Contour-based object orientation estimation

NASA Astrophysics Data System (ADS)

Alpatov, Boris; Babayan, Pavel

2016-04-01

Real-time object orientation estimation is an actual problem of computer vision nowadays. In this paper we propose an approach to estimate an orientation of objects lacking axial symmetry. Proposed algorithm is intended to estimate orientation of a specific known 3D object, so 3D model is required for learning. The proposed orientation estimation algorithm consists of 2 stages: learning and estimation. Learning stage is devoted to the exploring of studied object. Using 3D model we can gather set of training images by capturing 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. It minimizes the training image set. Gathered training image set is used for calculating descriptors, which will be used in the estimation stage of the algorithm. The estimation stage is focusing on matching process between an observed image descriptor and the training image descriptors. The experimental research was performed using a set of images of Airbus A380. The proposed orientation estimation algorithm showed good accuracy (mean error value less than 6°) in all case studies. The real-time performance of the algorithm was also demonstrated.

Predicting human skin absorption of chemicals: development of a novel quantitative structure activity relationship.

PubMed

Luo, Wen; Medrek, Sarah; Misra, Jatin; Nohynek, Gerhard J

2007-02-01

The objective of this study was to construct and validate a quantitative structure-activity relationship model for skin absorption. Such models are valuable tools for screening and prioritization in safety and efficacy evaluation, and risk assessment of drugs and chemicals. A database of 340 chemicals with percutaneous absorption was assembled. Two models were derived from the training set consisting 306 chemicals (90/10 random split). In addition to the experimental K(ow) values, over 300 2D and 3D atomic and molecular descriptors were analyzed using MDL's QsarIS computer program. Subsequently, the models were validated using both internal (leave-one-out) and external validation (test set) procedures. Using the stepwise regression analysis, three molecular descriptors were determined to have significant statistical correlation with K(p) (R2 = 0.8225): logK(ow), X0 (quantification of both molecular size and the degree of skeletal branching), and SsssCH (count of aromatic carbon groups). In conclusion, two models to estimate skin absorption were developed. When compared to other skin absorption QSAR models in the literature, our model incorporated more chemicals and explored a large number of descriptors. Additionally, our models are reasonably predictive and have met both internal and external statistical validations.

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.

Development of an online, publicly accessible naive Bayesian decision support tool for mammographic mass lesions based on the American College of Radiology (ACR) BI-RADS lexicon.

PubMed

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

2015-06-01

To develop and validate a decision support tool for mammographic mass lesions based on a standardized descriptor terminology (BI-RADS lexicon) to reduce variability of practice. We used separate training data (1,276 lesions, 138 malignant) and validation data (1,177 lesions, 175 malignant). We created naïve Bayes (NB) classifiers from the training data with tenfold cross-validation. Our "inclusive model" comprised BI-RADS categories, BI-RADS descriptors, and age as predictive variables; our "descriptor model" comprised BI-RADS descriptors and age. The resulting NB classifiers were applied to the validation data. We evaluated and compared classifier performance with ROC-analysis. In the training data, the inclusive model yields an AUC of 0.959; the descriptor model yields an AUC of 0.910 (P < 0.001). The inclusive model is superior to the clinical performance (BI-RADS categories alone, P < 0.001); the descriptor model performs similarly. When applied to the validation data, the inclusive model yields an AUC of 0.935; the descriptor model yields an AUC of 0.876 (P < 0.001). Again, the inclusive model is superior to the clinical performance (P < 0.001); the descriptor model performs similarly. We consider our classifier a step towards a more uniform interpretation of combinations of BI-RADS descriptors. We provide our classifier at www.ebm-radiology.com/nbmm/index.html . • We provide a decision support tool for mammographic masses at www.ebm-radiology.com/nbmm/index.html . • Our tool may reduce variability of practice in BI-RADS category assignment. • A formal analysis of BI-RADS descriptors may enhance radiologists' diagnostic performance.

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.

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.

Probabilistic Elastic Part Model: A Pose-Invariant Representation for Real-World Face Verification.

PubMed

Li, Haoxiang; Hua, Gang

2018-04-01

Pose variation remains to be a major challenge for real-world face recognition. We approach this problem through a probabilistic elastic part model. We extract local descriptors (e.g., LBP or SIFT) from densely sampled multi-scale image patches. By augmenting each descriptor with its location, a Gaussian mixture model (GMM) is trained to capture the spatial-appearance distribution of the face parts of all face images in the training corpus, namely the probabilistic elastic part (PEP) model. Each mixture component of the GMM is confined to be a spherical Gaussian to balance the influence of the appearance and the location terms, which naturally defines a part. Given one or multiple face images of the same subject, the PEP-model builds its PEP representation by sequentially concatenating descriptors identified by each Gaussian component in a maximum likelihood sense. We further propose a joint Bayesian adaptation algorithm to adapt the universally trained GMM to better model the pose variations between the target pair of faces/face tracks, which consistently improves face verification accuracy. Our experiments show that we achieve state-of-the-art face verification accuracy with the proposed representations on the Labeled Face in the Wild (LFW) dataset, the YouTube video face database, and the CMU MultiPIE dataset.

Evaluation of Controlled Vocabularies by Inter-Indexer Consistency

ERIC Educational Resources Information Center

Monreal, Concha Soler; Gil-Leiva, Isidoro

2011-01-01

Introduction: Several controlled vocabularies are used for indexing three journal articles to check if better or equal consistency rates are achieved with a list of descriptors than with a standard thesaurus and augmented thesaurus. Method: A terminology set for library and information Science was used to build a list of descriptors with…

Characterizing Changes in the Rate of Protein-Protein Dissociation upon Interface Mutation Using Hotspot Energy and Organization

PubMed Central

Agius, Rudi; Torchala, Mieczyslaw; Moal, Iain H.; Fernández-Recio, Juan; Bates, Paul A.

2013-01-01

Predicting the effects of mutations on the kinetic rate constants of protein-protein interactions is central to both the modeling of complex diseases and the design of effective peptide drug inhibitors. However, while most studies have concentrated on the determination of association rate constants, dissociation rates have received less attention. In this work we take a novel approach by relating the changes in dissociation rates upon mutation to the energetics and architecture of hotspots and hotregions, by performing alanine scans pre- and post-mutation. From these scans, we design a set of descriptors that capture the change in hotspot energy and distribution. The method is benchmarked on 713 kinetically characterized mutations from the SKEMPI database. Our investigations show that, with the use of hotspot descriptors, energies from single-point alanine mutations may be used for the estimation of off-rate mutations to any residue type and also multi-point mutations. A number of machine learning models are built from a combination of molecular and hotspot descriptors, with the best models achieving a Pearson's Correlation Coefficient of 0.79 with experimental off-rates and a Matthew's Correlation Coefficient of 0.6 in the detection of rare stabilizing mutations. Using specialized feature selection models we identify descriptors that are highly specific and, conversely, broadly important to predicting the effects of different classes of mutations, interface regions and complexes. Our results also indicate that the distribution of the critical stability regions across protein-protein interfaces is a function of complex size more strongly than interface area. In addition, mutations at the rim are critical for the stability of small complexes, but consistently harder to characterize. The relationship between hotregion size and the dissociation rate is also investigated and, using hotspot descriptors which model cooperative effects within hotregions, we show how the contribution of hotregions of different sizes, changes under different cooperative effects. PMID:24039569

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.

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.

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.

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.

Registration algorithm of point clouds based on multiscale normal features

NASA Astrophysics Data System (ADS)

Lu, Jun; Peng, Zhongtao; Su, Hang; Xia, GuiHua

2015-01-01

The point cloud registration technology for obtaining a three-dimensional digital model is widely applied in many areas. To improve the accuracy and speed of point cloud registration, a registration method based on multiscale normal vectors is proposed. The proposed registration method mainly includes three parts: the selection of key points, the calculation of feature descriptors, and the determining and optimization of correspondences. First, key points are selected from the point cloud based on the changes of magnitude of multiscale curvatures obtained by using principal components analysis. Then the feature descriptor of each key point is proposed, which consists of 21 elements based on multiscale normal vectors and curvatures. The correspondences in a pair of two point clouds are determined according to the descriptor's similarity of key points in the source point cloud and target point cloud. Correspondences are optimized by using a random sampling consistency algorithm and clustering technology. Finally, singular value decomposition is applied to optimized correspondences so that the rigid transformation matrix between two point clouds is obtained. Experimental results show that the proposed point cloud registration algorithm has a faster calculation speed, higher registration accuracy, and better antinoise performance.

Multiscale Region-Level VHR Image Change Detection via Sparse Change Descriptor and Robust Discriminative Dictionary Learning

PubMed Central

Xu, Yuan; Ding, Kun; Huo, Chunlei; Zhong, Zisha; Li, Haichang; Pan, Chunhong

2015-01-01

Very high resolution (VHR) image change detection is challenging due to the low discriminative ability of change feature and the difficulty of change decision in utilizing the multilevel contextual information. Most change feature extraction techniques put emphasis on the change degree description (i.e., in what degree the changes have happened), while they ignore the change pattern description (i.e., how the changes changed), which is of equal importance in characterizing the change signatures. Moreover, the simultaneous consideration of the classification robust to the registration noise and the multiscale region-consistent fusion is often neglected in change decision. To overcome such drawbacks, in this paper, a novel VHR image change detection method is proposed based on sparse change descriptor and robust discriminative dictionary learning. Sparse change descriptor combines the change degree component and the change pattern component, which are encoded by the sparse representation error and the morphological profile feature, respectively. Robust change decision is conducted by multiscale region-consistent fusion, which is implemented by the superpixel-level cosparse representation with robust discriminative dictionary and the conditional random field model. Experimental results confirm the effectiveness of the proposed change detection technique. PMID:25918748

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.

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.

Model based on GRID-derived descriptors for estimating CYP3A4 enzyme stability of potential drug candidates

NASA Astrophysics Data System (ADS)

Crivori, Patrizia; Zamora, Ismael; Speed, Bill; Orrenius, Christian; Poggesi, Italo

2004-03-01

A number of computational approaches are being proposed for an early optimization of ADME (absorption, distribution, metabolism and excretion) properties to increase the success rate in drug discovery. The present study describes the development of an in silico model able to estimate, from the three-dimensional structure of a molecule, the stability of a compound with respect to the human cytochrome P450 (CYP) 3A4 enzyme activity. Stability data were obtained by measuring the amount of unchanged compound remaining after a standardized incubation with human cDNA-expressed CYP3A4. The computational method transforms the three-dimensional molecular interaction fields (MIFs) generated from the molecular structure into descriptors (VolSurf and Almond procedures). The descriptors were correlated to the experimental metabolic stability classes by a partial least squares discriminant procedure. The model was trained using a set of 1800 compounds from the Pharmacia collection and was validated using two test sets: the first one including 825 compounds from the Pharmacia collection and the second one consisting of 20 known drugs. This model correctly predicted 75% of the first and 85% of the second test set and showed a precision above 86% to correctly select metabolically stable compounds. The model appears a valuable tool in the design of virtual libraries to bias the selection toward more stable compounds. Abbreviations: ADME - absorption, distribution, metabolism and excretion; CYP - cytochrome P450; MIFs - molecular interaction fields; HTS - high throughput screening; DDI - drug-drug interactions; 3D - three-dimensional; PCA - principal components analysis; CPCA - consensus principal components analysis; PLS - partial least squares; PLSD - partial least squares discriminant; GRIND - grid independent descriptors; GRID - software originally created and developed by Professor Peter Goodford.

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.

Virtual lock-and-key approach: the in silico revival of Fischer model by means of molecular descriptors.

PubMed

Lauria, Antonino; Tutone, Marco; Almerico, Anna Maria

2011-09-01

In the last years the application of computational methodologies in the medicinal chemistry fields has found an amazing development. All the efforts were focused on the searching of new leads featuring a close affinity on a specific biological target. Thus, different molecular modeling approaches in simulation of molecular behavior for a specific biological target were employed. In spite of the increasing reliability of computational methodologies, not always the designed lead, once synthesized and screened, are suitable for the chosen biological target. To give another chance to these compounds, this work tries to resume the old concept of Fischer lock-and-key model. The same can be done for the "re-purposing" of old drugs. In fact, it is known that drugs may have many physiological targets, therefore it may be useful to identify them. This aspect, called "polypharmacology", is known to be therapeutically essential in the different treatments. The proposed protocol, the virtual lock-and-key approach (VLKA), consists in the "virtualization" of biological targets through the respectively known inhibitors. In order to release a real lock it is necessary the key fits the pins of the lock. The molecular descriptors could be considered as pins. A tested compound can be considered a potential inhibitor of a biological target if the values of its molecular descriptors fall in the calculated range values for the set of known inhibitors. The proposed protocol permits to transform a biological target in a "lock model" starting from its known inhibitors. To release a real lock all pins must fit. In the proposed protocol, it was supposed that the higher is the number of fit pins, the higher will be the affinity to the considered biological target. Therefore, each biological target was converted in a sequence of "weighted" molecular descriptor range values (locks) by using the structural features of the known inhibitors. Each biological target lock was tested by performing a molecular descriptors "fitting" on known inhibitors not used in the model construction (keys or test set). The results showed a good predictive capability of the protocol (confidence level 80%). This method gives interesting and convenient results because of the user-defined descriptors and biological targets choice in the process of new inhibitors discovery. Copyright © 2011 Elsevier Masson SAS. All rights reserved.

Renal Function Descriptors in Neonates: Which Creatinine-Based Formula Best Describes Vancomycin Clearance?

PubMed

Bhongsatiern, Jiraganya; Stockmann, Chris; Yu, Tian; Constance, Jonathan E; Moorthy, Ganesh; Spigarelli, Michael G; Desai, Pankaj B; Sherwin, Catherine M T

2016-05-01

Growth and maturational changes have been identified as significant covariates in describing variability in clearance of renally excreted drugs such as vancomycin. Because of immaturity of clearance mechanisms, quantification of renal function in neonates is of importance. Several serum creatinine (SCr)-based renal function descriptors have been developed in adults and children, but none are selectively derived for neonates. This review summarizes development of the neonatal kidney and discusses assessment of the renal function regarding estimation of glomerular filtration rate using renal function descriptors. Furthermore, identification of the renal function descriptors that best describe the variability of vancomycin clearance was performed in a sample study of a septic neonatal cohort. Population pharmacokinetic models were developed applying a combination of age-weight, renal function descriptors, or SCr alone. In addition to age and weight, SCr or renal function descriptors significantly reduced variability of vancomycin clearance. The population pharmacokinetic models with Léger and modified Schwartz formulas were selected as the optimal final models, although the other renal function descriptors and SCr provided reasonably good fit to the data, suggesting further evaluation of the final models using external data sets and cross validation. The present study supports incorporation of renal function descriptors in the estimation of vancomycin clearance in neonates. © 2015, The American College of Clinical Pharmacology.

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.

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.

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.

Modeling the tendency for music to induce movement in humans: first correlations with low-level audio descriptors across music genres.

PubMed

Madison, Guy; Gouyon, Fabien; Ullén, Fredrik; Hörnström, Kalle

2011-10-01

Groove is often described as the experience of music that makes people tap their feet and want to dance. A high degree of consistency in ratings of groove across listeners indicates that physical properties of the sound signal contribute to groove (Madison, 2006). Here, correlations were assessed between listeners' ratings and a number of quantitative descriptors of rhythmic properties for one hundred music examples from five distinct traditional music genres. Groove was related to several different rhythmic properties, some of which were genre-specific and some of which were general across genres. Two descriptors corresponding to the density of events between beats and the salience of the beat, respectively, were strongly correlated with groove across domains. In contrast, systematic deviations from strict positions on the metrical grid, so-called microtiming, did not play any significant role. The results are discussed from a functional perspective of rhythmic music to enable and facilitate entrainment and precise synchronization among individuals.

Genome-wide association analysis to identify genotype × environment interaction for milk protein yield and level of somatic cell score as environmental descriptors in German Holsteins.

PubMed

Streit, M; Reinhardt, F; Thaller, G; Bennewitz, J

2013-01-01

Genotype by environment interaction (G × E) has been widely reported in dairy cattle. If the environment can be measured on a continuous scale, reaction norms can be applied to study G × E. The average herd milk production level has frequently been used as an environmental descriptor because it is influenced by the level of feeding or the feeding regimen. Another important environmental factor is the level of udder health and hygiene, for which the average herd somatic cell count might be a descriptor. In the present study, we conducted a genome-wide association analysis to identify single nucleotide polymorphisms (SNP) that affect intercept and slope of milk protein yield reaction norms when using the average herd test-day solution for somatic cell score as an environmental descriptor. Sire estimates for intercept and slope of the reaction norms were calculated from around 12 million daughter records, using linear reaction norm models. Sires were genotyped for ~54,000 SNP. The sire estimates were used as observations in the association analysis, using 1,797 sires. Significant SNP were confirmed in an independent validation set consisting of 500 sires. A known major gene affecting protein yield was included as a covariable in the statistical model. Sixty (21) SNP were confirmed for intercept with P ≤ 0.01 (P ≤ 0.001) in the validation set, and 28 and 11 SNP, respectively, were confirmed for slope. Most but not all SNP affecting slope also affected intercept. Comparison with an earlier study revealed that SNP affecting slope were, in general, also significant for slope when the environment was modeled by the average herd milk production level, although the two environmental descriptors were poorly correlated. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

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.

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

Developing a CD-CBM Anticipatory Approach for Cavitation - Defining a Model-Based Descriptor Consistent Across Processes, Phase 1 Final Report Context-Dependent Prognostics and Health Assessment: A New Paradigm for Condition-based Maintenance SBIR Topic No. N98-114

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

Allgood, G.O.; Dress, W.B.; Kercel, S.W.

1999-06-01

The objective of this research, and subsequent testing, was to identify specific features of cavitation that could be used as a model-based descriptor in a context-dependent condition-based maintenance (CD-CBM) anticipatory prognostic and health assessment model. This descriptor is based on the physics of the phenomena, capturing the salient features of the process dynamics. The test methodology and approach were developed to make the cavitation features the dominant effect in the process and collected signatures. This would allow the accurate characterization of the salient cavitation features at different operational states. By developing such an abstraction, these attributes can be used asmore » a general diagnostic for a system or any of its components. In this study, the particular focus will be pumps. As many as 90% of pump failures are catastrophic. They seem to be operating normally and fail abruptly without warning. This is true whether the failure is sudden hardware damage requiring repair, such as a gasket failure, or a transition into an undesired operating mode, such as cavitation. This means that conventional diagnostic methods fail to predict 90% of incipient failures and that in addressing this problem, model-based methods can add value where it is actually needed.« less

OPERA models for predicting physicochemical properties and environmental fate endpoints.

PubMed

Mansouri, Kamel; Grulke, Chris M; Judson, Richard S; Williams, Antony J

2018-03-08

The collection of chemical structure information and associated experimental data for quantitative structure-activity/property relationship (QSAR/QSPR) modeling is facilitated by an increasing number of public databases containing large amounts of useful data. However, the performance of QSAR models highly depends on the quality of the data and modeling methodology used. This study aims to develop robust QSAR/QSPR models for chemical properties of environmental interest that can be used for regulatory purposes. This study primarily uses data from the publicly available PHYSPROP database consisting of a set of 13 common physicochemical and environmental fate properties. These datasets have undergone extensive curation using an automated workflow to select only high-quality data, and the chemical structures were standardized prior to calculation of the molecular descriptors. The modeling procedure was developed based on the five Organization for Economic Cooperation and Development (OECD) principles for QSAR models. A weighted k-nearest neighbor approach was adopted using a minimum number of required descriptors calculated using PaDEL, an open-source software. The genetic algorithms selected only the most pertinent and mechanistically interpretable descriptors (2-15, with an average of 11 descriptors). The sizes of the modeled datasets varied from 150 chemicals for biodegradability half-life to 14,050 chemicals for logP, with an average of 3222 chemicals across all endpoints. The optimal models were built on randomly selected training sets (75%) and validated using fivefold cross-validation (CV) and test sets (25%). The CV Q 2 of the models varied from 0.72 to 0.95, with an average of 0.86 and an R 2 test value from 0.71 to 0.96, with an average of 0.82. Modeling and performance details are described in QSAR model reporting format and were validated by the European Commission's Joint Research Center to be OECD compliant. All models are freely available as an open-source, command-line application called OPEn structure-activity/property Relationship App (OPERA). OPERA models were applied to more than 750,000 chemicals to produce freely available predicted data on the U.S. Environmental Protection Agency's CompTox Chemistry Dashboard.

Developing Enhanced Blood–Brain Barrier Permeability Models: Integrating External Bio-Assay Data in QSAR Modeling

PubMed Central

Wang, Wenyi; Kim, Marlene T.; Sedykh, Alexander

2015-01-01

Purpose Experimental Blood–Brain Barrier (BBB) permeability models for drug molecules are expensive and time-consuming. As alternative methods, several traditional Quantitative Structure-Activity Relationship (QSAR) models have been developed previously. In this study, we aimed to improve the predictivity of traditional QSAR BBB permeability models by employing relevant public bio-assay data in the modeling process. Methods We compiled a BBB permeability database consisting of 439 unique compounds from various resources. The database was split into a modeling set of 341 compounds and a validation set of 98 compounds. Consensus QSAR modeling workflow was employed on the modeling set to develop various QSAR models. A five-fold cross-validation approach was used to validate the developed models, and the resulting models were used to predict the external validation set compounds. Furthermore, we used previously published membrane transporter models to generate relevant transporter profiles for target compounds. The transporter profiles were used as additional biological descriptors to develop hybrid QSAR BBB models. Results The consensus QSAR models have R2=0.638 for fivefold cross-validation and R2=0.504 for external validation. The consensus model developed by pooling chemical and transporter descriptors showed better predictivity (R2=0.646 for five-fold cross-validation and R2=0.526 for external validation). Moreover, several external bio-assays that correlate with BBB permeability were identified using our automatic profiling tool. Conclusions The BBB permeability models developed in this study can be useful for early evaluation of new compounds (e.g., new drug candidates). The combination of chemical and biological descriptors shows a promising direction to improve the current traditional QSAR models. PMID:25862462

A “loop” shape descriptor and its application to automated segmentation of airways from CT scans

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

Pu, Jiantao; Jin, Chenwang, E-mail: jcw76@163.com; Yu, Nan

2015-06-15

Purpose: A novel shape descriptor is presented to aid an automated identification of the airways depicted on computed tomography (CT) images. Methods: Instead of simplifying the tubular characteristic of the airways as an ideal mathematical cylindrical or circular shape, the proposed “loop” shape descriptor exploits the fact that the cross sections of any tubular structure (regardless of its regularity) always appear as a loop. In implementation, the authors first reconstruct the anatomical structures in volumetric CT as a three-dimensional surface model using the classical marching cubes algorithm. Then, the loop descriptor is applied to locate the airways with a concavemore » loop cross section. To deal with the variation of the airway walls in density as depicted on CT images, a multiple threshold strategy is proposed. A publicly available chest CT database consisting of 20 CT scans, which was designed specifically for evaluating an airway segmentation algorithm, was used for quantitative performance assessment. Measures, including length, branch count, and generations, were computed under the aid of a skeletonization operation. Results: For the test dataset, the airway length ranged from 64.6 to 429.8 cm, the generation ranged from 7 to 11, and the branch number ranged from 48 to 312. These results were comparable to the performance of the state-of-the-art algorithms validated on the same dataset. Conclusions: The authors’ quantitative experiment demonstrated the feasibility and reliability of the developed shape descriptor in identifying lung airways.« less

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.

Autocorrelation descriptor improvements for QSAR: 2DA_Sign and 3DA_Sign

NASA Astrophysics Data System (ADS)

Sliwoski, Gregory; Mendenhall, Jeffrey; Meiler, Jens

2016-03-01

Quantitative structure-activity relationship (QSAR) is a branch of computer aided drug discovery that relates chemical structures to biological activity. Two well established and related QSAR descriptors are two- and three-dimensional autocorrelation (2DA and 3DA). These descriptors encode the relative position of atoms or atom properties by calculating the separation between atom pairs in terms of number of bonds (2DA) or Euclidean distance (3DA). The sums of all values computed for a given small molecule are collected in a histogram. Atom properties can be added with a coefficient that is the product of atom properties for each pair. This procedure can lead to information loss when signed atom properties are considered such as partial charge. For example, the product of two positive charges is indistinguishable from the product of two equivalent negative charges. In this paper, we present variations of 2DA and 3DA called 2DA_Sign and 3DA_Sign that avoid information loss by splitting unique sign pairs into individual histograms. We evaluate these variations with models trained on nine datasets spanning a range of drug target classes. Both 2DA_Sign and 3DA_Sign significantly increase model performance across all datasets when compared with traditional 2DA and 3DA. Lastly, we find that limiting 3DA_Sign to maximum atom pair distances of 6 Å instead of 12 Å further increases model performance, suggesting that conformational flexibility may hinder performance with longer 3DA descriptors. Consistent with this finding, limiting the number of bonds in 2DA_Sign from 11 to 5 fails to improve performance.

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.

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

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.

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

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.

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

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.

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.

Uniting Cheminformatics and Chemical Theory To Predict the Intrinsic Aqueous Solubility of Crystalline Druglike Molecules

PubMed Central

2014-01-01

We present four models of solution free-energy prediction for druglike molecules utilizing cheminformatics descriptors and theoretically calculated thermodynamic values. We make predictions of solution free energy using physics-based theory alone and using machine learning/quantitative structure–property relationship (QSPR) models. We also develop machine learning models where the theoretical energies and cheminformatics descriptors are used as combined input. These models are used to predict solvation free energy. While direct theoretical calculation does not give accurate results in this approach, machine learning is able to give predictions with a root mean squared error (RMSE) of ∼1.1 log S units in a 10-fold cross-validation for our Drug-Like-Solubility-100 (DLS-100) dataset of 100 druglike molecules. We find that a model built using energy terms from our theoretical methodology as descriptors is marginally less predictive than one built on Chemistry Development Kit (CDK) descriptors. Combining both sets of descriptors allows a further but very modest improvement in the predictions. However, in some cases, this is a statistically significant enhancement. These results suggest that there is little complementarity between the chemical information provided by these two sets of descriptors, despite their different sources and methods of calculation. Our machine learning models are also able to predict the well-known Solubility Challenge dataset with an RMSE value of 0.9–1.0 log S units. PMID:24564264

A multivariate prediction model for Rho-dependent termination of transcription.

PubMed

Nadiras, Cédric; Eveno, Eric; Schwartz, Annie; Figueroa-Bossi, Nara; Boudvillain, Marc

2018-06-21

Bacterial transcription termination proceeds via two main mechanisms triggered either by simple, well-conserved (intrinsic) nucleic acid motifs or by the motor protein Rho. Although bacterial genomes can harbor hundreds of termination signals of either type, only intrinsic terminators are reliably predicted. Computational tools to detect the more complex and diversiform Rho-dependent terminators are lacking. To tackle this issue, we devised a prediction method based on Orthogonal Projections to Latent Structures Discriminant Analysis [OPLS-DA] of a large set of in vitro termination data. Using previously uncharacterized genomic sequences for biochemical evaluation and OPLS-DA, we identified new Rho-dependent signals and quantitative sequence descriptors with significant predictive value. Most relevant descriptors specify features of transcript C>G skewness, secondary structure, and richness in regularly-spaced 5'CC/UC dinucleotides that are consistent with known principles for Rho-RNA interaction. Descriptors collectively warrant OPLS-DA predictions of Rho-dependent termination with a ∼85% success rate. Scanning of the Escherichia coli genome with the OPLS-DA model identifies significantly more termination-competent regions than anticipated from transcriptomics and predicts that regions intrinsically refractory to Rho are primarily located in open reading frames. Altogether, this work delineates features important for Rho activity and describes the first method able to predict Rho-dependent terminators in bacterial genomes.

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.

A new adaptive L1-norm for optimal descriptor selection of high-dimensional QSAR classification model for anti-hepatitis C virus activity of thiourea derivatives.

PubMed

Algamal, Z Y; Lee, M H

2017-01-01

A high-dimensional quantitative structure-activity relationship (QSAR) classification model typically contains a large number of irrelevant and redundant descriptors. In this paper, a new design of descriptor selection for the QSAR classification model estimation method is proposed by adding a new weight inside L1-norm. The experimental results of classifying the anti-hepatitis C virus activity of thiourea derivatives demonstrate that the proposed descriptor selection method in the QSAR classification model performs effectively and competitively compared with other existing penalized methods in terms of classification performance on both the training and the testing datasets. Moreover, it is noteworthy that the results obtained in terms of stability test and applicability domain provide a robust QSAR classification model. It is evident from the results that the developed QSAR classification model could conceivably be employed for further high-dimensional QSAR classification studies.

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.

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.

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.

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.

QSAR Study and Molecular Design of Open-Chain Enaminones as Anticonvulsant Agents

PubMed Central

Garro Martinez, Juan C.; Duchowicz, Pablo R.; Estrada, Mario R.; Zamarbide, Graciela N.; Castro, Eduardo A.

2011-01-01

Present work employs the QSAR formalism to predict the ED50 anticonvulsant activity of ringed-enaminones, in order to apply these relationships for the prediction of unknown open-chain compounds containing the same types of functional groups in their molecular structure. Two different modeling approaches are applied with the purpose of comparing the consistency of our results: (a) the search of molecular descriptors via multivariable linear regressions; and (b) the calculation of flexible descriptors with the CORAL (CORrelation And Logic) program. Among the results found, we propose some potent candidate open-chain enaminones having ED50 values lower than 10 mg·kg−1 for corresponding pharmacological studies. These compounds are classified as Class 1 and Class 2 according to the Anticonvulsant Selection Project. PMID:22272137

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.

Blast and ballistic trajectories in combat casualties: a preliminary analysis using a cartesian positioning system with MDCT.

PubMed

Folio, Les R; Fischer, Tatjana; Shogan, Paul; Frew, Michael; Dwyer, Andrew; Provenzale, James M

2011-08-01

The purpose of this study is to determine the agreement with which radiologists identify wound paths in vivo on MDCT and calculate missile trajectories on the basis of Cartesian coordinates using a Cartesian positioning system (CPS). Three radiologists retrospectively identified 25 trajectories on MDCT in 19 casualties who sustained penetrating trauma in Iraq. Trajectories were described qualitatively in terms of directional path descriptors and quantitatively as trajectory vectors. Directional descriptors, trajectory angles, and angles between trajectories were calculated based on Cartesian coordinates of entrance and terminus or exit recorded in x, y image and table space (z) using a Trajectory Calculator created using spreadsheet software. The consistency of qualitative descriptor determinations was assessed in terms of frequency of observer agreement and multirater kappa statistics. Consistency of trajectory vectors was evaluated in terms of distribution of magnitude of the angles between vectors and the differences between their paraaxial and parasagittal angles. In 68% of trajectories, the observers' visual assessment of qualitative descriptors was congruent. Calculated descriptors agreed across observers in 60% of the trajectories. Estimated kappa also showed good agreement (0.65-0.79, p < 0.001); 70% of calculated paraaxial and parasagittal angles were within 20° across observers, and 61.3% of angles between trajectory vectors were within 20° across observers. Results show agreement of visually assessed and calculated qualitative descriptors and trajectory angles among observers. The Trajectory Calculator describes trajectories qualitatively similar to radiologists' visual assessment, showing the potential feasibility of automated trajectory analysis.

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.

Modeling of adipose/blood partition coefficient for environmental chemicals.

PubMed

Papadaki, K C; Karakitsios, S P; Sarigiannis, D A

2017-12-01

A Quantitative Structure Activity Relationship (QSAR) model was developed in order to predict the adipose/blood partition coefficient of environmental chemical compounds. The first step of QSAR modeling was the collection of inputs. Input data included the experimental values of adipose/blood partition coefficient and two sets of molecular descriptors for 67 organic chemical compounds; a) the descriptors from Linear Free Energy Relationship (LFER) and b) the PaDEL descriptors. The datasets were split to training and prediction set and were analysed using two statistical methods; Genetic Algorithm based Multiple Linear Regression (GA-MLR) and Artificial Neural Networks (ANN). The models with LFER and PaDEL descriptors, coupled with ANN, produced satisfying performance results. The fitting performance (R 2 ) of the models, using LFER and PaDEL descriptors, was 0.94 and 0.96, respectively. The Applicability Domain (AD) of the models was assessed and then the models were applied to a large number of chemical compounds with unknown values of adipose/blood partition coefficient. In conclusion, the proposed models were checked for fitting, validity and applicability. It was demonstrated that they are stable, reliable and capable to predict the values of adipose/blood partition coefficient of "data poor" chemical compounds that fall within the applicability domain. Copyright © 2017. Published by Elsevier Ltd.

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.

Chemically Aware Model Builder (camb): an R package for property and bioactivity modelling of small molecules.

PubMed

Murrell, Daniel S; Cortes-Ciriano, Isidro; van Westen, Gerard J P; Stott, Ian P; Bender, Andreas; Malliavin, Thérèse E; Glen, Robert C

2015-01-01

In silico predictive models have proved to be valuable for the optimisation of compound potency, selectivity and safety profiles in the drug discovery process. camb is an R package that provides an environment for the rapid generation of quantitative Structure-Property and Structure-Activity models for small molecules (including QSAR, QSPR, QSAM, PCM) and is aimed at both advanced and beginner R users. camb's capabilities include the standardisation of chemical structure representation, computation of 905 one-dimensional and 14 fingerprint type descriptors for small molecules, 8 types of amino acid descriptors, 13 whole protein sequence descriptors, filtering methods for feature selection, generation of predictive models (using an interface to the R package caret), as well as techniques to create model ensembles using techniques from the R package caretEnsemble). Results can be visualised through high-quality, customisable plots (R package ggplot2). Overall, camb constitutes an open-source framework to perform the following steps: (1) compound standardisation, (2) molecular and protein descriptor calculation, (3) descriptor pre-processing and model training, visualisation and validation, and (4) bioactivity/property prediction for new molecules. camb aims to speed model generation, in order to provide reproducibility and tests of robustness. QSPR and proteochemometric case studies are included which demonstrate camb's application.Graphical abstractFrom compounds and data to models: a complete model building workflow in one package.

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.

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

Modeling the Tendency for Music to Induce Movement in Humans: First Correlations with Low-Level Audio Descriptors across Music Genres

ERIC Educational Resources Information Center

Madison, Guy; Gouyon, Fabien; Ullen, Fredrik; Hornstrom, Kalle

2011-01-01

"Groove" is often described as the experience of music that makes people tap their feet and want to dance. A high degree of consistency in ratings of groove across listeners indicates that physical properties of the sound signal contribute to groove (Madison, 2006). Here, correlations were assessed between listeners' ratings and a number…

Toxicity prediction of ionic liquids based on Daphnia magna by using density functional theory

NASA Astrophysics Data System (ADS)

Nu’aim, M. N.; Bustam, M. A.

2018-04-01

By using a model called density functional theory, the toxicity of ionic liquids can be predicted and forecast. It is a theory that allowing the researcher to have a substantial tool for computation of the quantum state of atoms, molecules and solids, and molecular dynamics which also known as computer simulation method. It can be done by using structural feature based quantum chemical reactivity descriptor. The identification of ionic liquids and its Log[EC50] data are from literature data that available in Ismail Hossain thesis entitled “Synthesis, Characterization and Quantitative Structure Toxicity Relationship of Imidazolium, Pyridinium and Ammonium Based Ionic Liquids”. Each cation and anion of the ionic liquids were optimized and calculated. The geometry optimization and calculation from the software, produce the value of highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO). From the value of HOMO and LUMO, the value for other toxicity descriptors were obtained according to their formulas. The toxicity descriptor that involves are electrophilicity index, HOMO, LUMO, energy gap, chemical potential, hardness and electronegativity. The interrelation between the descriptors are being determined by using a multiple linear regression (MLR). From this MLR, all descriptors being analyzed and the descriptors that are significant were chosen. In order to develop the finest model equation for toxicity prediction of ionic liquids, the selected descriptors that are significant were used. The validation of model equation was performed with the Log[EC50] data from the literature and the final model equation was developed. A bigger range of ionic liquids which nearly 108 of ionic liquids can be predicted from this model equation.

An Analysis of Descriptors of Volatile Organic Compounds and Their Impact on Rate Constant for Reaction with Hydroxyl Radicals

DTIC Science & Technology

2018-05-01

the descriptors were correlated to experimental rate constants. The five descriptors fell into one of two categories: whole molecule descriptors or...model based on these correlations . Although that goal was not achieved in full, considerable progress has been made, and there is potential for a...readme.txt) and compiled. We then searched for correlations between the calculated properties from theory and the experimental measurements of reaction rate

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.

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.

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.

An orientation sensitive approach in biomolecule interaction quantitative structure-activity relationship modeling and its application in ion-exchange chromatography.

PubMed

Kittelmann, Jörg; Lang, Katharina M H; Ottens, Marcel; Hubbuch, Jürgen

2017-01-27

Quantitative structure-activity relationship (QSAR) modeling for prediction of biomolecule parameters has become an established technique in chromatographic purification process design. Unfortunately available descriptor sets fail to describe the orientation of biomolecules and the effects of ionic strength in the mobile phase on the interaction with the stationary phase. The literature describes several special descriptors used for chromatographic retention modeling, all of these do not describe the screening of electrostatic potential by the mobile phase in use. In this work we introduce two new approaches of descriptor calculations, namely surface patches and plane projection, which capture an oriented binding to charged surfaces and steric hindrance of the interaction with chromatographic ligands with regard to electrostatic potential screening by mobile phase ions. We present the use of the developed descriptor sets for predictive modeling of Langmuir isotherms for proteins at different pH values between pH 5 and 10 and varying ionic strength in the range of 10-100mM. The resulting model has a high correlation of calculated descriptors and experimental results, with a coefficient of determination of 0.82 and a predictive coefficient of determination of 0.92 for unknown molecular structures and conditions. The agreement of calculated molecular interaction orientations with both, experimental results as well as molecular dynamic simulations from literature is shown. The developed descriptors provide the means for improved QSAR models of chromatographic processes, as they reflect the complex interactions of biomolecules with chromatographic phases. Copyright © 2016 Elsevier B.V. All rights reserved.

Target recognition for ladar range image using slice image

NASA Astrophysics Data System (ADS)

Xia, Wenze; Han, Shaokun; Wang, Liang

2015-12-01

A shape descriptor and a complete shape-based recognition system using slice images as geometric feature descriptor for ladar range images are introduced. A slice image is a two-dimensional image generated by three-dimensional Hough transform and the corresponding mathematical transformation. The system consists of two processes, the model library construction and recognition. In the model library construction process, a series of range images are obtained after the model object is sampled at preset attitude angles. Then, all the range images are converted into slice images. The number of slice images is reduced by clustering analysis and finding a representation to reduce the size of the model library. In the recognition process, the slice image of the scene is compared with the slice image in the model library. The recognition results depend on the comparison. Simulated ladar range images are used to analyze the recognition and misjudgment rates, and comparison between the slice image representation method and moment invariants representation method is performed. The experimental results show that whether in conditions without noise or with ladar noise, the system has a high recognition rate and low misjudgment rate. The comparison experiment demonstrates that the slice image has better representation ability than moment invariants.

