halogen bond interactions is not necessarily a good predictor of stability. Finally, we investigate the correlation of the relative stability with simple chemical descriptors: changes in packing efficiency and hydrogen bond strength. We find some broad qualitative agreement with chemical intuition-more densely packed cocrystals with stronger hydrogen bonding tend to be more stable-but the relationship is weak, suggesting that such simple descriptors do not capture the complex balance of interactions driving cocrystallization. Our conclusions suggest that while cocrystallization is often a thermodynamically favorable process, it remains difficult to formulate general rules to guide synthesis, highlighting the continued importance of high-level computation in predicting and rationalizing such systems.

Evolution of mixing width induced by general Rayleigh-Taylor instability.

PubMed

Zhang, You-Sheng; He, Zhi-Wei; Gao, Fu-Jie; Li, Xin-Liang; Tian, Bao-Lin

2016-06-01

Turbulent mixing induced by Rayleigh-Taylor (RT) instability occurs ubiquitously in many natural phenomena and engineering applications. As the simplest and primary descriptor of the mixing process, the evolution of mixing width of the mixing zone plays a notable role in the flows. The flows generally involve complex varying acceleration histories and widely varying density ratios, two dominant factors affecting the evolution of mixing width. However, no satisfactory theory for predicting the evolution has yet been established. Here a theory determining the evolution of mixing width in general RT flows is established to reproduce, first, all of the documented experiments conducted for diverse (i.e., constant, impulsive, oscillating, decreasing, increasing, and complex) acceleration histories and all density ratios. The theory is established in terms of the conservation principle, with special consideration given to the asymmetry of the volume-averaged density fields occurring in actual flows. The results reveal the sensitivity or insensitivity of the evolution of a mixing front of a neighboring light or heavy fluid to the degree of asymmetry and thus explain the distinct evolutions in two experiments with the same configurations.

Immobilization thresholds of electrofishing relative to fish size

USGS Publications Warehouse

Dolan, C.R.; Miranda, L.E.

2003-01-01

Fish size and electrical waveforms have frequently been associated with variation in electrofishing effectiveness. Under controlled laboratory conditions, we measured the electrical power required by five electrical waveforms to immobilize eight fish species of diverse sizes and shapes. Fish size was indexed by total body length, surface area, volume, and weight; shape was indexed by the ratio of body length to body depth. Our objectives were to identify immobilization thresholds, elucidate the descriptors of fish size that were best associated with those immobilization thresholds, and determine whether the vulnerability of a species relative to other species remained constant across electrical treatments. The results confirmed that fish size is a key variable controlling the immobilization threshold and further suggested that the size descriptor best related to immobilization is fish volume. The peak power needed to immobilize fish decreased rapidly with increasing fish volume in small fish but decreased slowly for fish larger than 75-100 cm 3. Furthermore, when we controlled for size and shape, different waveforms did not favor particular species, possibly because of the overwhelming effect of body size. Many of the immobilization inconsistencies previously attributed to species might simply represent the effect of disparities in body size.

Interrelationships among carcinogenicity, mutagenicity, acute toxicity, and chemical structure in a genotoxicity data base

DOE Office of Scientific and Technical Information (OSTI.GOV)

Benigni, R.; Andreoli, C.; Giuliani, A.

1989-01-01

The interrelationships among carcinogenicity, mutagenicity, acute toxicity (LD50), and a number of molecular descriptors were studied by computerized data analysis methods on the data base generated by the International Program for the Evaluation of Short-Term Test for Carcinogens (IPESTTC). With the use of statistical regression methods, three main associations were evidenced: (1) the well-known correlation between carcinogenicity and mutagenicity; (2) a correlation between mutagenicity and toxicity (LD50 ip in mice); and (3) a correlation between toxicity and a recently introduced estimator of the free energy of binding of the molecules to biological receptors. As expected on the basis of themore » large variety of chemical classes represented in the IPESTTC data base, no simple relationship between mutagenicity or carcinogenicity and chemical descriptors was found. To overcome this problem, a new pattern recognition method (REPAD), developed by us for structure-activity studies of noncongeneric chemicals, has been used. This allowed us to highlight a significant difference between the whole patterns of relationships among chemicophysical variables in the two groups to active (mutagenicity and/or carcinogenic) and inactive chemicals. This approach generated a classification rule able to correctly assign about 80% of carcinogens or mutagens.« less

Interactions between commercial fishing and walleye pollock aggregations

NASA Astrophysics Data System (ADS)

Stienessen, Sarah; Wilson, Chris D.; Hallowed, Anne B.

2002-05-01

Scientists with the Alaska Fisheries Science Center are conducting a multiyear field experiment off the eastern side of Kodiak Island in the Gulf of Alaska to determine whether commercial fishing activities significantly affect the distribution and abundance of walleye pollock (Theragra chalcogramma), an important prey species of endangered Steller sea lions (Eumetopias jubatus). In support of this activity, spatio-temporal patterns were described for pollock aggregations. Acoustic-trawl surveys were conducted in two adjacent submarine troughs in August 2001. One trough served as a control site where fishing was prohibited and the other as a treatment site where fishing was allowed. Software, which included patch recognition algorithms, was used to extract acoustic data and generate patch size and shape-related variables to analyze fish aggregations. Important patch related descriptors included skewness, kurtosis, length, height, and density. Estimates of patch fractal dimensions, which relate school perimeter to school area, were less for juvenile than for adult aggregations, indicating a more complex school shape for adults. Comparisons of other patch descriptors were made between troughs and in the presence and absence of the fishery to determine whether trends in pollock aggregation dynamics were a result of the fishery or of naturally occurring events.

Machine learning study for the prediction of transdermal peptide

NASA Astrophysics Data System (ADS)

Jung, Eunkyoung; Choi, Seung-Hoon; Lee, Nam Kyung; Kang, Sang-Kee; Choi, Yun-Jaie; Shin, Jae-Min; Choi, Kihang; Jung, Dong Hyun

2011-04-01

In order to develop a computational method to rapidly evaluate transdermal peptides, we report approaches for predicting the transdermal activity of peptides on the basis of peptide sequence information using Artificial Neural Network (ANN), Partial Least Squares (PLS) and Support Vector Machine (SVM). We identified 269 transdermal peptides by the phage display technique and use them as the positive controls to develop and test machine learning models. Combinations of three descriptors with neural network architectures, the number of latent variables and the kernel functions are tried in training to make appropriate predictions. The capacity of models is evaluated by means of statistical indicators including sensitivity, specificity, and the area under the receiver operating characteristic curve (ROC score). In the ROC score-based comparison, three methods proved capable of providing a reasonable prediction of transdermal peptide. The best result is obtained by SVM model with a radial basis function and VHSE descriptors. The results indicate that it is possible to discriminate between transdermal peptides and random sequences using our models. We anticipate that our models will be applicable to prediction of transdermal peptide for large peptide database for facilitating efficient transdermal drug delivery through intact skin.

A SAR and QSAR study of new artemisinin compounds with antimalarial activity.

PubMed

Santos, Cleydson Breno R; Vieira, Josinete B; Lobato, Cleison C; Hage-Melim, Lorane I S; Souto, Raimundo N P; Lima, Clarissa S; Costa, Elizabeth V M; Brasil, Davi S B; Macêdo, Williams Jorge C; Carvalho, José Carlos T

2013-12-30

The Hartree-Fock method and the 6-31G** basis set were employed to calculate the molecular properties of artemisinin and 20 derivatives with antimalarial activity. Maps of molecular electrostatic potential (MEPs) and molecular docking were used to investigate the interaction between ligands and the receptor (heme). Principal component analysis and hierarchical cluster analysis were employed to select the most important descriptors related to activity. The correlation between biological activity and molecular properties was obtained using the partial least squares and principal component regression methods. The regression PLS and PCR models built in this study were also used to predict the antimalarial activity of 30 new artemisinin compounds with unknown activity. The models obtained showed not only statistical significance but also predictive ability. The significant molecular descriptors related to the compounds with antimalarial activity were the hydration energy (HE), the charge on the O11 oxygen atom (QO11), the torsion angle O1-O2-Fe-N2 (D2) and the maximum rate of R/Sanderson Electronegativity (RTe+). These variables led to a physical and structural explanation of the molecular properties that should be selected for when designing new ligands to be used as antimalarial agents.

Formulation optimization of aprepitant microemulsion-loaded silicated corn fiber gum particles for enhanced bioavailability.

PubMed

Kamboj, Sunil; Rana, Vikas

2016-08-01

The present investigation was aimed at development of silicate corn fiber gum (SCFG) particles as superior solid carrier for the preparation of Aprepitant (APT)-loaded self-emulsifying powder (SEP) system. 2(4) D-optimal mixture design with three level process variables was employed to develop SCFG particles, utilizing flow descriptors and hydrophobicity descriptors as response variables. The results indicated that blending of CFG (51.4% w/w) and magnesium silicate (MS) (48.6% w/w) using freeze-drying technique was found to have highest desirability (0.904). The developed SEP showed highest oil desorbing capacity, low self-emulsification time and highest drug content. It was observed that SCFG-SEP (F2 formulation) showed lowest PDI (0.2445 ± 0.03) as well as smallest particle size (127 ± 5.8 nm). The droplets were uniform and maintain their integrity after reconstitution (TEM analysis). Furthermore, APT-loaded SEP showed enhanced in vitro dissolution (4 folds) and ex vivo performance (7-fold enhanced Papp) as compared to pure APT. Furthermore, in vivo pharmacokinetic study showed that significant enhancement (p > 0.05) in Cmax was evident with APT-loaded F2 (SCFG-SEP) (1.93-fold) and F4 (Aerosil 200-SEP) (1.58-fold). The data also suggested increase in absorption rate when APT incorporated into SCFG-SEP. Thus, findings pointed toward enhanced bioavailability of APT when loaded into SCFG particles. Overall, the developed SCFG particles could be considered as a better alternative to already available solid carrier(s).

Characterization of a pepper collection (Capsicum frutescens L.) from Brazil.

PubMed

Lima, M F; Carvalho, S I C; Ragassi, C F; Bianchetti, L B; Faleiro, F G; Reifschneider, F J B

2017-08-31

Germplasm banks are essential as sources of genetic variability for plant breeding programs. To characterize a Brazilian Capsicum frutescens collection, 21 malagueta and 5 Tabasco hot pepper accessions were evaluated under field and greenhouse conditions regarding morphological and molecular traits, as well as resistance to viruses. Morphological characterization was performed using 53 IPGRI (International Plant Genetic Resources Institute) descriptors, 15 vegetative, 13 inflorescence, 22 fruit, and 3 seed. Molecular characterization was carried out with 60 polymorphic markers from 29 RAPD primers. The incidence of major viruses infecting Capsicum spp, Tomato spotted wilt virus (TSWV), Groundnut ringspot virus (GRSV), Potato virus Y (PVY), Pepper yellow mosaic virus (PepYMV), Pepper mild mottle virus (PMMoV), and Cucumber mosaic virus (CMV) was evaluated by ELISA. Based on the average genetic distance among genotypes, six groups were defined for the 53 IPGRI descriptors. When considering only 11 quantitative traits (five vegetative and six fruit), six groups were also determined, and the traits plant canopy width (56.05%) and days to fruiting (25.07%) most explained the genetic diversity among genotypes. Molecular analysis defined five groups of accessions with partial correspondence to the morphological characterization data. The incidence of viruses in field-grown plants varied among genotypes and according to virus species, from 5.6% (GRSV; CNPH 3286) to 100% (PMMoV; CNPH2871), and indicated some accessions as potential sources of virus resistance. These results demonstrate the genetic variability within the group of 26 hot pepper accessions, as well as virus-resistant genotypes that can be used in breeding programs.