Land use and land cover classification for rural residential areas in China using soft-probability cascading of multifeatures

NASA Astrophysics Data System (ADS)

Zhang, Bin; Liu, Yueyan; Zhang, Zuyu; Shen, Yonglin

2017-10-01

A multifeature soft-probability cascading scheme to solve the problem of land use and land cover (LULC) classification using high-spatial-resolution images to map rural residential areas in China is proposed. The proposed method is used to build midlevel LULC features. Local features are frequently considered as low-level feature descriptors in a midlevel feature learning method. However, spectral and textural features, which are very effective low-level features, are neglected. The acquisition of the dictionary of sparse coding is unsupervised, and this phenomenon reduces the discriminative power of the midlevel feature. Thus, we propose to learn supervised features based on sparse coding, a support vector machine (SVM) classifier, and a conditional random field (CRF) model to utilize the different effective low-level features and improve the discriminability of midlevel feature descriptors. First, three kinds of typical low-level features, namely, dense scale-invariant feature transform, gray-level co-occurrence matrix, and spectral features, are extracted separately. Second, combined with sparse coding and the SVM classifier, the probabilities of the different LULC classes are inferred to build supervised feature descriptors. Finally, the CRF model, which consists of two parts: unary potential and pairwise potential, is employed to construct an LULC classification map. Experimental results show that the proposed classification scheme can achieve impressive performance when the total accuracy reached about 87%.

Interplay of heritage and habitat in the distribution of bacterial signal transduction systems.

PubMed

Galperin, Michael Y; Higdon, Roger; Kolker, Eugene

2010-04-01

Comparative analysis of the complete genome sequences from a variety of poorly studied organisms aims at predicting ecological and behavioral properties of these organisms and helping in characterizing their habitats. This task requires finding appropriate descriptors that could be correlated with the core traits of each system and would allow meaningful comparisons. Using the relatively simple bacterial models, first attempts have been made to introduce suitable metrics to describe the complexity of organism's signaling machinery, which included introducing the "bacterial IQ" score. Here, we use an updated census of prokaryotic signal transduction systems to improve this parameter and evaluate its consistency within selected bacterial phyla. We also introduce a more elaborate descriptor, a set of profiles of relative abundance of members of each family of signal transduction proteins encoded in each genome. We show that these family profiles are well conserved within each genus and are often consistent within families of bacteria. Thus, they reflect evolutionary relationships between organisms as well as individual adaptations of each organism to its specific ecological niche.

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.

Categorical QSAR models for skin sensitization based on local lymph node assay measures and both ground and excited state 4D-fingerprint descriptors

NASA Astrophysics Data System (ADS)

Liu, Jianzhong; Kern, Petra S.; Gerberick, G. Frank; Santos-Filho, Osvaldo A.; Esposito, Emilio X.; Hopfinger, Anton J.; Tseng, Yufeng J.

2008-06-01

In previous studies we have developed categorical QSAR models for predicting skin-sensitization potency based on 4D-fingerprint (4D-FP) descriptors and in vivo murine local lymph node assay (LLNA) measures. Only 4D-FP derived from the ground state (GMAX) structures of the molecules were used to build the QSAR models. In this study we have generated 4D-FP descriptors from the first excited state (EMAX) structures of the molecules. The GMAX, EMAX and the combined ground and excited state 4D-FP descriptors (GEMAX) were employed in building categorical QSAR models. Logistic regression (LR) and partial least square coupled logistic regression (PLS-CLR), found to be effective model building for the LLNA skin-sensitization measures in our previous studies, were used again in this study. This also permitted comparison of the prior ground state models to those involving first excited state 4D-FP descriptors. Three types of categorical QSAR models were constructed for each of the GMAX, EMAX and GEMAX datasets: a binary model (2-state), an ordinal model (3-state) and a binary-binary model (two-2-state). No significant differences exist among the LR 2-state model constructed for each of the three datasets. However, the PLS-CLR 3-state and 2-state models based on the EMAX and GEMAX datasets have higher predictivity than those constructed using only the GMAX dataset. These EMAX and GMAX categorical models are also more significant and predictive than corresponding models built in our previous QSAR studies of LLNA skin-sensitization measures.

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.

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.

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.

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.

Development of a model for predicting reaction rate constants of organic chemicals with ozone at different temperatures.

PubMed

Li, Xuehua; Zhao, Wenxing; Li, Jing; Jiang, Jingqiu; Chen, Jianji; Chen, Jingwen

2013-08-01

To assess the persistence and fate of volatile organic compounds in the troposphere, the rate constants for the reaction with ozone (kO3) are needed. As kO3 values are only available for hundreds of compounds, and experimental determination of kO3 is costly and time-consuming, it is of importance to develop predictive models on kO3. In this study, a total of 379 logkO3 values at different temperatures were used to develop and validate a model for the prediction of kO3, based on quantum chemical descriptors, Dragon descriptors and structural fragments. Molecular descriptors were screened by stepwise multiple linear regression, and the model was constructed by partial least-squares regression. The cross validation coefficient QCUM(2) of the model is 0.836, and the external validation coefficient Qext(2) is 0.811, indicating that the model has high robustness and good predictive performance. The most significant descriptor explaining logkO3 is the BELm2 descriptor with connectivity information weighted atomic masses. kO3 increases with increasing BELm2, and decreases with increasing ionization potential. The applicability domain of the proposed model was visualized by the Williams plot. The developed model can be used to predict kO3 at different temperatures for a wide range of organic chemicals, including alkenes, cycloalkenes, haloalkenes, alkynes, oxygen-containing compounds, nitrogen-containing compounds (except primary amines) and aromatic compounds. Copyright © 2013 Elsevier Ltd. All rights reserved.

Quantum descriptors for predictive toxicology of halogenated aliphatic hydrocarbons.

PubMed

Trohalaki, S; Pachter, R

2003-04-01

In order to improve Quantitative Structure-Activity Relationships (QSARs) for halogenated aliphatics (HA) and to better understand the biophysical mechanism of toxic response to these ubiquitous chemicals, we employ improved quantum-mechanical descriptors to account for HA electrophilicity. We demonstrate that, unlike the lowest unoccupied molecular orbital energy, ELUMO, which was previously used as a descriptor, the electron affinity can be systematically improved by application of higher levels of theory. We also show that employing the reciprocal of ELUMO, which is more consistent with frontier molecular orbital (FMO) theory, improves the correlations with in vitro toxicity data. We offer explanations based on FMO theory for a result from our previous work, in which the LUMO energies of HA anions correlated surprisingly well with in vitro toxicity data. Additional descriptors are also suggested and interpreted in terms of the accepted biophysical mechanism of toxic response to HAs and new QSARs are derived for various chemical categories that compose the data set employed. These alternate descriptors provide important insight and could benefit other classes of compounds where the biophysical mechanism of toxic response involves dissociative attachment.

Predicting the activity of drugs for a group of imidazopyridine anticoccidial compounds.

PubMed

Si, Hongzong; Lian, Ning; Yuan, Shuping; Fu, Aiping; Duan, Yun-Bo; Zhang, Kejun; Yao, Xiaojun

2009-10-01

Gene expression programming (GEP) is a novel machine learning technique. The GEP is used to build nonlinear quantitative structure-activity relationship model for the prediction of the IC(50) for the imidazopyridine anticoccidial compounds. This model is based on descriptors which are calculated from the molecular structure. Four descriptors are selected from the descriptors' pool by heuristic method (HM) to build multivariable linear model. The GEP method produced a nonlinear quantitative model with a correlation coefficient and a mean error of 0.96 and 0.24 for the training set, 0.91 and 0.52 for the test set, respectively. It is shown that the GEP predicted results are in good agreement with experimental ones.

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.

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

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.

Molecular structure and gas chromatographic retention behavior of the components of Ylang-Ylang oil.

PubMed

Olivero, J; Gracia, T; Payares, P; Vivas, R; Díaz, D; Daza, E; Geerlings, P

1997-05-01

Using quantitative structure-retention relationships (QSRR) methodologies the Kovats gas chromatographic retention indices for both apolar (DB-1) and polar (DB-Wax) columns for 48 compounds from Ylang-Ylang essential oil were empirically predicted from calculated and experimental data on molecular structure. Topological, geometric, and electronic descriptors were obtained for model generation. Relationships between descriptors and the retention data reported were established by linear multiple regression, giving equations that can be used to predict the Kovats indices for compounds present in essential oils, both in DB-1 and DB-Wax columns. Factor analysis was performed to interpret the meaning of the descriptors included in the models. The prediction model for the DB-1 column includes descriptors such as Randic's first-order connectivity index (1X), the molecular surface (MSA), the sum of the atomic charge on all the hydrogens (QH), Randic's third-order connectivity index (3X) and the molecular electronegativity (chi). The prediction model for the DB-Wax column includes the first three descriptors mentioned for the DB-1 column (1X, MSA and QH) and the most negative charge (MNC), the global softness (S), and the difference between Randic's and Kier and Hall's third-order connectivity indexes (3X-3XV).

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.

Modeling and Prediction of Solvent Effect on Human Skin Permeability using Support Vector Regression and Random Forest.

PubMed

Baba, Hiromi; Takahara, Jun-ichi; Yamashita, Fumiyoshi; Hashida, Mitsuru

2015-11-01

The solvent effect on skin permeability is important for assessing the effectiveness and toxicological risk of new dermatological formulations in pharmaceuticals and cosmetics development. The solvent effect occurs by diverse mechanisms, which could be elucidated by efficient and reliable prediction models. However, such prediction models have been hampered by the small variety of permeants and mixture components archived in databases and by low predictive performance. Here, we propose a solution to both problems. We first compiled a novel large database of 412 samples from 261 structurally diverse permeants and 31 solvents reported in the literature. The data were carefully screened to ensure their collection under consistent experimental conditions. To construct a high-performance predictive model, we then applied support vector regression (SVR) and random forest (RF) with greedy stepwise descriptor selection to our database. The models were internally and externally validated. The SVR achieved higher performance statistics than RF. The (externally validated) determination coefficient, root mean square error, and mean absolute error of SVR were 0.899, 0.351, and 0.268, respectively. Moreover, because all descriptors are fully computational, our method can predict as-yet unsynthesized compounds. Our high-performance prediction model offers an attractive alternative to permeability experiments for pharmaceutical and cosmetic candidate screening and optimizing skin-permeable topical formulations.

SVM Based Descriptor Selection and Classification of Neurodegenerative Disease Drugs for Pharmacological Modeling.

PubMed

Shahid, Mohammad; Shahzad Cheema, Muhammad; Klenner, Alexander; Younesi, Erfan; Hofmann-Apitius, Martin

2013-03-01

Systems pharmacological modeling of drug mode of action for the next generation of multitarget drugs may open new routes for drug design and discovery. Computational methods are widely used in this context amongst which support vector machines (SVM) have proven successful in addressing the challenge of classifying drugs with similar features. We have applied a variety of such SVM-based approaches, namely SVM-based recursive feature elimination (SVM-RFE). We use the approach to predict the pharmacological properties of drugs widely used against complex neurodegenerative disorders (NDD) and to build an in-silico computational model for the binary classification of NDD drugs from other drugs. Application of an SVM-RFE model to a set of drugs successfully classified NDD drugs from non-NDD drugs and resulted in overall accuracy of ∼80 % with 10 fold cross validation using 40 top ranked molecular descriptors selected out of total 314 descriptors. Moreover, SVM-RFE method outperformed linear discriminant analysis (LDA) based feature selection and classification. The model reduced the multidimensional descriptors space of drugs dramatically and predicted NDD drugs with high accuracy, while avoiding over fitting. Based on these results, NDD-specific focused libraries of drug-like compounds can be designed and existing NDD-specific drugs can be characterized by a well-characterized set of molecular descriptors. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.

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

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.

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.

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.

Structural alignment of protein descriptors - a combinatorial model.

PubMed

Antczak, Maciej; Kasprzak, Marta; Lukasiak, Piotr; Blazewicz, Jacek

2016-09-17

Structural alignment of proteins is one of the most challenging problems in molecular biology. The tertiary structure of a protein strictly correlates with its function and computationally predicted structures are nowadays a main premise for understanding the latter. However, computationally derived 3D models often exhibit deviations from the native structure. A way to confirm a model is a comparison with other structures. The structural alignment of a pair of proteins can be defined with the use of a concept of protein descriptors. The protein descriptors are local substructures of protein molecules, which allow us to divide the original problem into a set of subproblems and, consequently, to propose a more efficient algorithmic solution. In the literature, one can find many applications of the descriptors concept that prove its usefulness for insight into protein 3D structures, but the proposed approaches are presented rather from the biological perspective than from the computational or algorithmic point of view. Efficient algorithms for identification and structural comparison of descriptors can become crucial components of methods for structural quality assessment as well as tertiary structure prediction. In this paper, we propose a new combinatorial model and new polynomial-time algorithms for the structural alignment of descriptors. The model is based on the maximum-size assignment problem, which we define here and prove that it can be solved in polynomial time. We demonstrate suitability of this approach by comparison with an exact backtracking algorithm. Besides a simplification coming from the combinatorial modeling, both on the conceptual and complexity level, we gain with this approach high quality of obtained results, in terms of 3D alignment accuracy and processing efficiency. All the proposed algorithms were developed and integrated in a computationally efficient tool descs-standalone, which allows the user to identify and structurally compare descriptors of biological molecules, such as proteins and RNAs. Both PDB (Protein Data Bank) and mmCIF (macromolecular Crystallographic Information File) formats are supported. The proposed tool is available as an open source project stored on GitHub ( https://github.com/mantczak/descs-standalone ).

Segmentation, modeling and classification of the compact objects in a pile

NASA Technical Reports Server (NTRS)

Gupta, Alok; Funka-Lea, Gareth; Wohn, Kwangyoen

1990-01-01

The problem of interpreting dense range images obtained from the scene of a heap of man-made objects is discussed. A range image interpretation system consisting of segmentation, modeling, verification, and classification procedures is described. First, the range image is segmented into regions and reasoning is done about the physical support of these regions. Second, for each region several possible three-dimensional interpretations are made based on various scenarios of the objects physical support. Finally each interpretation is tested against the data for its consistency. The superquadric model is selected as the three-dimensional shape descriptor, plus tapering deformations along the major axis. Experimental results obtained from some complex range images of mail pieces are reported to demonstrate the soundness and the robustness of our approach.

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.

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

Image-Based Airborne LiDAR Point Cloud Encoding for 3d Building Model Retrieval

NASA Astrophysics Data System (ADS)

Chen, Yi-Chen; Lin, Chao-Hung

2016-06-01

With the development of Web 2.0 and cyber city modeling, an increasing number of 3D models have been available on web-based model-sharing platforms with many applications such as navigation, urban planning, and virtual reality. Based on the concept of data reuse, a 3D model retrieval system is proposed to retrieve building models similar to a user-specified query. The basic idea behind this system is to reuse these existing 3D building models instead of reconstruction from point clouds. To efficiently retrieve models, the models in databases are compactly encoded by using a shape descriptor generally. However, most of the geometric descriptors in related works are applied to polygonal models. In this study, the input query of the model retrieval system is a point cloud acquired by Light Detection and Ranging (LiDAR) systems because of the efficient scene scanning and spatial information collection. Using Point clouds with sparse, noisy, and incomplete sampling as input queries is more difficult than that by using 3D models. Because that the building roof is more informative than other parts in the airborne LiDAR point cloud, an image-based approach is proposed to encode both point clouds from input queries and 3D models in databases. The main goal of data encoding is that the models in the database and input point clouds can be consistently encoded. Firstly, top-view depth images of buildings are generated to represent the geometry surface of a building roof. Secondly, geometric features are extracted from depth images based on height, edge and plane of building. Finally, descriptors can be extracted by spatial histograms and used in 3D model retrieval system. For data retrieval, the models are retrieved by matching the encoding coefficients of point clouds and building models. In experiments, a database including about 900,000 3D models collected from the Internet is used for evaluation of data retrieval. The results of the proposed method show a clear superiority over related methods.

Vapor–Liquid Equilibrium and Polarization Behavior of the GCP Water Model: Gaussian Charge-on-Spring versus Dipole Self-Consistent Field Approaches to Induced Polarization

DOE PAGES

Chialvo, Ariel A.; Moucka, Filip; Vlcek, Lukas; ...

2015-03-24

Here we implemented the Gaussian charge-on-spring (GCOS) version of the original self-consistent field implementation of the Gaussian Charge Polarizable water model and test its accuracy to represent the polarization behavior of the original model involving smeared charges and induced dipole moments. Moreover, for that purpose we adapted the recently developed multiple-particle-move (MPM) within the Gibbs and isochoric-isothermal ensembles Monte Carlo methods for the efficient simulation of polarizable fluids. We also assessed the accuracy of the GCOS representation by a direct comparison of the resulting vapor-liquid phase envelope, microstructure, and relevant microscopic descriptors of water polarization along the orthobaric curve againstmore » the corresponding quantities from the actual GCP water model.« less

An efficient descriptor model for designing materials for solar cells

NASA Astrophysics Data System (ADS)

Alharbi, Fahhad H.; Rashkeev, Sergey N.; El-Mellouhi, Fedwa; Lüthi, Hans P.; Tabet, Nouar; Kais, Sabre

2015-11-01

An efficient descriptor model for fast screening of potential materials for solar cell applications is presented. It works for both excitonic and non-excitonic solar cells materials, and in addition to the energy gap it includes the absorption spectrum (α(E)) of the material. The charge transport properties of the explored materials are modelled using the characteristic diffusion length (Ld) determined for the respective family of compounds. The presented model surpasses the widely used Scharber model developed for bulk heterojunction solar cells. Using published experimental data, we show that the presented model is more accurate in predicting the achievable efficiencies. To model both excitonic and non-excitonic systems, two different sets of parameters are used to account for the different modes of operation. The analysis of the presented descriptor model clearly shows the benefit of including α(E) and Ld in view of improved screening results.

Predicting allergic contact dermatitis: a hierarchical structure activity relationship (SAR) approach to chemical classification using topological and quantum chemical descriptors

NASA Astrophysics Data System (ADS)

Basak, Subhash C.; Mills, Denise; Hawkins, Douglas M.

2008-06-01

A hierarchical classification study was carried out based on a set of 70 chemicals—35 which produce allergic contact dermatitis (ACD) and 35 which do not. This approach was implemented using a regular ridge regression computer code, followed by conversion of regression output to binary data values. The hierarchical descriptor classes used in the modeling include topostructural (TS), topochemical (TC), and quantum chemical (QC), all of which are based solely on chemical structure. The concordance, sensitivity, and specificity are reported. The model based on the TC descriptors was found to be the best, while the TS model was extremely poor.

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.

3D-QSAR studies of some reversible Acetyl cholinesterase inhibitors based on CoMFA and ligand protein interaction fingerprints using PC-LS-SVM and PLS-LS-SVM.

PubMed

Ghafouri, Hamidreza; Ranjbar, Mohsen; Sakhteman, Amirhossein

2017-08-01

A great challenge in medicinal chemistry is to develop different methods for structural design based on the pattern of the previously synthesized compounds. In this study two different QSAR methods were established and compared for a series of piperidine acetylcholinesterase inhibitors. In one novel approach, PC-LS-SVM and PLS-LS-SVM was used for modeling 3D interaction descriptors, and in the other method the same nonlinear techniques were used to build QSAR equations based on field descriptors. Different validation methods were used to evaluate the models and the results revealed the more applicability and predictive ability of the model generated by field descriptors (Q 2 LOO-CV =1, R 2 ext =0.97). External validation criteria revealed that both methods can be used in generating reasonable QSAR models. It was concluded that due to ability of interaction descriptors in prediction of binding mode, using this approach can be implemented in future 3D-QSAR softwares. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Method for Automatic Surface Inspection Using a Model-Based 3D Descriptor.

PubMed

Madrigal, Carlos A; Branch, John W; Restrepo, Alejandro; Mery, Domingo

2017-10-02

Automatic visual inspection allows for the identification of surface defects in manufactured parts. Nevertheless, when defects are on a sub-millimeter scale, detection and recognition are a challenge. This is particularly true when the defect generates topological deformations that are not shown with strong contrast in the 2D image. In this paper, we present a method for recognizing surface defects in 3D point clouds. Firstly, we propose a novel 3D local descriptor called the Model Point Feature Histogram (MPFH) for defect detection. Our descriptor is inspired from earlier descriptors such as the Point Feature Histogram (PFH). To construct the MPFH descriptor, the models that best fit the local surface and their normal vectors are estimated. For each surface model, its contribution weight to the formation of the surface region is calculated and from the relative difference between models of the same region a histogram is generated representing the underlying surface changes. Secondly, through a classification stage, the points on the surface are labeled according to five types of primitives and the defect is detected. Thirdly, the connected components of primitives are projected to a plane, forming a 2D image. Finally, 2D geometrical features are extracted and by a support vector machine, the defects are recognized. The database used is composed of 3D simulated surfaces and 3D reconstructions of defects in welding, artificial teeth, indentations in materials, ceramics and 3D models of defects. The quantitative and qualitative results showed that the proposed method of description is robust to noise and the scale factor, and it is sufficiently discriminative for detecting some surface defects. The performance evaluation of the proposed method was performed for a classification task of the 3D point cloud in primitives, reporting an accuracy of 95%, which is higher than for other state-of-art descriptors. The rate of recognition of defects was close to 94%.

Chemometric modeling of 5-Phenylthiophenecarboxylic acid derivatives as anti-rheumatic agents.

PubMed

Adhikari, Nilanjan; Jana, Dhritiman; Halder, Amit K; Mondal, Chanchal; Maiti, Milan K; Jha, Tarun

2012-09-01

Arthritis involves joint inflammation, synovial proliferation and damage of cartilage. Interleukin-1 undergoes acute and chronic inflammatory mechanisms of arthritis. Non-steroidal anti-inflammatory drugs can produce symptomatic relief but cannot act through mechanisms of arthritis. Diseases modifying anti-rheumatoid drugs reduce the symptoms of arthritis like decrease in pain and disability score, reduction of swollen joints, articular index and serum concentration of acute phage proteins. Recently, some literature references are obtained on molecular modeling of antirheumatic agents. We have tried chemometric modeling through 2D-QSAR studies on a dataset of fifty-one compounds out of which forty-four 5-Phenylthiophenecarboxylic acid derivatives have IL-1 inhibitory activity and forty-six 5-Phenylthiophenecarboxylic acid derivatives have %AIA suppressive activity. The work was done to find out the structural requirements of these anti-rheumatic agents. 2D QSAR models were generated by 2D and 3D descriptors by using multiple linear regression and partial least square method where IL-1 antagonism was considered as the biological activity parameter. Statistically significant models were developed on the training set developed by k-means cluster analysis. Sterimol parameters, electronic interaction at atom number 9, 2D autocorrelation descriptors, information content descriptor, average connectivity index chi-3, radial distribution function, Balaban 3D index and 3D-MoRSE descriptors were found to play crucial roles to modulate IL-1 inhibitory activity. 2D autocorrelation descriptors like Broto-Moreau autocorrelation of topological structure-lag 3 weighted by atomic van der Waals volumes, Geary autocorrelation-lag 7 associated with weighted atomic Sanderson electronegativities and 3D-MoRSE descriptors like 3D-MoRSE-signal 22 related to atomic van der Waals volumes, 3D-MoRSE-signal 28 related to atomic van der Waals volumes and 3D-MoRSE-signal 9 which was unweighted, were found to play important roles to model %AIA suppressive activity.

A Method for Automatic Surface Inspection Using a Model-Based 3D Descriptor

PubMed Central

Branch, John W.

2017-01-01

Automatic visual inspection allows for the identification of surface defects in manufactured parts. Nevertheless, when defects are on a sub-millimeter scale, detection and recognition are a challenge. This is particularly true when the defect generates topological deformations that are not shown with strong contrast in the 2D image. In this paper, we present a method for recognizing surface defects in 3D point clouds. Firstly, we propose a novel 3D local descriptor called the Model Point Feature Histogram (MPFH) for defect detection. Our descriptor is inspired from earlier descriptors such as the Point Feature Histogram (PFH). To construct the MPFH descriptor, the models that best fit the local surface and their normal vectors are estimated. For each surface model, its contribution weight to the formation of the surface region is calculated and from the relative difference between models of the same region a histogram is generated representing the underlying surface changes. Secondly, through a classification stage, the points on the surface are labeled according to five types of primitives and the defect is detected. Thirdly, the connected components of primitives are projected to a plane, forming a 2D image. Finally, 2D geometrical features are extracted and by a support vector machine, the defects are recognized. The database used is composed of 3D simulated surfaces and 3D reconstructions of defects in welding, artificial teeth, indentations in materials, ceramics and 3D models of defects. The quantitative and qualitative results showed that the proposed method of description is robust to noise and the scale factor, and it is sufficiently discriminative for detecting some surface defects. The performance evaluation of the proposed method was performed for a classification task of the 3D point cloud in primitives, reporting an accuracy of 95%, which is higher than for other state-of-art descriptors. The rate of recognition of defects was close to 94%. PMID:28974037

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.

Real-Time Visual Tracking through Fusion Features

PubMed Central

Ruan, Yang; Wei, Zhenzhong

2016-01-01

Due to their high-speed, correlation filters for object tracking have begun to receive increasing attention. Traditional object trackers based on correlation filters typically use a single type of feature. In this paper, we attempt to integrate multiple feature types to improve the performance, and we propose a new DD-HOG fusion feature that consists of discriminative descriptors (DDs) and histograms of oriented gradients (HOG). However, fusion features as multi-vector descriptors cannot be directly used in prior correlation filters. To overcome this difficulty, we propose a multi-vector correlation filter (MVCF) that can directly convolve with a multi-vector descriptor to obtain a single-channel response that indicates the location of an object. Experiments on the CVPR2013 tracking benchmark with the evaluation of state-of-the-art trackers show the effectiveness and speed of the proposed method. Moreover, we show that our MVCF tracker, which uses the DD-HOG descriptor, outperforms the structure-preserving object tracker (SPOT) in multi-object tracking because of its high-speed and ability to address heavy occlusion. PMID:27347951

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.

High-order statistics of weber local descriptors for image representation.

PubMed

Han, Xian-Hua; Chen, Yen-Wei; Xu, Gang

2015-06-01

Highly discriminant visual features play a key role in different image classification applications. This study aims to realize a method for extracting highly-discriminant features from images by exploring a robust local descriptor inspired by Weber's law. The investigated local descriptor is based on the fact that human perception for distinguishing a pattern depends not only on the absolute intensity of the stimulus but also on the relative variance of the stimulus. Therefore, we firstly transform the original stimulus (the images in our study) into a differential excitation-domain according to Weber's law, and then explore a local patch, called micro-Texton, in the transformed domain as Weber local descriptor (WLD). Furthermore, we propose to employ a parametric probability process to model the Weber local descriptors, and extract the higher-order statistics to the model parameters for image representation. The proposed strategy can adaptively characterize the WLD space using generative probability model, and then learn the parameters for better fitting the training space, which would lead to more discriminant representation for images. In order to validate the efficiency of the proposed strategy, we apply three different image classification applications including texture, food images and HEp-2 cell pattern recognition, which validates that our proposed strategy has advantages over the state-of-the-art approaches.

Periodic table-based descriptors to encode cytotoxicity profile of metal oxide nanoparticles: a mechanistic QSTR approach.

PubMed

Kar, Supratik; Gajewicz, Agnieszka; Puzyn, Tomasz; Roy, Kunal; Leszczynski, Jerzy

2014-09-01

Nanotechnology has evolved as a frontrunner in the development of modern science. Current studies have established toxicity of some nanoparticles to human and environment. Lack of sufficient data and low adequacy of experimental protocols hinder comprehensive risk assessment of nanoparticles (NPs). In the present work, metal electronegativity (χ), the charge of the metal cation corresponding to a given oxide (χox), atomic number and valence electron number of the metal have been used as simple molecular descriptors to build up quantitative structure-toxicity relationship (QSTR) models for prediction of cytotoxicity of metal oxide NPs to bacteria Escherichia coli. These descriptors can be easily obtained from molecular formula and information acquired from periodic table in no time. It has been shown that a simple molecular descriptor χox can efficiently encode cytotoxicity of metal oxides leading to models with high statistical quality as well as interpretability. Based on this model and previously published experimental results, we have hypothesized the most probable mechanism of the cytotoxicity of metal oxide nanoparticles to E. coli. Moreover, the required information for descriptor calculation is independent of size range of NPs, nullifying a significant problem that various physical properties of NPs change for different size ranges. Copyright © 2014 Elsevier Inc. All rights reserved.

An infrastructure to mine molecular descriptors for ligand selection on virtual screening.

PubMed

Seus, Vinicius Rosa; Perazzo, Giovanni Xavier; Winck, Ana T; Werhli, Adriano V; Machado, Karina S

2014-01-01

The receptor-ligand interaction evaluation is one important step in rational drug design. The databases that provide the structures of the ligands are growing on a daily basis. This makes it impossible to test all the ligands for a target receptor. Hence, a ligand selection before testing the ligands is needed. One possible approach is to evaluate a set of molecular descriptors. With the aim of describing the characteristics of promising compounds for a specific receptor we introduce a data warehouse-based infrastructure to mine molecular descriptors for virtual screening (VS). We performed experiments that consider as target the receptor HIV-1 protease and different compounds for this protein. A set of 9 molecular descriptors are taken as the predictive attributes and the free energy of binding is taken as a target attribute. By applying the J48 algorithm over the data we obtain decision tree models that achieved up to 84% of accuracy. The models indicate which molecular descriptors and their respective values are relevant to influence good FEB results. Using their rules we performed ligand selection on ZINC database. Our results show important reduction in ligands selection to be applied in VS experiments; for instance, the best selection model picked only 0.21% of the total amount of drug-like ligands.

Notes on quantitative structure-properties relationships (QSPR) (1): A discussion on a QSPR dimensionality paradox (QSPR DP) and its quantum resolution.

PubMed

Carbó-Dorca, Ramon; Gallegos, Ana; Sánchez, Angel J

2009-05-01

Classical quantitative structure-properties relationship (QSPR) statistical techniques unavoidably present an inherent paradoxical computational context. They rely on the definition of a Gram matrix in descriptor spaces, which is used afterwards to reduce the original dimension via several possible kinds of algebraic manipulations. From there, effective models for the computation of unknown properties of known molecular structures are obtained. However, the reduced descriptor dimension causes linear dependence within the set of discrete vector molecular representations, leading to positive semi-definite Gram matrices in molecular spaces. To resolve this QSPR dimensionality paradox (QSPR DP) here is proposed to adopt as starting point the quantum QSPR (QQSPR) computational framework perspective, where density functions act as infinite dimensional descriptors. The fundamental QQSPR equation, deduced from employing quantum expectation value numerical evaluation, can be approximately solved in order to obtain models exempt of the QSPR DP. The substitution of the quantum similarity matrix by an empirical Gram matrix in molecular spaces, build up with the original non manipulated discrete molecular descriptor vectors, permits to obtain classical QSPR models with the same characteristics as in QQSPR, that is: possessing a certain degree of causality and explicitly independent of the descriptor dimension. 2008 Wiley Periodicals, Inc.

Using open source computational tools for predicting human metabolic stability and additional absorption, distribution, metabolism, excretion, and toxicity properties.

PubMed

Gupta, Rishi R; Gifford, Eric M; Liston, Ted; Waller, Chris L; Hohman, Moses; Bunin, Barry A; Ekins, Sean

2010-11-01

Ligand-based computational models could be more readily shared between researchers and organizations if they were generated with open source molecular descriptors [e.g., chemistry development kit (CDK)] and modeling algorithms, because this would negate the requirement for proprietary commercial software. We initially evaluated open source descriptors and model building algorithms using a training set of approximately 50,000 molecules and a test set of approximately 25,000 molecules with human liver microsomal metabolic stability data. A C5.0 decision tree model demonstrated that CDK descriptors together with a set of Smiles Arbitrary Target Specification (SMARTS) keys had good statistics [κ = 0.43, sensitivity = 0.57, specificity = 0.91, and positive predicted value (PPV) = 0.64], equivalent to those of models built with commercial Molecular Operating Environment 2D (MOE2D) and the same set of SMARTS keys (κ = 0.43, sensitivity = 0.58, specificity = 0.91, and PPV = 0.63). Extending the dataset to ∼193,000 molecules and generating a continuous model using Cubist with a combination of CDK and SMARTS keys or MOE2D and SMARTS keys confirmed this observation. When the continuous predictions and actual values were binned to get a categorical score we observed a similar κ statistic (0.42). The same combination of descriptor set and modeling method was applied to passive permeability and P-glycoprotein efflux data with similar model testing statistics. In summary, open source tools demonstrated predictive results comparable to those of commercial software with attendant cost savings. We discuss the advantages and disadvantages of open source descriptors and the opportunity for their use as a tool for organizations to share data precompetitively, avoiding repetition and assisting drug discovery.

Major Source of Error in QSPR Prediction of Intrinsic Thermodynamic Solubility of Drugs: Solid vs Nonsolid State Contributions?

PubMed

Abramov, Yuriy A

2015-06-01

The main purpose of this study is to define the major limiting factor in the accuracy of the quantitative structure-property relationship (QSPR) models of the thermodynamic intrinsic aqueous solubility of the drug-like compounds. For doing this, the thermodynamic intrinsic aqueous solubility property was suggested to be indirectly "measured" from the contributions of solid state, ΔGfus, and nonsolid state, ΔGmix, properties, which are estimated by the corresponding QSPR models. The QSPR models of ΔGfus and ΔGmix properties were built based on a set of drug-like compounds with available accurate measurements of fusion and thermodynamic solubility properties. For consistency ΔGfus and ΔGmix models were developed using similar algorithms and descriptor sets, and validated against the similar test compounds. Analysis of the relative performances of these two QSPR models clearly demonstrates that it is the solid state contribution which is the limiting factor in the accuracy and predictive power of the QSPR models of the thermodynamic intrinsic solubility. The performed analysis outlines a necessity of development of new descriptor sets for an accurate description of the long-range order (periodicity) phenomenon in the crystalline state. The proposed approach to the analysis of limitations and suggestions for improvement of QSPR-type models may be generalized to other applications in the pharmaceutical industry.

The proposal of architecture for chemical splitting to optimize QSAR models for aquatic toxicity.

PubMed

Colombo, Andrea; Benfenati, Emilio; Karelson, Mati; Maran, Uko

2008-06-01

One of the challenges in the field of quantitative structure-activity relationship (QSAR) analysis is the correct classification of a chemical compound to an appropriate model for the prediction of activity. Thus, in previous studies, compounds have been divided into distinct groups according to their mode of action or chemical class. In the current study, theoretical molecular descriptors were used to divide 568 organic substances into subsets with toxicity measured for the 96-h lethal median concentration for the Fathead minnow (Pimephales promelas). Simple constitutional descriptors such as the number of aliphatic and aromatic rings and a quantum chemical descriptor, maximum bond order of a carbon atom divide compounds into nine subsets. For each subset of compounds the automatic forward selection of descriptors was applied to construct QSAR models. Significant correlations were achieved for each subset of chemicals and all models were validated with the leave-one-out internal validation procedure (R(2)(cv) approximately 0.80). The results encourage to consider this alternative way for the prediction of toxicity using QSAR subset models without direct reference to the mechanism of toxic action or the traditional chemical classification.

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.

Quantitative structure-activation barrier relationship modeling for Diels-Alder ligations utilizing quantum chemical structural descriptors.

PubMed

Nandi, Sisir; Monesi, Alessandro; Drgan, Viktor; Merzel, Franci; Novič, Marjana

2013-10-30

In the present study, we show the correlation of quantum chemical structural descriptors with the activation barriers of the Diels-Alder ligations. A set of 72 non-catalysed Diels-Alder reactions were subjected to quantitative structure-activation barrier relationship (QSABR) under the framework of theoretical quantum chemical descriptors calculated solely from the structures of diene and dienophile reactants. Experimental activation barrier data were obtained from literature. Descriptors were computed using Hartree-Fock theory using 6-31G(d) basis set as implemented in Gaussian 09 software. Variable selection and model development were carried out by stepwise multiple linear regression methodology. Predictive performance of the quantitative structure-activation barrier relationship (QSABR) model was assessed by training and test set concept and by calculating leave-one-out cross-validated Q2 and predictive R2 values. The QSABR model can explain and predict 86.5% and 80% of the variances, respectively, in the activation energy barrier training data. Alternatively, a neural network model based on back propagation of errors was developed to assess the nonlinearity of the sought correlations between theoretical descriptors and experimental reaction barriers. A reasonable predictability for the activation barrier of the test set reactions was obtained, which enabled an exploration and interpretation of the significant variables responsible for Diels-Alder interaction between dienes and dienophiles. Thus, studies in the direction of QSABR modelling that provide efficient and fast prediction of activation barriers of the Diels-Alder reactions turn out to be a meaningful alternative to transition state theory based computation.

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.

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.

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.

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.

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.

Fourier Descriptor Analysis and Unification of Voice Range Profile Contours: Method and Applications

ERIC Educational Resources Information Center

Pabon, Peter; Ternstrom, Sten; Lamarche, Anick

2011-01-01

Purpose: To describe a method for unified description, statistical modeling, and comparison of voice range profile (VRP) contours, even from diverse sources. Method: A morphologic modeling technique, which is based on Fourier descriptors (FDs), is applied to the VRP contour. The technique, which essentially involves resampling of the curve of the…

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.

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.

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

Static sign language recognition using 1D descriptors and neural networks

NASA Astrophysics Data System (ADS)

Solís, José F.; Toxqui, Carina; Padilla, Alfonso; Santiago, César

2012-10-01

A frame work for static sign language recognition using descriptors which represents 2D images in 1D data and artificial neural networks is presented in this work. The 1D descriptors were computed by two methods, first one consists in a correlation rotational operator.1 and second is based on contour analysis of hand shape. One of the main problems in sign language recognition is segmentation; most of papers report a special color in gloves or background for hand shape analysis. In order to avoid the use of gloves or special clothing, a thermal imaging camera was used to capture images. Static signs were picked up from 1 to 9 digits of American Sign Language, a multilayer perceptron reached 100% recognition with cross-validation.

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.

Predictive Models of Acute Mountain Sickness after Rapid Ascent to Various Altitudes

DTIC Science & Technology

2013-01-01

unclassified relational mountain medicine database containing individ- ual ascent profiles, demographic and physiologic subject descriptors, and...course of AMS, and define the baseline demographics and physiologic descriptors that increase the risk of AMS. In addition, these models provide...substantiated this finding in un- acclimatized women (24). Other physiologic differences between men and women (i.e., differences in endothelial

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

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.

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.

The effects of geometric uncertainties on computational modelling of knee biomechanics

NASA Astrophysics Data System (ADS)

Meng, Qingen; Fisher, John; Wilcox, Ruth

2017-08-01

The geometry of the articular components of the knee is an important factor in predicting joint mechanics in computational models. There are a number of uncertainties in the definition of the geometry of cartilage and meniscus, and evaluating the effects of these uncertainties is fundamental to understanding the level of reliability of the models. In this study, the sensitivity of knee mechanics to geometric uncertainties was investigated by comparing polynomial-based and image-based knee models and varying the size of meniscus. The results suggested that the geometric uncertainties in cartilage and meniscus resulting from the resolution of MRI and the accuracy of segmentation caused considerable effects on the predicted knee mechanics. Moreover, even if the mathematical geometric descriptors can be very close to the imaged-based articular surfaces, the detailed contact pressure distribution produced by the mathematical geometric descriptors was not the same as that of the image-based model. However, the trends predicted by the models based on mathematical geometric descriptors were similar to those of the imaged-based models.

[Quantitative relationship between gas chromatographic retention time and structural parameters of alkylphenols].