Morphology and genetics of Anadenanthera colubrina var. cebil (Fabaceae) tree from salta (Northwestern Argentina).

PubMed

de Viana, Marta L; Giamminola, Eugenia; Russo, Roberta; Ciaccio, Mirella

2014-06-01

Anadenanthera colubrina var. cebil is an important tree species for its cultural, economic, and medicinal uses in South America. In order to characterize A. colubrina populations, we collected fruits from four different sites (San Bernardo, El Cebilar, Metán and El Gallinato) within the species distribution area in Salta Province, Northwestern Argentina. For this, a total of 75 fruits and seeds per site were collected and described using morphological (fruits size and weight; seed weight and number per fruit) and genetic descriptors (ribosomic DNA extraction and PCR; nucleotide alignment and phylogenetic analysis) with standard protocols. Our results showed that the San Bernardo population had the heaviest fruits and seeds (7.89 +/- 0.2g and 0.19 +/- 0.002, respectively), and the Cebilar population the lightest (6.25 +/- 0.18g and 0.15 +/- 0.002g, respectively). Fruits and seeds from Metán and El Gallinato showed similar and intermediate values. The proportion viable (39 to 55%) and aborted (43 to 57%) seeds was different, while the proportion of predated (1.7 to 4.2%) seeds was similar among populations. The genetic analysis showed variability of ITS sequences within the especies, and also when compared with the same Brazilian species. Both, morphologic and genetic descriptors showed a high level of similarity between San Bernardo and Metán, and between El Cebilar and El Gallinato populations. Further studies are needed to assess levels of phenotypic and genetic variability within and between populations of different plant species, since this information is crucial for biodiversity and germplasm long-term conservation.

Geomorphology Drives Amphibian Beta Diversity in Atlantic Forest Lowlands of Southeastern Brazil

PubMed Central

Luiz, Amom Mendes; Leão-Pires, Thiago Augusto; Sawaya, Ricardo J.

2016-01-01

Beta diversity patterns are the outcome of multiple processes operating at different scales. Amphibian assemblages seem to be affected by contemporary climate and dispersal-based processes. However, historical processes involved in present patterns of beta diversity remain poorly understood. We assess and disentangle geomorphological, climatic and spatial drivers of amphibian beta diversity in coastal lowlands of the Atlantic Forest, southeastern Brazil. We tested the hypothesis that geomorphological factors are more important in structuring anuran beta diversity than climatic and spatial factors. We obtained species composition via field survey (N = 766 individuals), museum specimens (N = 9,730) and literature records (N = 4,763). Sampling area was divided in four spatially explicit geomorphological units, representing historical predictors. Climatic descriptors were represented by the first two axis of a Principal Component Analysis. Spatial predictors in different spatial scales were described by Moran Eigenvector Maps. Redundancy Analysis was implemented to partition the explained variation of species composition by geomorphological, climatic and spatial predictors. Moreover, spatial autocorrelation analyses were used to test neutral theory predictions. Beta diversity was spatially structured in broader scales. Shared fraction between climatic and geomorphological variables was an important predictor of species composition (13%), as well as broad scale spatial predictors (13%). However, geomorphological variables alone were the most important predictor of beta diversity (42%). Historical factors related to geomorphology must have played a crucial role in structuring amphibian beta diversity. The complex relationships between geomorphological history and climatic gradients generated by the Serra do Mar Precambrian basements were also important. We highlight the importance of combining spatially explicit historical and contemporary predictors for understanding and disentangling major drivers of beta diversity patterns. PMID:27171522

Geomorphology Drives Amphibian Beta Diversity in Atlantic Forest Lowlands of Southeastern Brazil.

PubMed

Luiz, Amom Mendes; Leão-Pires, Thiago Augusto; Sawaya, Ricardo J

2016-01-01

Beta diversity patterns are the outcome of multiple processes operating at different scales. Amphibian assemblages seem to be affected by contemporary climate and dispersal-based processes. However, historical processes involved in present patterns of beta diversity remain poorly understood. We assess and disentangle geomorphological, climatic and spatial drivers of amphibian beta diversity in coastal lowlands of the Atlantic Forest, southeastern Brazil. We tested the hypothesis that geomorphological factors are more important in structuring anuran beta diversity than climatic and spatial factors. We obtained species composition via field survey (N = 766 individuals), museum specimens (N = 9,730) and literature records (N = 4,763). Sampling area was divided in four spatially explicit geomorphological units, representing historical predictors. Climatic descriptors were represented by the first two axis of a Principal Component Analysis. Spatial predictors in different spatial scales were described by Moran Eigenvector Maps. Redundancy Analysis was implemented to partition the explained variation of species composition by geomorphological, climatic and spatial predictors. Moreover, spatial autocorrelation analyses were used to test neutral theory predictions. Beta diversity was spatially structured in broader scales. Shared fraction between climatic and geomorphological variables was an important predictor of species composition (13%), as well as broad scale spatial predictors (13%). However, geomorphological variables alone were the most important predictor of beta diversity (42%). Historical factors related to geomorphology must have played a crucial role in structuring amphibian beta diversity. The complex relationships between geomorphological history and climatic gradients generated by the Serra do Mar Precambrian basements were also important. We highlight the importance of combining spatially explicit historical and contemporary predictors for understanding and disentangling major drivers of beta diversity patterns.

Use of in Vitro HTS-Derived Concentration–Response Data as Biological Descriptors Improves the Accuracy of QSAR Models of in Vivo Toxicity

PubMed Central

Sedykh, Alexander; Zhu, Hao; Tang, Hao; Zhang, Liying; Richard, Ann; Rusyn, Ivan; Tropsha, Alexander

2011-01-01

Background Quantitative high-throughput screening (qHTS) assays are increasingly being used to inform chemical hazard identification. Hundreds of chemicals have been tested in dozens of cell lines across extensive concentration ranges by the National Toxicology Program in collaboration with the National Institutes of Health Chemical Genomics Center. Objectives Our goal was to test a hypothesis that dose–response data points of the qHTS assays can serve as biological descriptors of assayed chemicals and, when combined with conventional chemical descriptors, improve the accuracy of quantitative structure–activity relationship (QSAR) models applied to prediction of in vivo toxicity end points. Methods We obtained cell viability qHTS concentration–response data for 1,408 substances assayed in 13 cell lines from PubChem; for a subset of these compounds, rodent acute toxicity half-maximal lethal dose (LD50) data were also available. We used the k nearest neighbor classification and random forest QSAR methods to model LD50 data using chemical descriptors either alone (conventional models) or combined with biological descriptors derived from the concentration–response qHTS data (hybrid models). Critical to our approach was the use of a novel noise-filtering algorithm to treat qHTS data. Results Both the external classification accuracy and coverage (i.e., fraction of compounds in the external set that fall within the applicability domain) of the hybrid QSAR models were superior to conventional models. Conclusions Concentration–response qHTS data may serve as informative biological descriptors of molecules that, when combined with conventional chemical descriptors, may considerably improve the accuracy and utility of computational approaches for predicting in vivo animal toxicity end points. PMID:20980217

Towards a metadata scheme for the description of materials - the description of microstructures

NASA Astrophysics Data System (ADS)

Schmitz, Georg J.; Böttger, Bernd; Apel, Markus; Eiken, Janin; Laschet, Gottfried; Altenfeld, Ralph; Berger, Ralf; Boussinot, Guillaume; Viardin, Alexandre

2016-01-01

The property of any material is essentially determined by its microstructure. Numerical models are increasingly the focus of modern engineering as helpful tools for tailoring and optimization of custom-designed microstructures by suitable processing and alloy design. A huge variety of software tools is available to predict various microstructural aspects for different materials. In the general frame of an integrated computational materials engineering (ICME) approach, these microstructure models provide the link between models operating at the atomistic or electronic scales, and models operating on the macroscopic scale of the component and its processing. In view of an improved interoperability of all these different tools it is highly desirable to establish a standardized nomenclature and methodology for the exchange of microstructure data. The scope of this article is to provide a comprehensive system of metadata descriptors for the description of a 3D microstructure. The presented descriptors are limited to a mere geometric description of a static microstructure and have to be complemented by further descriptors, e.g. for properties, numerical representations, kinetic data, and others in the future. Further attributes to each descriptor, e.g. on data origin, data uncertainty, and data validity range are being defined in ongoing work. The proposed descriptors are intended to be independent of any specific numerical representation. The descriptors defined in this article may serve as a first basis for standardization and will simplify the data exchange between different numerical models, as well as promote the integration of experimental data into numerical models of microstructures. An HDF5 template data file for a simple, three phase Al-Cu microstructure being based on the defined descriptors complements this article.

Towards a metadata scheme for the description of materials - the description of microstructures.

PubMed

Schmitz, Georg J; Böttger, Bernd; Apel, Markus; Eiken, Janin; Laschet, Gottfried; Altenfeld, Ralph; Berger, Ralf; Boussinot, Guillaume; Viardin, Alexandre

2016-01-01

The property of any material is essentially determined by its microstructure. Numerical models are increasingly the focus of modern engineering as helpful tools for tailoring and optimization of custom-designed microstructures by suitable processing and alloy design. A huge variety of software tools is available to predict various microstructural aspects for different materials. In the general frame of an integrated computational materials engineering (ICME) approach, these microstructure models provide the link between models operating at the atomistic or electronic scales, and models operating on the macroscopic scale of the component and its processing. In view of an improved interoperability of all these different tools it is highly desirable to establish a standardized nomenclature and methodology for the exchange of microstructure data. The scope of this article is to provide a comprehensive system of metadata descriptors for the description of a 3D microstructure. The presented descriptors are limited to a mere geometric description of a static microstructure and have to be complemented by further descriptors, e.g. for properties, numerical representations, kinetic data, and others in the future. Further attributes to each descriptor, e.g. on data origin, data uncertainty, and data validity range are being defined in ongoing work. The proposed descriptors are intended to be independent of any specific numerical representation. The descriptors defined in this article may serve as a first basis for standardization and will simplify the data exchange between different numerical models, as well as promote the integration of experimental data into numerical models of microstructures. An HDF5 template data file for a simple, three phase Al-Cu microstructure being based on the defined descriptors complements this article.