PubMed

Ruan, Xiaofang; Zhang, Ruisheng; Yao, Xiaojun; Liu, Mancang; Fan, Botao

2007-03-01

Alkylphenols are a group of permanent pollutants in the environment and could adversely disturb the human endocrine system. It is therefore important to effectively separate and measure the alkylphenols. To guide the chromatographic analysis of these compounds in practice, the development of quantitative relationship between the molecular structure and the retention time of alkylphenols becomes necessary. In this study, topological, constitutional, geometrical, electrostatic and quantum-chemical descriptors of 44 alkylphenols were calculated using a software, CODESSA, and these descriptors were pre-selected using the heuristic method. As a result, three-descriptor linear model (LM) was developed to describe the relationship between the molecular structure and the retention time of alkylphenols. Meanwhile, the non-linear regression model was also developed based on support vector machine (SVM) using the same three descriptors. The correlation coefficient (R(2)) for the LM and SVM was 0.98 and 0. 92, and the corresponding root-mean-square error was 0. 99 and 2. 77, respectively. By comparing the stability and prediction ability of the two models, it was found that the linear model was a better method for describing the quantitative relationship between the retention time of alkylphenols and the molecular structure. The results obtained suggested that the linear model could be applied for the chromatographic analysis of alkylphenols with known molecular structural parameters.

Model of twelve properties of a set of organic solvents with graph-theoretical and/or experimental parameters.

PubMed

Pogliani, Lionello

2010-01-30

Twelve properties of a highly heterogeneous class of organic solvents have been modeled with a graph-theoretical molecular connectivity modified (MC) method, which allows to encode the core electrons and the hydrogen atoms. The graph-theoretical method uses the concepts of simple, general, and complete graphs, where these last types of graphs are used to encode the core electrons. The hydrogen atoms have been encoded by the aid of a graph-theoretical perturbation parameter, which contributes to the definition of the valence delta, delta(v), a key parameter in molecular connectivity studies. The model of the twelve properties done with a stepwise search algorithm is always satisfactory, and it allows to check the influence of the hydrogen content of the solvent molecules on the choice of the type of descriptor. A similar argument holds for the influence of the halogen atoms on the type of core electron representation. In some cases the molar mass, and in a minor way, special "ad hoc" parameters have been used to improve the model. A very good model of the surface tension could be obtained by the aid of five experimental parameters. A mixed model method based on experimental parameters plus molecular connectivity indices achieved, instead, to consistently improve the model quality of five properties. To underline is the importance of the boiling point temperatures as descriptors in these last two model methodologies. Copyright 2009 Wiley Periodicals, Inc.

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.

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.

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.

Quantitative structure-toxicity relationship (QSTR) studies on the organophosphate insecticides.

PubMed

Can, Alper

2014-11-04

Organophosphate insecticides are the most commonly used pesticides in the world. In this study, quantitative structure-toxicity relationship (QSTR) models were derived for estimating the acute oral toxicity of organophosphate insecticides to male rats. The 20 chemicals of the training set and the seven compounds of the external testing set were described by means of using descriptors. Descriptors for lipophilicity, polarity and molecular geometry, as well as quantum chemical descriptors for energy were calculated. Model development to predict toxicity of organophosphate insecticides in different matrices was carried out using multiple linear regression. The model was validated internally and externally. In the present study, QSTR model was used for the first time to understand the inherent relationships between the organophosphate insecticide molecules and their toxicity behavior. Such studies provide mechanistic insight about structure-toxicity relationship and help in the design of less toxic insecticides. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Prediction of boiling points of organic compounds by QSPR tools.

PubMed

Dai, Yi-min; Zhu, Zhi-ping; Cao, Zhong; Zhang, Yue-fei; Zeng, Ju-lan; Li, Xun

2013-07-01

The novel electro-negativity topological descriptors of YC, WC were derived from molecular structure by equilibrium electro-negativity of atom and relative bond length of molecule. The quantitative structure-property relationships (QSPR) between descriptors of YC, WC as well as path number parameter P3 and the normal boiling points of 80 alkanes, 65 unsaturated hydrocarbons and 70 alcohols were obtained separately. The high-quality prediction models were evidenced by coefficient of determination (R(2)), the standard error (S), average absolute errors (AAE) and predictive parameters (Qext(2),RCV(2),Rm(2)). According to the regression equations, the influences of the length of carbon backbone, the size, the degree of branching of a molecule and the role of functional groups on the normal boiling point were analyzed. Comparison results with reference models demonstrated that novel topological descriptors based on the equilibrium electro-negativity of atom and the relative bond length were useful molecular descriptors for predicting the normal boiling points of organic compounds. Copyright © 2013 Elsevier Inc. All rights reserved.

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.

Linear and nonlinear methods in modeling the aqueous solubility of organic compounds.

PubMed

Catana, Cornel; Gao, Hua; Orrenius, Christian; Stouten, Pieter F W

2005-01-01

Solubility data for 930 diverse compounds have been analyzed using linear Partial Least Square (PLS) and nonlinear PLS methods, Continuum Regression (CR), and Neural Networks (NN). 1D and 2D descriptors from MOE package in combination with E-state or ISIS keys have been used. The best model was obtained using linear PLS for a combination between 22 MOE descriptors and 65 ISIS keys. It has a correlation coefficient (r2) of 0.935 and a root-mean-square error (RMSE) of 0.468 log molar solubility (log S(w)). The model validated on a test set of 177 compounds not included in the training set has r2 0.911 and RMSE 0.475 log S(w). The descriptors were ranked according to their importance, and at the top of the list have been found the 22 MOE descriptors. The CR model produced results as good as PLS, and because of the way in which cross-validation has been done it is expected to be a valuable tool in prediction besides PLS model. The statistics obtained using nonlinear methods did not surpass those got with linear ones. The good statistic obtained for linear PLS and CR recommends these models to be used in prediction when it is difficult or impossible to make experimental measurements, for virtual screening, combinatorial library design, and efficient leads optimization.

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 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%).

Assessment of tautomer distribution using the condensed reaction graph approach

NASA Astrophysics Data System (ADS)

Gimadiev, T. R.; Madzhidov, T. I.; Nugmanov, R. I.; Baskin, I. I.; Antipin, I. S.; Varnek, A.

2018-03-01

We report the first direct QSPR modeling of equilibrium constants of tautomeric transformations (logK T ) in different solvents and at different temperatures, which do not require intermediate assessment of acidity (basicity) constants for all tautomeric forms. The key step of the modeling consisted in the merging of two tautomers in one sole molecular graph ("condensed reaction graph") which enables to compute molecular descriptors characterizing entire equilibrium. The support vector regression method was used to build the models. The training set consisted of 785 transformations belonging to 11 types of tautomeric reactions with equilibrium constants measured in different solvents and at different temperatures. The models obtained perform well both in cross-validation (Q2 = 0.81 RMSE = 0.7 logK T units) and on two external test sets. Benchmarking studies demonstrate that our models outperform results obtained with DFT B3LYP/6-311 ++ G(d,p) and ChemAxon Tautomerizer applicable only in water at room temperature.

Pairwise registration of TLS point clouds using covariance descriptors and a non-cooperative game

NASA Astrophysics Data System (ADS)

Zai, Dawei; Li, Jonathan; Guo, Yulan; Cheng, Ming; Huang, Pengdi; Cao, Xiaofei; Wang, Cheng

2017-12-01

It is challenging to automatically register TLS point clouds with noise, outliers and varying overlap. In this paper, we propose a new method for pairwise registration of TLS point clouds. We first generate covariance matrix descriptors with an adaptive neighborhood size from point clouds to find candidate correspondences, we then construct a non-cooperative game to isolate mutual compatible correspondences, which are considered as true positives. The method was tested on three models acquired by two different TLS systems. Experimental results demonstrate that our proposed adaptive covariance (ACOV) descriptor is invariant to rigid transformation and robust to noise and varying resolutions. The average registration errors achieved on three models are 0.46 cm, 0.32 cm and 1.73 cm, respectively. The computational times cost on these models are about 288 s, 184 s and 903 s, respectively. Besides, our registration framework using ACOV descriptors and a game theoretic method is superior to the state-of-the-art methods in terms of both registration error and computational time. The experiment on a large outdoor scene further demonstrates the feasibility and effectiveness of our proposed pairwise registration framework.

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.

The Relevance Aura of Bibliographic Records.

ERIC Educational Resources Information Center

Brooks, Terrence A.

1997-01-01

Analyzes relevance assessments of topical descriptors for bibliographic records for two dimensions: (1) a vertical conceptual hierarchy of broad to narrow descriptors, and (2) a horizontal linkage of related terms. The data were analyzed for a semantic distance and semantic direction effect as postulated by the Semantic Distance Model. (Author/LRW)

Use of in Vitro HTS-Derived Concentration-Response Data as ...

EPA Pesticide Factsheets

Background: Quantitative high-throughput screening (qHTS) assays are increasingly being employed 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 NIH Chemical Genomics Center. Objectives: 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, may improve the accuracy of Quantitative Structure-Activity Relationship (QSAR) models applied to prediction of in vivo toxicity endpoints. Methods and Results: The cell viability qHTS concentration-response data for 1,408 substances assayed in 13 cell lines were obtained from PubChem; for a subset of these compounds rodent acute toxicity LD50 data were also available. The classification k Nearest Neighbor and Random Forest QSAR methods were employed for modeling LD50 data using either chemical descriptors alone (conventional models) or in combination 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. We show that 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 was superior to convent

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.'

The effects of geometric uncertainties on computational modelling of knee biomechanics

PubMed Central

Fisher, John; Wilcox, Ruth

2017-01-01

The geometry of the articular components of the knee is an important factor in predicting joint mechanics in computational models. There are a number of uncertainties in the definition of the geometry of cartilage and meniscus, and evaluating the effects of these uncertainties is fundamental to understanding the level of reliability of the models. In this study, the sensitivity of knee mechanics to geometric uncertainties was investigated by comparing polynomial-based and image-based knee models and varying the size of meniscus. The results suggested that the geometric uncertainties in cartilage and meniscus resulting from the resolution of MRI and the accuracy of segmentation caused considerable effects on the predicted knee mechanics. Moreover, even if the mathematical geometric descriptors can be very close to the imaged-based articular surfaces, the detailed contact pressure distribution produced by the mathematical geometric descriptors was not the same as that of the image-based model. However, the trends predicted by the models based on mathematical geometric descriptors were similar to those of the imaged-based models. PMID:28879008

Impact of metal ionic characteristics on adsorption potential of Ficus carica leaves using QSPR modeling.

PubMed

Batool, Fozia; Iqbal, Shahid; Akbar, Jamshed

2018-04-03

The present study describes Quantitative Structure Property Relationship (QSPR) modeling to relate metal ions characteristics with adsorption potential of Ficus carica leaves for 13 selected metal ions (Ca +2 , Cr +3 , Co +2 , Cu +2 , Cd +2 , K +1 , Mg +2 , Mn +2 , Na +1 , Ni +2 , Pb +2 , Zn +2 , and Fe +2 ) to generate QSPR model. A set of 21 characteristic descriptors were selected and relationship of these metal characteristics with adsorptive behavior of metal ions was investigated. Stepwise Multiple Linear Regression (SMLR) analysis and Artificial Neural Network (ANN) were applied for descriptors selection and model generation. Langmuir and Freundlich isotherms were also applied on adsorption data to generate proper correlation for experimental findings. Model generated indicated covalent index as the most significant descriptor, which is responsible for more than 90% predictive adsorption (α = 0.05). Internal validation of model was performed by measuring [Formula: see text] (0.98). The results indicate that present model is a useful tool for prediction of adsorptive behavior of different metal ions based on their ionic characteristics.

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.

Odor-color associations differ with verbal descriptors for odors: A comparison of three linguistically diverse groups.

PubMed

de Valk, Josje M; Wnuk, Ewelina; Huisman, John L A; Majid, Asifa

2017-08-01

People appear to have systematic associations between odors and colors. Previous research has emphasized the perceptual nature of these associations, but little attention has been paid to what role language might play. It is possible odor-color associations arise through a process of labeling; that is, participants select a descriptor for an odor and then choose a color accordingly (e.g., banana odor → "banana" label → yellow). If correct, this would predict odor-color associations would differ as odor descriptions differ. We compared speakers of Dutch (who overwhelmingly describe odors by referring to the source; e.g., smells like banana) with speakers of Maniq and Thai (who also describe odors with dedicated, abstract smell vocabulary; e.g., musty), and tested whether the type of descriptor mattered for odor-color associations. Participants were asked to select a color that they associated with an odor on two separate occasions (to test for consistency), and finally to label the odors. We found the hunter-gatherer Maniq showed few, if any, consistent or accurate odor-color associations. More importantly, we found the types of descriptors used to name the smells were related to the odor-color associations. When people used abstract smell terms to describe odors, they were less likely to choose a color match, but when they described an odor with a source-based term, their color choices more accurately reflected the odor source, particularly when the odor source was named correctly (e.g., banana odor → yellow). This suggests language is an important factor in odor-color cross-modal associations.

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.

QSAR modeling of human serum protein binding with several modeling techniques utilizing structure-information representation.

PubMed

Votano, Joseph R; Parham, Marc; Hall, L Mark; Hall, Lowell H; Kier, Lemont B; Oloff, Scott; Tropsha, Alexander

2006-11-30

Four modeling techniques, using topological descriptors to represent molecular structure, were employed to produce models of human serum protein binding (% bound) on a data set of 1008 experimental values, carefully screened from publicly available sources. To our knowledge, this data is the largest set on human serum protein binding reported for QSAR modeling. The data was partitioned into a training set of 808 compounds and an external validation test set of 200 compounds. Partitioning was accomplished by clustering the compounds in a structure descriptor space so that random sampling of 20% of the whole data set produced an external test set that is a good representative of the training set with respect to both structure and protein binding values. The four modeling techniques include multiple linear regression (MLR), artificial neural networks (ANN), k-nearest neighbors (kNN), and support vector machines (SVM). With the exception of the MLR model, the ANN, kNN, and SVM QSARs were ensemble models. Training set correlation coefficients and mean absolute error ranged from r2=0.90 and MAE=7.6 for ANN to r2=0.61 and MAE=16.2 for MLR. Prediction results from the validation set yielded correlation coefficients and mean absolute errors which ranged from r2=0.70 and MAE=14.1 for ANN to a low of r2=0.59 and MAE=18.3 for the SVM model. Structure descriptors that contribute significantly to the models are discussed and compared with those found in other published models. For the ANN model, structure descriptor trends with respect to their affects on predicted protein binding can assist the chemist in structure modification during the drug design process.

Prediction of drug transport processes using simple parameters and PLS statistics. The use of ACD/logP and ACD/ChemSketch descriptors.

PubMed

Osterberg, T; Norinder, U

2001-01-01

A method of modelling and predicting biopharmaceutical properties using simple theoretically computed molecular descriptors and multivariate statistics has been investigated for several data sets related to solubility, IAM chromatography, permeability across Caco-2 cell monolayers, human intestinal perfusion, brain-blood partitioning, and P-glycoprotein ATPase activity. The molecular descriptors (e.g. molar refractivity, molar volume, index of refraction, surface tension and density) and logP were computed with ACD/ChemSketch and ACD/logP, respectively. Good statistical models were derived that permit simple computational prediction of biopharmaceutical properties. All final models derived had R(2) values ranging from 0.73 to 0.95 and Q(2) values ranging from 0.69 to 0.86. The RMSEP values for the external test sets ranged from 0.24 to 0.85 (log scale).

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.

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.

Comparative study of solvation parameter models accounting the effects of mobile phase composition in reversed-phase liquid chromatography.

PubMed

Torres-Lapasió, J R; Ruiz-Angel, M J; García-Alvarez-Coque, M C

2007-09-28

Solvation parameter models relate linearly compound properties with five fundamental solute descriptors (excess molar refraction, dipolarity/polarizability, effective hydrogen-bond acidity and basicity, and McGowan volume). These models are widely used, due to the availability of protocols to obtain the descriptors, good performance, and general applicability. Several approaches to predict retention in reversed-phase liquid chromatography (RPLC) as a function of these descriptors and mobile phase composition are compared, assaying the performance with a set of 146 organic compounds of diverse nature, eluted with acetonitrile and methanol. The approaches are classified in two groups: those that only allow predictions of retention for the mobile phases used to build the models, and those valid at any other mobile phase composition. The first group includes the use of ratios between the regressed coefficients of the solvation models that are assumed to be characteristic for a column/solvent system, and the application of offsets to transfer the retention from a reference mobile phase to any other. Maximal accuracy in predictions corresponded, however, to the approaches in the second group, which were based on models that describe the retention as a function of mobile phase composition (expressed as the solvent volume fraction or a normalised polarity measurement), where the coefficients were made dependent on the solvent descriptors. The study revealed the properties that influence the retention and distinguish the particular behaviour of acetonitrile and methanol in RPLC.

Molecule kernels: a descriptor- and alignment-free quantitative structure-activity relationship approach.

PubMed

Mohr, Johannes A; Jain, Brijnesh J; Obermayer, Klaus

2008-09-01

Quantitative structure activity relationship (QSAR) analysis is traditionally based on extracting a set of molecular descriptors and using them to build a predictive model. In this work, we propose a QSAR approach based directly on the similarity between the 3D structures of a set of molecules measured by a so-called molecule kernel, which is independent of the spatial prealignment of the compounds. Predictors can be build using the molecule kernel in conjunction with the potential support vector machine (P-SVM), a recently proposed machine learning method for dyadic data. The resulting models make direct use of the structural similarities between the compounds in the test set and a subset of the training set and do not require an explicit descriptor construction. We evaluated the predictive performance of the proposed method on one classification and four regression QSAR datasets and compared its results to the results reported in the literature for several state-of-the-art descriptor-based and 3D QSAR approaches. In this comparison, the proposed molecule kernel method performed better than the other QSAR methods.

Motion control of planar parallel robot using the fuzzy descriptor system approach.

PubMed

Vermeiren, Laurent; Dequidt, Antoine; Afroun, Mohamed; Guerra, Thierry-Marie

2012-09-01

This work presents the control of a two-degree of freedom parallel robot manipulator. A quasi-LPV approach, through the so-called TS fuzzy model and LMI constraints problems is used. Moreover, in this context a way to derive interesting control laws is to keep the descriptor form of the mechanical system. Therefore, new LMI problems have to be defined that helps to reduce the conservatism of the usual results. Some relaxations are also proposed to leave the pure quadratic stability/stabilization framework. A comparison study between the classical control strategies from robotics and the control design using TS fuzzy descriptor models is carried out to show the interest of the proposed approach. Copyright © 2012 ISA. Published by Elsevier Ltd. All rights reserved.

Metal Oxide Nanomaterial QNAR Models: Available Structural Descriptors and Understanding of Toxicity Mechanisms

PubMed Central

Ying, Jiali; Zhang, Ting; Tang, Meng

2015-01-01

Metal oxide nanomaterials are widely used in various areas; however, the divergent published toxicology data makes it difficult to determine whether there is a risk associated with exposure to metal oxide nanomaterials. The application of quantitative structure activity relationship (QSAR) modeling in metal oxide nanomaterials toxicity studies can reduce the need for time-consuming and resource-intensive nanotoxicity tests. The nanostructure and inorganic composition of metal oxide nanomaterials makes this approach different from classical QSAR study; this review lists and classifies some structural descriptors, such as size, cation charge, and band gap energy, in recent metal oxide nanomaterials quantitative nanostructure activity relationship (QNAR) studies and discusses the mechanism of metal oxide nanomaterials toxicity based on these descriptors and traditional nanotoxicity tests. PMID:28347085

Integration of QSAR and SAR methods for the mechanistic interpretation of predictive models for carcinogenicity

PubMed Central

Fjodorova, Natalja; Novič, Marjana

2012-01-01

The knowledge-based Toxtree expert system (SAR approach) was integrated with the statistically based counter propagation artificial neural network (CP ANN) model (QSAR approach) to contribute to a better mechanistic understanding of a carcinogenicity model for non-congeneric chemicals using Dragon descriptors and carcinogenic potency for rats as a response. The transparency of the CP ANN algorithm was demonstrated using intrinsic mapping technique specifically Kohonen maps. Chemical structures were represented by Dragon descriptors that express the structural and electronic features of molecules such as their shape and electronic surrounding related to reactivity of molecules. It was illustrated how the descriptors are correlated with particular structural alerts (SAs) for carcinogenicity with recognized mechanistic link to carcinogenic activity. Moreover, the Kohonen mapping technique enables one to examine the separation of carcinogens and non-carcinogens (for rats) within a family of chemicals with a particular SA for carcinogenicity. The mechanistic interpretation of models is important for the evaluation of safety of chemicals. PMID:24688639

Solubility of organic compounds in octanol: Improved predictions based on the geometrical fragment approach.

PubMed

Mathieu, Didier

2017-09-01

Two new models are introduced to predict the solubility of chemicals in octanol (S oct ), taking advantage of the extensive character of log(S oct ) through a decomposition of molecules into so-called geometrical fragments (GF). They are extensively validated and their compliance with regulatory requirements is demonstrated. The first model requires just a molecular formula as input. Despite an extreme simplicity, it performs as well as an advanced random forest model involving 86 descriptors, with a root mean square error (RMSE) of 0.64 log units for an external test set of 100 molecules. For the second one, which requires the melting point T m as input, introducing GF descriptors reduces the RMSE from about 0.7 to <0.5 log units, a performance that could previously be obtained only through the use of Abraham descriptors. A script is provided for easy application of the models, taking into account the limits of their applicability domains. Copyright © 2017 Elsevier Ltd. All rights reserved.

Random forest models to predict aqueous solubility.

PubMed

Palmer, David S; O'Boyle, Noel M; Glen, Robert C; Mitchell, John B O

2007-01-01

Random Forest regression (RF), Partial-Least-Squares (PLS) regression, Support Vector Machines (SVM), and Artificial Neural Networks (ANN) were used to develop QSPR models for the prediction of aqueous solubility, based on experimental data for 988 organic molecules. The Random Forest regression model predicted aqueous solubility more accurately than those created by PLS, SVM, and ANN and offered methods for automatic descriptor selection, an assessment of descriptor importance, and an in-parallel measure of predictive ability, all of which serve to recommend its use. The prediction of log molar solubility for an external test set of 330 molecules that are solid at 25 degrees C gave an r2 = 0.89 and RMSE = 0.69 log S units. For a standard data set selected from the literature, the model performed well with respect to other documented methods. Finally, the diversity of the training and test sets are compared to the chemical space occupied by molecules in the MDL drug data report, on the basis of molecular descriptors selected by the regression analysis.

The importance of molecular structures, endpoints' values, and predictivity parameters in QSAR research: QSAR analysis of a series of estrogen receptor binders.

PubMed

Li, Jiazhong; Gramatica, Paola

2010-11-01

Quantitative structure-activity relationship (QSAR) methodology aims to explore the relationship between molecular structures and experimental endpoints, producing a model for the prediction of new data; the predictive performance of the model must be checked by external validation. Clearly, the qualities of chemical structure information and experimental endpoints, as well as the statistical parameters used to verify the external predictivity have a strong influence on QSAR model reliability. Here, we emphasize the importance of these three aspects by analyzing our models on estrogen receptor binders (Endocrine disruptor knowledge base (EDKB) database). Endocrine disrupting chemicals, which mimic or antagonize the endogenous hormones such as estrogens, are a hot topic in environmental and toxicological sciences. QSAR shows great values in predicting the estrogenic activity and exploring the interactions between the estrogen receptor and ligands. We have verified our previously published model for additional external validation on new EDKB chemicals. Having found some errors in the used 3D molecular conformations, we redevelop a new model using the same data set with corrected structures, the same method (ordinary least-square regression, OLS) and DRAGON descriptors. The new model, based on some different descriptors, is more predictive on external prediction sets. Three different formulas to calculate correlation coefficient for the external prediction set (Q2 EXT) were compared, and the results indicated that the new proposal of Consonni et al. had more reasonable results, consistent with the conclusions from regression line, Williams plot and root mean square error (RMSE) values. Finally, the importance of reliable endpoints values has been highlighted by comparing the classification assignments of EDKB with those of another estrogen receptor binders database (METI): we found that 16.1% assignments of the common compounds were opposite (20 among 124 common compounds). In order to verify the real assignments for these inconsistent compounds, we predicted these samples, as a blind external set, by our regression models and compared the results with the two databases. The results indicated that most of the predictions were consistent with METI. Furthermore, we built a kNN classification model using the 104 consistent compounds to predict those inconsistent ones, and most of the predictions were also in agreement with METI database.

A combined Fisher and Laplacian score for feature selection in QSAR based drug design using compounds with known and unknown activities.

PubMed

Valizade Hasanloei, Mohammad Amin; Sheikhpour, Razieh; Sarram, Mehdi Agha; Sheikhpour, Elnaz; Sharifi, Hamdollah

2018-02-01

Quantitative structure-activity relationship (QSAR) is an effective computational technique for drug design that relates the chemical structures of compounds to their biological activities. Feature selection is an important step in QSAR based drug design to select the most relevant descriptors. One of the most popular feature selection methods for classification problems is Fisher score which aim is to minimize the within-class distance and maximize the between-class distance. In this study, the properties of Fisher criterion were extended for QSAR models to define the new distance metrics based on the continuous activity values of compounds with known activities. Then, a semi-supervised feature selection method was proposed based on the combination of Fisher and Laplacian criteria which exploits both compounds with known and unknown activities to select the relevant descriptors. To demonstrate the efficiency of the proposed semi-supervised feature selection method in selecting the relevant descriptors, we applied the method and other feature selection methods on three QSAR data sets such as serine/threonine-protein kinase PLK3 inhibitors, ROCK inhibitors and phenol compounds. The results demonstrated that the QSAR models built on the selected descriptors by the proposed semi-supervised method have better performance than other models. This indicates the efficiency of the proposed method in selecting the relevant descriptors using the compounds with known and unknown activities. The results of this study showed that the compounds with known and unknown activities can be helpful to improve the performance of the combined Fisher and Laplacian based feature selection methods.

A combined Fisher and Laplacian score for feature selection in QSAR based drug design using compounds with known and unknown activities

NASA Astrophysics Data System (ADS)

Valizade Hasanloei, Mohammad Amin; Sheikhpour, Razieh; Sarram, Mehdi Agha; Sheikhpour, Elnaz; Sharifi, Hamdollah

2018-02-01

Quantitative structure-activity relationship (QSAR) is an effective computational technique for drug design that relates the chemical structures of compounds to their biological activities. Feature selection is an important step in QSAR based drug design to select the most relevant descriptors. One of the most popular feature selection methods for classification problems is Fisher score which aim is to minimize the within-class distance and maximize the between-class distance. In this study, the properties of Fisher criterion were extended for QSAR models to define the new distance metrics based on the continuous activity values of compounds with known activities. Then, a semi-supervised feature selection method was proposed based on the combination of Fisher and Laplacian criteria which exploits both compounds with known and unknown activities to select the relevant descriptors. To demonstrate the efficiency of the proposed semi-supervised feature selection method in selecting the relevant descriptors, we applied the method and other feature selection methods on three QSAR data sets such as serine/threonine-protein kinase PLK3 inhibitors, ROCK inhibitors and phenol compounds. The results demonstrated that the QSAR models built on the selected descriptors by the proposed semi-supervised method have better performance than other models. This indicates the efficiency of the proposed method in selecting the relevant descriptors using the compounds with known and unknown activities. The results of this study showed that the compounds with known and unknown activities can be helpful to improve the performance of the combined Fisher and Laplacian based feature selection methods.

A machine learning approach for ranking clusters of docked protein‐protein complexes by pairwise cluster comparison

PubMed Central

Pfeiffenberger, Erik; Chaleil, Raphael A.G.; Moal, Iain H.

2017-01-01

ABSTRACT Reliable identification of near‐native poses of docked protein–protein complexes is still an unsolved problem. The intrinsic heterogeneity of protein–protein interactions is challenging for traditional biophysical or knowledge based potentials and the identification of many false positive binding sites is not unusual. Often, ranking protocols are based on initial clustering of docked poses followed by the application of an energy function to rank each cluster according to its lowest energy member. Here, we present an approach of cluster ranking based not only on one molecular descriptor (e.g., an energy function) but also employing a large number of descriptors that are integrated in a machine learning model, whereby, an extremely randomized tree classifier based on 109 molecular descriptors is trained. The protocol is based on first locally enriching clusters with additional poses, the clusters are then characterized using features describing the distribution of molecular descriptors within the cluster, which are combined into a pairwise cluster comparison model to discriminate near‐native from incorrect clusters. The results show that our approach is able to identify clusters containing near‐native protein–protein complexes. In addition, we present an analysis of the descriptors with respect to their power to discriminate near native from incorrect clusters and how data transformations and recursive feature elimination can improve the ranking performance. Proteins 2017; 85:528–543. © 2016 Wiley Periodicals, Inc. PMID:27935158

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.

Analysis of calibrated seafloor backscatter for habitat classification methodology and case study of 158 spots in the Bay of Biscay and Celtic Sea

NASA Astrophysics Data System (ADS)

Fezzani, Ridha; Berger, Laurent

2018-06-01

An automated signal-based method was developed in order to analyse the seafloor backscatter data logged by calibrated multibeam echosounder. The processing consists first in the clustering of each survey sub-area into a small number of homogeneous sediment types, based on the backscatter average level at one or several incidence angles. Second, it uses their local average angular response to extract discriminant descriptors, obtained by fitting the field data to the Generic Seafloor Acoustic Backscatter parametric model. Third, the descriptors are used for seafloor type classification. The method was tested on the multi-year data recorded by a calibrated 90-kHz Simrad ME70 multibeam sonar operated in the Bay of Biscay, France and Celtic Sea, Ireland. It was applied for seafloor-type classification into 12 classes, to a dataset of 158 spots surveyed for demersal and benthic fauna study and monitoring. Qualitative analyses and classified clusters using extracted parameters show a good discriminatory potential, indicating the robustness of this approach.

Semi-automated detection of anterior cruciate ligament injury from MRI.

PubMed

Štajduhar, Ivan; Mamula, Mihaela; Miletić, Damir; Ünal, Gözde

2017-03-01

A radiologist's work in detecting various injuries or pathologies from radiological scans can be tiresome, time consuming and prone to errors. The field of computer-aided diagnosis aims to reduce these factors by introducing a level of automation in the process. In this paper, we deal with the problem of detecting the presence of anterior cruciate ligament (ACL) injury in a human knee. We examine the possibility of aiding the diagnosis process by building a decision-support model for detecting the presence of milder ACL injuries (not requiring operative treatment) and complete ACL ruptures (requiring operative treatment) from sagittal plane magnetic resonance (MR) volumes of human knees. Histogram of oriented gradient (HOG) descriptors and gist descriptors are extracted from manually selected rectangular regions of interest enveloping the wider cruciate ligament area. Performance of two machine-learning models is explored, coupled with both feature extraction methods: support vector machine (SVM) and random forests model. Model generalisation properties were determined by performing multiple iterations of stratified 10-fold cross validation whilst observing the area under the curve (AUC) score. Sagittal plane knee joint MR data was retrospectively gathered at the Clinical Hospital Centre Rijeka, Croatia, from 2007 until 2014. Type of ACL injury was established in a double-blind fashion by comparing the retrospectively set diagnosis against the prospective opinion of another radiologist. After clean up, the resulting dataset consisted of 917 usable labelled exam sequences of left or right knees. Experimental results suggest that a linear-kernel SVM learned from HOG descriptors has the best generalisation properties among the experimental models compared, having an area under the curve of 0.894 for the injury-detection problem and 0.943 for the complete-rupture-detection problem. Although the problem of performing semi-automated ACL-injury diagnosis by observing knee-joint MR volumes alone is a difficult one, experimental results suggest potential clinical application of computer-aided decision making, both for detecting milder injuries and detecting complete ruptures. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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.

Experimental and computational prediction of glass transition temperature of drugs.

PubMed

Alzghoul, Ahmad; Alhalaweh, Amjad; Mahlin, Denny; Bergström, Christel A S

2014-12-22

Glass transition temperature (Tg) is an important inherent property of an amorphous solid material which is usually determined experimentally. In this study, the relation between Tg and melting temperature (Tm) was evaluated using a data set of 71 structurally diverse druglike compounds. Further, in silico models for prediction of Tg were developed based on calculated molecular descriptors and linear (multilinear regression, partial least-squares, principal component regression) and nonlinear (neural network, support vector regression) modeling techniques. The models based on Tm predicted Tg with an RMSE of 19.5 K for the test set. Among the five computational models developed herein the support vector regression gave the best result with RMSE of 18.7 K for the test set using only four chemical descriptors. Hence, two different models that predict Tg of drug-like molecules with high accuracy were developed. If Tm is available, a simple linear regression can be used to predict Tg. However, the results also suggest that support vector regression and calculated molecular descriptors can predict Tg with equal accuracy, already before compound synthesis.

Position Estimation and Local Mapping Using Omnidirectional Images and Global Appearance Descriptors

PubMed Central

Berenguer, Yerai; Payá, Luis; Ballesta, Mónica; Reinoso, Oscar

2015-01-01

This work presents some methods to create local maps and to estimate the position of a mobile robot, using the global appearance of omnidirectional images. We use a robot that carries an omnidirectional vision system on it. Every omnidirectional image acquired by the robot is described only with one global appearance descriptor, based on the Radon transform. In the work presented in this paper, two different possibilities have been considered. In the first one, we assume the existence of a map previously built composed of omnidirectional images that have been captured from previously-known positions. The purpose in this case consists of estimating the nearest position of the map to the current position of the robot, making use of the visual information acquired by the robot from its current (unknown) position. In the second one, we assume that we have a model of the environment composed of omnidirectional images, but with no information about the location of where the images were acquired. The purpose in this case consists of building a local map and estimating the position of the robot within this map. Both methods are tested with different databases (including virtual and real images) taking into consideration the changes of the position of different objects in the environment, different lighting conditions and occlusions. The results show the effectiveness and the robustness of both methods. PMID:26501289

A dynamic appearance descriptor approach to facial actions temporal modeling.

PubMed

Jiang, Bihan; Valstar, Michel; Martinez, Brais; Pantic, Maja

2014-02-01

Both the configuration and the dynamics of facial expressions are crucial for the interpretation of human facial behavior. Yet to date, the vast majority of reported efforts in the field either do not take the dynamics of facial expressions into account, or focus only on prototypic facial expressions of six basic emotions. Facial dynamics can be explicitly analyzed by detecting the constituent temporal segments in Facial Action Coding System (FACS) Action Units (AUs)-onset, apex, and offset. In this paper, we present a novel approach to explicit analysis of temporal dynamics of facial actions using the dynamic appearance descriptor Local Phase Quantization from Three Orthogonal Planes (LPQ-TOP). Temporal segments are detected by combining a discriminative classifier for detecting the temporal segments on a frame-by-frame basis with Markov Models that enforce temporal consistency over the whole episode. The system is evaluated in detail over the MMI facial expression database, the UNBC-McMaster pain database, the SAL database, the GEMEP-FERA dataset in database-dependent experiments, in cross-database experiments using the Cohn-Kanade, and the SEMAINE databases. The comparison with other state-of-the-art methods shows that the proposed LPQ-TOP method outperforms the other approaches for the problem of AU temporal segment detection, and that overall AU activation detection benefits from dynamic appearance information.

Considering ionic state in modeling sorption of pharmaceuticals to sewage sludge.

PubMed

Rybacka, Aleksandra; Andersson, Patrik L

2016-12-01

Information on the partitioning of chemicals between particulate matter and water in sewage treatment plants (STPs) can be used to predict their subsequent environmental fate. However, this information can be challenging to acquire, especially for pharmaceuticals that are frequently present in ionized forms. This study investigated the relationship between the ionization state of active pharmaceutical ingredients (APIs) and their partitioning between water and sludge in STPs. We also investigated the underlying mechanisms of sludge sorption by using chemical descriptors based on ionized structures, and evaluated the usefulness of these descriptors in quantitative structure-property relationship (QSPR) modeling. K D values were collected for 110 APIs, which were classified as neutral, positive, or negative at pH 7. The models with the highest performance had the R 2 Y and Q 2 values of above 0.75 and 0.65, respectively. We found that the dominant intermolecular forces governing the interactions of neutral and positively charged APIs with sludge are hydrophobic, pi-pi, and dipole-dipole interactions, whereas the interactions of negatively charged APIs with sludge were mainly governed by covalent bonding as well as ion-ion, ion-dipole, and dipole-dipole interactions; hydrophobicity-driven interactions were rather unimportant. Including charge-related descriptors improved the models' performance by 5-10%, underlining the importance of electrostatic interactions. The use of descriptors calculated for ionized structures did not improve the model statistics for positive and negative APIs, but slightly increased model performance for neutral APIs. We attribute this to a better description of neutral zwitterions. Copyright © 2016 Elsevier Ltd. All rights reserved.

In silico prediction of nematic transition temperature for liquid crystals using quantitative structure-property relationship approaches.

PubMed

Fatemi, Mohammad Hossein; Ghorbanzad'e, Mehdi

2009-11-01

Quantitative structure-property relationship models for the prediction of the nematic transition temperature (T (N)) were developed by using multilinear regression analysis and a feedforward artificial neural network (ANN). A collection of 42 thermotropic liquid crystals was chosen as the data set. The data set was divided into three sets: for training, and an internal and external test set. Training and internal test sets were used for ANN model development, and the external test set was used for evaluation of the predictive power of the model. In order to build the models, a set of six descriptors were selected by the best multilinear regression procedure of the CODESSA program. These descriptors were: atomic charge weighted partial negatively charged surface area, relative negative charged surface area, polarity parameter/square distance, minimum most negative atomic partial charge, molecular volume, and the A component of moment of inertia, which encode geometrical and electronic characteristics of molecules. These descriptors were used as inputs to ANN. The optimized ANN model had 6:6:1 topology. The standard errors in the calculation of T (N) for the training, internal, and external test sets using the ANN model were 1.012, 4.910, and 4.070, respectively. To further evaluate the ANN model, a crossvalidation test was performed, which produced the statistic Q (2) = 0.9796 and standard deviation of 2.67 based on predicted residual sum of square. Also, the diversity test was performed to ensure the model's stability and prove its predictive capability. The obtained results reveal the suitability of ANN for the prediction of T (N) for liquid crystals using molecular structural descriptors.

QSAR Modeling and Prediction of Drug-Drug Interactions.

PubMed

Zakharov, Alexey V; Varlamova, Ekaterina V; Lagunin, Alexey A; Dmitriev, Alexander V; Muratov, Eugene N; Fourches, Denis; Kuz'min, Victor E; Poroikov, Vladimir V; Tropsha, Alexander; Nicklaus, Marc C

2016-02-01

Severe adverse drug reactions (ADRs) are the fourth leading cause of fatality in the U.S. with more than 100,000 deaths per year. As up to 30% of all ADRs are believed to be caused by drug-drug interactions (DDIs), typically mediated by cytochrome P450s, possibilities to predict DDIs from existing knowledge are important. We collected data from public sources on 1485, 2628, 4371, and 27,966 possible DDIs mediated by four cytochrome P450 isoforms 1A2, 2C9, 2D6, and 3A4 for 55, 73, 94, and 237 drugs, respectively. For each of these data sets, we developed and validated QSAR models for the prediction of DDIs. As a unique feature of our approach, the interacting drug pairs were represented as binary chemical mixtures in a 1:1 ratio. We used two types of chemical descriptors: quantitative neighborhoods of atoms (QNA) and simplex descriptors. Radial basis functions with self-consistent regression (RBF-SCR) and random forest (RF) were utilized to build QSAR models predicting the likelihood of DDIs for any pair of drug molecules. Our models showed balanced accuracy of 72-79% for the external test sets with a coverage of 81.36-100% when a conservative threshold for the model's applicability domain was applied. We generated virtually all possible binary combinations of marketed drugs and employed our models to identify drug pairs predicted to be instances of DDI. More than 4500 of these predicted DDIs that were not found in our training sets were confirmed by data from the DrugBank database.

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.