Descriptors of Oxygen-Evolution Activity for Oxides: A Statistical Evaluation

DOE PAGES

Hong, Wesley T.; Welsch, Roy E.; Shao-Horn, Yang

2015-12-16

Catalysts for oxygen electrochemical processes are critical for the commercial viability of renewable energy storage and conversion devices such as fuel cells, artificial photosynthesis, and metal-air batteries. Transition metal oxides are an excellent system for developing scalable, non-noble-metal-based catalysts, especially for the oxygen evolution reaction (OER). Central to the rational design of novel catalysts is the development of quantitative structure-activity relation-ships, which correlate the desired catalytic behavior to structural and/or elemental descriptors of materials. The ultimate goal is to use these relationships to guide materials design. In this study, 101 intrinsic OER activities of 51 perovskites were compiled from fivemore » studies in literature and additional measurements made for this work. We explored the behavior and performance of 14 descriptors of the metal-oxygen bond strength using a number of statistical approaches, including factor analysis and linear regression models. We found that these descriptors can be classified into five descriptor families and identify electron occupancy and metal-oxygen covalency as the dominant influences on the OER activity. However, multiple descriptors still need to be considered in order to develop strong predictive relationships, largely outperforming the use of only one or two descriptors (as conventionally done in the field). Here, we confirmed that the number of d electrons, charge-transfer energy (covalency), and optimality of eg occupancy play the important roles, but found that structural factors such as M-O-M bond angle and tolerance factor are relevant as well. With these tools, we demonstrate how statistical learning can be used to draw novel physical insights and combined with data mining to rapidly screen OER electrocatalysts across a wide chemical space.« less

Structural protein descriptors in 1-dimension and their sequence-based predictions.

PubMed

Kurgan, Lukasz; Disfani, Fatemeh Miri

2011-09-01

The last few decades observed an increasing interest in development and application of 1-dimensional (1D) descriptors of protein structure. These descriptors project 3D structural features onto 1D strings of residue-wise structural assignments. They cover a wide-range of structural aspects including conformation of the backbone, burying depth/solvent exposure and flexibility of residues, and inter-chain residue-residue contacts. We perform first-of-its-kind comprehensive comparative review of the existing 1D structural descriptors. We define, review and categorize ten structural descriptors and we also describe, summarize and contrast over eighty computational models that are used to predict these descriptors from the protein sequences. We show that the majority of the recent sequence-based predictors utilize machine learning models, with the most popular being neural networks, support vector machines, hidden Markov models, and support vector and linear regressions. These methods provide high-throughput predictions and most of them are accessible to a non-expert user via web servers and/or stand-alone software packages. We empirically evaluate several recent sequence-based predictors of secondary structure, disorder, and solvent accessibility descriptors using a benchmark set based on CASP8 targets. Our analysis shows that the secondary structure can be predicted with over 80% accuracy and segment overlap (SOV), disorder with over 0.9 AUC, 0.6 Matthews Correlation Coefficient (MCC), and 75% SOV, and relative solvent accessibility with PCC of 0.7 and MCC of 0.6 (0.86 when homology is used). We demonstrate that the secondary structure predicted from sequence without the use of homology modeling is as good as the structure extracted from the 3D folds predicted by top-performing template-based methods.

Towards the chemometric dissection of peptide - HLA-A*0201 binding affinity: comparison of local and global QSAR models

NASA Astrophysics Data System (ADS)

Doytchinova, Irini A.; Walshe, Valerie; Borrow, Persephone; Flower, Darren R.

2005-03-01

The affinities of 177 nonameric peptides binding to the HLA-A*0201 molecule were measured using a FACS-based MHC stabilisation assay and analysed using chemometrics. Their structures were described by global and local descriptors, QSAR models were derived by genetic algorithm, stepwise regression and PLS. The global molecular descriptors included molecular connectivity χ indices, κ shape indices, E-state indices, molecular properties like molecular weight and log P, and three-dimensional descriptors like polarizability, surface area and volume. The local descriptors were of two types. The first used a binary string to indicate the presence of each amino acid type at each position of the peptide. The second was also position-dependent but used five z-scales to describe the main physicochemical properties of the amino acids forming the peptides. The models were developed using a representative training set of 131 peptides and validated using an independent test set of 46 peptides. It was found that the global descriptors could not explain the variance in the training set nor predict the affinities of the test set accurately. Both types of local descriptors gave QSAR models with better explained variance and predictive ability. The results suggest that, in their interactions with the MHC molecule, the peptide acts as a complicated ensemble of multiple amino acids mutually potentiating each other.

Determination of polyparameter linear free energy relationship (pp-LFER) substance descriptors for established and alternative flame retardants.

PubMed

Stenzel, Angelika; Goss, Kai-Uwe; Endo, Satoshi

2013-02-05

Polyparameter linear free energy relationships (pp-LFERs) can predict partition coefficients for a multitude of environmental and biological phases with high accuracy. In this work, the pp-LFER substance descriptors of 40 established and alternative flame retardants (e.g., polybrominated diphenyl ethers, hexabromocyclododecane, bromobenzenes, trialkyl phosphates) were determined experimentally. In total, 251 data for gas-chromatographic (GC) retention times and liquid/liquid partition coefficients (K) were measured and used to calibrate the pp-LFER substance descriptors. Substance descriptors were validated through a comparison between predicted and experimental log K for the systems octanol/water (K(ow)), water/air (K(wa)), organic carbon/water (K(oc)) and liposome/water (K(lipw)), revealing a high reliability of pp-LFER predictions based on our descriptors. For instance, the difference between predicted and experimental log K(ow) was <0.3 log units for 17 out of 21 compounds for which experimental values were available. Moreover, we found an indication that the H-bond acceptor value (B) depends on the solvent for some compounds. Thus, for predicting environmentally relevant partition coefficients it is important to determine B values using measurements in aqueous systems. The pp-LFER descriptors calibrated in this study can be used to predict partition coefficients for which experimental data are unavailable, and the predicted values can serve as references for further experimental measurements.

3D molecular descriptors important for clinical success.

PubMed

Kombo, David C; Tallapragada, Kartik; Jain, Rachit; Chewning, Joseph; Mazurov, Anatoly A; Speake, Jason D; Hauser, Terry A; Toler, Steve

2013-02-25

The pharmacokinetic and safety profiles of clinical drug candidates are greatly influenced by their requisite physicochemical properties. In particular, it has been shown that 2D molecular descriptors such as fraction of Sp3 carbon atoms (Fsp3) and number of stereo centers correlate with clinical success. Using the proteomic off-target hit rate of nicotinic ligands, we found that shape-based 3D descriptors such as the radius of gyration and shadow indices discriminate off-target promiscuity better than do Fsp3 and the number of stereo centers. We have deduced the relevant descriptor values required for a ligand to be nonpromiscuous. Investigating the MDL Drug Data Report (MDDR) database as compounds move from the preclinical stage toward the market, we have found that these shape-based 3D descriptors predict clinical success of compounds at preclinical and phase1 stages vs compounds withdrawn from the market better than do Fsp3 and LogD. Further, these computed 3D molecular descriptors correlate well with experimentally observed solubility, which is among well-known physicochemical properties that drive clinical success. We also found that about 84% of launched drugs satisfy either Shadow index or Fsp3 criteria, whereas withdrawn and discontinued compounds fail to meet the same criteria. Our studies suggest that spherical compounds (rather than their elongated counterparts) with a minimal number of aromatic rings may exhibit a high propensity to advance from clinical trials to market.

Influence of Texture and Colour in Breast TMA Classification

PubMed Central

Fernández-Carrobles, M. Milagro; Bueno, Gloria; Déniz, Oscar; Salido, Jesús; García-Rojo, Marcial; González-López, Lucía

2015-01-01

Breast cancer diagnosis is still done by observation of biopsies under the microscope. The development of automated methods for breast TMA classification would reduce diagnostic time. This paper is a step towards the solution for this problem and shows a complete study of breast TMA classification based on colour models and texture descriptors. The TMA images were divided into four classes: i) benign stromal tissue with cellularity, ii) adipose tissue, iii) benign and benign anomalous structures, and iv) ductal and lobular carcinomas. A relevant set of features was obtained on eight different colour models from first and second order Haralick statistical descriptors obtained from the intensity image, Fourier, Wavelets, Multiresolution Gabor, M-LBP and textons descriptors. Furthermore, four types of classification experiments were performed using six different classifiers: (1) classification per colour model individually, (2) classification by combination of colour models, (3) classification by combination of colour models and descriptors, and (4) classification by combination of colour models and descriptors with a previous feature set reduction. The best result shows an average of 99.05% accuracy and 98.34% positive predictive value. These results have been obtained by means of a bagging tree classifier with combination of six colour models and the use of 1719 non-correlated (correlation threshold of 97%) textural features based on Statistical, M-LBP, Gabor and Spatial textons descriptors. PMID:26513238

Stargate GTM: Bridging Descriptor and Activity Spaces.

PubMed

Gaspar, Héléna A; Baskin, Igor I; Marcou, Gilles; Horvath, Dragos; Varnek, Alexandre

2015-11-23

Predicting the activity profile of a molecule or discovering structures possessing a specific activity profile are two important goals in chemoinformatics, which could be achieved by bridging activity and molecular descriptor spaces. In this paper, we introduce the "Stargate" version of the Generative Topographic Mapping approach (S-GTM) in which two different multidimensional spaces (e.g., structural descriptor space and activity space) are linked through a common 2D latent space. In the S-GTM algorithm, the manifolds are trained simultaneously in two initial spaces using the probabilities in the 2D latent space calculated as a weighted geometric mean of probability distributions in both spaces. S-GTM has the following interesting features: (1) activities are involved during the training procedure; therefore, the method is supervised, unlike conventional GTM; (2) using molecular descriptors of a given compound as input, the model predicts a whole activity profile, and (3) using an activity profile as input, areas populated by relevant chemical structures can be detected. To assess the performance of S-GTM prediction models, a descriptor space (ISIDA descriptors) of a set of 1325 GPCR ligands was related to a B-dimensional (B = 1 or 8) activity space corresponding to pKi values for eight different targets. S-GTM outperforms conventional GTM for individual activities and performs similarly to the Lasso multitask learning algorithm, although it is still slightly less accurate than the Random Forest method.

QSPR study of polychlorinated diphenyl ethers by molecular electronegativity distance vector (MEDV-4).

PubMed

Sun, Lili; Zhou, Liping; Yu, Yu; Lan, Yukun; Li, Zhiliang

2007-01-01

Polychlorinated diphenyl ethers (PCDEs) have received more and more concerns as a group of ubiquitous potential persistent organic pollutants (POPs). By using molecular electronegativity distance vector (MEDV-4), multiple linear regression (MLR) models are developed for sub-cooled liquid vapor pressures (P(L)), n-octanol/water partition coefficients (K(OW)) and sub-cooled liquid water solubilities (S(W,L)) of 209 PCDEs and diphenyl ether. The correlation coefficients (R) and the leave-one-out cross-validation (LOO) correlation coefficients (R(CV)) of all the 6-descriptor models for logP(L), logK(OW) and logS(W,L) are more than 0.98. By using stepwise multiple regression (SMR), the descriptors are selected and the resulting models are 5-descriptor model for logP(L), 4-descriptor model for logK(OW), and 6-descriptor model for logS(W,L), respectively. All these models exhibit excellent estimate capabilities for internal sample set and good predictive capabilities for external samples set. The consistency between observed and estimated/predicted values for logP(L) is the best (R=0.996, R(CV)=0.996), followed by logK(OW) (R=0.992, R(CV)=0.992) and logS(W,L) (R=0.983, R(CV)=0.980). By using MEDV-4 descriptors, the QSPR models can be used for prediction and the model predictions can hence extend the current database of experimental values.

A quantitative structure-activity relationship to predict efficacy of granular activated carbon adsorption to control emerging contaminants.

PubMed

Kennicutt, A R; Morkowchuk, L; Krein, M; Breneman, C M; Kilduff, J E

2016-08-01

A quantitative structure-activity relationship was developed to predict the efficacy of carbon adsorption as a control technology for endocrine-disrupting compounds, pharmaceuticals, and components of personal care products, as a tool for water quality professionals to protect public health. Here, we expand previous work to investigate a broad spectrum of molecular descriptors including subdivided surface areas, adjacency and distance matrix descriptors, electrostatic partial charges, potential energy descriptors, conformation-dependent charge descriptors, and Transferable Atom Equivalent (TAE) descriptors that characterize the regional electronic properties of molecules. We compare the efficacy of linear (Partial Least Squares) and non-linear (Support Vector Machine) machine learning methods to describe a broad chemical space and produce a user-friendly model. We employ cross-validation, y-scrambling, and external validation for quality control. The recommended Support Vector Machine model trained on 95 compounds having 23 descriptors offered a good balance between good performance statistics, low error, and low probability of over-fitting while describing a wide range of chemical features. The cross-validated model using a log-uptake (qe) response calculated at an aqueous equilibrium concentration (Ce) of 1 μM described the training dataset with an r(2) of 0.932, had a cross-validated r(2) of 0.833, and an average residual of 0.14 log units.

Estimation of descriptors for hydrogen-bonding compounds from chromatographic and liquid-liquid partition measurements.

PubMed

Lenca, Nicole; Atapattu, Sanka N; Poole, Colin F

2017-12-01

Retention factors obtained by gas chromatography and reversed-phase liquid chromatography on varied columns and partition constants in different liquid-liquid partition systems are used to estimate WSU descriptor values for 36 anilines and N-heterocyclic compounds, 13 amides and related compounds, and 45 phenols and alcohols. These compounds are suitable for use as calibration compounds to characterize separation systems covering the descriptor space E=0.2-3, S=0.4-2.1, A=0-1.5, B=0.1-1.5, L=2.5-10.0 and V=0.5-2.2. Hydrogen-bonding properties are discussed in terms of structure, the possibility of induction effects, intramolecular hydrogen bonding and steric factors for anilines, amides, phenols and alcohols. The relationship between these parameters and observed descriptor values are difficult to predict from structure but facilitate improving the general occupancy of the descriptor space by creating incremental changes in hydrogen-bonding properties. It is verified that the compounds included in this study can be merged with an existing database of compounds recommended for characterizing separation systems. Copyright © 2017 Elsevier B.V. All rights reserved.

Locator-Checker-Scaler Object Tracking Using Spatially Ordered and Weighted Patch Descriptor.

PubMed

Kim, Han-Ul; Kim, Chang-Su

2017-08-01

In this paper, we propose a simple yet effective object descriptor and a novel tracking algorithm to track a target object accurately. For the object description, we divide the bounding box of a target object into multiple patches and describe them with color and gradient histograms. Then, we determine the foreground weight of each patch to alleviate the impacts of background information in the bounding box. To this end, we perform random walk with restart (RWR) simulation. We then concatenate the weighted patch descriptors to yield the spatially ordered and weighted patch (SOWP) descriptor. For the object tracking, we incorporate the proposed SOWP descriptor into a novel tracking algorithm, which has three components: locator, checker, and scaler (LCS). The locator and the scaler estimate the center location and the size of a target, respectively. The checker determines whether it is safe to adjust the target scale in a current frame. These three components cooperate with one another to achieve robust tracking. Experimental results demonstrate that the proposed LCS tracker achieves excellent performance on recent benchmarks.

Artificial Intelligence Methods Applied to Parameter Detection of Atrial Fibrillation

NASA Astrophysics Data System (ADS)

Arotaritei, D.; Rotariu, C.

2015-09-01

In this paper we present a novel method to develop an atrial fibrillation (AF) based on statistical descriptors and hybrid neuro-fuzzy and crisp system. The inference of system produce rules of type if-then-else that care extracted to construct a binary decision system: normal of atrial fibrillation. We use TPR (Turning Point Ratio), SE (Shannon Entropy) and RMSSD (Root Mean Square of Successive Differences) along with a new descriptor, Teager- Kaiser energy, in order to improve the accuracy of detection. The descriptors are calculated over a sliding window that produce very large number of vectors (massive dataset) used by classifier. The length of window is a crisp descriptor meanwhile the rest of descriptors are interval-valued type. The parameters of hybrid system are adapted using Genetic Algorithm (GA) algorithm with fitness single objective target: highest values for sensibility and sensitivity. The rules are extracted and they are part of the decision system. The proposed method was tested using the Physionet MIT-BIH Atrial Fibrillation Database and the experimental results revealed a good accuracy of AF detection in terms of sensitivity and specificity (above 90%).

Innovative design method of automobile profile based on Fourier descriptor

NASA Astrophysics Data System (ADS)

Gao, Shuyong; Fu, Chaoxing; Xia, Fan; Shen, Wei

2017-10-01

Aiming at the innovation of the contours of automobile side, this paper presents an innovative design method of vehicle side profile based on Fourier descriptor. The design flow of this design method is: pre-processing, coordinate extraction, standardization, discrete Fourier transform, simplified Fourier descriptor, exchange descriptor innovation, inverse Fourier transform to get the outline of innovative design. Innovative concepts of the innovative methods of gene exchange among species and the innovative methods of gene exchange among different species are presented, and the contours of the innovative design are obtained separately. A three-dimensional model of a car is obtained by referring to the profile curve which is obtained by exchanging xenogeneic genes. The feasibility of the method proposed in this paper is verified by various aspects.

Learning moment-based fast local binary descriptor

NASA Astrophysics Data System (ADS)

Bellarbi, Abdelkader; Zenati, Nadia; Otmane, Samir; Belghit, Hayet

2017-03-01

Recently, binary descriptors have attracted significant attention due to their speed and low memory consumption; however, using intensity differences to calculate the binary descriptive vector is not efficient enough. We propose an approach to binary description called POLAR_MOBIL, in which we perform binary tests between geometrical and statistical information using moments in the patch instead of the classical intensity binary test. In addition, we introduce a learning technique used to select an optimized set of binary tests with low correlation and high variance. This approach offers high distinctiveness against affine transformations and appearance changes. An extensive evaluation on well-known benchmark datasets reveals the robustness and the effectiveness of the proposed descriptor, as well as its good performance in terms of low computation complexity when compared with state-of-the-art real-time local descriptors.

Automated classification of tropical shrub species: a hybrid of leaf shape and machine learning approach

PubMed Central

Murat, Miraemiliana; Abu, Arpah; Yap, Hwa Jen; Yong, Kien-Thai

2017-01-01

Plants play a crucial role in foodstuff, medicine, industry, and environmental protection. The skill of recognising plants is very important in some applications, including conservation of endangered species and rehabilitation of lands after mining activities. However, it is a difficult task to identify plant species because it requires specialized knowledge. Developing an automated classification system for plant species is necessary and valuable since it can help specialists as well as the public in identifying plant species easily. Shape descriptors were applied on the myDAUN dataset that contains 45 tropical shrub species collected from the University of Malaya (UM), Malaysia. Based on literature review, this is the first study in the development of tropical shrub species image dataset and classification using a hybrid of leaf shape and machine learning approach. Four types of shape descriptors were used in this study namely morphological shape descriptors (MSD), Histogram of Oriented Gradients (HOG), Hu invariant moments (Hu) and Zernike moments (ZM). Single descriptor, as well as the combination of hybrid descriptors were tested and compared. The tropical shrub species are classified using six different classifiers, which are artificial neural network (ANN), random forest (RF), support vector machine (SVM), k-nearest neighbour (k-NN), linear discriminant analysis (LDA) and directed acyclic graph multiclass least squares twin support vector machine (DAG MLSTSVM). In addition, three types of feature selection methods were tested in the myDAUN dataset, Relief, Correlation-based feature selection (CFS) and Pearson’s coefficient correlation (PCC). The well-known Flavia dataset and Swedish Leaf dataset were used as the validation dataset on the proposed methods. The results showed that the hybrid of all descriptors of ANN outperformed the other classifiers with an average classification accuracy of 98.23% for the myDAUN dataset, 95.25% for the Flavia dataset and 99.89% for the Swedish Leaf dataset. In addition, the Relief feature selection method achieved the highest classification accuracy of 98.13% after 80 (or 60%) of the original features were reduced, from 133 to 53 descriptors in the myDAUN dataset with the reduction in computational time. Subsequently, the hybridisation of four descriptors gave the best results compared to others. It is proven that the combination MSD and HOG were good enough for tropical shrubs species classification. Hu and ZM descriptors also improved the accuracy in tropical shrubs species classification in terms of invariant to translation, rotation and scale. ANN outperformed the others for tropical shrub species classification in this study. Feature selection methods can be used in the classification of tropical shrub species, as the comparable results could be obtained with the reduced descriptors and reduced in computational time and cost. PMID:28924506

Automated classification of tropical shrub species: a hybrid of leaf shape and machine learning approach.

PubMed

Murat, Miraemiliana; Chang, Siow-Wee; Abu, Arpah; Yap, Hwa Jen; Yong, Kien-Thai

2017-01-01

Plants play a crucial role in foodstuff, medicine, industry, and environmental protection. The skill of recognising plants is very important in some applications, including conservation of endangered species and rehabilitation of lands after mining activities. However, it is a difficult task to identify plant species because it requires specialized knowledge. Developing an automated classification system for plant species is necessary and valuable since it can help specialists as well as the public in identifying plant species easily. Shape descriptors were applied on the myDAUN dataset that contains 45 tropical shrub species collected from the University of Malaya (UM), Malaysia. Based on literature review, this is the first study in the development of tropical shrub species image dataset and classification using a hybrid of leaf shape and machine learning approach. Four types of shape descriptors were used in this study namely morphological shape descriptors (MSD), Histogram of Oriented Gradients (HOG), Hu invariant moments (Hu) and Zernike moments (ZM). Single descriptor, as well as the combination of hybrid descriptors were tested and compared. The tropical shrub species are classified using six different classifiers, which are artificial neural network (ANN), random forest (RF), support vector machine (SVM), k-nearest neighbour (k-NN), linear discriminant analysis (LDA) and directed acyclic graph multiclass least squares twin support vector machine (DAG MLSTSVM). In addition, three types of feature selection methods were tested in the myDAUN dataset, Relief, Correlation-based feature selection (CFS) and Pearson's coefficient correlation (PCC). The well-known Flavia dataset and Swedish Leaf dataset were used as the validation dataset on the proposed methods. The results showed that the hybrid of all descriptors of ANN outperformed the other classifiers with an average classification accuracy of 98.23% for the myDAUN dataset, 95.25% for the Flavia dataset and 99.89% for the Swedish Leaf dataset. In addition, the Relief feature selection method achieved the highest classification accuracy of 98.13% after 80 (or 60%) of the original features were reduced, from 133 to 53 descriptors in the myDAUN dataset with the reduction in computational time. Subsequently, the hybridisation of four descriptors gave the best results compared to others. It is proven that the combination MSD and HOG were good enough for tropical shrubs species classification. Hu and ZM descriptors also improved the accuracy in tropical shrubs species classification in terms of invariant to translation, rotation and scale. ANN outperformed the others for tropical shrub species classification in this study. Feature selection methods can be used in the classification of tropical shrub species, as the comparable results could be obtained with the reduced descriptors and reduced in computational time and cost.