Singular spectrum decomposition of Bouligand-Minkowski fractal descriptors: an application to the classification of texture Images

NASA Astrophysics Data System (ADS)

Florindo, João. Batista

2018-04-01

This work proposes the use of Singular Spectrum Analysis (SSA) for the classification of texture images, more specifically, to enhance the performance of the Bouligand-Minkowski fractal descriptors in this task. Fractal descriptors are known to be a powerful approach to model and particularly identify complex patterns in natural images. Nevertheless, the multiscale analysis involved in those descriptors makes them highly correlated. Although other attempts to address this point was proposed in the literature, none of them investigated the relation between the fractal correlation and the well-established analysis employed in time series. And SSA is one of the most powerful techniques for this purpose. The proposed method was employed for the classification of benchmark texture images and the results were compared with other state-of-the-art classifiers, confirming the potential of this analysis in image classification.

Quantitative structure-retention relationships applied to development of liquid chromatography gradient-elution method for the separation of sartans.

PubMed

Golubović, Jelena; Protić, Ana; Otašević, Biljana; Zečević, Mira

2016-04-01

QSRR are mathematically derived relationships between the chromatographic parameters determined for a representative series of analytes in given separation systems and the molecular descriptors accounting for the structural differences among the investigated analytes. Artificial neural network is a technique of data analysis, which sets out to emulate the human brain's way of working. The aim of the present work was to optimize separation of six angiotensin receptor antagonists, so-called sartans: losartan, valsartan, irbesartan, telmisartan, candesartan cilexetil and eprosartan in a gradient-elution HPLC method. For this purpose, ANN as a mathematical tool was used for establishing a QSRR model based on molecular descriptors of sartans and varied instrumental conditions. The optimized model can be further used for prediction of an external congener of sartans and analysis of the influence of the analyte structure, represented through molecular descriptors, on retention behaviour. Molecular descriptors included in modelling were electrostatic, geometrical and quantum-chemical descriptors: connolly solvent excluded volume non-1,4 van der Waals energy, octanol/water distribution coefficient, polarizability, number of proton-donor sites and number of proton-acceptor sites. Varied instrumental conditions were gradient time, buffer pH and buffer molarity. High prediction ability of the optimized network enabled complete separation of the analytes within the run time of 15.5 min under following conditions: gradient time of 12.5 min, buffer pH of 3.95 and buffer molarity of 25 mM. Applied methodology showed the potential to predict retention behaviour of an external analyte with the properties within the training space. Connolly solvent excluded volume, polarizability and number of proton-acceptor sites appeared to be most influential paramateres on retention behaviour of the sartans. Copyright © 2015 Elsevier B.V. All rights reserved.

The effect of geometrical presentation of multimodal cation-exchange ligands on selective recognition of hydrophobic regions on protein surfaces.

PubMed

Woo, James; Parimal, Siddharth; Brown, Matthew R; Heden, Ryan; Cramer, Steven M

2015-09-18

The effects of spatial organization of hydrophobic and charged moieties on multimodal (MM) cation-exchange ligands were examined by studying protein retention behavior on two commercial chromatographic media, Capto™ MMC and Nuvia™ cPrime™. Proteins with extended regions of surface-exposed aliphatic residues were found to have enhanced retention on the Capto MMC system as compared to the Nuvia cPrime resin. The results further indicated that while the Nuvia cPrime ligand had a strong preference for interactions with aromatic groups, the Capto MMC ligand appeared to interact with both aliphatic and aromatic clusters on the protein surfaces. These observations were formalized into a new set of protein surface property descriptors, which quantified the local distribution of electrostatic and hydrophobic potentials as well as distinguishing between aromatic and aliphatic properties. Using these descriptors, high-performing quantitative structure-activity relationship (QSAR) models (R(2)>0.88) were generated for both the Capto MMC and Nuvia cPrime datasets at pH 5 and pH 6. Descriptors of electrostatic properties were generally common across the four models; however both Capto MMC models included descriptors that quantified regions of aliphatic-based hydrophobicity in addition to aromatic descriptors. Retention was generally reduced by lowering the ligand densities on both MM resins. Notably, elution order was largely unaffected by the change in surface density, but smaller and more aliphatic proteins tended to be more affected by this drop in ligand density. This suggests that modulating the exposure, shape and density of the hydrophobic moieties in multimodal chromatographic systems can alter the preference for surface exposed aliphatic or aromatic residues, thus providing an additional dimension for modulating the selectivity of MM protein separation systems. Copyright © 2015 Elsevier B.V. All rights reserved.

Revealing cell cycle control by combining model-based detection of periodic expression with novel cis-regulatory descriptors

PubMed Central

Andersson, Claes R; Hvidsten, Torgeir R; Isaksson, Anders; Gustafsson, Mats G; Komorowski, Jan

2007-01-01

Background We address the issue of explaining the presence or absence of phase-specific transcription in budding yeast cultures under different conditions. To this end we use a model-based detector of gene expression periodicity to divide genes into classes depending on their behavior in experiments using different synchronization methods. While computational inference of gene regulatory circuits typically relies on expression similarity (clustering) in order to find classes of potentially co-regulated genes, this method instead takes advantage of known time profile signatures related to the studied process. Results We explain the regulatory mechanisms of the inferred periodic classes with cis-regulatory descriptors that combine upstream sequence motifs with experimentally determined binding of transcription factors. By systematic statistical analysis we show that periodic classes are best explained by combinations of descriptors rather than single descriptors, and that different combinations correspond to periodic expression in different classes. We also find evidence for additive regulation in that the combinations of cis-regulatory descriptors associated with genes periodically expressed in fewer conditions are frequently subsets of combinations associated with genes periodically expression in more conditions. Finally, we demonstrate that our approach retrieves combinations that are more specific towards known cell-cycle related regulators than the frequently used clustering approach. Conclusion The results illustrate how a model-based approach to expression analysis may be particularly well suited to detect biologically relevant mechanisms. Our new approach makes it possible to provide more refined hypotheses about regulatory mechanisms of the cell cycle and it can easily be adjusted to reveal regulation of other, non-periodic, cellular processes. PMID:17939860

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.

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.

One Shot Detection with Laplacian Object and Fast Matrix Cosine Similarity.

PubMed

Biswas, Sujoy Kumar; Milanfar, Peyman

2016-03-01

One shot, generic object detection involves searching for a single query object in a larger target image. Relevant approaches have benefited from features that typically model the local similarity patterns. In this paper, we combine local similarity (encoded by local descriptors) with a global context (i.e., a graph structure) of pairwise affinities among the local descriptors, embedding the query descriptors into a low dimensional but discriminatory subspace. Unlike principal components that preserve global structure of feature space, we actually seek a linear approximation to the Laplacian eigenmap that permits us a locality preserving embedding of high dimensional region descriptors. Our second contribution is an accelerated but exact computation of matrix cosine similarity as the decision rule for detection, obviating the computationally expensive sliding window search. We leverage the power of Fourier transform combined with integral image to achieve superior runtime efficiency that allows us to test multiple hypotheses (for pose estimation) within a reasonably short time. Our approach to one shot detection is training-free, and experiments on the standard data sets confirm the efficacy of our model. Besides, low computation cost of the proposed (codebook-free) object detector facilitates rather straightforward query detection in large data sets including movie videos.

Screening for High Conductivity/Low Viscosity Ionic Liquids Using Product Descriptors.

PubMed

Martin, Shawn; Pratt, Harry D; Anderson, Travis M

2017-07-01

We seek to optimize Ionic liquids (ILs) for application to redox flow batteries. As part of this effort, we have developed a computational method for suggesting ILs with high conductivity and low viscosity. Since ILs consist of cation-anion pairs, we consider a method for treating ILs as pairs using product descriptors for QSPRs, a concept borrowed from the prediction of protein-protein interactions in bioinformatics. We demonstrate the method by predicting electrical conductivity, viscosity, and melting point on a dataset taken from the ILThermo database on June 18 th , 2014. The dataset consists of 4,329 measurements taken from 165 ILs made up of 72 cations and 34 anions. We benchmark our QSPRs on the known values in the dataset then extend our predictions to screen all 2,448 possible cation-anion pairs in the dataset. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Screening for High Conductivity/Low Viscosity Ionic Liquids Using Product Descriptors

DOE PAGES

Martin, Shawn; Pratt, III, Harry D.; Anderson, Travis M.

2017-02-21

We seek to optimize Ionic liquids (ILs) for application to redox flow batteries. As part of this effort, we have developed a computational method for suggesting ILs with high conductivity and low viscosity. Since ILs consist of cation-anion pairs, we consider a method for treating ILs as pairs using product descriptors for QSPRs, a concept borrowed from the prediction of protein-protein interactions in bioinformatics. We demonstrate the method by predicting electrical conductivity, viscosity, and melting point on a dataset taken from the ILThermo database on June 18th, 2014. The dataset consists of 4,329 measurements taken from 165 ILs made upmore » of 72 cations and 34 anions. In conclusion, we benchmark our QSPRs on the known values in the dataset then extend our predictions to screen all 2,448 possible cation-anion pairs in the dataset.« less

Screening for High Conductivity/Low Viscosity Ionic Liquids Using Product Descriptors

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

Martin, Shawn; Pratt, III, Harry D.; Anderson, Travis M.

We seek to optimize Ionic liquids (ILs) for application to redox flow batteries. As part of this effort, we have developed a computational method for suggesting ILs with high conductivity and low viscosity. Since ILs consist of cation-anion pairs, we consider a method for treating ILs as pairs using product descriptors for QSPRs, a concept borrowed from the prediction of protein-protein interactions in bioinformatics. We demonstrate the method by predicting electrical conductivity, viscosity, and melting point on a dataset taken from the ILThermo database on June 18th, 2014. The dataset consists of 4,329 measurements taken from 165 ILs made upmore » of 72 cations and 34 anions. In conclusion, we benchmark our QSPRs on the known values in the dataset then extend our predictions to screen all 2,448 possible cation-anion pairs in the dataset.« less

Hierarchical human action recognition around sleeping using obscured posture information

NASA Astrophysics Data System (ADS)

Kudo, Yuta; Sashida, Takehiko; Aoki, Yoshimitsu

2015-04-01

This paper presents a new approach for human action recognition around sleeping with the human body parts locations and the positional relationship between human and sleeping environment. Body parts are estimated from the depth image obtained by a time-of-flight (TOF) sensor using oriented 3D normal vector. Issues in action recognition of sleeping situation are the demand of availability in darkness, and hiding of the human body by duvets. Therefore, the extraction of image features is difficult since color and edge features are obscured by covers. Thus, first in our method, positions of four parts of the body (head, torso, thigh, and lower leg) are estimated by using the shape model of bodily surface constructed by oriented 3D normal vector. This shape model can represent the surface shape of rough body, and is effective in robust posture estimation of the body hidden with duvets. Then, action descriptor is extracted from the position of each body part. The descriptor includes temporal variation of each part of the body and spatial vector of position of the parts and the bed. Furthermore, this paper proposes hierarchical action classes and classifiers to improve the indistinct action classification. Classifiers are composed of two layers, and recognize human action by using the action descriptor. First layer focuses on spatial descriptor and classifies action roughly. Second layer focuses on temporal descriptor and classifies action finely. This approach achieves a robust recognition of obscured human by using the posture information and the hierarchical action recognition.

Adding Temporal Characteristics to Geographical Schemata and Instances: A General Framework

NASA Astrophysics Data System (ADS)

Ota, Morishige

2018-05-01

This paper proposes the temporal general feature model (TGFM) as a meta-model for application schemata representing changes of real-world phenomena. It is not very easy to determine history directly from the current application schemata, even if the revision notes are attached to the specification. To solve this problem, the rules for description of the succession between previous and posterior components are added to the general feature model, thus resulting in TGFM. After discussing the concepts associated with the new model, simple examples of application schemata are presented as instances of TGFM. Descriptors for changing properties, the succession of changing properties in moving features, and the succession of features and associations are introduced. The modeling methods proposed in this paper will contribute to the acquisition of consistent and reliable temporal geospatial data.

From QSAR to QSIIR: Searching for Enhanced Computational Toxicology Models

PubMed Central

Zhu, Hao

2017-01-01

Quantitative Structure Activity Relationship (QSAR) is the most frequently used modeling approach to explore the dependency of biological, toxicological, or other types of activities/properties of chemicals on their molecular features. In the past two decades, QSAR modeling has been used extensively in drug discovery process. However, the predictive models resulted from QSAR studies have limited use for chemical risk assessment, especially for animal and human toxicity evaluations, due to the low predictivity of new compounds. To develop enhanced toxicity models with independently validated external prediction power, novel modeling protocols were pursued by computational toxicologists based on rapidly increasing toxicity testing data in recent years. This chapter reviews the recent effort in our laboratory to incorporate the biological testing results as descriptors in the toxicity modeling process. This effort extended the concept of QSAR to Quantitative Structure In vitro-In vivo Relationship (QSIIR). The QSIIR study examples provided in this chapter indicate that the QSIIR models that based on the hybrid (biological and chemical) descriptors are indeed superior to the conventional QSAR models that only based on chemical descriptors for several animal toxicity endpoints. We believe that the applications introduced in this review will be of interest and value to researchers working in the field of computational drug discovery and environmental chemical risk assessment. PMID:23086837

Ability of bottle cap color to facilitate accurate glaucoma patient-physician communication regarding medication identity

PubMed Central

Dave, Pujan; Villarreal, Guadalupe; Friedman, David S.; Kahook, Malik Y.; Ramulu, Pradeep Y.

2015-01-01

Objective To determine the accuracy of patient-physician communication regarding topical ophthalmic medication use based on bottle cap color, particularly amongst individuals who may have acquired color vision deficiency from glaucoma. Design Cross-sectional, clinical study. Participants Patients ≥ 18 years old with primary open-angle, primary angle-closure, pseudoexfoliation, or pigment dispersion glaucoma, bilateral visual acuity of 20/400 or better, and no concurrent conditions that may affect color vision. Methods One hundred patients provided color descriptions of 11 distinct medication bottle caps. Patient-produced color descriptors were then presented to three physicians. Each physician matched each color descriptor to the medication they thought the descriptor was describing. Main Outcome Measures Frequency of patient-physician agreement, occurring when all three physicians accurately matched the patient-produced color descriptor to the correct medication. Multivariate regression models evaluated whether patient-physician agreement decreased with degree of better-eye visual field (VF) damage, color descriptor heterogeneity, and/or color vision deficiency, as determined by Hardy-Rand-Rittler (HRR) score and the Lanthony D15 testing index (D15 CCI). Results Subjects had a mean age of 69 (±11) years, with mean VF mean deviation of −4.7 (±6.0) and −10.9 (±8.4) dB in the better- and worse-seeing eyes, respectively. Patients produced 102 unique color descriptors to describe the colors of the 11 tested bottle caps. Among individual patients, the mean number of medications demonstrating patient-physician agreement was 6.1/11 (55.5%). Agreement was less than 15% for 4 medications (prednisolone acetate [generic], betaxolol HCl [Betoptic], brinzolamide/brimonidine [Simbrinza], and latanoprost [Xalatan]). Lower HRR scores and higher D15 CCI (both indicating worse color vision) were associated with greater VF damage (p<0.001). Extent of color vision deficiency and color descriptor heterogeneity were the only significant predictors of patient-physician agreement in multivariate models (odds of agreement = 0.90 per 1 point decrement in HRR score, p<0.001; odds of agreement = 0.30 for medications exhibiting high heterogeneity [≥ 11 descriptors], p=0.007). Conclusions Physician understanding of patient medication usage based solely on bottle cap color is frequently incorrect, particularly in glaucoma patients who may have color vision deficiency. Errors based on communication using bottle cap color alone may be common and could lead to confusion and harm. PMID:26260280

Structure-reactivity modeling using mixture-based representation of chemical reactions.

PubMed

Polishchuk, Pavel; Madzhidov, Timur; Gimadiev, Timur; Bodrov, Andrey; Nugmanov, Ramil; Varnek, Alexandre

2017-09-01

We describe a novel approach of reaction representation as a combination of two mixtures: a mixture of reactants and a mixture of products. In turn, each mixture can be encoded using an earlier reported approach involving simplex descriptors (SiRMS). The feature vector representing these two mixtures results from either concatenated product and reactant descriptors or the difference between descriptors of products and reactants. This reaction representation doesn't need an explicit labeling of a reaction center. The rigorous "product-out" cross-validation (CV) strategy has been suggested. Unlike the naïve "reaction-out" CV approach based on a random selection of items, the proposed one provides with more realistic estimation of prediction accuracy for reactions resulting in novel products. The new methodology has been applied to model rate constants of E2 reactions. It has been demonstrated that the use of the fragment control domain applicability approach significantly increases prediction accuracy of the models. The models obtained with new "mixture" approach performed better than those required either explicit (Condensed Graph of Reaction) or implicit (reaction fingerprints) reaction center labeling.

Differentiating gold nanorod samples using particle size and shape distributions from transmission electron microscope images

NASA Astrophysics Data System (ADS)

Grulke, Eric A.; Wu, Xiaochun; Ji, Yinglu; Buhr, Egbert; Yamamoto, Kazuhiro; Song, Nam Woong; Stefaniak, Aleksandr B.; Schwegler-Berry, Diane; Burchett, Woodrow W.; Lambert, Joshua; Stromberg, Arnold J.

2018-04-01

Size and shape distributions of gold nanorod samples are critical to their physico-chemical properties, especially their longitudinal surface plasmon resonance. This interlaboratory comparison study developed methods for measuring and evaluating size and shape distributions for gold nanorod samples using transmission electron microscopy (TEM) images. The objective was to determine whether two different samples, which had different performance attributes in their application, were different with respect to their size and/or shape descriptor distributions. Touching particles in the captured images were identified using a ruggedness shape descriptor. Nanorods could be distinguished from nanocubes using an elongational shape descriptor. A non-parametric statistical test showed that cumulative distributions of an elongational shape descriptor, that is, the aspect ratio, were statistically different between the two samples for all laboratories. While the scale parameters of size and shape distributions were similar for both samples, the width parameters of size and shape distributions were statistically different. This protocol fulfills an important need for a standardized approach to measure gold nanorod size and shape distributions for applications in which quantitative measurements and comparisons are important. Furthermore, the validated protocol workflow can be automated, thus providing consistent and rapid measurements of nanorod size and shape distributions for researchers, regulatory agencies, and industry.

Predicting Subtype Selectivity for Adenosine Receptor Ligands with Three-Dimensional Biologically Relevant Spectrum (BRS-3D)

NASA Astrophysics Data System (ADS)

He, Song-Bing; Ben Hu; Kuang, Zheng-Kun; Wang, Dong; Kong, De-Xin

2016-11-01

Adenosine receptors (ARs) are potential therapeutic targets for Parkinson’s disease, diabetes, pain, stroke and cancers. Prediction of subtype selectivity is therefore important from both therapeutic and mechanistic perspectives. In this paper, we introduced a shape similarity profile as molecular descriptor, namely three-dimensional biologically relevant spectrum (BRS-3D), for AR selectivity prediction. Pairwise regression and discrimination models were built with the support vector machine methods. The average determination coefficient (r2) of the regression models was 0.664 (for test sets). The 2B-3 (A2B vs A3) model performed best with q2 = 0.769 for training sets (10-fold cross-validation), and r2 = 0.766, RMSE = 0.828 for test sets. The models’ robustness and stability were validated with 100 times resampling and 500 times Y-randomization. We compared the performance of BRS-3D with 3D descriptors calculated by MOE. BRS-3D performed as good as, or better than, MOE 3D descriptors. The performances of the discrimination models were also encouraging, with average accuracy (ACC) 0.912 and MCC 0.792 (test set). The 2A-3 (A2A vs A3) selectivity discrimination model (ACC = 0.882 and MCC = 0.715 for test set) outperformed an earlier reported one (ACC = 0.784). These results demonstrated that, through multiple conformation encoding, BRS-3D can be used as an effective molecular descriptor for AR subtype selectivity prediction.

Comparison of the applicability domain of a quantitative structure-activity relationship for estrogenicity with a large chemical inventory.

PubMed

Netzeva, Tatiana I; Gallegos Saliner, Ana; Worth, Andrew P

2006-05-01

The aim of the present study was to illustrate that it is possible and relatively straightforward to compare the domain of applicability of a quantitative structure-activity relationship (QSAR) model in terms of its physicochemical descriptors with a large inventory of chemicals. A training set of 105 chemicals with data for relative estrogenic gene activation, obtained in a recombinant yeast assay, was used to develop the QSAR. A binary classification model for predicting active versus inactive chemicals was developed using classification tree analysis and two descriptors with a clear physicochemical meaning (octanol-water partition coefficient, or log Kow, and the number of hydrogen bond donors, or n(Hdon)). The model demonstrated a high overall accuracy (90.5%), with a sensitivity of 95.9% and a specificity of 78.1%. The robustness of the model was evaluated using the leave-many-out cross-validation technique, whereas the predictivity was assessed using an artificial external test set composed of 12 compounds. The domain of the QSAR training set was compared with the chemical space covered by the European Inventory of Existing Commercial Chemical Substances (EINECS), as incorporated in the CDB-EC software, in the log Kow / n(Hdon) plane. The results showed that the training set and, therefore, the applicability domain of the QSAR model covers a small part of the physicochemical domain of the inventory, even though a simple method for defining the applicability domain (ranges in the descriptor space) was used. However, a large number of compounds are located within the narrow descriptor window.

Detecting reactive islands using Lagrangian descriptors and the relevance to transition path sampling.

PubMed

Patra, Sarbani; Keshavamurthy, Srihari

2018-02-14

It has been known for sometime now that isomerization reactions, classically, are mediated by phase space structures called reactive islands (RI). RIs provide one possible route to correct for the nonstatistical effects in the reaction dynamics. In this work, we map out the reactive islands for the two dimensional Müller-Brown model potential and show that the reactive islands are intimately linked to the issue of rare event sampling. In particular, we establish the sensitivity of the so called committor probabilities, useful quantities in the transition path sampling technique, to the hierarchical RI structures. Mapping out the RI structure for high dimensional systems, however, is a challenging task. Here, we show that the technique of Lagrangian descriptors is able to effectively identify the RI hierarchy in the model system. Based on our results, we suggest that the Lagrangian descriptors can be useful for detecting RIs in high dimensional systems.

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®.

osFP: a web server for predicting the oligomeric states of fluorescent proteins.

PubMed

Simeon, Saw; Shoombuatong, Watshara; Anuwongcharoen, Nuttapat; Preeyanon, Likit; Prachayasittikul, Virapong; Wikberg, Jarl E S; Nantasenamat, Chanin

2016-01-01

Currently, monomeric fluorescent proteins (FP) are ideal markers for protein tagging. The prediction of oligomeric states is helpful for enhancing live biomedical imaging. Computational prediction of FP oligomeric states can accelerate the effort of protein engineering efforts of creating monomeric FPs. To the best of our knowledge, this study represents the first computational model for predicting and analyzing FP oligomerization directly from the amino acid sequence. After data curation, an exhaustive data set consisting of 397 non-redundant FP oligomeric states was compiled from the literature. Results from benchmarking of the protein descriptors revealed that the model built with amino acid composition descriptors was the top performing model with accuracy, sensitivity and specificity in excess of 80% and MCC greater than 0.6 for all three data subsets (e.g. training, tenfold cross-validation and external sets). The model provided insights on the important residues governing the oligomerization of FP. To maximize the benefit of the generated predictive model, it was implemented as a web server under the R programming environment. osFP affords a user-friendly interface that can be used to predict the oligomeric state of FP using the protein sequence. The advantage of osFP is that it is platform-independent meaning that it can be accessed via a web browser on any operating system and device. osFP is freely accessible at http://codes.bio/osfp/ while the source code and data set is provided on GitHub at https://github.com/chaninn/osFP/.Graphical Abstract.

Multiple QSAR models, pharmacophore pattern and molecular docking analysis for anticancer activity of α, β-unsaturated carbonyl-based compounds, oxime and oxime ether analogues

NASA Astrophysics Data System (ADS)

Masand, Vijay H.; El-Sayed, Nahed N. E.; Bambole, Mukesh U.; Quazi, Syed A.

2018-04-01

Multiple discrete quantitative structure-activity relationships (QSARs) models were constructed for the anticancer activity of α, β-unsaturated carbonyl-based compounds, oxime and oxime ether analogues with a variety of substituents like sbnd Br, sbnd OH, -OMe, etc. at different positions. A big pool of descriptors was considered for QSAR model building. Genetic algorithm (GA), available in QSARINS-Chem, was executed to choose optimum number and set of descriptors to create the multi-linear regression equations for a dataset of sixty-nine compounds. The newly developed five parametric models were subjected to exhaustive internal and external validation along with Y-scrambling using QSARINS-Chem, according to the OECD principles for QSAR model validation. The models were built using easily interpretable descriptors and accepted after confirming statistically robustness with high external predictive ability. The five parametric models were found to have R2 = 0.80 to 0.86, R2ex = 0.75 to 0.84, and CCCex = 0.85 to 0.90. The models indicate that frequency of nitrogen and oxygen atoms separated by five bonds from each other and internal electronic environment of the molecule have correlation with the anticancer activity.

Computer-assisted shape descriptors for skull morphology in craniosynostosis.

PubMed

Shim, Kyu Won; Lee, Min Jin; Lee, Myung Chul; Park, Eun Kyung; Kim, Dong Seok; Hong, Helen; Kim, Yong Oock

2016-03-01

Our aim was to develop a novel method for characterizing common skull deformities with high sensitivity and specificity, based on two-dimensional (2D) shape descriptors in computed tomography (CT) images. Between 2003 and 2014, 44 normal subjects and 39 infants with craniosynostosis (sagittal, 29; bicoronal, 10) enrolled for analysis. Mean age overall was 16 months (range, 1-120 months), with a male:female ratio of 56:29. Two reference planes, sagittal (S-plane: through top of lateral ventricle) and coronal (C-plane: at maximum dimension of fourth ventricle), were utilized to formulate three 2D shape descriptors (cranial index [CI], cranial radius index [CR], and cranial extreme spot index [CES]), which were then applied to S- and C-plane target images of both groups. In infants with sagittal craniosynostosis, CI in S-plane (S-CI) usually was <1.0 (mean, 0.78; range, 0.67-0.95), with CR consistently at 3 and a characteristic CES pattern of two discrete hot spots oriented diagonally. In the bicoronal craniosynostosis subset, CI was >1.0 (mean 1.11; range, 1.04-1.25), with CR at -3 and a CES pattern of four discrete diagonally oriented hot spots. Scatter plots underscored the highly intuitive joint performance of CI and CES in distinguishing normal and deformed states. Altogether, these novel 2D shape descriptors enabled effective discrimination of sagittal and bicoronal skull deformities. Newly developed 2D shape descriptors for cranial CT imaging enabled recognition of common skull deformities with statistical significance, perhaps providing impetus for automated CT-based diagnosis of craniosynostosis.

On the Development and Use of Large Chemical Similarity Networks, Informatics Best Practices and Novel Chemical Descriptors towards Materials Quantitative Structure Property Relationships

ERIC Educational Resources Information Center

Krein, Michael

2011-01-01

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…

Quaternion-Based Texture Analysis of Multiband Satellite Images: Application to the Estimation of Aboveground Biomass in the East Region of Cameroon.

PubMed

Djiongo Kenfack, Cedrigue Boris; Monga, Olivier; Mpong, Serge Moto; Ndoundam, René

2018-03-01

Within the last decade, several approaches using quaternion numbers to handle and model multiband images in a holistic manner were introduced. The quaternion Fourier transform can be efficiently used to model texture in multidimensional data such as color images. For practical application, multispectral satellite data appear as a primary source for measuring past trends and monitoring changes in forest carbon stocks. In this work, we propose a texture-color descriptor based on the quaternion Fourier transform to extract relevant information from multiband satellite images. We propose a new multiband image texture model extraction, called FOTO++, in order to address biomass estimation issues. The first stage consists in removing noise from the multispectral data while preserving the edges of canopies. Afterward, color texture descriptors are extracted thanks to a discrete form of the quaternion Fourier transform, and finally the support vector regression method is used to deduce biomass estimation from texture indices. Our texture features are modeled using a vector composed with the radial spectrum coming from the amplitude of the quaternion Fourier transform. We conduct several experiments in order to study the sensitivity of our model to acquisition parameters. We also assess its performance both on synthetic images and on real multispectral images of Cameroonian forest. The results show that our model is more robust to acquisition parameters than the classical Fourier Texture Ordination model (FOTO). Our scheme is also more accurate for aboveground biomass estimation. We stress that a similar methodology could be implemented using quaternion wavelets. These results highlight the potential of the quaternion-based approach to study multispectral satellite images.

A new biodegradation prediction model specific to petroleum hydrocarbons.

PubMed

Howard, Philip; Meylan, William; Aronson, Dallas; Stiteler, William; Tunkel, Jay; Comber, Michael; Parkerton, Thomas F

2005-08-01

A new predictive model for determining quantitative primary biodegradation half-lives of individual petroleum hydrocarbons has been developed. This model uses a fragment-based approach similar to that of several other biodegradation models, such as those within the Biodegradation Probability Program (BIOWIN) estimation program. In the present study, a half-life in days is estimated using multiple linear regression against counts of 31 distinct molecular fragments. The model was developed using a data set consisting of 175 compounds with environmentally relevant experimental data that was divided into training and validation sets. The original fragments from the Ministry of International Trade and Industry BIOWIN model were used initially as structural descriptors and additional fragments were then added to better describe the ring systems found in petroleum hydrocarbons and to adjust for nonlinearity within the experimental data. The training and validation sets had r2 values of 0.91 and 0.81, respectively.

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.

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

A novel class sensitive hashing technique for large-scale content-based remote sensing image retrieval

NASA Astrophysics Data System (ADS)

Reato, Thomas; Demir, Begüm; Bruzzone, Lorenzo

2017-10-01

This paper presents a novel class sensitive hashing technique in the framework of large-scale content-based remote sensing (RS) image retrieval. The proposed technique aims at representing each image with multi-hash codes, each of which corresponds to a primitive (i.e., land cover class) present in the image. To this end, the proposed method consists of a three-steps algorithm. The first step is devoted to characterize each image by primitive class descriptors. These descriptors are obtained through a supervised approach, which initially extracts the image regions and their descriptors that are then associated with primitives present in the images. This step requires a set of annotated training regions to define primitive classes. A correspondence between the regions of an image and the primitive classes is built based on the probability of each primitive class to be present at each region. All the regions belonging to the specific primitive class with a probability higher than a given threshold are highly representative of that class. Thus, the average value of the descriptors of these regions is used to characterize that primitive. In the second step, the descriptors of primitive classes are transformed into multi-hash codes to represent each image. This is achieved by adapting the kernel-based supervised locality sensitive hashing method to multi-code hashing problems. The first two steps of the proposed technique, unlike the standard hashing methods, allow one to represent each image by a set of primitive class sensitive descriptors and their hash codes. Then, in the last step, the images in the archive that are very similar to a query image are retrieved based on a multi-hash-code-matching scheme. Experimental results obtained on an archive of aerial images confirm the effectiveness of the proposed technique in terms of retrieval accuracy when compared to the standard hashing methods.

Obscure phenomena in statistical analysis of quantitative structure-activity relationships. Part 1: Multicollinearity of physicochemical descriptors.

PubMed

Mager, P P; Rothe, H

1990-10-01

Multicollinearity of physicochemical descriptors leads to serious consequences in quantitative structure-activity relationship (QSAR) analysis, such as incorrect estimators and test statistics of regression coefficients of the ordinary least-squares (OLS) model applied usually to QSARs. Beside the diagnosis of the known simple collinearity, principal component regression analysis (PCRA) also allows the diagnosis of various types of multicollinearity. Only if the absolute values of PCRA estimators are order statistics that decrease monotonically, the effects of multicollinearity can be circumvented. Otherwise, obscure phenomena may be observed, such as good data recognition but low predictive model power of a QSAR model.

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.

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

Shreddability of pizza Mozzarella cheese predicted using physicochemical properties.

PubMed

Banville, V; Morin, P; Pouliot, Y; Britten, M

2014-07-01

This study used rheological techniques such as uniaxial compression, wire cutting, and dynamic oscillatory shear to probe the physical properties of pizza Mozzarella cheeses. Predictive models were built using compositional and textural descriptors to predict cheese shreddability. Experimental cheeses were made using milk with (0.25% wt/wt) or without denatured whey protein and renneted at pH 6.5 or 6.4. The cheeses were aged for 8, 22, or 36 d and then tested at 4, 13, or 22°C for textural attributes using 11 descriptors. Adding denatured whey protein and reducing the milk renneting pH strongly affected cheese mechanical properties, but these effects were usually dependent on testing temperature. Cheeses were generally weaker as they aged. None of the compositional or rheological descriptors taken alone could predict the shredding behavior of the cheeses. Using the stepwise method, an objective selection of a few (<4) relevant descriptors made it possible to predict the production of fines (R(2)=0.82), the percentage of long shreds (R(2)=0.67), and to a lesser degree, the adhesion of cheese to the shredding blade (R(2)=0.45). The principal component analysis markedly contrasted the adhesion of cheese to the shredding blade with other shredding properties such as the production of fines or long shreds. The predictive models and principal component analysis can help manufacturers select relevant descriptors for the development of cheese with optimal mechanical behavior under shredding conditions. Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

A review on principles, theory and practices of 2D-QSAR.

PubMed

Roy, Kunal; Das, Rudra Narayan

2014-01-01

The central axiom of science purports the explanation of every natural phenomenon using all possible logics coming from pure as well as mixed scientific background. The quantitative structure-activity relationship (QSAR) analysis is a study correlating the behavioral manifestation of compounds with their structures employing the interdisciplinary knowledge of chemistry, mathematics, biology as well as physics. Several studies have attempted to mathematically correlate the chemistry and property (physicochemical/ biological/toxicological) of molecules using various computationally or experimentally derived quantitative parameters termed as descriptors. The dimensionality of the descriptors depends on the type of algorithm employed and defines the nature of QSAR analysis. The most interesting feature of predictive QSAR models is that the behavior of any new or even hypothesized molecule can be predicted by the use of the mathematical equations. The phrase "2D-QSAR" signifies development of QSAR models using 2D-descriptors. Such predictor variables are the most widely practised ones because of their simple and direct mathematical algorithmic nature involving no time consuming energy computations and having reproducible operability. 2D-descriptors have a deluge of contributions in extracting chemical attributes and they are also capable of representing the 3D molecular features to some extent; although in no case they should be considered as the ultimate one, since they often suffer from the problems of intercorrelation, insufficient chemical information as well as lack of interpretation. However, by following rational approaches, novel 2D-descriptors may be developed to obviate various existing problems giving potential 2D-QSAR equations, thereby solving the innumerable chemical mysteries still unexplored.

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.

QSPR for predicting chloroform formation in drinking water disinfection.

PubMed

Luilo, G B; Cabaniss, S E

2011-01-01

Chlorination is the most widely used technique for water disinfection, but may lead to the formation of chloroform (trichloromethane; TCM) and other by-products. This article reports the first quantitative structure-property relationship (QSPR) for predicting the formation of TCM in chlorinated drinking water. Model compounds (n = 117) drawn from 10 literature sources were divided into training data (n = 90, analysed by five-way leave-many-out internal cross-validation) and external validation data (n = 27). QSPR internal cross-validation had Q² = 0.94 and root mean square error (RMSE) of 0.09 moles TCM per mole compound, consistent with external validation Q2 of 0.94 and RMSE of 0.08 moles TCM per mole compound, and met criteria for high predictive power and robustness. In contrast, log TCM QSPR performed poorly and did not meet the criteria for predictive power. The QSPR predictions were consistent with experimental values for TCM formation from tannic acid and for model fulvic acid structures. The descriptors used are consistent with a relatively small number of important TCM precursor structures based upon 1,3-dicarbonyls or 1,3-diphenols.

A Novel Cylindrical Representation for Characterizing Intrinsic Properties of Protein Sequences.

PubMed

Yu, Jia-Feng; Dou, Xiang-Hua; Wang, Hong-Bo; Sun, Xiao; Zhao, Hui-Ying; Wang, Ji-Hua

2015-06-22

The composition and sequence order of amino acid residues are the two most important characteristics to describe a protein sequence. Graphical representations facilitate visualization of biological sequences and produce biologically useful numerical descriptors. In this paper, we propose a novel cylindrical representation by placing the 20 amino acid residue types in a circle and sequence positions along the z axis. This representation allows visualization of the composition and sequence order of amino acids at the same time. Ten numerical descriptors and one weighted numerical descriptor have been developed to quantitatively describe intrinsic properties of protein sequences on the basis of the cylindrical model. Their applications to similarity/dissimilarity analysis of nine ND5 proteins indicated that these numerical descriptors are more effective than several classical numerical matrices. Thus, the cylindrical representation obtained here provides a new useful tool for visualizing and charactering protein sequences. An online server is available at http://biophy.dzu.edu.cn:8080/CNumD/input.jsp .

“Hybrid Topics” -- Facilitating the Interpretation of Topics Through the Addition of MeSH Descriptors to Bags of Words

PubMed Central

Yu, Zhiguo; Nguyen, Thang; Dhombres, Ferdinand; Johnson, Todd; Bodenreider, Olivier

2018-01-01

Extracting and understanding information, themes and relationships from large collections of documents is an important task for biomedical researchers. Latent Dirichlet Allocation is an unsupervised topic modeling technique using the bag-of-words assumption that has been applied extensively to unveil hidden thematic information within large sets of documents. In this paper, we added MeSH descriptors to the bag-of-words assumption to generate ‘hybrid topics’, which are mixed vectors of words and descriptors. We evaluated this approach on the quality and interpretability of topics in both a general corpus and a specialized corpus. Our results demonstrated that the coherence of ‘hybrid topics’ is higher than that of regular bag-of-words topics in the specialized corpus. We also found that the proportion of topics that are not associated with MeSH descriptors is higher in the specialized corpus than in the general corpus. PMID:29295179

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.

Numerical study of wall shear stress-based descriptors in the human left coronary artery.

PubMed

Pinto, S I S; Campos, J B L M

2016-10-01

The present work is about the application of wall shear stress descriptors - time averaged wall shear stress (TAWSS), oscillating shear index (OSI) and relative residence time (RRT) - to the study of blood flow in the left coronary artery (LCA). These descriptors aid the prediction of disturbed flow conditions in the vessels and play a significant role in the detection of potential zones of atherosclerosis development. Hemodynamic descriptors data were obtained, numerically, through ANSYS® software, for the LCA of a patient-specific geometry and for a 3D idealized model. Comparing both cases, the results are coherent, in terms of location and magnitude. Low TAWSS, high OSI and high RRT values are observed in the bifurcation - potential zone of atherosclerosis appearance. The dissimilarities observed in the TAWSS values, considering blood as a Newtonian or non-Newtonian fluid, releases the importance of the correct blood rheologic caracterization. Moreover, for a higher Reynolds number, the TAWSS values decrease in the bifurcation and along the LAD branch, increasing the probability of plaques deposition. Furthermore, for a stenotic LCA model, very low TAWSS and high RRT values in front and behind the stenosis are observed, indicating the probable extension, in the flow direction, of the lesion.

Modeling Geometric-Temporal Context With Directional Pyramid Co-Occurrence for Action Recognition.