BioSig-Air-Force

DOE Office of Scientific and Technical Information (OSTI.GOV)

2011-07-15

1) Configured servers: In coordination with the INSIGHT team, a hardware configuration was selected. Two nodes were purchased, configured, and shipped with compatible OS and database installation. The servers have been stress tested for reliability as they use leading edge technologies. Each node has two CPUs and 12 cores per CPU with maximum onboard memory for high performance. 2) LIM and Experimental module: The original BioSig system was developed for cancer research. Accordingly, the LIM system its corresponding web pages are being modified to facilitate (i) pathogene-donor interactions, (ii) media composition, (iii) chemical and siRNA plate configurations. The LIM systemmore » has been redesigned. The revised system allows design of new media and tracking it from lot-to-lot so that variations in the phenotypic responses can be tracked to a specific media and lot number. Similar associations are also possible with other experimental factors (e.g., donor-pathoge, siRNA, and chemical). Furthermore, the design of the experimental variables has also been revised to (i) interact with the newly developed LIM system, (ii) simplify experimental specifications, and (iii) test for potential operator's error during the data entry. Part of the complication has been due to the handshake between multiple teams that provide the small molecule plates and the team that creates assay plates. Our efforts have focused to harmonize these interactions (e.g., various data formats) so that each assay plate can be mapped to its source so that a correct set of experimental variables can be associated with each image. For example, depending upon the source of the chemical plates, they may have different formats. We have developed a canonical representation that registers SMILES code, for each chemical compound, along with its physiochemical properties. The schema for LIM conjunction with customized Web pages. 3) Import of Images and computed descriptors module: In coordination with the INSIGHT team, policies were designed to route images and computed representation into BioSig. This module (i) examines for completion of image analysis, and imports images, computed masks, and descriptors into BioSig. A database API for efficient retrieval of selection of descriptors (among thousands) was designed and implemented. 4) Computed segmentation masks from external software were imported, boundaries computed, and overlaid on images for quality control.« less

Genetic variability of a Brazilian Capsicum frutescens germplasm collection using morphological characteristics and SSR markers.

PubMed

Carvalho, S I C; Bianchetti, L B; Ragassi, C F; Ribeiro, C S C; Reifschneider, F J B; Buso, G S C; Faleiro, F G

2017-07-06

Characterization studies provide essential information for the conservation and use of germplasm in plant breeding programs. In this study, 103 Capsicum frutescens L. accessions from the Active Germplasm Bank of Embrapa Hortaliças, representative of all five Brazilian geographic regions, were characterized based on morphological characteristics and microsatellite (or simple sequence repeat - SSR) molecular markers. Morphological characterization was carried out using 57 descriptors, and molecular characterization was based on 239 alleles from 24 microsatellite loci. From the estimates of genetic distances among accessions, based on molecular characterization, a cluster analysis was carried out, and a dendrogram was established. Correlations between morphological and molecular variables were also estimated. Twelve morphological descriptors were monomorphic for the set of C. frutescens accessions, and those with the highest degree of polymorphism were stem length (14.0 to 62.0 cm), stem diameter (1.0 to 4.2 cm), days to flowering (90 to 129), days to fruiting (100 to 140), fruit weight (0.1 to 1.4 g), fruit length (0.6 to 4.6 cm), and fruit wall thickness (0.25 to 1.5 mm). The polymorphism information content for the SSR loci varied from 0.36 (EPMS 417) to 0.75 (CA49), with an overall mean of 0.57. The correlation value between morphological and molecular characterization data was 0.6604, which was statistically significant. Fourteen accessions were described as belonging to the morphological type tabasco, 85 were described as malagueta, and four were malaguetinha, a morphological type confirmed in this study. The typical morphological pattern of malagueta was described. Six similarity groups were established for C. frutescens based on the dendrogram and are discussed individually. The genetic variability analyzed in the study highlights the importance of characterizing genetic resources available for the development of new C. frutescens cultivars with the potential for various niche markets.

Application of the QSPR approach to the boiling points of azeotropes.

PubMed

Katritzky, Alan R; Stoyanova-Slavova, Iva B; Tämm, Kaido; Tamm, Tarmo; Karelson, Mati

2011-04-21

CODESSA Pro derivative descriptors were calculated for a data set of 426 azeotropic mixtures by the centroid approximation and the weighted-contribution-factor approximation. The two approximations produced almost identical four-descriptor QSPR models relating the structural characteristic of the individual components of azeotropes to the azeotropic boiling points. These models were supported by internal and external validations. The descriptors contributing to the QSPR models are directly related to the three components of the enthalpy (heat) of vaporization.

Structural, electronic, topological and vibrational properties of a series of N-benzylamides derived from Maca (Lepidium meyenii) combining spectroscopic studies with ONION calculations

NASA Astrophysics Data System (ADS)

Chain, Fernando E.; Ladetto, María Florencia; Grau, Alfredo; Catalán, César A. N.; Brandán, Silvia Antonia

2016-02-01

In the present work, the structural, topological and vibrational properties of four members of the N-benzylamides series derived from Maca (Lepidium meyenii) whose names are, N-benzylpentadecanamide, N-benzylhexadecanamide, N-benzylheptadecanamide and N-benzyloctadecanamide, were studied combining the FTIR, FT-Raman and 1H and 13C-NMR spectroscopies with density functional theory (DFT) and ONION calculations. Furthermore, the N-benzylacetamide, N-benzylpropilamide and N-benzyl hexanamide derivatives were also studied in order to compare their properties with those computed for the four macamides. These seven N-benzylamides series have a common structure, C8H8NO-R, being R the side chain [-(CH2)n-CH3] with a variable n number of CH2 groups. Here, the atomic charges, molecular electrostatic potentials, stabilization energies, topological properties of those macamides were analyzed as a function of the number of C atoms of the side chain while the frontier orbitals were used to compute the gap energies and some descriptors in order to predict their reactivities and behaviors in function of the longitude of the side chain. Here, the force fields, the complete vibrational assignments and the corresponding force constants were only reported for N-benzylacetamide, N-benzyl hexanamide and N-benzylpentadecanamide due to the high number of vibration normal modes that present the remains macamides.

TOXICO-CHEMINFORMATICS AND QSAR MODELING OF ...

EPA Pesticide Factsheets

This abstract concludes that QSAR approaches combined with toxico-chemoinformatics descriptors can enhance predictive toxicology models. This abstract concludes that QSAR approaches combined with toxico-chemoinformatics descriptors can enhance predictive toxicology models.

Using DFT methodology for more reliable predictive models: Design of inhibitors of Golgi α-Mannosidase II.

PubMed

Bobovská, Adela; Tvaroška, Igor; Kóňa, Juraj

2016-05-01

Human Golgi α-mannosidase II (GMII), a zinc ion co-factor dependent glycoside hydrolase (E.C.3.2.1.114), is a pharmaceutical target for the design of inhibitors with anti-cancer activity. The discovery of an effective inhibitor is complicated by the fact that all known potent inhibitors of GMII are involved in unwanted co-inhibition with lysosomal α-mannosidase (LMan, E.C.3.2.1.24), a relative to GMII. Routine empirical QSAR models for both GMII and LMan did not work with a required accuracy. Therefore, we have developed a fast computational protocol to build predictive models combining interaction energy descriptors from an empirical docking scoring function (Glide-Schrödinger), Linear Interaction Energy (LIE) method, and quantum mechanical density functional theory (QM-DFT) calculations. The QSAR models were built and validated with a library of structurally diverse GMII and LMan inhibitors and non-active compounds. A critical role of QM-DFT descriptors for the more accurate prediction abilities of the models is demonstrated. The predictive ability of the models was significantly improved when going from the empirical docking scoring function to mixed empirical-QM-DFT QSAR models (Q(2)=0.78-0.86 when cross-validation procedures were carried out; and R(2)=0.81-0.83 for a testing set). The average error for the predicted ΔGbind decreased to 0.8-1.1kcalmol(-1). Also, 76-80% of non-active compounds were successfully filtered out from GMII and LMan inhibitors. The QSAR models with the fragmented QM-DFT descriptors may find a useful application in structure-based drug design where pure empirical and force field methods reached their limits and where quantum mechanics effects are critical for ligand-receptor interactions. The optimized models will apply in lead optimization processes for GMII drug developments. Copyright © 2016 Elsevier Inc. All rights reserved.

feets: feATURE eXTRACTOR for tIME sERIES

NASA Astrophysics Data System (ADS)

Cabral, Juan; Sanchez, Bruno; Ramos, Felipe; Gurovich, Sebastián; Granitto, Pablo; VanderPlas, Jake

2018-06-01

feets characterizes and analyzes light-curves from astronomical photometric databases for modelling, classification, data cleaning, outlier detection and data analysis. It uses machine learning algorithms to determine the numerical descriptors that characterize and distinguish the different variability classes of light-curves; these range from basic statistical measures such as the mean or standard deviation to complex time-series characteristics such as the autocorrelation function. The library is not restricted to the astronomical field and could also be applied to any kind of time series. This project is a derivative work of FATS (ascl:1711.017).

What scientists want from their research ethics committee.

PubMed

Keith-Spiegel, Patricia; Tabachnick, Barbara

2006-03-01

Whereas investigators have directed considerable criticism against Institutional Review Boards (IRBs), the desirable characteristics of IRBs have not previously been empirically determined. A sample of 886 experienced biomedical and social and behavioral scientists rated 45 descriptors of IRB actions and functions as to their importance. Predictions derived from organizational justice research findings in other work settings were generally borne out. Investigators place high value on the fairness and respectful consideration of their IRBs. Expected differences between biomedical and social behavioral researchers and other variables were unfounded. Recommendations are offered for educating IRBs to accord researchers greater respect and fair treatment.

Music Identification System Using MPEG-7 Audio Signature Descriptors

PubMed Central

You, Shingchern D.; Chen, Wei-Hwa; Chen, Woei-Kae

2013-01-01

This paper describes a multiresolution system based on MPEG-7 audio signature descriptors for music identification. Such an identification system may be used to detect illegally copied music circulated over the Internet. In the proposed system, low-resolution descriptors are used to search likely candidates, and then full-resolution descriptors are used to identify the unknown (query) audio. With this arrangement, the proposed system achieves both high speed and high accuracy. To deal with the problem that a piece of query audio may not be inside the system's database, we suggest two different methods to find the decision threshold. Simulation results show that the proposed method II can achieve an accuracy of 99.4% for query inputs both inside and outside the database. Overall, it is highly possible to use the proposed system for copyright control. PMID:23533359

Multiple node remote messaging

DOEpatents

Blumrich, Matthias A.; Chen, Dong; Gara, Alan G.; Giampapa, Mark E.; Heidelberger, Philip; Ohmacht, Martin; Salapura, Valentina; Steinmacher-Burow, Burkhard; Vranas, Pavlos

2010-08-31

A method for passing remote messages in a parallel computer system formed as a network of interconnected compute nodes includes that a first compute node (A) sends a single remote message to a remote second compute node (B) in order to control the remote second compute node (B) to send at least one remote message. The method includes various steps including controlling a DMA engine at first compute node (A) to prepare the single remote message to include a first message descriptor and at least one remote message descriptor for controlling the remote second compute node (B) to send at least one remote message, including putting the first message descriptor into an injection FIFO at the first compute node (A) and sending the single remote message and the at least one remote message descriptor to the second compute node (B).

Modular Chemical Descriptor Language (MCDL): Stereochemical modules

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gakh, Andrei A; Burnett, Michael N; Trepalin, Sergei V.

2011-01-01

In our previous papers we introduced the Modular Chemical Descriptor Language (MCDL) for providing a linear representation of chemical information. A subsequent development was the MCDL Java Chemical Structure Editor which is capable of drawing chemical structures from linear representations and generating MCDL descriptors from structures. In this paper we present MCDL modules and accompanying software that incorporate unique representation of molecular stereochemistry based on Cahn-Ingold-Prelog and Fischer ideas in constructing stereoisomer descriptors. The paper also contains additional discussions regarding canonical representation of stereochemical isomers, and brief algorithm descriptions of the open source LINDES, Java applet, and Open Babel MCDLmore » processing module software packages. Testing of the upgraded MCDL Java Chemical Structure Editor on compounds taken from several large and diverse chemical databases demonstrated satisfactory performance for storage and processing of stereochemical information in MCDL format.« less

Applications of genetic algorithms on the structure-activity relationship analysis of some cinnamamides.

PubMed

Hou, T J; Wang, J M; Liao, N; Xu, X J

1999-01-01

Quantitative structure-activity relationships (QSARs) for 35 cinnamamides were studied. By using a genetic algorithm (GA), a group of multiple regression models with high fitness scores was generated. From the statistical analyses of the descriptors used in the evolution procedure, the principal features affecting the anticonvulsant activity were found. The significant descriptors include the partition coefficient, the molar refraction, the Hammet sigma constant of the substituents on the benzene ring, and the formation energy of the molecules. It could be found that the steric complementarity and the hydrophobic interaction between the inhibitors and the receptor were very important to the biological activity, while the contribution of the electronic effect was not so obvious. Moreover, by construction of the spline models for these four principal descriptors, the effective range for each descriptor was identified.

Conceptual DFT Descriptors of Amino Acids with Potential Corrosion Inhibition Properties Calculated with the Latest Minnesota Density Functionals.

PubMed

Frau, Juan; Glossman-Mitnik, Daniel

2017-01-01

Amino acids and peptides have the potential to perform as corrosion inhibitors. The chemical reactivity descriptors that arise from Conceptual DFT for the twenty natural amino acids have been calculated by using the latest Minnesota family of density functionals. In order to verify the validity of the calculation of the descriptors directly from the HOMO and LUMO, a comparison has been performed with those obtained through ΔSCF results. Moreover, the active sites for nucleophilic and electrophilic attacks have been identified through Fukui function indices, the dual descriptor Δf( r ) and the electrophilic and nucleophilic Parr functions. The results could be of interest as a starting point for the study of large peptides where the calculation of the radical cation and anion of each system may be computationally harder and costly.

Preventing messaging queue deadlocks in a DMA environment

DOEpatents

Blocksome, Michael A; Chen, Dong; Gooding, Thomas; Heidelberger, Philip; Parker, Jeff

2014-01-14

Embodiments of the invention may be used to manage message queues in a parallel computing environment to prevent message queue deadlock. A direct memory access controller of a compute node may determine when a messaging queue is full. In response, the DMA may generate and interrupt. An interrupt handler may stop the DMA and swap all descriptors from the full messaging queue into a larger queue (or enlarge the original queue). The interrupt handler then restarts the DMA. Alternatively, the interrupt handler stops the DMA, allocates a memory block to hold queue data, and then moves descriptors from the full messaging queue into the allocated memory block. The interrupt handler then restarts the DMA. During a normal messaging advance cycle, a messaging manager attempts to inject the descriptors in the memory block into other messaging queues until the descriptors have all been processed.

Completion processing for data communications instructions

DOEpatents

Blocksome, Michael A.; Kumar, Sameer; Jeffrey, Parker J.

2014-06-10

Completion processing of data communications instructions in a distributed computing environment with computers coupled for data communications through communications adapters and an active messaging interface (`AMI`), injecting for data communications instructions into slots in an injection FIFO buffer a transfer descriptor, at least some of the instructions specifying callback functions; injecting a completion descriptor for each instruction that specifies a callback function into an injection FIFO buffer slot having a corresponding slot in a pending callback list; listing in the pending callback list callback functions specified by data communications instructions; processing each descriptor in the injection FIFO buffer, setting a bit in a completion bit mask corresponding to the slot in the FIFO where the completion descriptor was injected; and calling by the AMI any callback functions in the pending callback list as indicated by set bits in the completion bit mask.

Learning Optimized Local Difference Binaries for Scalable Augmented Reality on Mobile Devices.

PubMed

Xin Yang; Kwang-Ting Cheng

2014-06-01

The efficiency, robustness and distinctiveness of a feature descriptor are critical to the user experience and scalability of a mobile augmented reality (AR) system. However, existing descriptors are either too computationally expensive to achieve real-time performance on a mobile device such as a smartphone or tablet, or not sufficiently robust and distinctive to identify correct matches from a large database. As a result, current mobile AR systems still only have limited capabilities, which greatly restrict their deployment in practice. In this paper, we propose a highly efficient, robust and distinctive binary descriptor, called Learning-based Local Difference Binary (LLDB). LLDB directly computes a binary string for an image patch using simple intensity and gradient difference tests on pairwise grid cells within the patch. To select an optimized set of grid cell pairs, we densely sample grid cells from an image patch and then leverage a modified AdaBoost algorithm to automatically extract a small set of critical ones with the goal of maximizing the Hamming distance between mismatches while minimizing it between matches. Experimental results demonstrate that LLDB is extremely fast to compute and to match against a large database due to its high robustness and distinctiveness. Compared to the state-of-the-art binary descriptors, primarily designed for speed, LLDB has similar efficiency for descriptor construction, while achieving a greater accuracy and faster matching speed when matching over a large database with 2.3M descriptors on mobile devices.

Improved nucleic acid descriptors for siRNA efficacy prediction.

PubMed

Sciabola, Simone; Cao, Qing; Orozco, Modesto; Faustino, Ignacio; Stanton, Robert V

2013-02-01

Although considerable progress has been made recently in understanding how gene silencing is mediated by the RNAi pathway, the rational design of effective sequences is still a challenging task. In this article, we demonstrate that including three-dimensional descriptors improved the discrimination between active and inactive small interfering RNAs (siRNAs) in a statistical model. Five descriptor types were used: (i) nucleotide position along the siRNA sequence, (ii) nucleotide composition in terms of presence/absence of specific combinations of di- and trinucleotides, (iii) nucleotide interactions by means of a modified auto- and cross-covariance function, (iv) nucleotide thermodynamic stability derived by the nearest neighbor model representation and (v) nucleic acid structure flexibility. The duplex flexibility descriptors are derived from extended molecular dynamics simulations, which are able to describe the sequence-dependent elastic properties of RNA duplexes, even for non-standard oligonucleotides. The matrix of descriptors was analysed using three statistical packages in R (partial least squares, random forest, and support vector machine), and the most predictive model was implemented in a modeling tool we have made publicly available through SourceForge. Our implementation of new RNA descriptors coupled with appropriate statistical algorithms resulted in improved model performance for the selection of siRNA candidates when compared with publicly available siRNA prediction tools and previously published test sets. Additional validation studies based on in-house RNA interference projects confirmed the robustness of the scoring procedure in prospective studies.

Three-Dimensional Object Recognition and Registration for Robotic Grasping Systems Using a Modified Viewpoint Feature Histogram

PubMed Central

Chen, Chin-Sheng; Chen, Po-Chun; Hsu, Chih-Ming

2016-01-01

This paper presents a novel 3D feature descriptor for object recognition and to identify poses when there are six-degrees-of-freedom for mobile manipulation and grasping applications. Firstly, a Microsoft Kinect sensor is used to capture 3D point cloud data. A viewpoint feature histogram (VFH) descriptor for the 3D point cloud data then encodes the geometry and viewpoint, so an object can be simultaneously recognized and registered in a stable pose and the information is stored in a database. The VFH is robust to a large degree of surface noise and missing depth information so it is reliable for stereo data. However, the pose estimation for an object fails when the object is placed symmetrically to the viewpoint. To overcome this problem, this study proposes a modified viewpoint feature histogram (MVFH) descriptor that consists of two parts: a surface shape component that comprises an extended fast point feature histogram and an extended viewpoint direction component. The MVFH descriptor characterizes an object’s pose and enhances the system’s ability to identify objects with mirrored poses. Finally, the refined pose is further estimated using an iterative closest point when the object has been recognized and the pose roughly estimated by the MVFH descriptor and it has been registered on a database. The estimation results demonstrate that the MVFH feature descriptor allows more accurate pose estimation. The experiments also show that the proposed method can be applied in vision-guided robotic grasping systems. PMID:27886080

A short feature vector for image matching: The Log-Polar Magnitude feature descriptor

PubMed Central

Hast, Anders; Wählby, Carolina; Sintorn, Ida-Maria

2017-01-01

The choice of an optimal feature detector-descriptor combination for image matching often depends on the application and the image type. In this paper, we propose the Log-Polar Magnitude feature descriptor—a rotation, scale, and illumination invariant descriptor that achieves comparable performance to SIFT on a large variety of image registration problems but with much shorter feature vectors. The descriptor is based on the Log-Polar Transform followed by a Fourier Transform and selection of the magnitude spectrum components. Selecting different frequency components allows optimizing for image patterns specific for a particular application. In addition, by relying only on coordinates of the found features and (optionally) feature sizes our descriptor is completely detector independent. We propose 48- or 56-long feature vectors that potentially can be shortened even further depending on the application. Shorter feature vectors result in better memory usage and faster matching. This combined with the fact that the descriptor does not require a time-consuming feature orientation estimation (the rotation invariance is achieved solely by using the magnitude spectrum of the Log-Polar Transform) makes it particularly attractive to applications with limited hardware capacity. Evaluation is performed on the standard Oxford dataset and two different microscopy datasets; one with fluorescence and one with transmission electron microscopy images. Our method performs better than SURF and comparable to SIFT on the Oxford dataset, and better than SIFT on both microscopy datasets indicating that it is particularly useful in applications with microscopy images. PMID:29190737

An image retrieval framework for real-time endoscopic image retargeting.

PubMed

Ye, Menglong; Johns, Edward; Walter, Benjamin; Meining, Alexander; Yang, Guang-Zhong

2017-08-01

Serial endoscopic examinations of a patient are important for early diagnosis of malignancies in the gastrointestinal tract. However, retargeting for optical biopsy is challenging due to extensive tissue variations between examinations, requiring the method to be tolerant to these changes whilst enabling real-time retargeting. This work presents an image retrieval framework for inter-examination retargeting. We propose both a novel image descriptor tolerant of long-term tissue changes and a novel descriptor matching method in real time. The descriptor is based on histograms generated from regional intensity comparisons over multiple scales, offering stability over long-term appearance changes at the higher levels, whilst remaining discriminative at the lower levels. The matching method then learns a hashing function using random forests, to compress the string and allow for fast image comparison by a simple Hamming distance metric. A dataset that contains 13 in vivo gastrointestinal videos was collected from six patients, representing serial examinations of each patient, which includes videos captured with significant time intervals. Precision-recall for retargeting shows that our new descriptor outperforms a number of alternative descriptors, whilst our hashing method outperforms a number of alternative hashing approaches. We have proposed a novel framework for optical biopsy in serial endoscopic examinations. A new descriptor, combined with a novel hashing method, achieves state-of-the-art retargeting, with validation on in vivo videos from six patients. Real-time performance also allows for practical integration without disturbing the existing clinical workflow.