PubMed

Yuan, Chunfeng; Li, Xi; Hu, Weiming; Ling, Haibin; Maybank, Stephen J

2014-02-01

In this paper, we present a new geometric-temporal representation for visual action recognition based on local spatio-temporal features. First, we propose a modified covariance descriptor under the log-Euclidean Riemannian metric to represent the spatio-temporal cuboids detected in the video sequences. Compared with previously proposed covariance descriptors, our descriptor can be measured and clustered in Euclidian space. Second, to capture the geometric-temporal contextual information, we construct a directional pyramid co-occurrence matrix (DPCM) to describe the spatio-temporal distribution of the vector-quantized local feature descriptors extracted from a video. DPCM characterizes the co-occurrence statistics of local features as well as the spatio-temporal positional relationships among the concurrent features. These statistics provide strong descriptive power for action recognition. To use DPCM for action recognition, we propose a directional pyramid co-occurrence matching kernel to measure the similarity of videos. The proposed method achieves the state-of-the-art performance and improves on the recognition performance of the bag-of-visual-words (BOVWs) models by a large margin on six public data sets. For example, on the KTH data set, it achieves 98.78% accuracy while the BOVW approach only achieves 88.06%. On both Weizmann and UCF CIL data sets, the highest possible accuracy of 100% is achieved.

QSAR, QSPR and QSRR in Terms of 3-D-MoRSE Descriptors for In Silico Screening of Clofibric Acid Analogues.

PubMed

Di Tullio, Maurizio; Maccallini, Cristina; Ammazzalorso, Alessandra; Giampietro, Letizia; Amoroso, Rosa; De Filippis, Barbara; Fantacuzzi, Marialuigia; Wiczling, Paweł; Kaliszan, Roman

2012-07-01

A series of 27 analogues of clofibric acid, mostly heteroarylalkanoic derivatives, have been analyzed by a novel high-throughput reversed-phase HPLC method employing combined gradient of eluent's pH and organic modifier content. The such determined hydrophobicity (lipophilicity) parameters, log kw , and acidity constants, pKa , were subjected to multiple regression analysis to get a QSRR (Quantitative StructureRetention Relationships) and a QSPR (Quantitative Structure-Property Relationships) equation, respectively, describing these pharmacokinetics-determining physicochemical parameters in terms of the calculation chemistry derived structural descriptors. The previously determined in vitro log EC50 values - transactivation activity towards PPARα (human Peroxisome Proliferator-Activated Receptor α) - have also been described in a QSAR (Quantitative StructureActivity Relationships) equation in terms of the 3-D-MoRSE descriptors (3D-Molecule Representation of Structures based on Electron diffraction descriptors). The QSAR model derived can serve for an a priori prediction of bioactivity in vitro of any designed analogue, whereas the QSRR and the QSPR models can be used to evaluate lipophilicity and acidity, respectively, of the compounds, and hence to rational guide selection of structures of proper pharmacokinetics. Copyright © 2012 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Discrimination of Active and Weakly Active Human BACE1 Inhibitors Using Self-Organizing Map and Support Vector Machine.

PubMed

Li, Hang; Wang, Maolin; Gong, Ya-Nan; Yan, Aixia

2016-01-01

β-secretase (BACE1) is an aspartyl protease, which is considered as a novel vital target in Alzheimer`s disease therapy. We collected a data set of 294 BACE1 inhibitors, and built six classification models to discriminate active and weakly active inhibitors using Kohonen's Self-Organizing Map (SOM) method and Support Vector Machine (SVM) method. Each molecular descriptor was calculated using the program ADRIANA.Code. We adopted two different methods: random method and Self-Organizing Map method, for training/test set split. The descriptors were selected by F-score and stepwise linear regression analysis. The best SVM model Model2C has a good prediction performance on test set with prediction accuracy, sensitivity (SE), specificity (SP) and Matthews correlation coefficient (MCC) of 89.02%, 90%, 88%, 0.78, respectively. Model 1A is the best SOM model, whose accuracy and MCC of the test set were 94.57% and 0.98, respectively. The lone pair electronegativity and polarizability related descriptors importantly contributed to bioactivity of BACE1 inhibitor. The Extended-Connectivity Finger-Prints_4 (ECFP_4) analysis found some vitally key substructural features, which could be helpful for further drug design research. The SOM and SVM models built in this study can be obtained from the authors by email or other contacts.

Adaptive Granulation-Based Prediction for Energy System of Steel Industry.

PubMed

Wang, Tianyu; Han, Zhongyang; Zhao, Jun; Wang, Wei

2018-01-01

The flow variation tendency of byproduct gas plays a crucial role for energy scheduling in steel industry. An accurate prediction of its future trends will be significantly beneficial for the economic profits of steel enterprise. In this paper, a long-term prediction model for the energy system is proposed by providing an adaptive granulation-based method that considers the production semantics involved in the fluctuation tendency of the energy data, and partitions them into a series of information granules. To fully reflect the corresponding data characteristics of the formed unequal-length temporal granules, a 3-D feature space consisting of the timespan, the amplitude and the linetype is designed as linguistic descriptors. In particular, a collaborative-conditional fuzzy clustering method is proposed to granularize the tendency-based feature descriptors and specifically measure the amplitude variation of industrial data which plays a dominant role in the feature space. To quantify the performance of the proposed method, a series of real-world industrial data coming from the energy data center of a steel plant is employed to conduct the comparative experiments. The experimental results demonstrate that the proposed method successively satisfies the requirements of the practically viable prediction.

Simultaneous feature selection and parameter optimisation using an artificial ant colony: case study of melting point prediction.

PubMed

O'Boyle, Noel M; Palmer, David S; Nigsch, Florian; Mitchell, John Bo

2008-10-29

We present a novel feature selection algorithm, Winnowing Artificial Ant Colony (WAAC), that performs simultaneous feature selection and model parameter optimisation for the development of predictive quantitative structure-property relationship (QSPR) models. The WAAC algorithm is an extension of the modified ant colony algorithm of Shen et al. (J Chem Inf Model 2005, 45: 1024-1029). We test the ability of the algorithm to develop a predictive partial least squares model for the Karthikeyan dataset (J Chem Inf Model 2005, 45: 581-590) of melting point values. We also test its ability to perform feature selection on a support vector machine model for the same dataset. Starting from an initial set of 203 descriptors, the WAAC algorithm selected a PLS model with 68 descriptors which has an RMSE on an external test set of 46.6 degrees C and R2 of 0.51. The number of components chosen for the model was 49, which was close to optimal for this feature selection. The selected SVM model has 28 descriptors (cost of 5, epsilon of 0.21) and an RMSE of 45.1 degrees C and R2 of 0.54. This model outperforms a kNN model (RMSE of 48.3 degrees C, R2 of 0.47) for the same data and has similar performance to a Random Forest model (RMSE of 44.5 degrees C, R2 of 0.55). However it is much less prone to bias at the extremes of the range of melting points as shown by the slope of the line through the residuals: -0.43 for WAAC/SVM, -0.53 for Random Forest. With a careful choice of objective function, the WAAC algorithm can be used to optimise machine learning and regression models that suffer from overfitting. Where model parameters also need to be tuned, as is the case with support vector machine and partial least squares models, it can optimise these simultaneously. The moving probabilities used by the algorithm are easily interpreted in terms of the best and current models of the ants, and the winnowing procedure promotes the removal of irrelevant descriptors.

Predicting p Ka values from EEM atomic charges

PubMed Central

2013-01-01

The acid dissociation constant p Ka is a very important molecular property, and there is a strong interest in the development of reliable and fast methods for p Ka prediction. We have evaluated the p Ka prediction capabilities of QSPR models based on empirical atomic charges calculated by the Electronegativity Equalization Method (EEM). Specifically, we collected 18 EEM parameter sets created for 8 different quantum mechanical (QM) charge calculation schemes. Afterwards, we prepared a training set of 74 substituted phenols. Additionally, for each molecule we generated its dissociated form by removing the phenolic hydrogen. For all the molecules in the training set, we then calculated EEM charges using the 18 parameter sets, and the QM charges using the 8 above mentioned charge calculation schemes. For each type of QM and EEM charges, we created one QSPR model employing charges from the non-dissociated molecules (three descriptor QSPR models), and one QSPR model based on charges from both dissociated and non-dissociated molecules (QSPR models with five descriptors). Afterwards, we calculated the quality criteria and evaluated all the QSPR models obtained. We found that QSPR models employing the EEM charges proved as a good approach for the prediction of p Ka (63% of these models had R2 > 0.9, while the best had R2 = 0.924). As expected, QM QSPR models provided more accurate p Ka predictions than the EEM QSPR models but the differences were not significant. Furthermore, a big advantage of the EEM QSPR models is that their descriptors (i.e., EEM atomic charges) can be calculated markedly faster than the QM charge descriptors. Moreover, we found that the EEM QSPR models are not so strongly influenced by the selection of the charge calculation approach as the QM QSPR models. The robustness of the EEM QSPR models was subsequently confirmed by cross-validation. The applicability of EEM QSPR models for other chemical classes was illustrated by a case study focused on carboxylic acids. In summary, EEM QSPR models constitute a fast and accurate p Ka prediction approach that can be used in virtual screening. PMID:23574978

Artificial neural network study on organ-targeting peptides

NASA Astrophysics Data System (ADS)

Jung, Eunkyoung; Kim, Junhyoung; Choi, Seung-Hoon; Kim, Minkyoung; Rhee, Hokyoung; Shin, Jae-Min; Choi, Kihang; Kang, Sang-Kee; Lee, Nam Kyung; Choi, Yun-Jaie; Jung, Dong Hyun

2010-01-01

We report a new approach to studying organ targeting of peptides on the basis of peptide sequence information. The positive control data sets consist of organ-targeting peptide sequences identified by the peroral phage-display technique for four organs, and the negative control data are prepared from random sequences. The capacity of our models to make appropriate predictions is validated by statistical indicators including sensitivity, specificity, enrichment curve, and the area under the receiver operating characteristic (ROC) curve (the ROC score). VHSE descriptor produces statistically significant training models and the models with simple neural network architectures show slightly greater predictive power than those with complex ones. The training and test set statistics indicate that our models could discriminate between organ-targeting and random sequences. We anticipate that our models will be applicable to the selection of organ-targeting peptides for generating peptide drugs or peptidomimetics.

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.

Hypercognitive seizures - Proposal of a new term for the phenomenon forced thinking in epilepsy.

PubMed

Stephani, C; Koubeissi, M

2017-08-01

Here we propose the term hypercognitive seizures as a descriptor for seizures that manifest as a transient mental experience of intrusive thoughts or words that do not consist mainly of reminiscence. Currently, the term forced thinking is used to describe this uncommon seizure semiology, which has also been elicited by electrical brain stimulation. The available literature on forced thinking shows discordant interpretations of its meaning, justifying the suggestion of a new descriptor. In this paper, we would like to suggest and explain the term hypercognitive seizure and argue that this type of seizure lateralizes to the dominant hemisphere. Copyright © 2017 Elsevier B.V. All rights reserved.

Development of QSAR models for predicting the binding affinity of endocrine disrupting chemicals to eight fish estrogen receptor.

PubMed

He, Junyi; Peng, Tao; Yang, Xianhai; Liu, Huihui

2018-02-01

Endocrine disrupting effect has become a central point of concern, and various biological mechanisms involve in the disruption of endocrine system. Recently, we have explored the mechanism of disrupting hormonal transport protein, through the binding affinity of sex hormone-binding globulin in different fish species. This study, serving as a companion article, focused on the mechanism of activating/inhibiting hormone receptor, by investigating the binding interaction of chemicals with the estrogen receptor (ER) of different fish species. We collected the relative binding affinity (RBA) of chemicals with 17β-estradiol binding to the ER of eight fish species. With this parameter as the endpoints, quantitative structure-activity relationship (QSAR) models were established using DRAGON descriptors. Statistical results indicated that the developed models had satisfactory goodness of fit, robustness and predictive ability. The Euclidean distance and Williams plot verified that these models had wide application domains, which covered a large number of structurally diverse chemicals. Based on the screened descriptors, we proposed an appropriate mechanism interpretation for the binding potency. Additionally, even though the same chemical had different affinities for ER from different fish species, the affinity of ER exhibited a high correlation for fish species within the same Order (i.e., Salmoniformes, Cypriniformes, Perciformes), which consistent with that in our previous study. Hence, when performing the endocrine disrupting effect assessment, the species diversity should be taken into account, but maybe the fish species in the same Order can be grouped together. Copyright © 2017 Elsevier Inc. All rights reserved.

Action Recognition Using Nonnegative Action Component Representation and Sparse Basis Selection.

PubMed

Wang, Haoran; Yuan, Chunfeng; Hu, Weiming; Ling, Haibin; Yang, Wankou; Sun, Changyin

2014-02-01

In this paper, we propose using high-level action units to represent human actions in videos and, based on such units, a novel sparse model is developed for human action recognition. There are three interconnected components in our approach. First, we propose a new context-aware spatial-temporal descriptor, named locally weighted word context, to improve the discriminability of the traditionally used local spatial-temporal descriptors. Second, from the statistics of the context-aware descriptors, we learn action units using the graph regularized nonnegative matrix factorization, which leads to a part-based representation and encodes the geometrical information. These units effectively bridge the semantic gap in action recognition. Third, we propose a sparse model based on a joint l2,1-norm to preserve the representative items and suppress noise in the action units. Intuitively, when learning the dictionary for action representation, the sparse model captures the fact that actions from the same class share similar units. The proposed approach is evaluated on several publicly available data sets. The experimental results and analysis clearly demonstrate the effectiveness of the proposed approach.

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.

Effect analysis of quantum chemical descriptors and substituent characteristics on Henry's law constants of polybrominated diphenyl ethers at different temperatures.

PubMed

Long, Jiang; Youli, Qiu; Yu, Li

2017-11-01

Twelve substituent descriptors, 17 quantum chemical descriptors and 1/T were selected to establish a quantitative structure-property relationship (QSPR) model of Henry's law constants for 7 polybrominated diphenyl ethers (PBDEs) at five different temperatures. Then, the lgH of 202 congeners at different temperatures were predicted. The variation rule and regulating mechanism of lgH was studied from the perspectives of both quantum chemical descriptors and substituent characteristics. The R 2 for modeling and testing sets of the final QSPR model are 0.977 and 0.979, respectively, thus indicating good fitness and predictive ability for Henry' law constants of PBDEs at different temperatures. The favorable hydrogen binding sites are the 5,5',6,6'-positions for high substituent congeners and the O atom of the ether bond for low substituent congeners, which affects the interaction between PBDEs and water molecules. lgH is negatively and linearly correlated with 1/T, and the variation trends of lgH with temperature are primarily regulated by individual substituent characteristics, wherein: the more substituents involved, the smaller the lgH. The significant sequence for the main effect of substituent positions is para>meta>ortho, where the ortho-positions are mainly involved in second-order interaction effect (64.01%). Having two substituents in the same ring also provides a significant effect, with 81.36% of second-order interaction effects, particularly where there is an adjacent distribution (55.02%). Copyright © 2017 Elsevier Inc. All rights reserved.

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.

Rank Order Entropy: why one metric is not enough

PubMed Central

McLellan, Margaret R.; Ryan, M. Dominic; Breneman, Curt M.

2011-01-01

The use of Quantitative Structure-Activity Relationship models to address problems in drug discovery has a mixed history, generally resulting from the mis-application of QSAR models that were either poorly constructed or used outside of their domains of applicability. This situation has motivated the development of a variety of model performance metrics (r2, PRESS r2, F-tests, etc) designed to increase user confidence in the validity of QSAR predictions. In a typical workflow scenario, QSAR models are created and validated on training sets of molecules using metrics such as Leave-One-Out or many-fold cross-validation methods that attempt to assess their internal consistency. However, few current validation methods are designed to directly address the stability of QSAR predictions in response to changes in the information content of the training set. Since the main purpose of QSAR is to quickly and accurately estimate a property of interest for an untested set of molecules, it makes sense to have a means at hand to correctly set user expectations of model performance. In fact, the numerical value of a molecular prediction is often less important to the end user than knowing the rank order of that set of molecules according to their predicted endpoint values. Consequently, a means for characterizing the stability of predicted rank order is an important component of predictive QSAR. Unfortunately, none of the many validation metrics currently available directly measure the stability of rank order prediction, making the development of an additional metric that can quantify model stability a high priority. To address this need, this work examines the stabilities of QSAR rank order models created from representative data sets, descriptor sets, and modeling methods that were then assessed using Kendall Tau as a rank order metric, upon which the Shannon Entropy was evaluated as a means of quantifying rank-order stability. Random removal of data from the training set, also known as Data Truncation Analysis (DTA), was used as a means for systematically reducing the information content of each training set while examining both rank order performance and rank order stability in the face of training set data loss. The premise for DTA ROE model evaluation is that the response of a model to incremental loss of training information will be indicative of the quality and sufficiency of its training set, learning method, and descriptor types to cover a particular domain of applicability. This process is termed a “rank order entropy” evaluation, or ROE. By analogy with information theory, an unstable rank order model displays a high level of implicit entropy, while a QSAR rank order model which remains nearly unchanged during training set reductions would show low entropy. In this work, the ROE metric was applied to 71 data sets of different sizes, and was found to reveal more information about the behavior of the models than traditional metrics alone. Stable, or consistently performing models, did not necessarily predict rank order well. Models that performed well in rank order did not necessarily perform well in traditional metrics. In the end, it was shown that ROE metrics suggested that some QSAR models that are typically used should be discarded. ROE evaluation helps to discern which combinations of data set, descriptor set, and modeling methods lead to usable models in prioritization schemes, and provides confidence in the use of a particular model within a specific domain of applicability. PMID:21875058

Ability of Bottle Cap Color to Facilitate Accurate Patient-Physician Communication Regarding Medication Identity in Patients with Glaucoma.

PubMed

Dave, Pujan; Villarreal, Guadalupe; Friedman, David S; Kahook, Malik Y; Ramulu, Pradeep Y

2015-12-01

To determine the accuracy of patient-physician communication regarding topical ophthalmic medication use based on bottle cap color, particularly among individuals who may have acquired color vision deficiency from glaucoma. Cross-sectional, clinical study. Patients aged ≥18 years with primary open-angle, primary angle-closure, pseudoexfoliation, or pigment dispersion glaucoma, bilateral visual acuity of ≥20/400, and no concurrent conditions that may affect color vision. A total of 100 patients provided color descriptions of 11 distinct medication bottle caps. Color descriptors were then presented to 3 physicians. Physicians matched each color descriptor to the medication they thought the descriptor was describing. Frequency of patient-physician agreement, occurring when all 3 physicians accurately matched the color descriptor to the correct medication. Multivariate regression models evaluated whether patient-physician agreement decreased with degree of better-eye visual field (VF) damage, color descriptor heterogeneity, or color vision deficiency, as determined by the Hardy-Rand-Rittler (HRR) score and Lanthony D15 color confusion index (D15 CCI). Subjects had a mean age of 69 (±11) years, with VF mean deviation of -4.7 (±6.0) and -10.9 (±8.4) decibels (dB) in the better- and worse-seeing eyes, respectively. Patients produced 102 unique color descriptors to describe the colors of the 11 bottle caps. Among individual patients, the mean number of medications demonstrating agreement was 6.1/11 (55.5%). Agreement was less than 15% for 4 medications (prednisolone acetate [generic], betaxolol HCl [Betoptic; Alcon Laboratories Inc., Fort Worth, TX], brinzolamide/brimonidine [Simbrinza; Alcon Laboratories Inc.], and latanoprost [Xalatan; Pfizer, Inc., New York, NY]). Lower HRR scores and higher D15 CCI (both indicating worse color vision) were associated with greater VF damage (P < 0.001). Extent of color vision deficiency and color descriptor heterogeneity significantly predicted agreement in multivariate models (odds of agreement = 0.90 per 1 point decrement in HRR score, P < 0.001; odds of agreement = 0.30 for medications exhibiting high heterogeneity [≥11 descriptors], P = 0.007). Physician understanding of patient medication use based solely on bottle cap color is frequently incorrect, particularly in patients with glaucoma who may have color vision deficiency. Errors based on communication using bottle cap color alone may be common and could lead to confusion and harm. Copyright © 2015 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.

Novel Uses of In Vitro Data to Develop Quantitative Biological Activity Relationship Models for in Vivo Carcinogenicity Prediction.

PubMed

Pradeep, Prachi; Povinelli, Richard J; Merrill, Stephen J; Bozdag, Serdar; Sem, Daniel S

2015-04-01

The availability of large in vitro datasets enables better insight into the mode of action of chemicals and better identification of potential mechanism(s) of toxicity. Several studies have shown that not all in vitro assays can contribute as equal predictors of in vivo carcinogenicity for development of hybrid Quantitative Structure Activity Relationship (QSAR) models. We propose two novel approaches for the use of mechanistically relevant in vitro assay data in the identification of relevant biological descriptors and development of Quantitative Biological Activity Relationship (QBAR) models for carcinogenicity prediction. We demonstrate that in vitro assay data can be used to develop QBAR models for in vivo carcinogenicity prediction via two case studies corroborated with firm scientific rationale. The case studies demonstrate the similarities between QBAR and QSAR modeling in: (i) the selection of relevant descriptors to be used in the machine learning algorithm, and (ii) the development of a computational model that maps chemical or biological descriptors to a toxic endpoint. The results of both the case studies show: (i) improved accuracy and sensitivity which is especially desirable under regulatory requirements, and (ii) overall adherence with the OECD/REACH guidelines. Such mechanism based models can be used along with QSAR models for prediction of mechanistically complex toxic endpoints. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Quantitative structure-property relationships for predicting sorption of pharmaceuticals to sewage sludge during waste water treatment processes.

PubMed

Berthod, L; Whitley, D C; Roberts, G; Sharpe, A; Greenwood, R; Mills, G A

2017-02-01

Understanding the sorption of pharmaceuticals to sewage sludge during waste water treatment processes is important for understanding their environmental fate and in risk assessments. The degree of sorption is defined by the sludge/water partition coefficient (K d ). Experimental K d values (n=297) for active pharmaceutical ingredients (n=148) in primary and activated sludge were collected from literature. The compounds were classified by their charge at pH7.4 (44 uncharged, 60 positively and 28 negatively charged, and 16 zwitterions). Univariate models relating log K d to log K ow for each charge class showed weak correlations (maximum R 2 =0.51 for positively charged) with no overall correlation for the combined dataset (R 2 =0.04). Weaker correlations were found when relating log K d to log D ow . Three sets of molecular descriptors (Molecular Operating Environment, VolSurf and ParaSurf) encoding a range of physico-chemical properties were used to derive multivariate models using stepwise regression, partial least squares and Bayesian artificial neural networks (ANN). The best predictive performance was obtained with ANN, with R 2 =0.62-0.69 for these descriptors using the complete dataset. Use of more complex Vsurf and ParaSurf descriptors showed little improvement over Molecular Operating Environment descriptors. The most influential descriptors in the ANN models, identified by automatic relevance determination, highlighted the importance of hydrophobicity, charge and molecular shape effects in these sorbate-sorbent interactions. The heterogeneous nature of the different sewage sludges used to measure K d limited the predictability of sorption from physico-chemical properties of the pharmaceuticals alone. Standardization of test materials for the measurement of K d would improve comparability of data from different studies, in the long-term leading to better quality environmental risk assessments. Copyright © 2016 British Geological Survey, NERC. Published by Elsevier B.V. All rights reserved.

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients.

PubMed

Freitas, Alex A; Limbu, Kriti; Ghafourian, Taravat

2015-01-01

Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Graphical AbstractDecision trees for the prediction of tissue partition coefficient and volume of distribution of drugs.

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.

QSPR modeling of octanol/water partition coefficient for vitamins by optimal descriptors calculated with SMILES.

PubMed

Toropov, A A; Toropova, A P; Raska, I

2008-04-01

Simplified molecular input line entry system (SMILES) has been utilized in constructing quantitative structure-property relationships (QSPR) for octanol/water partition coefficient of vitamins and organic compounds of different classes by optimal descriptors. Statistical characteristics of the best model (vitamins) are the following: n=17, R(2)=0.9841, s=0.634, F=931 (training set); n=7, R(2)=0.9928, s=0.773, F=690 (test set). Using this approach for modeling octanol/water partition coefficient for a set of organic compounds gives a model that is statistically characterized by n=69, R(2)=0.9872, s=0.156, F=5184 (training set) and n=70, R(2)=0.9841, s=0.179, F=4195 (test set).

A Investigation of the Verbal Description of Trombone Tone Quality with Respect to Selected Attributes of Sound

NASA Astrophysics Data System (ADS)

Stroeher, Michael Steven

The purpose of this study was to determine the physical elements which experienced trombonists associate with selected descriptors in characterizing the tone quality of that instrument. Stimuli sampled from live trombone tones and synthesized into musical phrases represented 17 variations in (1) presence/absence of attack transient, (2) rise time, (3) duration, (4) number of harmonics, (5) upper limit of harmonicity, (6) spectral envelope shape, and (7) frequency. A vocabulary of 20 adjectives appropriate for describing trombone tone quality was established by a postal survey of 49 college trombone instructors. A pretest was presented to 28 University of North Texas trombone students, who rated the 17 stimuli in terms of the 20 adjectives on a Likert-type scale with reliability estimates of.8997 to.9439 (coefficient alpha). The pretest established the lack of significance of the attack transient in subjects' descriptions. Factor analysis of responses determined similarities in word usages and allowed the number of descriptors to be reduced to eight and the number of sound stimuli to nine for the final investigation. Subjects of the final study consisted of 161 trombonists at universities in Texas, Oklahoma, Colorado, Michigan and North Carolina, who rated the nine stimuli in terms of the eight chosen adjectives on Likert-type scales, with an overall reliability estimate of.7121 (coefficient alpha). Although the low reliability indicated some lack of agreement in descriptor usage, multiple regression analysis established relationships among subjects' use of descriptors and physical attributes of the stimuli. Subjects' judgements of bright were associated with tones of higher frequency; centered, good, dark and warm with faster rise time, longer duration and relatively fewer harmonics; pinched with a high number of harmonics and slower rise time; fuzzy with slow rise time and the presence of high-frequency inharmonic noise. Subjects did not use the word full with any degree of consistency.

Developing a contributing factor classification scheme for Rasmussen's AcciMap: Reliability and validity evaluation.

PubMed

Goode, N; Salmon, P M; Taylor, N Z; Lenné, M G; Finch, C F

2017-10-01

One factor potentially limiting the uptake of Rasmussen's (1997) Accimap method by practitioners is the lack of a contributing factor classification scheme to guide accident analyses. This article evaluates the intra- and inter-rater reliability and criterion-referenced validity of a classification scheme developed to support the use of Accimap by led outdoor activity (LOA) practitioners. The classification scheme has two levels: the system level describes the actors, artefacts and activity context in terms of 14 codes; the descriptor level breaks the system level codes down into 107 specific contributing factors. The study involved 11 LOA practitioners using the scheme on two separate occasions to code a pre-determined list of contributing factors identified from four incident reports. Criterion-referenced validity was assessed by comparing the codes selected by LOA practitioners to those selected by the method creators. Mean intra-rater reliability scores at the system (M = 83.6%) and descriptor (M = 74%) levels were acceptable. Mean inter-rater reliability scores were not consistently acceptable for both coding attempts at the system level (M T1  = 68.8%; M T2  = 73.9%), and were poor at the descriptor level (M T1  = 58.5%; M T2  = 64.1%). Mean criterion referenced validity scores at the system level were acceptable (M T1  = 73.9%; M T2  = 75.3%). However, they were not consistently acceptable at the descriptor level (M T1  = 67.6%; M T2  = 70.8%). Overall, the results indicate that the classification scheme does not currently satisfy reliability and validity requirements, and that further work is required. The implications for the design and development of contributing factors classification schemes are discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Structure-activity relationships for serotonin transporter and dopamine receptor selectivity.

PubMed

Agatonovic-Kustrin, Snezana; Davies, Paul; Turner, Joseph V

2009-05-01

Antipsychotic medications have a diverse pharmacology with affinity for serotonergic, dopaminergic, adrenergic, histaminergic and cholinergic receptors. Their clinical use now also includes the treatment of mood disorders, thought to be mediated by serotonergic receptor activity. The aim of our study was to characterise the molecular properties of antipsychotic agents, and to develop a model that would indicate molecular specificity for the dopamine (D(2)) receptor and the serotonin (5-HT) transporter. Back-propagation artificial neural networks (ANNs) were trained on a dataset of 47 ligands categorically assigned antidepressant or antipsychotic utility. The structure of each compound was encoded with 63 calculated molecular descriptors. ANN parameters including hidden neurons and input descriptors were optimised based on sensitivity analyses, with optimum models containing between four and 14 descriptors. Predicted binding preferences were in excellent agreement with clinical antipsychotic or antidepressant utility. Validated models were further tested by use of an external prediction set of five drugs with unknown mechanism of action. The SAR models developed revealed the importance of simple molecular characteristics for differential binding to the D(2) receptor and the 5-HT transporter. These included molecular size and shape, solubility parameters, hydrogen donating potential, electrostatic parameters, stereochemistry and presence of nitrogen. The developed models and techniques employed are expected to be useful in the rational design of future therapeutic agents.

Model for the partition of neutral compounds between n-heptane and formamide.

PubMed

Karunasekara, Thushara; Poole, Colin F

2010-04-01

Partition coefficients for 84 varied compounds were determined for n-heptane-formamide biphasic partition system and used to derive a model for the distribution of neutral compounds between the n-heptane-rich and formamide-rich layers. The partition coefficients, log K(p), were correlated through the solvation parameter model giving log K(p)=0.083+0.559E-2.244S-3.250A-1.614B+2.387V with a multiple correlation coefficient of 0.996, standard error of the estimate 0.139, and Fisher statistic 1791. In the model, the solute descriptors are excess molar refraction, E, dipolarity/polarizability, S, overall hydrogen-bond acidity, A, overall hydrogen-bond basicity, B, and McGowan's characteristic volume, V. The model is expected to be able to estimate further values of the partition coefficient to about 0.13 log units for the same descriptor space covered by the calibration compounds (E=-0.26-2.29, S=0-1.93, A=0-1.25, B=0.02-1.58, and V=0.78-2.50). The n-heptane-formamide partition system is shown to have different selectivity to other totally organic biphasic systems and to be suitable for estimating descriptor values for compounds of low water solubility and/or stability.

Agenda toward the development of a rational noise descriptor system relevant to human annoyance by en route aircraft noise

NASA Technical Reports Server (NTRS)

Garbell, Maurice A.

1990-01-01

A rational, internationally consistent, noise descriptor system is needed to express existing and predicted en route aircraft noise levels in terms closely correlated to the annoyance perceived by people and physiologically identifiable in people, to provide guidance for aircraft and powerplant design, flight management, land-use planning, and building codes. Expanding on previous discussions, a new comprehensive statement of the specific questions that must be resolved by needed research, and the nature and quality of proof that must be adduced to justify further steps toward the drafting and adoption of new international en route aircraft-noise standards is sought. The single noise-descriptor system envisioned must be valid for widely varying aircraft-noise frequency spectra, including time-variant components and agreeable and disagreeable discrete tones and combinations of tones. The measures and criteria established by the system must be valid at high and low immission levels, at high and low ambient noise levels, for great and small number of noise events, and outdoors and indoors.

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.

Stochastic Analysis and Design of Heterogeneous Microstructural Materials System

NASA Astrophysics Data System (ADS)

Xu, Hongyi

Advanced materials system refers to new materials that are comprised of multiple traditional constituents but complex microstructure morphologies, which lead to superior properties over the conventional materials. To accelerate the development of new advanced materials system, the objective of this dissertation is to develop a computational design framework and the associated techniques for design automation of microstructure materials systems, with an emphasis on addressing the uncertainties associated with the heterogeneity of microstructural materials. Five key research tasks are identified: design representation, design evaluation, design synthesis, material informatics and uncertainty quantification. Design representation of microstructure includes statistical characterization and stochastic reconstruction. This dissertation develops a new descriptor-based methodology, which characterizes 2D microstructures using descriptors of composition, dispersion and geometry. Statistics of 3D descriptors are predicted based on 2D information to enable 2D-to-3D reconstruction. An efficient sequential reconstruction algorithm is developed to reconstruct statistically equivalent random 3D digital microstructures. In design evaluation, a stochastic decomposition and reassembly strategy is developed to deal with the high computational costs and uncertainties induced by material heterogeneity. The properties of Representative Volume Elements (RVE) are predicted by stochastically reassembling SVE elements with stochastic properties into a coarse representation of the RVE. In design synthesis, a new descriptor-based design framework is developed, which integrates computational methods of microstructure characterization and reconstruction, sensitivity analysis, Design of Experiments (DOE), metamodeling and optimization the enable parametric optimization of the microstructure for achieving the desired material properties. Material informatics is studied to efficiently reduce the dimension of microstructure design space. This dissertation develops a machine learning-based methodology to identify the key microstructure descriptors that highly impact properties of interest. In uncertainty quantification, a comparative study on data-driven random process models is conducted to provide guidance for choosing the most accurate model in statistical uncertainty quantification. Two new goodness-of-fit metrics are developed to provide quantitative measurements of random process models' accuracy. The benefits of the proposed methods are demonstrated by the example of designing the microstructure of polymer nanocomposites. This dissertation provides material-generic, intelligent modeling/design methodologies and techniques to accelerate the process of analyzing and designing new microstructural materials system.

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.

Comparison of Multiple Linear Regressions and Neural Networks based QSAR models for the design of new antitubercular compounds.

PubMed

Ventura, Cristina; Latino, Diogo A R S; Martins, Filomena

2013-01-01

The performance of two QSAR methodologies, namely Multiple Linear Regressions (MLR) and Neural Networks (NN), towards the modeling and prediction of antitubercular activity was evaluated and compared. A data set of 173 potentially active compounds belonging to the hydrazide family and represented by 96 descriptors was analyzed. Models were built with Multiple Linear Regressions (MLR), single Feed-Forward Neural Networks (FFNNs), ensembles of FFNNs and Associative Neural Networks (AsNNs) using four different data sets and different types of descriptors. The predictive ability of the different techniques used were assessed and discussed on the basis of different validation criteria and results show in general a better performance of AsNNs in terms of learning ability and prediction of antitubercular behaviors when compared with all other methods. MLR have, however, the advantage of pinpointing the most relevant molecular characteristics responsible for the behavior of these compounds against Mycobacterium tuberculosis. The best results for the larger data set (94 compounds in training set and 18 in test set) were obtained with AsNNs using seven descriptors (R(2) of 0.874 and RMSE of 0.437 against R(2) of 0.845 and RMSE of 0.472 in MLRs, for test set). Counter-Propagation Neural Networks (CPNNs) were trained with the same data sets and descriptors. From the scrutiny of the weight levels in each CPNN and the information retrieved from MLRs, a rational design of potentially active compounds was attempted. Two new compounds were synthesized and tested against M. tuberculosis showing an activity close to that predicted by the majority of the models. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

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.

Temporal variability in the importance of hydrologic, biotic, and climatic descriptors of dissolved oxygen dynamics in a shallow tidal-marsh creek

NASA Astrophysics Data System (ADS)

Nelson, N.; Munoz-Carpena, R.; Neale, P.; Tzortziou, M.; Megonigal, P.

2017-12-01

Due to strong abiotic forcing, dissolved oxygen (DO) in shallow tidal creeks often disobeys the conventional explanation of general aquatic DO cycling as biologically-regulated. In the present work, we seek to quantify the relative importance of abiotic (hydrologic and climatic), and biotic (primary productivity as represented by chlorophyll-a) descriptors of tidal creek DO. By fitting multiple linear regression models of DO to hourly chlorophyll-a, water quality, hydrology, and weather data collected in a tidal creek of a Chesapeake Bay marsh (Maryland, USA), temporal shifts (summer - early winter) in the relative importance of tidal creek DO descriptors were uncovered. Moreover, this analysis identified an alternative approach to evaluating tidal stage as a driver of DO by dividing stage into two DO-relevant variables: stage above and below bankfull depth. Within the hydrologic variable class, stage below bankfull depth dominated as an important descriptor, thus highlighting the role of pore water drainage and mixing as influential processes forcing tidal creek DO. Study findings suggest that tidal creek DO dynamics are explained by a balance of hydrologic, climatic, and biotic descriptors during warmer seasons due to many of these variables (i.e., chlorophyll-a, water temperature) acting as tracers of estuarine-marsh water mixing; conversely, in early winter months when estuarine and marsh waters differ less distinctly, hydrologic variables increase in relative importance as descriptors of tidal creek DO. These findings underline important distinctions in the underlying mechanisms dictating DO variability in shallow tidal marsh-creek environments relative to open water estuarine systems.

Temporal variability in the importance of hydrologic, biotic, and climatic descriptors of dissolved oxygen dynamics in a shallow tidal-marsh creek

NASA Astrophysics Data System (ADS)

Nelson, Natalie G.; Muñoz-Carpena, Rafael; Neale, Patrick J.; Tzortziou, Maria; Megonigal, J. Patrick

2017-08-01

Due to strong abiotic forcing, dissolved oxygen (DO) in shallow tidal creeks often disobeys the conventional explanation of general aquatic DO cycling as biologically regulated. In the present work, we seek to quantify the relative importance of abiotic (hydrologic and climatic), and biotic (primary productivity as represented by chlorophyll-a) descriptors of tidal creek DO. By fitting multiple linear regression models of DO to hourly chlorophyll-a, water quality, hydrology, and weather data collected in a tidal creek of a Chesapeake Bay marsh (Maryland, USA), temporal shifts (summer-early winter) in the relative importance of tidal creek DO descriptors were uncovered. Moreover, this analysis identified an alternative approach to evaluating tidal stage as a driver of DO by dividing stage into two DO-relevant variables: stage above and below bankfull depth. Within the hydrologic variable class, stage below bankfull depth dominated as an important descriptor, thus highlighting the role of pore water drainage and mixing as influential processes forcing tidal creek DO. Study findings suggest that tidal creek DO dynamics are explained by a balance of hydrologic, climatic, and biotic descriptors during warmer seasons due to many of these variables (i.e., chlorophyll-a, water temperature) acting as tracers of estuarine-marsh water mixing; conversely, in early winter months when estuarine and marsh waters differ less distinctly, hydrologic variables increase in relative importance as descriptors of tidal creek DO. These findings underline important distinctions in the underlying mechanisms dictating DO variability in shallow tidal marsh-creek environments relative to open water estuarine systems.

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

Nanoparticle surface characterization and clustering through concentration-dependent surface adsorption modeling.

PubMed

Chen, Ran; Zhang, Yuntao; Sahneh, Faryad Darabi; Scoglio, Caterina M; Wohlleben, Wendel; Haase, Andrea; Monteiro-Riviere, Nancy A; Riviere, Jim E

2014-09-23

Quantitative characterization of nanoparticle interactions with their surrounding environment is vital for safe nanotechnological development and standardization. A recent quantitative measure, the biological surface adsorption index (BSAI), has demonstrated promising applications in nanomaterial surface characterization and biological/environmental prediction. This paper further advances the approach beyond the application of five descriptors in the original BSAI to address the concentration dependence of the descriptors, enabling better prediction of the adsorption profile and more accurate categorization of nanomaterials based on their surface properties. Statistical analysis on the obtained adsorption data was performed based on three different models: the original BSAI, a concentration-dependent polynomial model, and an infinite dilution model. These advancements in BSAI modeling showed a promising development in the application of quantitative predictive modeling in biological applications, nanomedicine, and environmental safety assessment of nanomaterials.

IRIS Toxicological Review of Vanadium Pentoxide (External Review Draft)

EPA Science Inventory

This vanadium pentoxide reassessment consists of an oral reference dose (RfD), an inhalation reference concentration (RfC), an inhalation unit risk (IUR) and a cancer weight of evidence descriptor. This is the first assessment developing an RfC or IUR for this compound. This as...

Harmony Search as a Powerful Tool for Feature Selection in QSPR Study of the Drugs Lipophilicity.

PubMed

Bahadori, Behnoosh; Atabati, Morteza

2017-01-01

Aims & Scope: Lipophilicity represents one of the most studied and most frequently used fundamental physicochemical properties. In the present work, harmony search (HS) algorithm is suggested to feature selection in quantitative structure-property relationship (QSPR) modeling to predict lipophilicity of neutral, acidic, basic and amphotheric drugs that were determined by UHPLC. Harmony search is a music-based metaheuristic optimization algorithm. It was affected by the observation that the aim of music is to search for a perfect state of harmony. Semi-empirical quantum-chemical calculations at AM1 level were used to find the optimum 3D geometry of the studied molecules and variant descriptors (1497 descriptors) were calculated by the Dragon software. The selected descriptors by harmony search algorithm (9 descriptors) were applied for model development using multiple linear regression (MLR). In comparison with other feature selection methods such as genetic algorithm and simulated annealing, harmony search algorithm has better results. The root mean square error (RMSE) with and without leave-one out cross validation (LOOCV) were obtained 0.417 and 0.302, respectively. The results were compared with those obtained from the genetic algorithm and simulated annealing methods and it showed that the HS is a helpful tool for feature selection with fine performance. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

In silico prediction of ROCK II inhibitors by different classification approaches.

PubMed

Cai, Chuipu; Wu, Qihui; Luo, Yunxia; Ma, Huili; Shen, Jiangang; Zhang, Yongbin; Yang, Lei; Chen, Yunbo; Wen, Zehuai; Wang, Qi

2017-11-01

ROCK II is an important pharmacological target linked to central nervous system disorders such as Alzheimer's disease. The purpose of this research is to generate ROCK II inhibitor prediction models by machine learning approaches. Firstly, four sets of descriptors were calculated with MOE 2010 and PaDEL-Descriptor, and optimized by F-score and linear forward selection methods. In addition, four classification algorithms were used to initially build 16 classifiers with k-nearest neighbors [Formula: see text], naïve Bayes, Random forest, and support vector machine. Furthermore, three sets of structural fingerprint descriptors were introduced to enhance the predictive capacity of classifiers, which were assessed with fivefold cross-validation, test set validation and external test set validation. The best two models, MFK + MACCS and MLR + SubFP, have both MCC values of 0.925 for external test set. After that, a privileged substructure analysis was performed to reveal common chemical features of ROCK II inhibitors. Finally, binding modes were analyzed to identify relationships between molecular descriptors and activity, while main interactions were revealed by comparing the docking interaction of the most potent and the weakest ROCK II inhibitors. To the best of our knowledge, this is the first report on ROCK II inhibitors utilizing machine learning approaches that provides a new method for discovering novel ROCK II inhibitors.

QSAR as a random event: modeling of nanoparticles uptake in PaCa2 cancer cells.

PubMed

Toropov, Andrey A; Toropova, Alla P; Puzyn, Tomasz; Benfenati, Emilio; Gini, Giuseppina; Leszczynska, Danuta; Leszczynski, Jerzy

2013-06-01

Quantitative structure-property/activity relationships (QSPRs/QSARs) are a tool to predict various endpoints for various substances. The "classic" QSPR/QSAR analysis is based on the representation of the molecular structure by the molecular graph. However, simplified molecular input-line entry system (SMILES) gradually becomes most popular representation of the molecular structure in the databases available on the Internet. Under such circumstances, the development of molecular descriptors calculated directly from SMILES becomes attractive alternative to "classic" descriptors. The CORAL software (http://www.insilico.eu/coral) is provider of SMILES-based optimal molecular descriptors which are aimed to correlate with various endpoints. We analyzed data set on nanoparticles uptake in PaCa2 pancreatic cancer cells. The data set includes 109 nanoparticles with the same core but different surface modifiers (small organic molecules). The concept of a QSAR as a random event is suggested in opposition to "classic" QSARs which are based on the only one distribution of available data into the training and the validation sets. In other words, five random splits into the "visible" training set and the "invisible" validation set were examined. The SMILES-based optimal descriptors (obtained by the Monte Carlo technique) for these splits are calculated with the CORAL software. The statistical quality of all these models is good. Copyright © 2013 Elsevier Ltd. All rights reserved.

[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.

Prediction of passive blood-brain partitioning: straightforward and effective classification models based on in silico derived physicochemical descriptors

PubMed Central

Vilar, Santiago; Chakrabarti, Mayukh; Costanzi, Stefano

2010-01-01

The distribution of compounds between blood and brain is a very important consideration for new candidate drug molecules. In this paper, we describe the derivation of two linear discriminant analysis (LDA) models for the prediction of passive blood-brain partitioning, expressed in terms of log BB values. The models are based on computationally derived physicochemical descriptors, namely the octanol/water partition coefficient (log P), the topological polar surface area (TPSA) and the total number of acidic and basic atoms, and were obtained using a homogeneous training set of 307 compounds, for all of which the published experimental log BB data had been determined in vivo. In particular, since molecules with log BB > 0.3 cross the blood-brain barrier (BBB) readily while molecules with log BB < −1 are poorly distributed to the brain, on the basis of these thresholds we derived two distinct models, both of which show a percentage of good classification of about 80%. Notably, the predictive power of our models was confirmed by the analysis of a large external dataset of compounds with reported activity on the central nervous system (CNS) or lack thereof. The calculation of straightforward physicochemical descriptors is the only requirement for the prediction of the log BB of novel compounds through our models, which can be conveniently applied in conjunction with drug design and virtual screenings. PMID:20427217

Prediction of passive blood-brain partitioning: straightforward and effective classification models based on in silico derived physicochemical descriptors.

PubMed

Vilar, Santiago; Chakrabarti, Mayukh; Costanzi, Stefano

2010-06-01

The distribution of compounds between blood and brain is a very important consideration for new candidate drug molecules. In this paper, we describe the derivation of two linear discriminant analysis (LDA) models for the prediction of passive blood-brain partitioning, expressed in terms of logBB values. The models are based on computationally derived physicochemical descriptors, namely the octanol/water partition coefficient (logP), the topological polar surface area (TPSA) and the total number of acidic and basic atoms, and were obtained using a homogeneous training set of 307 compounds, for all of which the published experimental logBB data had been determined in vivo. In particular, since molecules with logBB>0.3 cross the blood-brain barrier (BBB) readily while molecules with logBB<-1 are poorly distributed to the brain, on the basis of these thresholds we derived two distinct models, both of which show a percentage of good classification of about 80%. Notably, the predictive power of our models was confirmed by the analysis of a large external dataset of compounds with reported activity on the central nervous system (CNS) or lack thereof. The calculation of straightforward physicochemical descriptors is the only requirement for the prediction of the logBB of novel compounds through our models, which can be conveniently applied in conjunction with drug design and virtual screenings. Published by Elsevier Inc.

A Novel Two-Step Hierarchical Quantitative Structure–Activity Relationship Modeling Work Flow for Predicting Acute Toxicity of Chemicals in Rodents

PubMed Central

Zhu, Hao; Ye, Lin; Richard, Ann; Golbraikh, Alexander; Wright, Fred A.; Rusyn, Ivan; Tropsha, Alexander

2009-01-01

Background Accurate prediction of in vivo toxicity from in vitro testing is a challenging problem. Large public–private consortia have been formed with the goal of improving chemical safety assessment by the means of high-throughput screening. Objective A wealth of available biological data requires new computational approaches to link chemical structure, in vitro data, and potential adverse health effects. Methods and results A database containing experimental cytotoxicity values for in vitro half-maximal inhibitory concentration (IC50) and in vivo rodent median lethal dose (LD50) for more than 300 chemicals was compiled by Zentralstelle zur Erfassung und Bewertung von Ersatz- und Ergaenzungsmethoden zum Tierversuch (ZEBET; National Center for Documentation and Evaluation of Alternative Methods to Animal Experiments). The application of conventional quantitative structure–activity relationship (QSAR) modeling approaches to predict mouse or rat acute LD50 values from chemical descriptors of ZEBET compounds yielded no statistically significant models. The analysis of these data showed no significant correlation between IC50 and LD50. However, a linear IC50 versus LD50 correlation could be established for a fraction of compounds. To capitalize on this observation, we developed a novel two-step modeling approach as follows. First, all chemicals are partitioned into two groups based on the relationship between IC50 and LD50 values: One group comprises compounds with linear IC50 versus LD50 relationships, and another group comprises the remaining compounds. Second, we built conventional binary classification QSAR models to predict the group affiliation based on chemical descriptors only. Third, we developed k-nearest neighbor continuous QSAR models for each subclass to predict LD50 values from chemical descriptors. All models were extensively validated using special protocols. Conclusions The novelty of this modeling approach is that it uses the relationships between in vivo and in vitro data only to inform the initial construction of the hierarchical two-step QSAR models. Models resulting from this approach employ chemical descriptors only for external prediction of acute rodent toxicity. PMID:19672406

A novel two-step hierarchical quantitative structure-activity relationship modeling work flow for predicting acute toxicity of chemicals in rodents.

PubMed

Zhu, Hao; Ye, Lin; Richard, Ann; Golbraikh, Alexander; Wright, Fred A; Rusyn, Ivan; Tropsha, Alexander

2009-08-01

Accurate prediction of in vivo toxicity from in vitro testing is a challenging problem. Large public-private consortia have been formed with the goal of improving chemical safety assessment by the means of high-throughput screening. A wealth of available biological data requires new computational approaches to link chemical structure, in vitro data, and potential adverse health effects. A database containing experimental cytotoxicity values for in vitro half-maximal inhibitory concentration (IC(50)) and in vivo rodent median lethal dose (LD(50)) for more than 300 chemicals was compiled by Zentralstelle zur Erfassung und Bewertung von Ersatz- und Ergaenzungsmethoden zum Tierversuch (ZEBET; National Center for Documentation and Evaluation of Alternative Methods to Animal Experiments). The application of conventional quantitative structure-activity relationship (QSAR) modeling approaches to predict mouse or rat acute LD(50) values from chemical descriptors of ZEBET compounds yielded no statistically significant models. The analysis of these data showed no significant correlation between IC(50) and LD(50). However, a linear IC(50) versus LD(50) correlation could be established for a fraction of compounds. To capitalize on this observation, we developed a novel two-step modeling approach as follows. First, all chemicals are partitioned into two groups based on the relationship between IC(50) and LD(50) values: One group comprises compounds with linear IC(50) versus LD(50) relationships, and another group comprises the remaining compounds. Second, we built conventional binary classification QSAR models to predict the group affiliation based on chemical descriptors only. Third, we developed k-nearest neighbor continuous QSAR models for each subclass to predict LD(50) values from chemical descriptors. All models were extensively validated using special protocols. The novelty of this modeling approach is that it uses the relationships between in vivo and in vitro data only to inform the initial construction of the hierarchical two-step QSAR models. Models resulting from this approach employ chemical descriptors only for external prediction of acute rodent toxicity.

In silico quantitative structure-toxicity relationship study of aromatic nitro compounds.

PubMed

Pasha, Farhan Ahmad; Neaz, Mohammad Morshed; Cho, Seung Joo; Ansari, Mohiuddin; Mishra, Sunil Kumar; Tiwari, Sharvan

2009-05-01

Small molecules often have toxicities that are a function of molecular structural features. Minor variations in structural features can make large difference in such toxicity. Consequently, in silico techniques may be used to correlate such molecular toxicities with their structural features. Relative to nine different sets of aromatic nitro compounds having known observed toxicities against different targets, we developed ligand-based 2D quantitative structure-toxicity relationship models using 20 selected topological descriptors. The topological descriptors have several advantages such as conformational independency, facile and less time-consuming computation to yield good results. Multiple linear regression analysis was used to correlate variations of toxicity with molecular properties. The information index on molecular size, lopping centric index and Kier flexibility index were identified as fundamental descriptors for different kinds of toxicity, and further showed that molecular size, branching and molecular flexibility might be particularly important factors in quantitative structure-toxicity relationship analysis. This study revealed that topological descriptor-guided quantitative structure-toxicity relationship provided a very useful, cost and time-efficient, in silico tool for describing small-molecule toxicities.

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.

Metabolic biotransformation half-lives in fish: QSAR modeling and consensus analysis.

PubMed

Papa, Ester; van der Wal, Leon; Arnot, Jon A; Gramatica, Paola

2014-02-01

Bioaccumulation in fish is a function of competing rates of chemical uptake and elimination. For hydrophobic organic chemicals bioconcentration, bioaccumulation and biomagnification potential are high and the biotransformation rate constant is a key parameter. Few measured biotransformation rate constant data are available compared to the number of chemicals that are being evaluated for bioaccumulation hazard and for exposure and risk assessment. Three new Quantitative Structure-Activity Relationships (QSARs) for predicting whole body biotransformation half-lives (HLN) in fish were developed and validated using theoretical molecular descriptors that seek to capture structural characteristics of the whole molecule and three data set splitting schemes. The new QSARs were developed using a minimal number of theoretical descriptors (n=9) and compared to existing QSARs developed using fragment contribution methods that include up to 59 descriptors. The predictive statistics of the models are similar thus further corroborating the predictive performance of the different QSARs; Q(2)ext ranges from 0.75 to 0.77, CCCext ranges from 0.86 to 0.87, RMSE in prediction ranges from 0.56 to 0.58. The new QSARs provide additional mechanistic insights into the biotransformation capacity of organic chemicals in fish by including whole molecule descriptors and they also include information on the domain of applicability for the chemical of interest. Advantages of consensus modeling for improving overall prediction and minimizing false negative errors in chemical screening assessments, for identifying potential sources of residual error in the empirical HLN database, and for identifying structural features that are not well represented in the HLN dataset to prioritize future testing needs are illustrated. © 2013.

CADASTER QSPR Models for Predictions of Melting and Boiling Points of Perfluorinated Chemicals.

PubMed

Bhhatarai, Barun; Teetz, Wolfram; Liu, Tao; Öberg, Tomas; Jeliazkova, Nina; Kochev, Nikolay; Pukalov, Ognyan; Tetko, Igor V; Kovarich, Simona; Papa, Ester; Gramatica, Paola

2011-03-14

Quantitative structure property relationship (QSPR) studies on per- and polyfluorinated chemicals (PFCs) on melting point (MP) and boiling point (BP) are presented. The training and prediction chemicals used for developing and validating the models were selected from Syracuse PhysProp database and literatures. The available experimental data sets were split in two different ways: a) random selection on response value, and b) structural similarity verified by self-organizing-map (SOM), in order to propose reliable predictive models, developed only on the training sets and externally verified on the prediction sets. Individual linear and non-linear approaches based models developed by different CADASTER partners on 0D-2D Dragon descriptors, E-state descriptors and fragment based descriptors as well as consensus model and their predictions are presented. In addition, the predictive performance of the developed models was verified on a blind external validation set (EV-set) prepared using PERFORCE database on 15 MP and 25 BP data respectively. This database contains only long chain perfluoro-alkylated chemicals, particularly monitored by regulatory agencies like US-EPA and EU-REACH. QSPR models with internal and external validation on two different external prediction/validation sets and study of applicability-domain highlighting the robustness and high accuracy of the models are discussed. Finally, MPs for additional 303 PFCs and BPs for 271 PFCs were predicted for which experimental measurements are unknown. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Thermal regimes, nonnative trout, and their influences on native Bull Trout in the Upper Klamath River Basin, Oregon

USGS Publications Warehouse

Benjamin, Joseph R.; Heltzel, Jeannie; Dunham, Jason B.; Heck, Michael; Banish, Nolan P.

2016-01-01

The occurrence of fish species may be strongly influenced by a stream’s thermal regime (magnitude, frequency, variation, and timing). For instance, magnitude and frequency provide information about sublethal temperatures, variability in temperature can affect behavioral thermoregulation and bioenergetics, and timing of thermal events may cue life history events, such as spawning and migration. We explored the relationship between thermal regimes and the occurrences of native Bull Trout Salvelinus confluentus and nonnative Brook Trout Salvelinus fontinalis and Brown Trout Salmo trutta across 87 sites in the upper Klamath River basin, Oregon. Our objectives were to associate descriptors of the thermal regime with trout occurrence, predict the probability of Bull Trout occurrence, and estimate upper thermal tolerances of the trout species. We found that each species was associated with a different suite of thermal regime descriptors. Bull Trout were present at sites that were cooler, had fewer high-temperature events, had less variability, and took longer to warm. Brook Trout were also observed at cooler sites with fewer high-temperature events, but the sites were more variable and Brook Trout occurrence was not associated with a timing descriptor. In contrast, Brown Trout were present at sites that were warmer and reached higher temperatures faster, but they were not associated with frequency or variability descriptors. Among the descriptors considered, magnitude (specifically June degree-days) was the most important in predicting the probability of Bull Trout occurrence, and model predictions were strengthened by including Brook Trout occurrence. Last, all three trout species exhibited contrasting patterns of tolerating longer exposures to lower temperatures. Tolerance limits for Bull Trout were lower than those for Brook Trout and Brown Trout, with contrasts especially evident for thermal maxima. Our results confirm the value of exploring a suite of thermal regime descriptors for understanding the distribution and occurrence of fishes. Moreover, these descriptors and their relationships to fish should be considered with future changes in land use, water use, or climate.

A penalized quantitative structure-property relationship study on melting point of energetic carbocyclic nitroaromatic compounds using adaptive bridge penalty.

PubMed

Al-Fakih, A M; Algamal, Z Y; Lee, M H; Aziz, M

2018-05-01

A penalized quantitative structure-property relationship (QSPR) model with adaptive bridge penalty for predicting the melting points of 92 energetic carbocyclic nitroaromatic compounds is proposed. To ensure the consistency of the descriptor selection of the proposed penalized adaptive bridge (PBridge), we proposed a ridge estimator ([Formula: see text]) as an initial weight in the adaptive bridge penalty. The Bayesian information criterion was applied to ensure the accurate selection of the tuning parameter ([Formula: see text]). The PBridge based model was internally and externally validated based on [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], the Y-randomization test, [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] and the applicability domain. The validation results indicate that the model is robust and not due to chance correlation. The descriptor selection and prediction performance of PBridge for the training dataset outperforms the other methods used. PBridge shows the highest [Formula: see text] of 0.959, [Formula: see text] of 0.953, [Formula: see text] of 0.949 and [Formula: see text] of 0.959, and the lowest [Formula: see text] and [Formula: see text]. For the test dataset, PBridge shows a higher [Formula: see text] of 0.945 and [Formula: see text] of 0.948, and a lower [Formula: see text] and [Formula: see text], indicating its better prediction performance. The results clearly reveal that the proposed PBridge is useful for constructing reliable and robust QSPRs for predicting melting points prior to synthesizing new organic compounds.

Correlating Reactivity and Selectivity to Cyclopentadienyl Ligand Properties in Rh(III)-Catalyzed C-H Activation Reactions: An Experimental and Computational Study.

PubMed

Piou, Tiffany; Romanov-Michailidis, Fedor; Romanova-Michaelides, Maria; Jackson, Kelvin E; Semakul, Natthawat; Taggart, Trevor D; Newell, Brian S; Rithner, Christopher D; Paton, Robert S; Rovis, Tomislav

2017-01-25

Cp X Rh(III)-catalyzed C-H functionalization reactions are a proven method for the efficient assembly of small molecules. However, rationalization of the effects of cyclopentadienyl (Cp X ) ligand structure on reaction rate and selectivity has been viewed as a black box, and a truly systematic study is lacking. Consequently, predicting the outcomes of these reactions is challenging because subtle variations in ligand structure can cause notable changes in reaction behavior. A predictive tool is, nonetheless, of considerable value to the community as it would greatly accelerate reaction development. Designing a data set in which the steric and electronic properties of the Cp X Rh(III) catalysts were systematically varied allowed us to apply multivariate linear regression algorithms to establish correlations between these catalyst-based descriptors and the regio-, diastereoselectivity, and rate of model reactions. This, in turn, led to the development of quantitative predictive models that describe catalyst performance. Our newly described cone angles and Sterimol parameters for Cp X ligands served as highly correlative steric descriptors in the regression models. Through rational design of training and validation sets, key diastereoselectivity outliers were identified. Computations reveal the origins of the outstanding stereoinduction displayed by these outliers. The results are consistent with partial η 5 -η 3 ligand slippage that occurs in the transition state of the selectivity-determining step. In addition to the instructive value of our study, we believe that the insights gained are transposable to other group 9 transition metals and pave the way toward rational design of C-H functionalization catalysts.

Secure access control and large scale robust representation for online multimedia event detection.

PubMed

Liu, Changyu; Lu, Bin; Li, Huiling

2014-01-01

We developed an online multimedia event detection (MED) system. However, there are a secure access control issue and a large scale robust representation issue when we want to integrate traditional event detection algorithms into the online environment. For the first issue, we proposed a tree proxy-based and service-oriented access control (TPSAC) model based on the traditional role based access control model. Verification experiments were conducted on the CloudSim simulation platform, and the results showed that the TPSAC model is suitable for the access control of dynamic online environments. For the second issue, inspired by the object-bank scene descriptor, we proposed a 1000-object-bank (1000OBK) event descriptor. Feature vectors of the 1000OBK were extracted from response pyramids of 1000 generic object detectors which were trained on standard annotated image datasets, such as the ImageNet dataset. A spatial bag of words tiling approach was then adopted to encode these feature vectors for bridging the gap between the objects and events. Furthermore, we performed experiments in the context of event classification on the challenging TRECVID MED 2012 dataset, and the results showed that the robust 1000OBK event descriptor outperforms the state-of-the-art approaches.

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.

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.

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.

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 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.

PyBioMed: a python library for various molecular representations of chemicals, proteins and DNAs and their interactions.

PubMed

Dong, Jie; Yao, Zhi-Jiang; Zhang, Lin; Luo, Feijun; Lin, Qinlu; Lu, Ai-Ping; Chen, Alex F; Cao, Dong-Sheng

2018-03-20

With the increasing development of biotechnology and informatics technology, publicly available data in chemistry and biology are undergoing explosive growth. Such wealthy information in these data needs to be extracted and transformed to useful knowledge by various data mining methods. Considering the amazing rate at which data are accumulated in chemistry and biology fields, new tools that process and interpret large and complex interaction data are increasingly important. So far, there are no suitable toolkits that can effectively link the chemical and biological space in view of molecular representation. To further explore these complex data, an integrated toolkit for various molecular representation is urgently needed which could be easily integrated with data mining algorithms to start a full data analysis pipeline. Herein, the python library PyBioMed is presented, which comprises functionalities for online download for various molecular objects by providing different IDs, the pretreatment of molecular structures, the computation of various molecular descriptors for chemicals, proteins, DNAs and their interactions. PyBioMed is a feature-rich and highly customized python library used for the characterization of various complex chemical and biological molecules and interaction samples. The current version of PyBioMed could calculate 775 chemical descriptors and 19 kinds of chemical fingerprints, 9920 protein descriptors based on protein sequences, more than 6000 DNA descriptors from nucleotide sequences, and interaction descriptors from pairwise samples using three different combining strategies. Several examples and five real-life applications were provided to clearly guide the users how to use PyBioMed as an integral part of data analysis projects. By using PyBioMed, users are able to start a full pipelining from getting molecular data, pretreating molecules, molecular representation to constructing machine learning models conveniently. PyBioMed provides various user-friendly and highly customized APIs to calculate various features of biological molecules and complex interaction samples conveniently, which aims at building integrated analysis pipelines from data acquisition, data checking, and descriptor calculation to modeling. PyBioMed is freely available at http://projects.scbdd.com/pybiomed.html .

Defense RDT&E Online System (DROLS) Handbook

DTIC Science & Technology

1993-07-01

of the descriptor TROPICAL DISEASES hierarchically will produce the same results as a cumulated search of the following terms: CHOLERA DENGUE ...Header List The Source header List is a two volume listing of all source names arranged in alphabetical order. Each en ~try consists of: Source Name...BB Belgium ................................................................ BE Belize

Across the Threshold: A Call for ILR Proficiency Descriptor Banks

ERIC Educational Resources Information Center

Holman, Don

2017-01-01

Whereas the Interagency Language Roundtable Language Skill Level Descriptions broke new ground for assessing proficiency in foreign languages, the need for user-oriented (rather than assessment-oriented) proficiency scales has led, especially in Europe, to the creation of scales consisting of positively formulated "can-do" statements,…

Towards reporting standards for neuropsychological study results: A proposal to minimize communication errors with standardized qualitative descriptors for normalized test scores.

PubMed

Schoenberg, Mike R; Rum, Ruba S

2017-11-01

Rapid, clear and efficient communication of neuropsychological results is essential to benefit patient care. Errors in communication are a lead cause of medical errors; nevertheless, there remains a lack of consistency in how neuropsychological scores are communicated. A major limitation in the communication of neuropsychological results is the inconsistent use of qualitative descriptors for standardized test scores and the use of vague terminology. PubMed search from 1 Jan 2007 to 1 Aug 2016 to identify guidelines or consensus statements for the description and reporting of qualitative terms to communicate neuropsychological test scores was conducted. The review found the use of confusing and overlapping terms to describe various ranges of percentile standardized test scores. In response, we propose a simplified set of qualitative descriptors for normalized test scores (Q-Simple) as a means to reduce errors in communicating test results. The Q-Simple qualitative terms are: 'very superior', 'superior', 'high average', 'average', 'low average', 'borderline' and 'abnormal/impaired'. A case example illustrates the proposed Q-Simple qualitative classification system to communicate neuropsychological results for neurosurgical planning. The Q-Simple qualitative descriptor system is aimed as a means to improve and standardize communication of standardized neuropsychological test scores. Research are needed to further evaluate neuropsychological communication errors. Conveying the clinical implications of neuropsychological results in a manner that minimizes risk for communication errors is a quintessential component of evidence-based practice. Copyright © 2017 Elsevier B.V. All rights reserved.

Simultaneous feature selection and parameter optimisation using an artificial ant colony: case study of melting point prediction

PubMed Central

O'Boyle, Noel M; Palmer, David S; Nigsch, Florian; Mitchell, John BO

2008-01-01

Background We present a novel feature selection algorithm, Winnowing Artificial Ant Colony (WAAC), that performs simultaneous feature selection and model parameter optimisation for the development of predictive quantitative structure-property relationship (QSPR) models. The WAAC algorithm is an extension of the modified ant colony algorithm of Shen et al. (J Chem Inf Model 2005, 45: 1024–1029). We test the ability of the algorithm to develop a predictive partial least squares model for the Karthikeyan dataset (J Chem Inf Model 2005, 45: 581–590) of melting point values. We also test its ability to perform feature selection on a support vector machine model for the same dataset. Results Starting from an initial set of 203 descriptors, the WAAC algorithm selected a PLS model with 68 descriptors which has an RMSE on an external test set of 46.6°C and R2 of 0.51. The number of components chosen for the model was 49, which was close to optimal for this feature selection. The selected SVM model has 28 descriptors (cost of 5, ε of 0.21) and an RMSE of 45.1°C and R2 of 0.54. This model outperforms a kNN model (RMSE of 48.3°C, R2 of 0.47) for the same data and has similar performance to a Random Forest model (RMSE of 44.5°C, R2 of 0.55). However it is much less prone to bias at the extremes of the range of melting points as shown by the slope of the line through the residuals: -0.43 for WAAC/SVM, -0.53 for Random Forest. Conclusion With a careful choice of objective function, the WAAC algorithm can be used to optimise machine learning and regression models that suffer from overfitting. Where model parameters also need to be tuned, as is the case with support vector machine and partial least squares models, it can optimise these simultaneously. The moving probabilities used by the algorithm are easily interpreted in terms of the best and current models of the ants, and the winnowing procedure promotes the removal of irrelevant descriptors. PMID:18959785

How young water fractions can delineate travel time distributions in contrasting catchments

NASA Astrophysics Data System (ADS)

Lutz, Stefanie; Zink, Matthias; Merz, Ralf

2017-04-01

Travel time distributions (TTDs) are crucial descriptors of flow and transport processes in catchments. Tracking fluxes of environmental tracers such as stable water isotopes offers a practicable method to determine TTDs. The mean transit time (MTT) is the most commonly reported statistic of TTDs; however, MTT assessments are prone to large aggregation biases resulting from spatial heterogeneity and non-stationarity in real-world catchments. Recently, the young water fraction (Fyw) has been introduced as a more robust statistic that can be derived from seasonal tracer cycles. In this study, we aimed at improving the assessment of TTDs by using Fyw as additional information in lumped isotope models. First, we calculated Fyw from monthly δ18O-samples for 24 contrasting sub-catchments in a meso-scale catchment (3300 km2). Fyw ranged from 0.01 to 0.27 (mean= 0.11) and was not significantly correlated with catchment characteristics (e.g., mean slope, catchment area, and baseflow index) apart from the dominant soil type. Second, assuming gamma-shaped TTDs, we determined time-invariant TTDs for each sub-catchment by optimization of lumped isotope models using the convolution integral method. Whereas multiple optimization runs for the same sub-catchment showed a wide range of TTD parameters, the use of Fyw as additional information allowed constraining this range and thus improving the assessment of MTTs. Hence, the best model fit to observed isotope data might not be the desired solution, as the resulting TTD might define a young water fraction non-consistent with the tracer-cycle based Fyw. Given that the latter is a robust descriptor of fast-flow contribution, isotope models should instead aim at accurately describing both Fyw and the isotope time series in order to improve our understanding of flow and transport in catchments.

Evaluating the Effectiveness of Various Blast Loading Descriptors as Occupant Injury Predictors for Underbody Blast Events

DTIC Science & Technology

2014-01-09

of Hybrid III ATD LSDYNA model with FTSS v7.1.6 finite element dummy 6 Unclassified: Distribution Statement A. Approved for public release...descriptors as occupant injury predictors for underbody blast events Recording injury metrics Response from the dummy especially pelvic acceleration and...Ciip(H&ad CG,2) "’"’ "-......--------, I Max : 122.669 @59.81 7!; Time, ms Pelvic Z acceleration, g I I Clip: -4.75737 Ts:97.4138 Te: 104.414

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.

Eyeglasses Lens Contour Extraction from Facial Images Using an Efficient Shape Description

PubMed Central

Borza, Diana; Darabant, Adrian Sergiu; Danescu, Radu

2013-01-01

This paper presents a system that automatically extracts the position of the eyeglasses and the accurate shape and size of the frame lenses in facial images. The novelty brought by this paper consists in three key contributions. The first one is an original model for representing the shape of the eyeglasses lens, using Fourier descriptors. The second one is a method for generating the search space starting from a finite, relatively small number of representative lens shapes based on Fourier morphing. Finally, we propose an accurate lens contour extraction algorithm using a multi-stage Monte Carlo sampling technique. Multiple experiments demonstrate the effectiveness of our approach. PMID:24152926

RED: a set of molecular descriptors based on Renyi entropy.

PubMed

Delgado-Soler, Laura; Toral, Raul; Tomás, M Santos; Rubio-Martinez, Jaime

2009-11-01

New molecular descriptors, RED (Renyi entropy descriptors), based on the generalized entropies introduced by Renyi are presented. Topological descriptors based on molecular features have proven to be useful for describing molecular profiles. Renyi entropy is used as a variability measure to contract a feature-pair distribution composing the descriptor vector. The performance of RED descriptors was tested for the analysis of different sets of molecular distances, virtual screening, and pharmacological profiling. A free parameter of the Renyi entropy has been optimized for all the considered applications.

RS-predictor: a new tool for predicting sites of cytochrome P450-mediated metabolism applied to CYP 3A4.

PubMed

Zaretzki, Jed; Bergeron, Charles; Rydberg, Patrik; Huang, Tao-wei; Bennett, Kristin P; Breneman, Curt M

2011-07-25

This article describes RegioSelectivity-Predictor (RS-Predictor), a new in silico method for generating predictive models of P450-mediated metabolism for drug-like compounds. Within this method, potential sites of metabolism (SOMs) are represented as "metabolophores": A concept that describes the hierarchical combination of topological and quantum chemical descriptors needed to represent the reactivity of potential metabolic reaction sites. RS-Predictor modeling involves the use of metabolophore descriptors together with multiple-instance ranking (MIRank) to generate an optimized descriptor weight vector that encodes regioselectivity trends across all cases in a training set. The resulting pathway-independent (O-dealkylation vs N-oxidation vs Csp(3) hydroxylation, etc.), isozyme-specific regioselectivity model may be used to predict potential metabolic liabilities. In the present work, cross-validated RS-Predictor models were generated for a set of 394 substrates of CYP 3A4 as a proof-of-principle for the method. Rank aggregation was then employed to merge independently generated predictions for each substrate into a single consensus prediction. The resulting consensus RS-Predictor models were shown to reliably identify at least one observed site of metabolism in the top two rank-positions on 78% of the substrates. Comparisons between RS-Predictor and previously described regioselectivity prediction methods reveal new insights into how in silico metabolite prediction methods should be compared.

Isoelectric point is an inadequate descriptor of MS2, Phi X 174 and PRD1 phages adhesion on abiotic surfaces.

PubMed

Dika, Christelle; Duval, Jérôme F L; Francius, Gregory; Perrin, Aline; Gantzer, Christophe

2015-05-15

MS2, Phi X 174 and PRD1 bacteriophages are commonly used as surrogates to evaluate pathogenic virus behavior in natural aquatic media. The interfacial properties of these model soft bioparticles are herein discussed in connection with their propensities to adhere onto abiotic surfaces that differ in terms of surface charges and hydrophobicities. The phages considered in this work exhibit distinct multilayered surface structures and their electrostatic charges are evaluated from the dependence of their electrophoretic mobilities on electrolyte concentration at neutral pH on the basis of electrokinetic theory for soft (bio)particles. The charges of the viruses probed by electrokinetics vary according to the sequence Phi X 174⩽PRD1≪MS2, where '<' stands for 'less charged than'. The hydrophobic/hydrophilic balances of the phages are further derived from their adhesions onto model hydrophobic and hydrophilic self-assembled mono-layers. The corresponding results lead to the following hydrophobicity sequence Phi X 174≪MS2

Self-organizing maps of molecular descriptors for sesquiterpene lactones and their application to the chemotaxonomy of the Asteraceae family.

PubMed

Scotti, Marcus T; Emerenciano, Vicente; Ferreira, Marcelo J P; Scotti, Luciana; Stefani, Ricardo; da Silva, Marcelo S; Mendonça Junior, Francisco Jaime B

2012-04-20

The Asteraceae, one of the largest families among angiosperms, is chemically characterised by the production of sesquiterpene lactones (SLs). A total of 1,111 SLs, which were extracted from 658 species, 161 genera, 63 subtribes and 15 tribes of Asteraceae, were represented and registered in two dimensions in the SISTEMATX, an in-house software system, and were associated with their botanical sources. The respective 11 block of descriptors: Constitutional, Functional groups, BCUT, Atom-centred, 2D autocorrelations, Topological, Geometrical, RDF, 3D-MoRSE, GETAWAY and WHIM were used as input data to separate the botanical occurrences through self-organising maps. Maps that were generated with each descriptor divided the Asteraceae tribes, with total index values between 66.7% and 83.6%. The analysis of the results shows evident similarities among the Heliantheae, Helenieae and Eupatorieae tribes as well as between the Anthemideae and Inuleae tribes. Those observations are in agreement with systematic classifications that were proposed by Bremer, which use mainly morphological and molecular data, therefore chemical markers partially corroborate with these classifications. The results demonstrate that the atom-centred and RDF descriptors can be used as a tool for taxonomic classification in low hierarchical levels, such as tribes. Descriptors obtained through fragments or by the two-dimensional representation of the SL structures were sufficient to obtain significant results, and better results were not achieved by using descriptors derived from three-dimensional representations of SLs. Such models based on physico-chemical properties can project new design SLs, similar structures from literature or even unreported structures in two-dimensional chemical space. Therefore, the generated SOMs can predict the most probable tribe where a biologically active molecule can be found according Bremer classification.

Prioritization of in silico models and molecular descriptors for the assessment of ready biodegradability.

PubMed

Fernández, Alberto; Rallo, Robert; Giralt, Francesc

2015-10-01

Ready biodegradability is a key property for evaluating the long-term effects of chemicals on the environment and human health. As such, it is used as a screening test for the assessment of persistent, bioaccumulative and toxic substances. Regulators encourage the use of non-testing methods, such as in silico models, to save money and time. A dataset of 757 chemicals was collected to assess the performance of four freely available in silico models that predict ready biodegradability. They were applied to develop a new consensus method that prioritizes the use of each individual model according to its performance on chemical subsets driven by the presence or absence of different molecular descriptors. This consensus method was capable of almost eliminating unpredictable chemicals, while the performance of combined models was substantially improved with respect to that of the individual models. Copyright © 2015 Elsevier Inc. All rights reserved.

Computer-assisted quantification of the skull deformity for craniosynostosis from 3D head CT images using morphological descriptor and hierarchical classification

NASA Astrophysics Data System (ADS)

Lee, Min Jin; Hong, Helen; Shim, Kyu Won; Kim, Yong Oock

2017-03-01

This paper proposes morphological descriptors representing the degree of skull deformity for craniosynostosis in head CT images and a hierarchical classifier model distinguishing among normal and different types of craniosynostosis. First, to compare deformity surface model with mean normal surface model, mean normal surface models are generated for each age range and the mean normal surface model is deformed to the deformity surface model via multi-level threestage registration. Second, four shape features including local distance and area ratio indices are extracted in each five cranial bone. Finally, hierarchical SVM classifier is proposed to distinguish between the normal and deformity. As a result, the proposed method showed improved classification results compared to traditional cranial index. Our method can be used for the early diagnosis, surgical planning and postsurgical assessment of craniosynostosis as well as quantitative analysis of skull deformity.

Use of Cell Viability Assay Data Improves the Prediction Accuracy of Conventional Quantitative Structure–Activity Relationship Models of Animal Carcinogenicity

PubMed Central

Zhu, Hao; Rusyn, Ivan; Richard, Ann; Tropsha, Alexander

2008-01-01

Background To develop efficient approaches for rapid evaluation of chemical toxicity and human health risk of environmental compounds, the National Toxicology Program (NTP) in collaboration with the National Center for Chemical Genomics has initiated a project on high-throughput screening (HTS) of environmental chemicals. The first HTS results for a set of 1,408 compounds tested for their effects on cell viability in six different cell lines have recently become available via PubChem. Objectives We have explored these data in terms of their utility for predicting adverse health effects of the environmental agents. Methods and results Initially, the classification k nearest neighbor (kNN) quantitative structure–activity relationship (QSAR) modeling method was applied to the HTS data only, for a curated data set of 384 compounds. The resulting models had prediction accuracies for training, test (containing 275 compounds together), and external validation (109 compounds) sets as high as 89%, 71%, and 74%, respectively. We then asked if HTS results could be of value in predicting rodent carcinogenicity. We identified 383 compounds for which data were available from both the Berkeley Carcinogenic Potency Database and NTP–HTS studies. We found that compounds classified by HTS as “actives” in at least one cell line were likely to be rodent carcinogens (sensitivity 77%); however, HTS “inactives” were far less informative (specificity 46%). Using chemical descriptors only, kNN QSAR modeling resulted in 62.3% prediction accuracy for rodent carcinogenicity applied to this data set. Importantly, the prediction accuracy of the model was significantly improved (72.7%) when chemical descriptors were augmented by HTS data, which were regarded as biological descriptors. Conclusions Our studies suggest that combining NTP–HTS profiles with conventional chemical descriptors could considerably improve the predictive power of computational approaches in toxicology. PMID:18414635

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.

Automatic Mrf-Based Registration of High Resolution Satellite Video Data

NASA Astrophysics Data System (ADS)

Platias, C.; Vakalopoulou, M.; Karantzalos, K.