Prediction of the Fate of Organic Compounds in the Environment From Their Molecular Properties: A Review

PubMed Central

Mamy, Laure; Patureau, Dominique; Barriuso, Enrique; Bedos, Carole; Bessac, Fabienne; Louchart, Xavier; Martin-laurent, Fabrice; Miege, Cecile; Benoit, Pierre

2015-01-01

A comprehensive review of quantitative structure-activity relationships (QSAR) allowing the prediction of the fate of organic compounds in the environment from their molecular properties was done. The considered processes were water dissolution, dissociation, volatilization, retention on soils and sediments (mainly adsorption and desorption), degradation (biotic and abiotic), and absorption by plants. A total of 790 equations involving 686 structural molecular descriptors are reported to estimate 90 environmental parameters related to these processes. A significant number of equations was found for dissociation process (pKa), water dissolution or hydrophobic behavior (especially through the KOW parameter), adsorption to soils and biodegradation. A lack of QSAR was observed to estimate desorption or potential of transfer to water. Among the 686 molecular descriptors, five were found to be dominant in the 790 collected equations and the most generic ones: four quantum-chemical descriptors, the energy of the highest occupied molecular orbital (EHOMO) and the energy of the lowest unoccupied molecular orbital (ELUMO), polarizability (α) and dipole moment (μ), and one constitutional descriptor, the molecular weight. Keeping in mind that the combination of descriptors belonging to different categories (constitutional, topological, quantum-chemical) led to improve QSAR performances, these descriptors should be considered for the development of new QSAR, for further predictions of environmental parameters. This review also allows finding of the relevant QSAR equations to predict the fate of a wide diversity of compounds in the environment. PMID:25866458

Abnormal brain processing of affective and sensory pain descriptors in chronic pain patients.

PubMed

Sitges, Carolina; García-Herrera, Manuel; Pericás, Miquel; Collado, Dolores; Truyols, Magdalena; Montoya, Pedro

2007-12-01

Previous research has suggested that chronic pain patients might be particularly vulnerable to the effects of negative mood during information processing. However, there is little evidence for abnormal brain processing of affective and sensory pain-related information in chronic pain. Behavioral and brain responses, to pain descriptors and pleasant words, were examined in chronic pain patients and healthy controls during a self-endorsement task. Eighteen patients with fibromyalgia (FM), 18 patients with chronic musculoskeletal pain due to identifiable physical injury (MSK), and 16 healthy controls were asked to decide whether word targets described their current or past experience of pain. The number of self-endorsed words, elapsed time to endorse the words, and event-related potentials (ERPs) elicited by words, were recorded. Data revealed that chronic pain patients used more affective and sensory pain descriptors, and were slower in responding to self-endorsed pain descriptors than to pleasant words. In addition, it was found that affective pain descriptors elicited significantly more enhanced positive ERP amplitudes than pleasant words in MSK pain patients; whereas sensory pain descriptors elicited greater positive ERP amplitudes than affective pain words in healthy controls. These data support the notion of abnormal information processing in chronic pain patients, which might be characterized by a lack of dissociation between sensory and affective components of pain-related information, and by an exaggerated rumination over word meaning during the encoding of self-referent information about pain.

Prediction of the Fate of Organic Compounds in the Environment From Their Molecular Properties: A Review.

PubMed

Mamy, Laure; Patureau, Dominique; Barriuso, Enrique; Bedos, Carole; Bessac, Fabienne; Louchart, Xavier; Martin-Laurent, Fabrice; Miege, Cecile; Benoit, Pierre

2015-06-18

A comprehensive review of quantitative structure-activity relationships (QSAR) allowing the prediction of the fate of organic compounds in the environment from their molecular properties was done. The considered processes were water dissolution, dissociation, volatilization, retention on soils and sediments (mainly adsorption and desorption), degradation (biotic and abiotic), and absorption by plants. A total of 790 equations involving 686 structural molecular descriptors are reported to estimate 90 environmental parameters related to these processes. A significant number of equations was found for dissociation process (pK a ), water dissolution or hydrophobic behavior (especially through the K OW parameter), adsorption to soils and biodegradation. A lack of QSAR was observed to estimate desorption or potential of transfer to water. Among the 686 molecular descriptors, five were found to be dominant in the 790 collected equations and the most generic ones: four quantum-chemical descriptors, the energy of the highest occupied molecular orbital (E HOMO ) and the energy of the lowest unoccupied molecular orbital (E LUMO ), polarizability (α) and dipole moment (μ), and one constitutional descriptor, the molecular weight. Keeping in mind that the combination of descriptors belonging to different categories (constitutional, topological, quantum-chemical) led to improve QSAR performances, these descriptors should be considered for the development of new QSAR, for further predictions of environmental parameters. This review also allows finding of the relevant QSAR equations to predict the fate of a wide diversity of compounds in the environment.

Exploring Job Satisfaction of Nursing Faculty: Theoretical Approaches.

PubMed

Wang, Yingchen; Liesveld, Judy

2015-01-01

The Future of Nursing report identified the shortage of nursing faculty as 1 of the barriers to nursing education. In light of this, it is becoming increasingly important to understand the work-life of nursing faculty. The current research focused on job satisfaction of nursing faculty from 4 theoretical perspectives: human capital theory, which emphasizes the expected monetary and nonmonetary returns for any career choices; structural theory, which emphasizes the impact of institutional features on job satisfaction; positive extrinsic environment by self-determination theory, which asserts that a positive extrinsic environment promotes competency and effective outcomes at work; and psychological theory, which emphasizes the proposed relationship between job performance and satisfaction. In addition to the measures for human capital theory, institutional variables (from structural theory and self-determination theory), and productivity measures (from psychological theory), the authors also selected sets of variables for personal characteristics to investigate their effects on job satisfaction. The results indicated that variables related to human capital theory, especially salary, contributed the most to job satisfaction, followed by those related to institutional variables. Personal variables and productivity variables as a whole contributed as well. The only other variable with marginal significance was faculty's perception of institutional support for teaching. Published by Elsevier Inc.

Texture Descriptors Ensembles Enable Image-Based Classification of Maturation of Human Stem Cell-Derived Retinal Pigmented Epithelium

PubMed Central

Caetano dos Santos, Florentino Luciano; Skottman, Heli; Juuti-Uusitalo, Kati; Hyttinen, Jari

2016-01-01

Aims A fast, non-invasive and observer-independent method to analyze the homogeneity and maturity of human pluripotent stem cell (hPSC) derived retinal pigment epithelial (RPE) cells is warranted to assess the suitability of hPSC-RPE cells for implantation or in vitro use. The aim of this work was to develop and validate methods to create ensembles of state-of-the-art texture descriptors and to provide a robust classification tool to separate three different maturation stages of RPE cells by using phase contrast microscopy images. The same methods were also validated on a wide variety of biological image classification problems, such as histological or virus image classification. Methods For image classification we used different texture descriptors, descriptor ensembles and preprocessing techniques. Also, three new methods were tested. The first approach was an ensemble of preprocessing methods, to create an additional set of images. The second was the region-based approach, where saliency detection and wavelet decomposition divide each image in two different regions, from which features were extracted through different descriptors. The third method was an ensemble of Binarized Statistical Image Features, based on different sizes and thresholds. A Support Vector Machine (SVM) was trained for each descriptor histogram and the set of SVMs combined by sum rule. The accuracy of the computer vision tool was verified in classifying the hPSC-RPE cell maturation level. Dataset and Results The RPE dataset contains 1862 subwindows from 195 phase contrast images. The final descriptor ensemble outperformed the most recent stand-alone texture descriptors, obtaining, for the RPE dataset, an area under ROC curve (AUC) of 86.49% with the 10-fold cross validation and 91.98% with the leave-one-image-out protocol. The generality of the three proposed approaches was ascertained with 10 more biological image datasets, obtaining an average AUC greater than 97%. Conclusions Here we showed that the developed ensembles of texture descriptors are able to classify the RPE cell maturation stage. Moreover, we proved that preprocessing and region-based decomposition improves many descriptors’ accuracy in biological dataset classification. Finally, we built the first public dataset of stem cell-derived RPE cells, which is publicly available to the scientific community for classification studies. The proposed tool is available at https://www.dei.unipd.it/node/2357 and the RPE dataset at http://www.biomeditech.fi/data/RPE_dataset/. Both are available at https://figshare.com/s/d6fb591f1beb4f8efa6f. PMID:26895509

Dannie Heineman Prize for Mathematical Physics: Applying mathematical techniques to solve important problems in quantum theory

NASA Astrophysics Data System (ADS)

Bender, Carl

2017-01-01

The theory of complex variables is extremely useful because it helps to explain the mathematical behavior of functions of a real variable. Complex variable theory also provides insight into the nature of physical theories. For example, it provides a simple and beautiful picture of quantization and it explains the underlying reason for the divergence of perturbation theory. By using complex-variable methods one can generalize conventional Hermitian quantum theories into the complex domain. The result is a new class of parity-time-symmetric (PT-symmetric) theories whose remarkable physical properties have been studied and verified in many recent laboratory experiments.

Conformational Entropy as Collective Variable for Proteins.

PubMed

Palazzesi, Ferruccio; Valsson, Omar; Parrinello, Michele

2017-10-05

Many enhanced sampling methods rely on the identification of appropriate collective variables. For proteins, even small ones, finding appropriate descriptors has proven challenging. Here we suggest that the NMR S 2 order parameter can be used to this effect. We trace the validity of this statement to the suggested relation between S 2 and conformational entropy. Using the S 2 order parameter and a surrogate for the protein enthalpy in conjunction with metadynamics or variationally enhanced sampling, we are able to reversibly fold and unfold a small protein and draw its free energy at a fraction of the time that is needed in unbiased simulations. We also use S 2 in combination with the free energy flooding method to compute the unfolding rate of this peptide. We repeat this calculation at different temperatures to obtain the unfolding activation energy.

Principal component analysis on molecular descriptors as an alternative point of view in the search of new Hsp90 inhibitors.

PubMed

Lauria, Antonino; Ippolito, Mario; Almerico, Anna Maria

2009-10-01

Inhibiting a protein that regulates multiple signal transduction pathways in cancer cells is an attractive goal for cancer therapy. Heat shock protein 90 (Hsp90) is one of the most promising molecular targets for such an approach. In fact, Hsp90 is a ubiquitous molecular chaperone protein that is involved in folding, activating and assembling of many key mediators of signal transduction, cellular growth, differentiation, stress-response and apoptothic pathways. With the aim to analyze which molecular descriptors have the higher importance in the binding interactions of these classes, we first performed molecular docking experiments on the 187 Hsp90 inhibitors included in the BindingDB, a public database of measured binding affinities. Further, for each frozen conformation obtained from the docking, a set of 250 molecular descriptors was calculated, and the resulting Structure/Descriptors matrix was submitted to Principal Component Analysis. From the factor scores it emerged a good clusterization among similar compounds both in terms of structural class and activity spectrum, while examination of the loadings of the first two factors also allowed to study the classes of descriptors which mainly contribute to each one.