2016-06-01

In this paper we propose a deformable registration framework for high resolution satellite video data able to automatically and accurately co-register satellite video frames and/or register them to a reference map/image. The proposed approach performs non-rigid registration, formulates a Markov Random Fields (MRF) model, while efficient linear programming is employed for reaching the lowest potential of the cost function. The developed approach has been applied and validated on satellite video sequences from Skybox Imaging and compared with a rigid, descriptor-based registration method. Regarding the computational performance, both the MRF-based and the descriptor-based methods were quite efficient, with the first one converging in some minutes and the second in some seconds. Regarding the registration accuracy the proposed MRF-based method significantly outperformed the descriptor-based one in all the performing experiments.

Robust decentralised stabilisation of uncertain large-scale interconnected nonlinear descriptor systems via proportional plus derivative feedback

NASA Astrophysics Data System (ADS)

Li, Jian; Zhang, Qingling; Ren, Junchao; Zhang, Yanhao

2017-10-01

This paper studies the problem of robust stability and stabilisation for uncertain large-scale interconnected nonlinear descriptor systems via proportional plus derivative state feedback or proportional plus derivative output feedback. The basic idea of this work is to use the well-known differential mean value theorem to deal with the nonlinear model such that the considered nonlinear descriptor systems can be transformed into linear parameter varying systems. By using a parameter-dependent Lyapunov function, a decentralised proportional plus derivative state feedback controller and decentralised proportional plus derivative output feedback controller are designed, respectively such that the closed-loop system is quadratically normal and quadratically stable. Finally, a hypersonic vehicle practical simulation example and numerical example are given to illustrate the effectiveness of the results obtained in this paper.

Selection of morphoagronomic descriptors for the characterization of accessions of cassava of the Eastern Brazilian Amazon.

PubMed

Silva, R S; Moura, E F; Farias-Neto, J T; Ledo, C A S; Sampaio, J E

2017-04-13

The aim of this study was to select morphoagronomic descriptors to characterize cassava accessions representative of Eastern Brazilian Amazonia. It was characterized 262 accessions using 21 qualitative descriptors. The multiple-correspondence analysis (MCA) technique was applied using the criteria: contribution of the descriptor in the last factorial axis of analysis in successive cycles (SMCA); reverse order of the descriptor's contribution in the last factorial axis of analysis with all descriptors ('O'´p') of Jolliffe's method; mean of the contribution orders of the descriptor in the first three factorial axes in the analysis with all descriptors ('Os') together with ('O'´p'); and order of contribution of weighted mean in the first three factorial axes in the analysis of all descriptors ('Oz'). The dissimilarity coefficient was measured by the method of multicategorical variables. The correlation among the matrix generated with all descriptors and matrices based on each criteria varied (r = 0.21, r = 0.97, r = 0.98, r = 0.13 for SMCA, 'Os', 'Oz' and 'O'´p', respectively). The least informative descriptors were discarded independently and according to both 'Os' and 'Oz' criteria. Thirteen descriptors were capable to discriminate the accessions and to represent the morphological variability of accessions sampled in Brazilian Eastern Amazonia: color of apical leaves, petiole color, color of stem exterior, external color of storage root, color of stem cortex, color of root pulp, texture of root epidermis, color of leaf vein, color of stem epidermis, color of end branches of adult plant, branching habit, root shape, and constriction of root.

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.

Discovery of Novel HIV-1 Integrase Inhibitors Using QSAR-Based Virtual Screening of the NCI Open Database.

PubMed

Ko, Gene M; Garg, Rajni; Bailey, Barbara A; Kumar, Sunil

2016-01-01

Quantitative structure-activity relationship (QSAR) models can be used as a predictive tool for virtual screening of chemical libraries to identify novel drug candidates. The aims of this paper were to report the results of a study performed for descriptor selection, QSAR model development, and virtual screening for identifying novel HIV-1 integrase inhibitor drug candidates. First, three evolutionary algorithms were compared for descriptor selection: differential evolution-binary particle swarm optimization (DE-BPSO), binary particle swarm optimization, and genetic algorithms. Next, three QSAR models were developed from an ensemble of multiple linear regression, partial least squares, and extremely randomized trees models. A comparison of the performances of three evolutionary algorithms showed that DE-BPSO has a significant improvement over the other two algorithms. QSAR models developed in this study were used in consensus as a predictive tool for virtual screening of the NCI Open Database containing 265,242 compounds to identify potential novel HIV-1 integrase inhibitors. Six compounds were predicted to be highly active (plC50 > 6) by each of the three models. The use of a hybrid evolutionary algorithm (DE-BPSO) for descriptor selection and QSAR model development in drug design is a novel approach. Consensus modeling may provide better predictivity by taking into account a broader range of chemical properties within the data set conducive for inhibition that may be missed by an individual model. The six compounds identified provide novel drug candidate leads in the design of next generation HIV- 1 integrase inhibitors targeting drug resistant mutant viruses.

A scale-invariant keypoint detector in log-polar space

NASA Astrophysics Data System (ADS)

Tao, Tao; Zhang, Yun

2017-02-01

The scale-invariant feature transform (SIFT) algorithm is devised to detect keypoints via the difference of Gaussian (DoG) images. However, the DoG data lacks the high-frequency information, which can lead to a performance drop of the algorithm. To address this issue, this paper proposes a novel log-polar feature detector (LPFD) to detect scale-invariant blubs (keypoints) in log-polar space, which, in contrast, can retain all the image information. The algorithm consists of three components, viz. keypoint detection, descriptor extraction and descriptor matching. Besides, the algorithm is evaluated in detecting keypoints from the INRIA dataset by comparing with the SIFT algorithm and one of its fast versions, the speed up robust features (SURF) algorithm in terms of three performance measures, viz. correspondences, repeatability, correct matches and matching score.

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.

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.

NMF-Based Image Quality Assessment Using Extreme Learning Machine.

PubMed

Wang, Shuigen; Deng, Chenwei; Lin, Weisi; Huang, Guang-Bin; Zhao, Baojun

2017-01-01

Numerous state-of-the-art perceptual image quality assessment (IQA) algorithms share a common two-stage process: distortion description followed by distortion effects pooling. As for the first stage, the distortion descriptors or measurements are expected to be effective representatives of human visual variations, while the second stage should well express the relationship among quality descriptors and the perceptual visual quality. However, most of the existing quality descriptors (e.g., luminance, contrast, and gradient) do not seem to be consistent with human perception, and the effects pooling is often done in ad-hoc ways. In this paper, we propose a novel full-reference IQA metric. It applies non-negative matrix factorization (NMF) to measure image degradations by making use of the parts-based representation of NMF. On the other hand, a new machine learning technique [extreme learning machine (ELM)] is employed to address the limitations of the existing pooling techniques. Compared with neural networks and support vector regression, ELM can achieve higher learning accuracy with faster learning speed. Extensive experimental results demonstrate that the proposed metric has better performance and lower computational complexity in comparison with the relevant state-of-the-art approaches.

Three-Dimensional Biologically Relevant Spectrum (BRS-3D): Shape Similarity Profile Based on PDB Ligands as Molecular Descriptors.

PubMed

Hu, Ben; Kuang, Zheng-Kun; Feng, Shi-Yu; Wang, Dong; He, Song-Bing; Kong, De-Xin

2016-11-17

The crystallized ligands in the Protein Data Bank (PDB) can be treated as the inverse shapes of the active sites of corresponding proteins. Therefore, the shape similarity between a molecule and PDB ligands indicated the possibility of the molecule to bind with the targets. In this paper, we proposed a shape similarity profile that can be used as a molecular descriptor for ligand-based virtual screening. First, through three-dimensional (3D) structural clustering, 300 diverse ligands were extracted from the druggable protein-ligand database, sc-PDB. Then, each of the molecules under scrutiny was flexibly superimposed onto the 300 ligands. Superimpositions were scored by shape overlap and property similarity, producing a 300 dimensional similarity array termed the "Three-Dimensional Biologically Relevant Spectrum (BRS-3D)". Finally, quantitative or discriminant models were developed with the 300 dimensional descriptor using machine learning methods (support vector machine). The effectiveness of this approach was evaluated using 42 benchmark data sets from the G protein-coupled receptor (GPCR) ligand library and the GPCR decoy database (GLL/GDD). We compared the performance of BRS-3D with other 2D and 3D state-of-the-art molecular descriptors. The results showed that models built with BRS-3D performed best for most GLL/GDD data sets. We also applied BRS-3D in histone deacetylase 1 inhibitors screening and GPCR subtype selectivity prediction. The advantages and disadvantages of this approach are discussed.

In silico design of anti-atherogenic biomaterials.

PubMed

Lewis, Daniel R; Kholodovych, Vladyslav; Tomasini, Michael D; Abdelhamid, Dalia; Petersen, Latrisha K; Welsh, William J; Uhrich, Kathryn E; Moghe, Prabhas V

2013-10-01

Atherogenesis, the uncontrolled deposition of modified lipoproteins in inflamed arteries, serves as a focal trigger of cardiovascular disease (CVD). Polymeric biomaterials have been envisioned to counteract atherogenesis based on their ability to repress scavenger mediated uptake of oxidized lipoprotein (oxLDL) in macrophages. Following the conceptualization in our laboratories of a new library of amphiphilic macromolecules (AMs), assembled from sugar backbones, aliphatic chains and poly(ethylene glycol) tails, a more rational approach is necessary to parse the diverse features such as charge, hydrophobicity, sugar composition and stereochemistry. In this study, we advance a computational biomaterials design approach to screen and elucidate anti-atherogenic biomaterials with high efficacy. AMs were quantified in terms of not only 1D (molecular formula) and 2D (molecular connectivity) descriptors, but also new 3D (molecular geometry) descriptors of AMs modeled by coarse-grained molecular dynamics (MD) followed by all-atom MD simulations. Quantitative structure-activity relationship (QSAR) models for anti-atherogenic activity were then constructed by screening a total of 1164 descriptors against the corresponding, experimentally measured potency of AM inhibition of oxLDL uptake in human monocyte-derived macrophages. Five key descriptors were identified to provide a strong linear correlation between the predicted and observed anti-atherogenic activity values, and were then used to correctly forecast the efficacy of three newly designed AMs. Thus, a new ligand-based drug design framework was successfully adapted to computationally screen and design biomaterials with cardiovascular therapeutic properties. Copyright © 2013 Elsevier Ltd. All rights reserved.

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.

Hybrid optimal descriptors as a tool to predict skin sensitization in accordance to OECD principles.

PubMed

Toropova, Alla P; Toropov, Andrey A

2017-06-05

Skin sensitization (allergic contact dermatitis) is a widespread problem arising from the contact of chemicals with the skin. The detection of molecular features with undesired effect for skin is complex task owing to unclear biochemical mechanisms and unclearness of conditions of action of chemicals to skin. The development of computational methods for estimation of this endpoint in order to reduce animal testing is recommended (Cosmetics Directive EC regulation 1907/2006; EU Regulation, Regulation, 1223/2009). The CORAL software (http://www.insilico.eu/coral) gives good predictive models for the skin sensitization. Simplified molecular input-line entry system (SMILES) together with molecular graph are used to represent the molecular structure for these models. So-called hybrid optimal descriptors are used to establish quantitative structure-activity relationships (QSARs). The aim of this study is the estimation of the predictive potential of the hybrid descriptors. Three different distributions into the training (≈70%), calibration (≈15%), and validation (≈15%) sets are studied. QSAR for these three distributions are built up with using the Monte Carlo technique. The statistical characteristics of these models for external validation set are used as a measure of predictive potential of these models. The best model, according to the above criterion, is characterized by n validation =29, r 2 validation =0.8596, RMSE validation =0.489. Mechanistic interpretation and domain of applicability for these models are defined. Copyright © 2017 Elsevier B.V. All rights reserved.

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 evaluation of distributed damage in concrete based on sinusoidal modeling of the ultrasonic response.

PubMed

Sepehrinezhad, Alireza; Toufigh, Vahab

2018-05-25

Ultrasonic wave attenuation is an effective descriptor of distributed damage in inhomogeneous materials. Methods developed to measure wave attenuation have the potential to provide an in-site evaluation of existing concrete structures insofar as they are accurate and time-efficient. In this study, material classification and distributed damage evaluation were investigated based on the sinusoidal modeling of the response from the through-transmission ultrasonic tests on polymer concrete specimens. The response signal was modeled as single or the sum of damping sinusoids. Due to the inhomogeneous nature of concrete materials, model parameters may vary from one specimen to another. Therefore, these parameters are not known in advance and should be estimated while the response signal is being received. The modeling procedure used in this study involves a data-adaptive algorithm to estimate the parameters online. Data-adaptive algorithms are used due to a lack of knowledge of the model parameters. The damping factor was estimated as a descriptor of the distributed damage. The results were compared in two different cases as follows: (1) constant excitation frequency with varying concrete mixtures and (2) constant mixture with varying excitation frequencies. The specimens were also loaded up to their ultimate compressive strength to investigate the effect of distributed damage in the response signal. The results of the estimation indicated that the damping was highly sensitive to the change in material inhomogeneity, even in comparable mixtures. In addition to the proposed method, three methods were employed to compare the results based on their accuracy in the classification of materials and the evaluation of the distributed damage. It is shown that the estimated damping factor is not only sensitive to damage in the final stages of loading, but it is also applicable in evaluating micro damages in the earlier stages providing a reliable descriptor of damage. In addition, the modified amplitude ratio method is introduced as an improvement of the classical method. The proposed methods were validated to be effective descriptors of distributed damage. The presented models were also in good agreement with the experimental data. Copyright © 2018 Elsevier B.V. All rights reserved.

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.

Lacosamide derivatives with anticonvulsant activity as carbonic anhydrase inhibitors. Molecular modeling, docking and QSAR analysis.

PubMed

Garro Martinez, Juan C; Vega-Hissi, Esteban G; Andrada, Matías F; Duchowicz, Pablo R; Torrens, Francisco; Estrada, Mario R

2014-01-01

Lacosamide is an anticonvulsant drug which presents carbonic anhydrase inhibition. In this paper, we analyzed the apparent relationship between both activities performing a molecular modeling, docking and QSAR studies on 18 lacosamide derivatives with known anticonvulsant activity. Docking results suggested the zinc-binding site of carbonic anhydrase is a possible target of lacosamide and lacosamide derivatives making favorable Van der Waals interactions with Asn67, Gln92, Phe131 and Thr200. The mathematical models revealed a poor relationship between the anticonvulsant activity and molecular descriptors obtained from DFT and docking calculations. However, a QSAR model was developed using Dragon software descriptors. The statistic parameters of the model are: correlation coefficient, R=0.957 and standard deviation, S=0.162. Our results provide new valuable information regarding the relationship between both activities and contribute important insights into the essential molecular requirements for the anticonvulsant activity.

Linear and nonlinear models for predicting fish bioconcentration factors for pesticides.

PubMed

Yuan, Jintao; Xie, Chun; Zhang, Ting; Sun, Jinfang; Yuan, Xuejie; Yu, Shuling; Zhang, Yingbiao; Cao, Yunyuan; Yu, Xingchen; Yang, Xuan; Yao, Wu

2016-08-01

This work is devoted to the applications of the multiple linear regression (MLR), multilayer perceptron neural network (MLP NN) and projection pursuit regression (PPR) to quantitative structure-property relationship analysis of bioconcentration factors (BCFs) of pesticides tested on Bluegill (Lepomis macrochirus). Molecular descriptors of a total of 107 pesticides were calculated with the DRAGON Software and selected by inverse enhanced replacement method. Based on the selected DRAGON descriptors, a linear model was built by MLR, nonlinear models were developed using MLP NN and PPR. The robustness of the obtained models was assessed by cross-validation and external validation using test set. Outliers were also examined and deleted to improve predictive power. Comparative results revealed that PPR achieved the most accurate predictions. This study offers useful models and information for BCF prediction, risk assessment, and pesticide formulation. Copyright © 2016 Elsevier Ltd. All rights reserved.

Implicit Shape Models for Object Detection in 3d Point Clouds

NASA Astrophysics Data System (ADS)

Velizhev, A.; Shapovalov, R.; Schindler, K.

2012-07-01

We present a method for automatic object localization and recognition in 3D point clouds representing outdoor urban scenes. The method is based on the implicit shape models (ISM) framework, which recognizes objects by voting for their center locations. It requires only few training examples per class, which is an important property for practical use. We also introduce and evaluate an improved version of the spin image descriptor, more robust to point density variation and uncertainty in normal direction estimation. Our experiments reveal a significant impact of these modifications on the recognition performance. We compare our results against the state-of-the-art method and get significant improvement in both precision and recall on the Ohio dataset, consisting of combined aerial and terrestrial LiDAR scans of 150,000 m2 of urban area in total.

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.

Development of TLSER model and QSAR model for predicting partition coefficients of hydrophobic organic chemicals between low density polyethylene film and water.

PubMed

Liu, Huihui; Wei, Mengbi; Yang, Xianhai; Yin, Cen; He, Xiao

2017-01-01

Partition coefficients are vital parameters for measuring accurately the chemicals concentrations by passive sampling devices. Given the wide use of low density polyethylene (LDPE) film in passive sampling, we developed a theoretical linear solvation energy relationship (TLSER) model and a quantitative structure-activity relationship (QSAR) model for the prediction of the partition coefficient of chemicals between LDPE and water (K pew ). For chemicals with the octanol-water partition coefficient (log K ow ) <8, a TLSER model with V x (McGowan volume) and qA - (the most negative charge on O, N, S, X atoms) as descriptors was developed, but the model had relatively low determination coefficient (R 2 ) and cross-validated coefficient (Q 2 ). In order to further explore the theoretical mechanisms involved in the partition process, a QSAR model with four descriptors (MLOGP (Moriguchi octanol-water partition coeff.), P_VSA_s_3 (P_VSA-like on I-state, bin 3), Hy (hydrophilic factor) and NssO (number of atoms of type ssO)) was established, and statistical analysis indicated that the model had satisfactory goodness-of-fit, robustness and predictive ability. For chemicals with log K OW >8, a TLSER model with V x and a QSAR model with MLOGP as descriptor were developed. This is the first paper to explore the models for highly hydrophobic chemicals. The applicability domain of the models, characterized by the Euclidean distance-based method and Williams plot, covered a large number of structurally diverse chemicals, which included nearly all the common hydrophobic organic compounds. Additionally, through mechanism interpretation, we explored the structural features those governing the partition behavior of chemicals between LDPE and water. Copyright © 2016 Elsevier B.V. All rights reserved.

Human action recognition based on point context tensor shape descriptor

NASA Astrophysics Data System (ADS)

Li, Jianjun; Mao, Xia; Chen, Lijiang; Wang, Lan

2017-07-01

Motion trajectory recognition is one of the most important means to determine the identity of a moving object. A compact and discriminative feature representation method can improve the trajectory recognition accuracy. This paper presents an efficient framework for action recognition using a three-dimensional skeleton kinematic joint model. First, we put forward a rotation-scale-translation-invariant shape descriptor based on point context (PC) and the normal vector of hypersurface to jointly characterize local motion and shape information. Meanwhile, an algorithm for extracting the key trajectory based on the confidence coefficient is proposed to reduce the randomness and computational complexity. Second, to decrease the eigenvalue decomposition time complexity, a tensor shape descriptor (TSD) based on PC that can globally capture the spatial layout and temporal order to preserve the spatial information of each frame is proposed. Then, a multilinear projection process is achieved by tensor dynamic time warping to map the TSD to a low-dimensional tensor subspace of the same size. Experimental results show that the proposed shape descriptor is effective and feasible, and the proposed approach obtains considerable performance improvement over the state-of-the-art approaches with respect to accuracy on a public action dataset.

Objective scoring of transformed foci in BALB/c 3T3 cell transformation assay by statistical image descriptors.

PubMed

Urani, C; Corvi, R; Callegaro, G; Stefanini, F M

2013-09-01

In vitro cell transformation assays (CTAs) have been shown to model important stages of in vivo carcinogenesis and have the potential to predict carcinogenicity in humans. Advantages of CTAs are their ability of revealing both genotoxic and non-genotoxic carcinogens while reducing both experimental costs and the number of animals used. The endpoint of the CTA is foci formation, and requires classification under light microscopy based on morphology. Thus current limitations for the wide adoption of the assay partially depend on a fair degree of subjectivity in foci scoring. An objective evaluation may be obtained after separating foci from background monolayer in the digital image, and quantifying values of statistical descriptors which are selected to capture eye-scored morphological features. The aim of this study was to develop statistical descriptors to be applied to transformed foci of BALB/c 3T3, which cover foci size, multilayering and invasive cell growth into the background monolayer. Proposed descriptors were applied to a database of 407 foci images to explore the numerical features, and to illustrate open problems and potential solutions. Copyright © 2013 Elsevier Ltd. All rights reserved.

QSPR models for various physical properties of carbohydrates based on molecular mechanics and quantum chemical calculations.

PubMed

Dyekjaer, Jane Dannow; Jónsdóttir, Svava Osk

2004-01-22

Quantitative Structure-Property Relationships (QSPR) have been developed for a series of monosaccharides, including the physical properties of partial molar heat capacity, heat of solution, melting point, heat of fusion, glass-transition temperature, and solid state density. The models were based on molecular descriptors obtained from molecular mechanics and quantum chemical calculations, combined with other types of descriptors. Saccharides exhibit a large degree of conformational flexibility, therefore a methodology for selecting the energetically most favorable conformers has been developed, and was used for the development of the QSPR models. In most cases good correlations were obtained for monosaccharides. For five of the properties predictions were made for disaccharides, and the predicted values for the partial molar heat capacities were in excellent agreement with experimental values.

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.

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.

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.

Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou's PseAAC.

PubMed

Contreras-Torres, Ernesto

2018-06-02

In this study, I introduce novel global and local 0D-protein descriptors based on a statistical quantity named Total Sum of Squares (TSS). This quantity represents the sum of the squares differences of amino acid properties from the arithmetic mean property. As an extension, the amino acid-types and amino acid-groups formalisms are used for describing zones of interest in proteins. To assess the effectiveness of the proposed descriptors, a Nearest Neighbor model for predicting the major four protein structural classes was built. This model has a success rate of 98.53% on the jackknife cross-validation test; this performance being superior to other reported methods despite the simplicity of the predictor. Additionally, this predictor has an average success rate of 98.35% in different cross-validation tests performed. A value of 0.98 for the Kappa statistic clearly discriminates this model from a random predictor. The results obtained by the Nearest Neighbor model demonstrated the ability of the proposed descriptors not only to reflect relevant biochemical information related to the structural classes of proteins but also to allow appropriate interpretability. It can thus be expected that the current method may play a supplementary role to other existing approaches for protein structural class prediction and other protein attributes. Copyright © 2018 Elsevier Ltd. All rights reserved.

Insights into the structural basis of N2 and O6 substituted guanine derivatives as cyclin-dependent kinase 2 (CDK2) inhibitors: prediction of the binding modes and potency of the inhibitors by docking and ONIOM calculations.

PubMed

Alzate-Morales, Jans H; Caballero, Julio; Vergara Jague, Ariela; González Nilo, Fernando D

2009-04-01

N2 and O6 substituted guanine derivatives are well-known as potent and selective CDK2 inhibitors. The ability of molecular docking using the program AutoDock3 and the hybrid method ONIOM, to obtain some quantum chemical descriptors with the aim to successfully rank these inhibitors, was assessed. The quantum chemical descriptors were used to explain the affinity, of the series studied, by a model of the CDK2 binding site. The initial structures were obtained from docking studies and the ONIOM method was applied with only a single point energy calculation on the protein-ligand structure. We obtained a good correlation model between the ONIOM derived quantum chemical descriptor "H-bond interaction energy" and the experimental biological activity, with a correlation coefficient value of R = 0.80 for 75 compounds. To the best of our knowledge, this is the first time that both methodologies are used in conjunction in order to obtain a correlation model. The model suggests that electrostatic interactions are the principal driving force in this protein-ligand interaction. Overall, the approach was successful for the cases considered, and it suggests that could be useful for the design of inhibitors in the lead optimization phase of drug discovery.

Modeling and Prediction of Drug Dispersability in Polyvinylpyrrolidone-Vinyl Acetate Copolymer Using a Molecular Descriptor.

PubMed

DeBoyace, Kevin; Buckner, Ira S; Gong, Yuchuan; Ju, Tzu-Chi Rob; Wildfong, Peter L D

2018-01-01

The expansion of a novel in silico model for the prediction of the dispersability of 18 model compounds with polyvinylpyrrolidone-vinyl acetate copolymer is described. The molecular descriptor R3m (atomic mass weighted 3rd-order autocorrelation index) is shown to be predictive of the formation of amorphous solid dispersions at 2 drug loadings (15% and 75% w/w) using 2 preparation methods (melt quenching and solvent evaporation using a rotary evaporator). Cosolidified samples were characterized using a suite of analytical techniques, which included differential scanning calorimetry, powder X-ray diffraction, pair distribution function analysis, polarized light microscopy, and hot stage microscopy. Logistic regression was applied, where appropriate, to model the success and failure of compound dispersability in polyvinylpyrrolidone-vinyl acetate copolymer. R3m had combined prediction accuracy greater than 90% for tested samples. The usefulness of this descriptor appears to be associated with the presence of heavy atoms in the molecular structure of the active pharmaceutical ingredient, and their location with respect to the geometric center of the molecule. Given the higher electronegativity and atomic volume of these types of atoms, it is hypothesized that they may impact the molecular mobility of the active pharmaceutical ingredient, or increase the likelihood of forming nonbonding interactions with the carrier polymer. Copyright © 2018 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Secure Access Control and Large Scale Robust Representation for Online Multimedia Event Detection

PubMed Central

Liu, Changyu; Li, Huiling

2014-01-01

We developed an online multimedia event detection (MED) system. However, there are a secure access control issue and a large scale robust representation issue when we want to integrate traditional event detection algorithms into the online environment. For the first issue, we proposed a tree proxy-based and service-oriented access control (TPSAC) model based on the traditional role based access control model. Verification experiments were conducted on the CloudSim simulation platform, and the results showed that the TPSAC model is suitable for the access control of dynamic online environments. For the second issue, inspired by the object-bank scene descriptor, we proposed a 1000-object-bank (1000OBK) event descriptor. Feature vectors of the 1000OBK were extracted from response pyramids of 1000 generic object detectors which were trained on standard annotated image datasets, such as the ImageNet dataset. A spatial bag of words tiling approach was then adopted to encode these feature vectors for bridging the gap between the objects and events. Furthermore, we performed experiments in the context of event classification on the challenging TRECVID MED 2012 dataset, and the results showed that the robust 1000OBK event descriptor outperforms the state-of-the-art approaches. PMID:25147840

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.

QSAR and 3D-QSAR studies applied to compounds with anticonvulsant activity.

PubMed

Garro Martinez, Juan C; Vega-Hissi, Esteban G; Andrada, Matías F; Estrada, Mario R

2015-01-01

Quantitative structure-activity relationships (QSAR and 3D-QSAR) have been applied in the last decade to obtain a reliable statistical model for the prediction of the anticonvulsant activities of new chemical entities. However, despite the large amount of information on QSAR, no recent review has published and discussed this data in detail. In this review, the authors provide a detailed discussion of QSAR studies that have been applied to compounds with anticonvulsant activity published between the years 2003 and 2013. They also evaluate the mathematical approaches and the main software used to develop the QSAR and 3D-QSAR model. QSAR methodologies continue to attract the attention of researchers and provide valuable information for the development of new potentially active compounds including those with anticonvulsant activity. This has been helped in part by improvements in the size and performance of computers; the development of specific software and the development of novel molecular descriptors, which have given rise to new and more predictive QSAR models. The extensive development of descriptors, and the way by which descriptor values are derived, have allowed the evolution of the QSAR methods. This evolution could strengthen the QSAR methods as an important tool in research and development of new and more potent anticonvulsant agents.

Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties

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

von Lilienfeld, O. Anatole; Ramakrishnan, Raghunathan; Rupp, Matthias

We introduce a fingerprint representation of molecules based on a Fourier series of atomic radial distribution functions. This fingerprint is unique (except for chirality), continuous, and differentiable with respect to atomic coordinates and nuclear charges. It is invariant with respect to translation, rotation, and nuclear permutation, and requires no preconceived knowledge about chemical bonding, topology, or electronic orbitals. As such, it meets many important criteria for a good molecular representation, suggesting its usefulness for machine learning models of molecular properties trained across chemical compound space. To assess the performance of this new descriptor, we have trained machine learning models ofmore » molecular enthalpies of atomization for training sets with up to 10 k organic molecules, drawn at random from a published set of 134 k organic molecules with an average atomization enthalpy of over 1770 kcal/mol. We validate the descriptor on all remaining molecules of the 134 k set. For a training set of 10 k molecules, the fingerprint descriptor achieves a mean absolute error of 8.0 kcal/mol. This is slightly worse than the performance attained using the Coulomb matrix, another popular alternative, reaching 6.2 kcal/mol for the same training and test sets. (c) 2015 Wiley Periodicals, Inc.« less

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

Predicting Retention Times of Naturally Occurring Phenolic Compounds in Reversed-Phase Liquid Chromatography: A Quantitative Structure-Retention Relationship (QSRR) Approach

PubMed Central

Akbar, Jamshed; Iqbal, Shahid; Batool, Fozia; Karim, Abdul; Chan, Kim Wei

2012-01-01

Quantitative structure-retention relationships (QSRRs) have successfully been developed for naturally occurring phenolic compounds in a reversed-phase liquid chromatographic (RPLC) system. A total of 1519 descriptors were calculated from the optimized structures of the molecules using MOPAC2009 and DRAGON softwares. The data set of 39 molecules was divided into training and external validation sets. For feature selection and mapping we used step-wise multiple linear regression (SMLR), unsupervised forward selection followed by step-wise multiple linear regression (UFS-SMLR) and artificial neural networks (ANN). Stable and robust models with significant predictive abilities in terms of validation statistics were obtained with negation of any chance correlation. ANN models were found better than remaining two approaches. HNar, IDM, Mp, GATS2v, DISP and 3D-MoRSE (signals 22, 28 and 32) descriptors based on van der Waals volume, electronegativity, mass and polarizability, at atomic level, were found to have significant effects on the retention times. The possible implications of these descriptors in RPLC have been discussed. All the models are proven to be quite able to predict the retention times of phenolic compounds and have shown remarkable validation, robustness, stability and predictive performance. PMID:23203132

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.

Evaluation of a novel electronic eigenvalue (EEVA) molecular descriptor for QSAR/QSPR studies: validation using a benchmark steroid data set.

PubMed

Tuppurainen, Kari; Viisas, Marja; Laatikainen, Reino; Peräkylä, Mikael

2002-01-01

A novel electronic eigenvalue (EEVA) descriptor of molecular structure for use in the derivation of predictive QSAR/QSPR models is described. Like other spectroscopic QSAR/QSPR descriptors, EEVA is also invariant as to the alignment of the structures concerned. Its performance was tested with respect to the CBG (corticosteroid binding globulin) affinity of 31 benchmark steroids. It appeared that the electronic structure of the steroids, i.e., the "spectra" derived from molecular orbital energies, is directly related to the CBG binding affinities. The predictive ability of EEVA is compared to other QSAR approaches, and its performance is discussed in the context of the Hammett equation. The good performance of EEVA is an indication of the essential quantum mechanical nature of QSAR. The EEVA method is a supplement to conventional 3D QSAR methods, which employ fields or surface properties derived from Coulombic and van der Waals interactions.

The General Aviation Dynamics Model Volume III. Systems Manual.

DTIC Science & Technology

1979-07-01

SEXi and SEX2 or in some other way, as desired by the user. The descriptors of an index are used as stubs (row descriptors) and/or spanners (column...BAR A produces the following plot [7 SUWPLO~TS DATASET A, 1970 TO 1975 8+ .QL xx YXXMX 4+ MXUXX IDLMXX XXXXXX * yyx :mx0(c XXX *m XXXX XX XXXXX XMxK...VXXXCK 2+ mm~ XXXXX MMXXX I~CUM LXXMXx *X2~ XXX xXX =uXX ImXXXLMX * xx Iac lxxno IXxc= )LccIQX * XX= )LXXX ILMKXX X.XXXX IXXCX SHOW

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.

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.

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.

A novel prediction approach for antimalarial activities of Trimethoprim, Pyrimethamine, and Cycloguanil analogues using extremely randomized trees.

PubMed

Nattee, Cholwich; Khamsemanan, Nirattaya; Lawtrakul, Luckhana; Toochinda, Pisanu; Hannongbua, Supa

2017-01-01

Malaria is still one of the most serious diseases in tropical regions. This is due in part to the high resistance against available drugs for the inhibition of parasites, Plasmodium, the cause of the disease. New potent compounds with high clinical utility are urgently needed. In this work, we created a novel model using a regression tree to study structure-activity relationships and predict the inhibition constant, K i of three different antimalarial analogues (Trimethoprim, Pyrimethamine, and Cycloguanil) based on their molecular descriptors. To the best of our knowledge, this work is the first attempt to study the structure-activity relationships of all three analogues combined. The most relevant descriptors and appropriate parameters of the regression tree are harvested using extremely randomized trees. These descriptors are water accessible surface area, Log of the aqueous solubility, total hydrophobic van der Waals surface area, and molecular refractivity. Out of all possible combinations of these selected parameters and descriptors, the tree with the strongest coefficient of determination is selected to be our prediction model. Predicted K i values from the proposed model show a strong coefficient of determination, R 2 =0.996, to experimental K i values. From the structure of the regression tree, compounds with high accessible surface area of all hydrophobic atoms (ASA_H) and low aqueous solubility of inhibitors (Log S) generally possess low K i values. Our prediction model can also be utilized as a screening test for new antimalarial drug compounds which may reduce the time and expenses for new drug development. New compounds with high predicted K i should be excluded from further drug development. It is also our inference that a threshold of ASA_H greater than 575.80 and Log S less than or equal to -4.36 is a sufficient condition for a new compound to possess a low K i . Copyright © 2016 Elsevier Inc. All rights reserved.

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.

QSAR Analysis of 2-Amino or 2-Methyl-1-Substituted Benzimidazoles Against Pseudomonas aeruginosa

PubMed Central

Podunavac-Kuzmanović, Sanja O.; Cvetković, Dragoljub D.; Barna, Dijana J.

2009-01-01

A set of benzimidazole derivatives were tested for their inhibitory activities against the Gram-negative bacterium Pseudomonas aeruginosa and minimum inhibitory concentrations were determined for all the compounds. Quantitative structure activity relationship (QSAR) analysis was applied to fourteen of the abovementioned derivatives using a combination of various physicochemical, steric, electronic, and structural molecular descriptors. A multiple linear regression (MLR) procedure was used to model the relationships between molecular descriptors and the antibacterial activity of the benzimidazole derivatives. The stepwise regression method was used to derive the most significant models as a calibration model for predicting the inhibitory activity of this class of molecules. The best QSAR models were further validated by a leave one out technique as well as by the calculation of statistical parameters for the established theoretical models. To confirm the predictive power of the models, an external set of molecules was used. High agreement between experimental and predicted inhibitory values, obtained in the validation procedure, indicated the good quality of the derived QSAR models. PMID:19468332

Identification of molecular descriptors for design of novel Isoalloxazine derivatives as potential Acetylcholinesterase inhibitors against Alzheimer's disease.

PubMed

Gurung, Arun Bahadur; Aguan, Kripamoy; Mitra, Sivaprasad; Bhattacharjee, Atanu

2017-06-01

In Alzheimer's disease (AD), the level of Acetylcholine (ACh) neurotransmitter is reduced. Since Acetylcholinesterase (AChE) cleaves ACh, inhibitors of AChE are very much sought after for AD treatment. The side effects of current inhibitors necessitate development of newer AChE inhibitors. Isoalloxazine derivatives have proved to be promising (AChE) inhibitors. However, their structure-activity relationship studies have not been reported till date. In the present work, various quantitative structure-activity relationship (QSAR) building methods such as multiple linear regression (MLR), partial least squares ,and principal component regression were employed to derive 3D-QSAR models using steric and electrostatic field descriptors. Statistically significant model was obtained using MLR coupled with stepwise selection method having r 2  = .9405, cross validated r 2 (q 2 ) = .6683, and a high predictability (pred_r 2  = .6206 and standard error, pred_r 2 se = .2491). Steric and electrostatic contribution plot revealed three electrostatic fields E_496, E_386 and E_577 and one steric field S_60 contributing towards biological activity. A ligand-based 3D-pharmacophore model was generated consisting of eight pharmacophore features. Isoalloxazine derivatives were docked against human AChE, which revealed critical residues implicated in hydrogen bonds as well as hydrophobic interactions. The binding modes of docked complexes (AChE_IA1 and AChE_IA14) were validated by molecular dynamics simulation which showed their stable trajectories in terms of root mean square deviation and molecular mechanics/Poisson-Boltzmann surface area binding free energy analysis revealed key residues contributing significantly to overall binding energy. The present study may be useful in the design of more potent Isoalloxazine derivatives as AChE inhibitors.

Quantitative structure carcinogenicity relationship for detecting structural alerts in nitroso-compounds

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

Helguera, Aliuska Morales; Molecular Simulation and Drug Design, Chemical Bioactive Center, Central University of Las Villas, Santa Clara, 54830, Villa Clara; Department of Chemistry, Central University of Las Villas, Santa Clara, 54830, Villa Clara

2008-09-01

In this work, Quantitative Structure-Activity Relationship (QSAR) modelling was used as a tool for predicting the carcinogenic potency of a set of 39 nitroso-compounds, which have been bioassayed in male rats by using the oral route of administration. The optimum QSAR model provided evidence of good fit and performance of predicitivity from training set. It was able to account for about 84% of the variance in the experimental activity and exhibited high values of the determination coefficients of cross validations, leave one out and bootstrapping (q{sup 2}{sub LOO} = 78.53 and q{sup 2}{sub Boot} = 74.97). Such a model wasmore » based on spectral moments weighted with Gasteiger-Marsilli atomic charges, polarizability and hydrophobicity, as well as with Abraham indexes, specifically the summation solute hydrogen bond basicity and the combined dipolarity/polarizability. This is the first study to have explored the possibility of combining Abraham solute descriptors with spectral moments. A reasonable interpretation of these molecular descriptors from a toxicological point of view was achieved by means of taking into account bond contributions. The set of relationships so derived revealed the importance of the length of the alkyl chains for determining carcinogenic potential of the chemicals analysed, and were able to explain the difference between mono-substituted and di-substituted nitrosoureas as well as to discriminate between isomeric structures with hydroxyl-alkyl and alkyl substituents in different positions. Moreover, they allowed the recognition of structural alerts in classical structures of two potent nitrosamines, consistent with their biotransformation. These results indicate that this new approach has the potential for improving carcinogenicity predictions based on the identification of structural alerts.« less

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.

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).

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.

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.

A New Shape Description Method Using Angular Radial Transform

NASA Astrophysics Data System (ADS)

Lee, Jong-Min; Kim, Whoi-Yul

Shape is one of the primary low-level image features in content-based image retrieval. In this paper we propose a new shape description method that consists of a rotationally invariant angular radial transform descriptor (IARTD). The IARTD is a feature vector that combines the magnitude and aligned phases of the angular radial transform (ART) coefficients. A phase correction scheme is employed to produce the aligned phase so that the IARTD is invariant to rotation. The distance between two IARTDs is defined by combining differences in the magnitudes and aligned phases. In an experiment using the MPEG-7 shape dataset, the proposed method outperforms existing methods; the average BEP of the proposed method is 57.69%, while the average BEPs of the invariant Zernike moments descriptor and the traditional ART are 41.64% and 36.51%, respectively.

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.