Object Tracking Using Adaptive Covariance Descriptor and Clustering-Based Model Updating for Visual Surveillance

PubMed Central

Qin, Lei; Snoussi, Hichem; Abdallah, Fahed

2014-01-01

We propose a novel approach for tracking an arbitrary object in video sequences for visual surveillance. The first contribution of this work is an automatic feature extraction method that is able to extract compact discriminative features from a feature pool before computing the region covariance descriptor. As the feature extraction method is adaptive to a specific object of interest, we refer to the region covariance descriptor computed using the extracted features as the adaptive covariance descriptor. The second contribution is to propose a weakly supervised method for updating the object appearance model during tracking. The method performs a mean-shift clustering procedure among the tracking result samples accumulated during a period of time and selects a group of reliable samples for updating the object appearance model. As such, the object appearance model is kept up-to-date and is prevented from contamination even in case of tracking mistakes. We conducted comparing experiments on real-world video sequences, which confirmed the effectiveness of the proposed approaches. The tracking system that integrates the adaptive covariance descriptor and the clustering-based model updating method accomplished stable object tracking on challenging video sequences. PMID:24865883

MBR-SIFT: A mirror reflected invariant feature descriptor using a binary representation for image matching.

PubMed

Su, Mingzhe; Ma, Yan; Zhang, Xiangfen; Wang, Yan; Zhang, Yuping

2017-01-01

The traditional scale invariant feature transform (SIFT) method can extract distinctive features for image matching. However, it is extremely time-consuming in SIFT matching because of the use of the Euclidean distance measure. Recently, many binary SIFT (BSIFT) methods have been developed to improve matching efficiency; however, none of them is invariant to mirror reflection. To address these problems, in this paper, we present a horizontal or vertical mirror reflection invariant binary descriptor named MBR-SIFT, in addition to a novel image matching approach. First, 16 cells in the local region around the SIFT keypoint are reorganized, and then the 128-dimensional vector of the SIFT descriptor is transformed into a reconstructed vector according to eight directions. Finally, the MBR-SIFT descriptor is obtained after binarization and reverse coding. To improve the matching speed and accuracy, a fast matching algorithm that includes a coarse-to-fine two-step matching strategy in addition to two similarity measures for the MBR-SIFT descriptor are proposed. Experimental results on the UKBench dataset show that the proposed method not only solves the problem of mirror reflection, but also ensures desirable matching accuracy and speed.

MBR-SIFT: A mirror reflected invariant feature descriptor using a binary representation for image matching

PubMed Central

Su, Mingzhe; Ma, Yan; Zhang, Xiangfen; Wang, Yan; Zhang, Yuping

2017-01-01

The traditional scale invariant feature transform (SIFT) method can extract distinctive features for image matching. However, it is extremely time-consuming in SIFT matching because of the use of the Euclidean distance measure. Recently, many binary SIFT (BSIFT) methods have been developed to improve matching efficiency; however, none of them is invariant to mirror reflection. To address these problems, in this paper, we present a horizontal or vertical mirror reflection invariant binary descriptor named MBR-SIFT, in addition to a novel image matching approach. First, 16 cells in the local region around the SIFT keypoint are reorganized, and then the 128-dimensional vector of the SIFT descriptor is transformed into a reconstructed vector according to eight directions. Finally, the MBR-SIFT descriptor is obtained after binarization and reverse coding. To improve the matching speed and accuracy, a fast matching algorithm that includes a coarse-to-fine two-step matching strategy in addition to two similarity measures for the MBR-SIFT descriptor are proposed. Experimental results on the UKBench dataset show that the proposed method not only solves the problem of mirror reflection, but also ensures desirable matching accuracy and speed. PMID:28542537

The implementation of aerial object recognition algorithm based on contour descriptor in FPGA-based on-board vision system

NASA Astrophysics Data System (ADS)

Babayan, Pavel; Smirnov, Sergey; Strotov, Valery

2017-10-01

This paper describes the aerial object recognition algorithm for on-board and stationary vision system. Suggested algorithm is intended to recognize the objects of a specific kind using the set of the reference objects defined by 3D models. The proposed algorithm based on the outer contour descriptor building. The algorithm consists of two stages: learning and recognition. Learning stage is devoted to the exploring of reference objects. Using 3D models we can build the database containing training images by rendering the 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. Gathered training image set is used for calculating descriptors, which will be used in the recognition stage of the algorithm. The recognition stage is focusing on estimating the similarity of the captured object and the reference objects by matching an observed image descriptor and the reference object descriptors. The experimental research was performed using a set of the models of the aircraft of the different types (airplanes, helicopters, UAVs). The proposed orientation estimation algorithm showed good accuracy in all case studies. The real-time performance of the algorithm in FPGA-based vision system was demonstrated.

Chemical Sensor Array Response Modeling Using Quantitative Structure-Activity Relationships Technique

NASA Astrophysics Data System (ADS)

Shevade, Abhijit V.; Ryan, Margaret A.; Homer, Margie L.; Zhou, Hanying; Manfreda, Allison M.; Lara, Liana M.; Yen, Shiao-Pin S.; Jewell, April D.; Manatt, Kenneth S.; Kisor, Adam K.

We have developed a Quantitative Structure-Activity Relationships (QSAR) based approach to correlate the response of chemical sensors in an array with molecular descriptors. A novel molecular descriptor set has been developed; this set combines descriptors of sensing film-analyte interactions, representing sensor response, with a basic analyte descriptor set commonly used in QSAR studies. The descriptors are obtained using a combination of molecular modeling tools and empirical and semi-empirical Quantitative Structure-Property Relationships (QSPR) methods. The sensors under investigation are polymer-carbon sensing films which have been exposed to analyte vapors at parts-per-million (ppm) concentrations; response is measured as change in film resistance. Statistically validated QSAR models have been developed using Genetic Function Approximations (GFA) for a sensor array for a given training data set. The applicability of the sensor response models has been tested by using it to predict the sensor activities for test analytes not considered in the training set for the model development. The validated QSAR sensor response models show good predictive ability. The QSAR approach is a promising computational tool for sensing materials evaluation and selection. It can also be used to predict response of an existing sensing film to new target analytes.

Salient aspects of PBP2A-inhibition; A QSAR Study.

PubMed

Ogunleye, Adewale J; Eniafe, Gabriel O; Inyang, Olumide K; Adewumi, Benjamin; Omotuyi, Olaposi I

2018-05-15

Backgound: Inhibition of penicillin binding protein 2A (PBP2A) represents a sound drug design strategy in combatting Methicillin resistant Staphylococcus aureus (MRSA). Considering the urgent need for effective antimicrobials in combatting MRSA infections, we have developed a statistically robust ensemble of molecular descriptors (1, 2, & 3-D) from compounds targeting PBP2A in vivo. 37 (training set: 26, test set: 11) PBP2A-inhibitors were submitted for descriptor generation after which an unsupervised, non-exhaustive genetic algorithm (GA) was deployed for fishing out the best descriptor subset. Assignment of descriptors to a regression model was accomplished with the Partial Least Square (PLS) algorithm. At the end, an ensemble of 30 descriptors accurately predicted the ligand bioactivity, IC50 (R = 0.9996, R2 = 0.9992, R2a = 0.9949, SEE =, 0.2297 Q2LOO = 0.9741). Inferentially, we noticed that the overall efficacy of this model greatly depends on atomic polarizability and negative charge (electron) density. Besides the formula derived, the high dimensional model also offers critical insights into salient cheminformatics parameter to note during hit-to-lead PBP2A-antagonist optimization. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Automatic summarization of soccer highlights using audio-visual descriptors.

PubMed

Raventós, A; Quijada, R; Torres, Luis; Tarrés, Francesc

2015-01-01

Automatic summarization generation of sports video content has been object of great interest for many years. Although semantic descriptions techniques have been proposed, many of the approaches still rely on low-level video descriptors that render quite limited results due to the complexity of the problem and to the low capability of the descriptors to represent semantic content. In this paper, a new approach for automatic highlights summarization generation of soccer videos using audio-visual descriptors is presented. The approach is based on the segmentation of the video sequence into shots that will be further analyzed to determine its relevance and interest. Of special interest in the approach is the use of the audio information that provides additional robustness to the overall performance of the summarization system. For every video shot a set of low and mid level audio-visual descriptors are computed and lately adequately combined in order to obtain different relevance measures based on empirical knowledge rules. The final summary is generated by selecting those shots with highest interest according to the specifications of the user and the results of relevance measures. A variety of results are presented with real soccer video sequences that prove the validity of the approach.

Plant Identification Based on Leaf Midrib Cross-Section Images Using Fractal Descriptors.

PubMed

da Silva, Núbia Rosa; Florindo, João Batista; Gómez, María Cecilia; Rossatto, Davi Rodrigo; Kolb, Rosana Marta; Bruno, Odemir Martinez

2015-01-01

The correct identification of plants is a common necessity not only to researchers but also to the lay public. Recently, computational methods have been employed to facilitate this task, however, there are few studies front of the wide diversity of plants occurring in the world. This study proposes to analyse images obtained from cross-sections of leaf midrib using fractal descriptors. These descriptors are obtained from the fractal dimension of the object computed at a range of scales. In this way, they provide rich information regarding the spatial distribution of the analysed structure and, as a consequence, they measure the multiscale morphology of the object of interest. In Biology, such morphology is of great importance because it is related to evolutionary aspects and is successfully employed to characterize and discriminate among different biological structures. Here, the fractal descriptors are used to identify the species of plants based on the image of their leaves. A large number of samples are examined, being 606 leaf samples of 50 species from Brazilian flora. The results are compared to other imaging methods in the literature and demonstrate that fractal descriptors are precise and reliable in the taxonomic process of plant species identification.

Geographic profiling and animal foraging.

PubMed

Le Comber, Steven C; Nicholls, Barry; Rossmo, D Kim; Racey, Paul A

2006-05-21

Geographic profiling was originally developed as a statistical tool for use in criminal cases, particularly those involving serial killers and rapists. It is designed to help police forces prioritize lists of suspects by using the location of crime scenes to identify the areas in which the criminal is most likely to live. Two important concepts are the buffer zone (criminals are less likely to commit crimes in the immediate vicinity of their home) and distance decay (criminals commit fewer crimes as the distance from their home increases). In this study, we show how the techniques of geographic profiling may be applied to animal data, using as an example foraging patterns in two sympatric colonies of pipistrelle bats, Pipistrellus pipistrellus and P. pygmaeus, in the northeast of Scotland. We show that if model variables are fitted to known roost locations, these variables may be used as numerical descriptors of foraging patterns. We go on to show that these variables can be used to differentiate patterns of foraging in these two species.

Some elements of a theory of multidimensional complex variables. I - General theory. II - Expansions of analytic functions and application to fluid flows

NASA Technical Reports Server (NTRS)

Martin, E. Dale

1989-01-01

The paper introduces a new theory of N-dimensional complex variables and analytic functions which, for N greater than 2, is both a direct generalization and a close analog of the theory of ordinary complex variables. The algebra in the present theory is a commutative ring, not a field. Functions of a three-dimensional variable were defined and the definition of the derivative then led to analytic functions.