Predicting highly-connected hubs in protein interaction networks by QSAR and biological data descriptors

PubMed Central

Hsing, Michael; Byler, Kendall; Cherkasov, Artem

2009-01-01

Hub proteins (those engaged in most physical interactions in a protein interaction network (PIN) have recently gained much research interest due to their essential role in mediating cellular processes and their potential therapeutic value. It is straightforward to identify hubs if the underlying PIN is experimentally determined; however, theoretical hub prediction remains a very challenging task, as physicochemical properties that differentiate hubs from less connected proteins remain mostly uncharacterized. To adequately distinguish hubs from non-hub proteins we have utilized over 1300 protein descriptors, some of which represent QSAR (quantitative structure-activity relationship) parameters, and some reflect sequence-derived characteristics of proteins including domain composition and functional annotations. Those protein descriptors, together with available protein interaction data have been processed by a machine learning method (boosting trees) and resulted in the development of hub classifiers that are capable of predicting highly interacting proteins for four model organisms: Escherichia coli, Saccharomyces cerevisiae, Drosophila melanogaster and Homo sapiens. More importantly, through the analyses of the most relevant protein descriptors, we are able to demonstrate that hub proteins not only share certain common physicochemical and structural characteristics that make them different from non-hub counterparts, but they also exhibit species-specific characteristics that should be taken into account when analyzing different PINs. The developed prediction models can be used for determining highly interacting proteins in the four studied species to assist future proteomics experiments and PIN analyses. Availability The source code and executable program of the hub classifier are available for download at: http://www.cnbi2.ca/hub-analysis/ PMID:20198194

Correlating subjective and objective descriptors of ultra high molecular weight wear particles from total joint prostheses.

PubMed

McMullin, Brian T; Leung, Ming-Ying; Shanbhag, Arun S; McNulty, Donald; Mabrey, Jay D; Agrawal, C Mauli

2006-02-01

A total of 750 images of individual ultra-high molecular weight polyethylene (UHMWPE) particles isolated from periprosthetic failed hip, knee, and shoulder arthroplasties were extracted from archival scanning electron micrographs. Particle size and morphology was subsequently analyzed using computerized image analysis software utilizing five descriptors found in ASTM F1877-98, a standard for quantitative description of wear debris. An online survey application was developed to display particle images, and allowed ten respondents to classify particle morphologies according to commonly used terminology as fibers, flakes, or granules. Particles were categorized based on a simple majority of responses. All descriptors were evaluated using a one-way ANOVA and Tukey-Kramer test for all-pairs comparison among each class of particles. A logistic regression model using half of the particles included in the survey was then used to develop a mathematical scheme to predict whether a given particle should be classified as a fiber, flake, or granule based on its quantitative measurements. The validity of the model was then assessed using the other half of the survey particles and compared with human responses. Comparison of the quantitative measurements of isolated particles showed that the morphologies of each particle type classified by respondents were statistically different from one another (p<0.05). The average agreement between mathematical prediction and human respondents was 83.5% (standard error 0.16%). These data suggest that computerized descriptors can be feasibly correlated with subjective terminology, thus providing a basis for a common vocabulary for particle description which can be translated into quantitative dimensions.

Correlating subjective and objective descriptors of ultra high molecular weight wear particles from total joint prostheses

PubMed Central

McMullin, Brian T.; Leung, Ming-Ying; Shanbhag, Arun S.; McNulty, Donald; Mabrey, Jay D.; Agrawal, C. Mauli

2014-01-01

A total of 750 images of individual ultra-high molecular weight polyethylene (UHMWPE) particles isolated from periprosthetic failed hip, knee, and shoulder arthroplasties were extracted from archival scanning electron micrographs. Particle size and morphology was subsequently analyzed using computerized image analysis software utilizing five descriptors found in ASTM F1877-98, a standard for quantitative description of wear debris. An online survey application was developed to display particle images, and allowed ten respondents to classify particle morphologies according to commonly used terminology as fibers, flakes, or granules. Particles were categorized based on a simple majority of responses. All descriptors were evaluated using a one-way ANOVA and Tukey–Kramer test for all-pairs comparison among each class of particles. A logistic regression model using half of the particles included in the survey was then used to develop a mathematical scheme to predict whether a given particle should be classified as a fiber, flake, or granule based on its quantitative measurements. The validity of the model was then assessed using the other half of the survey particles and compared with human responses. Comparison of the quantitative measurements of isolated particles showed that the morphologies of each particle type classified by respondents were statistically different from one another (po0:05). The average agreement between mathematical prediction and human respondents was 83.5% (standard error 0.16%). These data suggest that computerized descriptors can be feasibly correlated with subjective terminology, thus providing a basis for a common vocabulary for particle description which can be translated into quantitative dimensions. PMID:16112725

The model of the long-range effect in solids: Evolution of structure, clusters of internal boundaries, and their statistical descriptors

NASA Astrophysics Data System (ADS)

Herega, Alexander; Sukhanov, Volodymyr; Vyrovoy, Valery

2017-12-01

It is known that the multifocal mechanism of genesis of structure of heterogeneous materials provokes intensive formation of internal boundaries. In the present papers, the dependence of the structure and properties of material on the characteristic size and shape, the number and size distribution, and the character of interaction of individual internal boundaries and their clusters is studied. The limitation on the applicability of the material damage coefficient is established; the effective information descriptor of internal boundaries is proposed. An idea of the effect of long-range interaction in irradiated solids on the realization of the second-order phase transition is introduced; a phenomenological percolation model of the effect is proposed.

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.

Oral LD50 toxicity modeling and prediction of per- and polyfluorinated chemicals on rat and mouse.

PubMed

Bhhatarai, Barun; Gramatica, Paola

2011-05-01

Quantitative structure-activity relationship (QSAR) analyses were performed using the LD(50) oral toxicity data of per- and polyfluorinated chemicals (PFCs) on rodents: rat and mouse. PFCs are studied under the EU project CADASTER which uses the available experimental data for prediction and prioritization of toxic chemicals for risk assessment by using the in silico tools. The methodology presented here applies chemometrical analysis on the existing experimental data and predicts the toxicity of new compounds. QSAR analyses were performed on the available 58 mouse and 50 rat LD(50) oral data using multiple linear regression (MLR) based on theoretical molecular descriptors selected by genetic algorithm (GA). Training and prediction sets were prepared a priori from available experimental datasets in terms of structure and response. These sets were used to derive statistically robust and predictive (both internally and externally) models. The structural applicability domain (AD) of the models were verified on 376 per- and polyfluorinated chemicals including those in REACH preregistration list. The rat and mouse endpoints were predicted by each model for the studied compounds, and finally 30 compounds, all perfluorinated, were prioritized as most important for experimental toxicity analysis under the project. In addition, cumulative study on compounds within the AD of all four models, including two earlier published models on LC(50) rodent analysis was studied and the cumulative toxicity trend was observed using principal component analysis (PCA). The similarities and the differences observed in terms of descriptors and chemical/mechanistic meaning encoded by descriptors to prioritize the most toxic compounds are highlighted.

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

3D-QSAR and docking studies on 4-anilinoquinazoline and 4-anilinoquinoline epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors

NASA Astrophysics Data System (ADS)

Assefa, Haregewein; Kamath, Shantaram; Buolamwini, John K.

2003-08-01

The overexpression and/or mutation of the epidermal growth factor receptor (EGFR) tyrosine kinase has been observed in many human solid tumors, and is under intense investigation as a novel anticancer molecular target. Comparative 3D-QSAR analyses using different alignments were undertaken employing comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) for 122 anilinoquinazoline and 50 anilinoquinoline inhibitors of EGFR kinase. The SYBYL multifit alignment rule was applied to three different conformational templates, two obtained from a MacroModel Monte Carlo conformational search, and one from the bound conformation of erlotinib in complex with EGFR in the X-ray crystal structure. In addition, a flexible ligand docking alignment obtained with the GOLD docking program, and a novel flexible receptor-guided consensus dynamics alignment obtained with the DISCOVER program in the INSIGHTII modeling package were also investigated. 3D-QSAR models with q2 values up to 0.70 and r2 values up to 0.97 were obtained. Among the 4-anilinoquinazoline set, the q2 values were similar, but the ability of the different conformational models to predict the activities of an external test set varied considerably. In this regard, the model derived using the X-ray crystallographically determined bioactive conformation of erlotinib afforded the best predictive model. Electrostatic, hydrophobic and H-bond donor descriptors contributed the most to the QSAR models of the 4-anilinoquinazolines, whereas electrostatic, hydrophobic and H-bond acceptor descriptors contributed the most to the 4-anilinoquinoline QSAR, particularly the H-bond acceptor descriptor. A novel receptor-guided consensus dynamics alignment has also been introduced for 3D-QSAR studies. This new alignment method may incorporate to some extent ligand-receptor induced fit effects into 3D-QSAR models.

Feature extraction and descriptor calculation methods for automatic georeferencing of Philippines' first microsatellite imagery

NASA Astrophysics Data System (ADS)

Tupas, M. E. A.; Dasallas, J. A.; Jiao, B. J. D.; Magallon, B. J. P.; Sempio, J. N. H.; Ramos, M. K. F.; Aranas, R. K. D.; Tamondong, A. M.

2017-10-01

The FAST-SIFT corner detector and descriptor extractor combination was used to automatically georeference DIWATA-1 Spaceborne Multispectral Imager images. Features from the Fast Accelerated Segment Test (FAST) algorithm detects corners or keypoints in an image, and these robustly detected keypoints have well-defined positions. Descriptors were computed using Scale-Invariant Feature Transform (SIFT) extractor. FAST-SIFT method effectively SMI same-subscene images detected by the NIR sensor. The method was also tested in stitching NIR images with varying subscene swept by the camera. The slave images were matched to the master image. The keypoints served as the ground control points. Random sample consensus was used to eliminate fall-out matches and ensure accuracy of the feature points from which the transformation parameters were derived. Keypoints are matched based on their descriptor vector. Nearest-neighbor matching is employed based on a metric distance between the descriptors. The metrics include Euclidean and city block, among others. Rough matching outputs not only the correct matches but also the faulty matches. A previous work in automatic georeferencing incorporates a geometric restriction. In this work, we applied a simplified version of the learning method. RANSAC was used to eliminate fall-out matches and ensure accuracy of the feature points. This method identifies if a point fits the transformation function and returns inlier matches. The transformation matrix was solved by Affine, Projective, and Polynomial models. The accuracy of the automatic georeferencing method were determined by calculating the RMSE of interest points, selected randomly, between the master image and transformed slave image.

Synthesis and quantitative structure-activity relationship (QSAR) study of novel isoxazoline and oxime derivatives of podophyllotoxin as insecticidal agents.

PubMed

Wang, Yi; Shao, Yonghua; Wang, Yangyang; Fan, Lingling; Yu, Xiang; Zhi, Xiaoyan; Yang, Chun; Qu, Huan; Yao, Xiaojun; Xu, Hui

2012-08-29

In continuation of our program aimed at the discovery and development of natural-product-based insecticidal agents, 33 isoxazoline and oxime derivatives of podophyllotoxin modified in the C and D rings were synthesized and their structures were characterized by Proton nuclear magnetic resonance ((1)H NMR), high-resolution mass spectrometry (HRMS), electrospray ionization-mass spectrometry (ESI-MS), optical rotation, melting point (mp), and infrared (IR) spectroscopy. The stereochemical configurations of compounds 5e, 5f, and 9f were unambiguously determined by X-ray crystallography. Their insecticidal activity was evaluated against the pre-third-instar larvae of northern armyworm, Mythimna separata (Walker), in vivo. Compounds 5e, 9c, 11g, and 11h especially exhibited more promising insecticidal activity than toosendanin, a commercial botanical insecticide extracted from Melia azedarach . A genetic algorithm combined with multiple linear regression (GA-MLR) calculation is performed by the MOBY DIGS package. Five selected descriptors are as follows: one two-dimensional (2D) autocorrelation descriptor (GATS4e), one edge adjacency indice (EEig06x), one RDF descriptor (RDF080v), one three-dimensional (3D) MoRSE descriptor (Mor09v), and one atom-centered fragment (H-052) descriptor. Quantitative structure-activity relationship studies demonstrated that the insecticidal activity of these compounds was mainly influenced by many factors, such as electronic distribution, steric factors, etc. For this model, the standard deviation error in prediction (SDEP) is 0.0592, the correlation coefficient (R(2)) is 0.861, and the leave-one-out cross-validation correlation coefficient (Q(2)loo) is 0.797.

Modeling the Biodegradability of Chemical Compounds Using the Online CHEmical Modeling Environment (OCHEM)

PubMed Central

Vorberg, Susann

2013-01-01

Abstract Biodegradability describes the capacity of substances to be mineralized by free‐living bacteria. It is a crucial property in estimating a compound’s long‐term impact on the environment. The ability to reliably predict biodegradability would reduce the need for laborious experimental testing. However, this endpoint is difficult to model due to unavailability or inconsistency of experimental data. Our approach makes use of the Online Chemical Modeling Environment (OCHEM) and its rich supply of machine learning methods and descriptor sets to build classification models for ready biodegradability. These models were analyzed to determine the relationship between characteristic structural properties and biodegradation activity. The distinguishing feature of the developed models is their ability to estimate the accuracy of prediction for each individual compound. The models developed using seven individual descriptor sets were combined in a consensus model, which provided the highest accuracy. The identified overrepresented structural fragments can be used by chemists to improve the biodegradability of new chemical compounds. The consensus model, the datasets used, and the calculated structural fragments are publicly available at http://ochem.eu/article/31660. PMID:27485201

Automated detection of microaneurysms using robust blob descriptors

NASA Astrophysics Data System (ADS)

Adal, K.; Ali, S.; Sidibé, D.; Karnowski, T.; Chaum, E.; Mériaudeau, F.

2013-03-01

Microaneurysms (MAs) are among the first signs of diabetic retinopathy (DR) that can be seen as round dark-red structures in digital color fundus photographs of retina. In recent years, automated computer-aided detection and diagnosis (CAD) of MAs has attracted many researchers due to its low-cost and versatile nature. In this paper, the MA detection problem is modeled as finding interest points from a given image and several interest point descriptors are introduced and integrated with machine learning techniques to detect MAs. The proposed approach starts by applying a novel fundus image contrast enhancement technique using Singular Value Decomposition (SVD) of fundus images. Then, Hessian-based candidate selection algorithm is applied to extract image regions which are more likely to be MAs. For each candidate region, robust low-level blob descriptors such as Speeded Up Robust Features (SURF) and Intensity Normalized Radon Transform are extracted to characterize candidate MA regions. The combined features are then classified using SVM which has been trained using ten manually annotated training images. The performance of the overall system is evaluated on Retinopathy Online Challenge (ROC) competition database. Preliminary results show the competitiveness of the proposed candidate selection techniques against state-of-the art methods as well as the promising future for the proposed descriptors to be used in the localization of MAs from fundus images.

Predicting Cell Association of Surface-Modified Nanoparticles Using Protein Corona Structure - Activity Relationships (PCSAR).

PubMed

Kamath, Padmaja; Fernandez, Alberto; Giralt, Francesc; Rallo, Robert

2015-01-01

Nanoparticles are likely to interact in real-case application scenarios with mixtures of proteins and biomolecules that will absorb onto their surface forming the so-called protein corona. Information related to the composition of the protein corona and net cell association was collected from literature for a library of surface-modified gold and silver nanoparticles. For each protein in the corona, sequence information was extracted and used to calculate physicochemical properties and statistical descriptors. Data cleaning and preprocessing techniques including statistical analysis and feature selection methods were applied to remove highly correlated, redundant and non-significant features. A weighting technique was applied to construct specific signatures that represent the corona composition for each nanoparticle. Using this basic set of protein descriptors, a new Protein Corona Structure-Activity Relationship (PCSAR) that relates net cell association with the physicochemical descriptors of the proteins that form the corona was developed and validated. The features that resulted from the feature selection were in line with already published literature, and the computational model constructed on these features had a good accuracy (R(2)LOO=0.76 and R(2)LMO(25%)=0.72) and stability, with the advantage that the fingerprints based on physicochemical descriptors were independent of the specific proteins that form the corona.

Genetic algorithm applied to the selection of factors in principal component-artificial neural networks: application to QSAR study of calcium channel antagonist activity of 1,4-dihydropyridines (nifedipine analogous).

PubMed

Hemmateenejad, Bahram; Akhond, Morteza; Miri, Ramin; Shamsipur, Mojtaba

2003-01-01

A QSAR algorithm, principal component-genetic algorithm-artificial neural network (PC-GA-ANN), has been applied to a set of newly synthesized calcium channel blockers, which are of special interest because of their role in cardiac diseases. A data set of 124 1,4-dihydropyridines bearing different ester substituents at the C-3 and C-5 positions of the dihydropyridine ring and nitroimidazolyl, phenylimidazolyl, and methylsulfonylimidazolyl groups at the C-4 position with known Ca(2+) channel binding affinities was employed in this study. Ten different sets of descriptors (837 descriptors) were calculated for each molecule. The principal component analysis was used to compress the descriptor groups into principal components. The most significant descriptors of each set were selected and used as input for the ANN. The genetic algorithm (GA) was used for the selection of the best set of extracted principal components. A feed forward artificial neural network with a back-propagation of error algorithm was used to process the nonlinear relationship between the selected principal components and biological activity of the dihydropyridines. A comparison between PC-GA-ANN and routine PC-ANN shows that the first model yields better prediction ability.

Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval.

PubMed

Wei, Xiu-Shen; Luo, Jian-Hao; Wu, Jianxin; Zhou, Zhi-Hua

2017-06-01

Deep convolutional neural network models pre-trained for the ImageNet classification task have been successfully adopted to tasks in other domains, such as texture description and object proposal generation, but these tasks require annotations for images in the new domain. In this paper, we focus on a novel and challenging task in the pure unsupervised setting: fine-grained image retrieval. Even with image labels, fine-grained images are difficult to classify, letting alone the unsupervised retrieval task. We propose the selective convolutional descriptor aggregation (SCDA) method. The SCDA first localizes the main object in fine-grained images, a step that discards the noisy background and keeps useful deep descriptors. The selected descriptors are then aggregated and the dimensionality is reduced into a short feature vector using the best practices we found. The SCDA is unsupervised, using no image label or bounding box annotation. Experiments on six fine-grained data sets confirm the effectiveness of the SCDA for fine-grained image retrieval. Besides, visualization of the SCDA features shows that they correspond to visual attributes (even subtle ones), which might explain SCDA's high-mean average precision in fine-grained retrieval. Moreover, on general image retrieval data sets, the SCDA achieves comparable retrieval results with the state-of-the-art general image retrieval approaches.

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.

Physicochemical properties/descriptors governing the solubility and partitioning of chemicals in water-solvent-gas systems. Part 2. Solubility in 1-octanol.

PubMed

Raevsky, O A; Perlovich, G L; Schaper, K-J

2007-01-01

On the basis of octanol solubility data (log S(o)) for 218 structurally diverse solid chemicals it was shown that the exclusive consideration of melting points did not provide satisfactory results in the quantitative prediction of this parameter (s = 0.92). The application of HYBOT physicochemical descriptors separately (s = 0.94) and together with melting points (s = 0.70) in the framework of a common regression model also was not successful, although contributions of volume-related and H-bond terms to solubility in octanol were identified. It was proposed that the main reason for such behaviour was the different crystal lattice interaction of different classes of chemicals. Successful calculations of the solubility in octanol of chemicals of interest were performed on the basis of the experimental solubility of structurally/physicochemically/numerically similar nearest neighbours with consideration of their difference in physicochemical parameters (molecular polarisability, H-bond acceptor and donor factors (s = 0.66)) and of these descriptors together with melting point differences (s = 0.38). Good results were obtained for all compounds having nearest neighbours with sufficient similarity, expressed by Tanimoto indexes, and by distances in the scaled 3D descriptor space. Obviously the success of this approach depends on the size of the database.

Fast human pose estimation using 3D Zernike descriptors

NASA Astrophysics Data System (ADS)

Berjón, Daniel; Morán, Francisco

2012-03-01

Markerless video-based human pose estimation algorithms face a high-dimensional problem that is frequently broken down into several lower-dimensional ones by estimating the pose of each limb separately. However, in order to do so they need to reliably locate the torso, for which they typically rely on time coherence and tracking algorithms. Their losing track usually results in catastrophic failure of the process, requiring human intervention and thus precluding their usage in real-time applications. We propose a very fast rough pose estimation scheme based on global shape descriptors built on 3D Zernike moments. Using an articulated model that we configure in many poses, a large database of descriptor/pose pairs can be computed off-line. Thus, the only steps that must be done on-line are the extraction of the descriptors for each input volume and a search against the database to get the most likely poses. While the result of such process is not a fine pose estimation, it can be useful to help more sophisticated algorithms to regain track or make more educated guesses when creating new particles in particle-filter-based tracking schemes. We have achieved a performance of about ten fps on a single computer using a database of about one million entries.

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.

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.

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.

Issues and solutions for storage, retrieval, and searching of MPEG-7 documents

NASA Astrophysics Data System (ADS)

Chang, Yuan-Chi; Lo, Ming-Ling; Smith, John R.

2000-10-01

The ongoing MPEG-7 standardization activity aims at creating a standard for describing multimedia content in order to facilitate the interpretation of the associated information content. Attempting to address a broad range of applications, MPEG-7 has defined a flexible framework consisting of Descriptors, Description Schemes, and Description Definition Language. Descriptors and Description Schemes describe features, structure and semantics of multimedia objects. They are written in the Description Definition Language (DDL). In the most recent revision, DDL applies XML (Extensible Markup Language) Schema with MPEG-7 extensions. DDL has constructs that support inclusion, inheritance, reference, enumeration, choice, sequence, and abstract type of Description Schemes and Descriptors. In order to enable multimedia systems to use MPEG-7, a number of important problems in storing, retrieving and searching MPEG-7 documents need to be solved. This paper reports on initial finding on issues and solutions of storing and accessing MPEG-7 documents. In particular, we discuss the benefits of using a virtual document management framework based on XML Access Server (XAS) in order to bridge the MPEG-7 multimedia applications and database systems. The need arises partly because MPEG-7 descriptions need customized storage schema, indexing and search engines. We also discuss issues arising in managing dependence and cross-description scheme search.

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.

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.

Prediction of anticancer property of bowsellic acid derivatives by quantitative structure activity relationship analysis and molecular docking study.

PubMed

Satpathy, Raghunath; Guru, R K; Behera, R; Nayak, B

2015-01-01

Boswellic acid consists of a series of pentacyclic triterpene molecules that are produced by the plant Boswellia serrata. The potential applications of Bowsellic acid for treatment of cancer have been focused here. To predict the property of the bowsellic acid derivatives as anticancer compounds by various computational approaches. In this work, all total 65 derivatives of bowsellic acids from the PubChem database were considered for the study. After energy minimization of the ligands various types of molecular descriptors were computed and corresponding two-dimensional quantitative structure activity relationship (QSAR) models were obtained by taking Andrews coefficient as the dependent variable. Different types of comparative analysis were used for QSAR study are multiple linear regression, partial least squares, support vector machines and artificial neural network. From the study geometrical descriptors shows the highest correlation coefficient, which indicates the binding factor of the compound. To evaluate the anticancer property molecular docking study of six selected ligands based on Andrews affinity were performed with nuclear factor-kappa protein kinase (Protein Data Bank ID 4G3D), which is an established therapeutic target for cancers. Along with QSAR study and docking result, it was predicted that bowsellic acid can also be treated as a potential anticancer compound. Along with QSAR study and docking result, it was predicted that bowsellic acid can also be treated as a potential anticancer compound.

Compilation and physicochemical classification analysis of a diverse hERG inhibition database

NASA Astrophysics Data System (ADS)

Didziapetris, Remigijus; Lanevskij, Kiril

2016-12-01

A large and chemically diverse hERG inhibition data set comprised of 6690 compounds was constructed on the basis of ChEMBL bioactivity database and original publications dealing with experimental determination of hERG activities using patch-clamp and competitive displacement assays. The collected data were converted to binary format at 10 µM activity threshold and subjected to gradient boosting machine classification analysis using a minimal set of physicochemical and topological descriptors. The tested parameters involved lipophilicity (log P), ionization (p K a ), polar surface area, aromaticity, molecular size and flexibility. The employed approach allowed classifying the compounds with an overall 75-80 % accuracy, even though it only accounted for non-specific interactions between hERG and ligand molecules. The observed descriptor-response profiles were consistent with common knowledge about hERG ligand binding site, but also revealed several important quantitative trends, as well as slight inter-assay variability in hERG inhibition data. The results suggest that even weakly basic groups (p K a < 6) might substantially contribute to hERG inhibition potential, whereas the role of lipophilicity depends on the compound's ionization state, and the influence of log P decreases in the order of bases > zwitterions > neutrals > acids. Given its robust performance and clear physicochemical interpretation, the proposed model may provide valuable information to direct drug discovery efforts towards compounds with reduced risk of hERG-related cardiotoxicity.

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.

Describing contrast across scales

NASA Astrophysics Data System (ADS)

Syed, Sohaib Ali; Iqbal, Muhammad Zafar; Riaz, Muhammad Mohsin

2017-06-01

Due to its sensitive nature against illumination and noise distributions, contrast is not widely used for image description. On the contrary, the human perception of contrast along different spatial frequency bandwidths provides a powerful discriminator function that can be modeled in a robust manner against local illumination. Based upon this observation, a dense local contrast descriptor is proposed and its potential in different applications of computer vision is discussed. Extensive experiments reveal that this simple yet effective description performs well in comparison with state of the art image descriptors. We also show the importance of this description in multiresolution pansharpening framework.

Public Databases Supporting Computational Toxicology

EPA Science Inventory

A major goal of the emerging field of computational toxicology is the development of screening-level models that predict potential toxicity of chemicals from a combination of mechanistic in vitro assay data and chemical structure descriptors. In order to build these models, resea...

Assessment of the Nature and Severity of Pain Using SF-MPQ for Cancer Patients at the National Institute of Oncology in Rabat in 2015.

PubMed

Rouahi, Nabila; Ouazzani Touhami, Zineb; Ahyayauch, Hasna; El Mlili, Nisrin; Filali-Maltouf, A; Belkhadir, Zakaria

2016-01-01

Cancer is a worldwide health problem and pain is among the most common and unpleasant effects affecting well-being of cancer patients. Accurate description of pain can help physicians to improve its management. Many English tools have been developed to assess pain. Only a limited number of these are applied in Arab countries. Our aim was to assess the quality, the nature and the severity of pain using the short McGill Pain Questionnaire (SF-MPQ) on cancer patients in the National Institute of Oncology (NIO) in Rabat, Morocco. The tool used was the SF-MPQ inspired from the Arabic version of the MPQ. The subjects were cancer patients (N=182) attending the NIO, from 24th October 2015 to 8th January 2016, ≥18 years old, experiencing pain and coming to have or to update their pain medication. The rate of participation was 96.3%. Eight patients haddif culties to express their pain using descriptors, but could use the Visual Analogue Scale (VAS) and the body diagram. The most frequent sensory descriptors were 'Throbbing', 'Shooting', 'Hot-Burning'. The most used affective descriptor was 'Tiring-Exhausting'. The mean VAS was 6.6 (2.4). The mean score of all items was 11.9 (7.8). The patients were suffering from severe pain. The internal consistency of the form was acceptable. The findings indicate that most of the patients attending the pain center of the NIO could use the descriptors of the SF-MPQ to describe their pain. They indicate the usefulness of the SF-MPQ to assess the nature and the severity of pain in cancer patients. This tool should now be tested in other Moroccan and Arabic contexts associated with other tools in clinical trials.

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.

Mathematical modeling of tetrahydroimidazole benzodiazepine-1-one derivatives as an anti HIV agent

NASA Astrophysics Data System (ADS)

Ojha, Lokendra Kumar

2017-07-01

The goal of the present work is the study of drug receptor interaction via QSAR (Quantitative Structure-Activity Relationship) analysis for 89 set of TIBO (Tetrahydroimidazole Benzodiazepine-1-one) derivatives. MLR (Multiple Linear Regression) method is utilized to generate predictive models of quantitative structure-activity relationships between a set of molecular descriptors and biological activity (IC50). The best QSAR model was selected having a correlation coefficient (r) of 0.9299 and Standard Error of Estimation (SEE) of 0.5022, Fisher Ratio (F) of 159.822 and Quality factor (Q) of 1.852. This model is statistically significant and strongly favours the substitution of sulphur atom, IS i.e. indicator parameter for -Z position of the TIBO derivatives. Two other parameter logP (octanol-water partition coefficient) and SAG (Surface Area Grid) also played a vital role in the generation of best QSAR model. All three descriptor shows very good stability towards data variation in leave-one-out (LOO).

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.

A systematic approach to prioritize drug targets using machine learning, a molecular descriptor-based classification model, and high-throughput screening of plant derived molecules: a case study in oral cancer.

PubMed

Randhawa, Vinay; Kumar Singh, Anil; Acharya, Vishal

2015-12-01

Systems-biology inspired identification of drug targets and machine learning-based screening of small molecules which modulate their activity have the potential to revolutionize modern drug discovery by complementing conventional methods. To utilize the effectiveness of such pipelines, we first analyzed the dysregulated gene pairs between control and tumor samples and then implemented an ensemble-based feature selection approach to prioritize targets in oral squamous cell carcinoma (OSCC) for therapeutic exploration. Based on the structural information of known inhibitors of CXCR4-one of the best targets identified in this study-a feature selection was implemented for the identification of optimal structural features (molecular descriptor) based on which a classification model was generated. Furthermore, the CXCR4-centered descriptor-based classification model was finally utilized to screen a repository of plant derived small-molecules to obtain potential inhibitors. The application of our methodology may assist effective selection of the best targets which may have previously been overlooked, that in turn will lead to the development of new oral cancer medications. The small molecules identified in this study can be ideal candidates for trials as potential novel anti-oral cancer agents. Importantly, distinct steps of this whole study may provide reference for the analysis of other complex human diseases.

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.

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.

Revising an Engineering Design Rubric: A Case Study Illustrating Principles and Practices to Ensure Technical Quality of Rubrics

ERIC Educational Resources Information Center

Goldberg, Gail Lynn

2014-01-01

This article provides a detailed account of a rubric revision process to address seven common problems to which rubrics are prone: lack of consistency and parallelism; the presence of "orphan" and "widow" words and phrases; redundancy in descriptors; inconsistency in the focus of qualifiers; limited routes to partial credit;…

Prediction of pesticide acute toxicity using two-dimensional chemical descriptors and target species classification

EPA Science Inventory

Previous modelling of the median lethal dose (oral rat LD50) has indicated that local class-based models yield better correlations than global models. We evaluated the hypothesis that dividing the dataset by pesticidal mechanisms would improve prediction accuracy. A linear discri...

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.

One-Shot Learning of Human Activity With an MAP Adapted GMM and Simplex-HMM.

PubMed

Rodriguez, Mario; Orrite, Carlos; Medrano, Carlos; Makris, Dimitrios

2016-05-10

This paper presents a novel activity class representation using a single sequence for training. The contribution of this representation lays on the ability to train an one-shot learning recognition system, useful in new scenarios where capturing and labeling sequences is expensive or impractical. The method uses a universal background model of local descriptors obtained from source databases available on-line and adapts it to a new sequence in the target scenario through a maximum a posteriori adaptation. Each activity sample is encoded in a sequence of normalized bag of features and modeled by a new hidden Markov model formulation, where the expectation-maximization algorithm for training is modified to deal with observations consisting in vectors in a unit simplex. Extensive experiments in recognition have been performed using one-shot learning over the public datasets Weizmann, KTH, and IXMAS. These experiments demonstrate the discriminative properties of the representation and the validity of application in recognition systems, achieving state-of-the-art results.

Quantitative prediction of solvation free energy in octanol of organic compounds.

PubMed

Delgado, Eduardo J; Jaña, Gonzalo A

2009-03-01

The free energy of solvation, DeltaGS0, in octanol of organic compounds is quantitatively predicted from the molecular structure. The model, involving only three molecular descriptors, is obtained by multiple linear regression analysis from a data set of 147 compounds containing diverse organic functions, namely, halogenated and non-halogenated alkanes, alkenes, alkynes, aromatics, alcohols, aldehydes, ketones, amines, ethers and esters; covering a DeltaGS0 range from about -50 to 0 kJ.mol(-1). The model predicts the free energy of solvation with a squared correlation coefficient of 0.93 and a standard deviation, 2.4 kJ.mol(-1), just marginally larger than the generally accepted value of experimental uncertainty. The involved molecular descriptors have definite physical meaning corresponding to the different intermolecular interactions occurring in the bulk liquid phase. The model is validated with an external set of 36 compounds not included in the training set.

Quantitative Prediction of Solvation Free Energy in Octanol of Organic Compounds

PubMed Central

Delgado, Eduardo J.; Jaña, Gonzalo A.

2009-01-01

The free energy of solvation, ΔGS0, in octanol of organic compunds is quantitatively predicted from the molecular structure. The model, involving only three molecular descriptors, is obtained by multiple linear regression analysis from a data set of 147 compounds containing diverse organic functions, namely, halogenated and non-halogenated alkanes, alkenes, alkynes, aromatics, alcohols, aldehydes, ketones, amines, ethers and esters; covering a ΔGS0 range from about −50 to 0 kJ·mol−1. The model predicts the free energy of solvation with a squared correlation coefficient of 0.93 and a standard deviation, 2.4 kJ·mol−1, just marginally larger than the generally accepted value of experimental uncertainty. The involved molecular descriptors have definite physical meaning corresponding to the different intermolecular interactions occurring in the bulk liquid phase. The model is validated with an external set of 36 compounds not included in the training set. PMID:19399236

Optimal descriptor as a translator of eclectic data into endpoint prediction: mutagenicity of fullerene as a mathematical function of conditions.

PubMed

Toropov, Andrey A; Toropova, Alla P

2014-06-01

The experimental data on the bacterial reverse mutation test on C60 nanoparticles (TA100) is examined as an endpoint. By means of the optimal descriptors calculated with the Monte Carlo method a mathematical model of the endpoint has been built up. The model is the mathematical function of (i) dose (g/plate); (ii) metabolic activation (i.e. with S9 mix or without S9 mix); and (iii) illumination (i.e. dark or irradiation). The statistical quality of the model is the following: n=10, r(2)=0.7549, q(2)=0.5709, s=7.67, F=25 (Training set); n=5, r(2)=0.8987, s=18.4 (Calibration set); and n=5, r(2)=0.6968, s=10.9 (Validation set). Copyright © 2013 Elsevier Ltd. All rights reserved.

Aping humans: age and sex effects in chimpanzee (Pan troglodytes) and human (Homo sapiens) personality.

PubMed

King, James E; Weiss, Alexander; Sisco, Melissa M

2008-11-01

Ratings of 202 chimpanzees on 43 personality descriptor adjectives were used to calculate scores on five domains analogous to the human Five-Factor Model and a chimpanzee-specific Dominance domain. Male and female chimpanzees were divided into five age groups ranging from juvenile to old adult. Internal consistencies and interrater reliabilities of factors were stable across age groups and approximately 6.8 year retest reliabilities were high. Age-related declines in Extraversion and Openness and increases in Agreeableness and Conscientiousness paralleled human age differences. The mean change in absolute standardized units for all five factors was virtually identical in humans and chimpanzees after adjustment for different developmental rates. Consistent with their aggressive behavior in the wild, male chimpanzees were rated as more aggressive, emotional, and impulsive than females. Chimpanzee sex differences in personality were greater than comparable human gender differences. These findings suggest that chimpanzee and human personality develop via an unfolding maturational process. (PsycINFO Database Record (c) 2008 APA, all rights reserved).

Anticorrosive Effects of Some Thiophene Derivatives Against the Corrosion of Iron: A Computational Study

NASA Astrophysics Data System (ADS)

Guo, Lei; Safi, Zaki S.; Kaya, Savas; Shi, Wei; Tüzün, Burak; Altunay, Nail; Kaya, Cemal

2018-05-01

It is known that iron is one of the most widely used metals in industrial production. In this work, the inhibition performances of three thiophene derivatives on the corrosion of iron were investigated in the light of several theoretical approaches. In the section including DFT calculations, several global reactivity descriptors such as EHOMO, ELUMO, ionization energy (I), electron affinity (A), HOMO-LUMO energy gap (ΔE), chemical hardness (η), softness (σ), as well as local reactivity descriptors like Fukui indices, local softness, and local electrophilicity were considered and discussed. The adsorption behaviors of considered thiophene derivatives on Fe(110) surface were investigated using molecular dynamics simulation approach. To determine the most active corrosion inhibitor among studied thiophene derivatives, we used the principle component analysis (PCA) and agglomerative hierarchical cluster analysis (AHCA). Accordingly, all data obtained using various theoretical calculation techniques are consistent with experiments.

A computer program for creating keyword indexes to textual data files

USGS Publications Warehouse

Moody, David W.

1972-01-01

A keyword-in-context (KWIC) or out-of-context (KWOC) index is a convenient means of organizing information. This keyword index program can be used to create either KWIC or KWOC indexes of bibliographic references or other types of information punched on. cards, typed on optical scanner sheets, or retrieved from various Department of Interior data bases using the Generalized Information Processing System (GIPSY). The index consists of a 'bibliographic' section and a keyword-section based on the permutation of. document titles, project titles, environmental impact statement titles, maps, etc. or lists of descriptors. The program can also create a back-of-the-book index to documents from a list of descriptors. By providing the user with a wide range of input and output options, the program provides the researcher, manager, or librarian with a means of-maintaining a list and index to documents in. a small library, reprint collection, or office file.

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.

Improving quantitative structure-activity relationship models using Artificial Neural Networks trained with dropout.

PubMed

Mendenhall, Jeffrey; Meiler, Jens

2016-02-01

Dropout is an Artificial Neural Network (ANN) training technique that has been shown to improve ANN performance across canonical machine learning (ML) datasets. Quantitative Structure Activity Relationship (QSAR) datasets used to relate chemical structure to biological activity in Ligand-Based Computer-Aided Drug Discovery pose unique challenges for ML techniques, such as heavily biased dataset composition, and relatively large number of descriptors relative to the number of actives. To test the hypothesis that dropout also improves QSAR ANNs, we conduct a benchmark on nine large QSAR datasets. Use of dropout improved both enrichment false positive rate and log-scaled area under the receiver-operating characteristic curve (logAUC) by 22-46 % over conventional ANN implementations. Optimal dropout rates are found to be a function of the signal-to-noise ratio of the descriptor set, and relatively independent of the dataset. Dropout ANNs with 2D and 3D autocorrelation descriptors outperform conventional ANNs as well as optimized fingerprint similarity search methods.

Improving Quantitative Structure-Activity Relationship Models using Artificial Neural Networks Trained with Dropout

PubMed Central

Mendenhall, Jeffrey; Meiler, Jens

2016-01-01

Dropout is an Artificial Neural Network (ANN) training technique that has been shown to improve ANN performance across canonical machine learning (ML) datasets. Quantitative Structure Activity Relationship (QSAR) datasets used to relate chemical structure to biological activity in Ligand-Based Computer-Aided Drug Discovery (LB-CADD) pose unique challenges for ML techniques, such as heavily biased dataset composition, and relatively large number of descriptors relative to the number of actives. To test the hypothesis that dropout also improves QSAR ANNs, we conduct a benchmark on nine large QSAR datasets. Use of dropout improved both Enrichment false positive rate (FPR) and log-scaled area under the receiver-operating characteristic curve (logAUC) by 22–46% over conventional ANN implementations. Optimal dropout rates are found to be a function of the signal-to-noise ratio of the descriptor set, and relatively independent of the dataset. Dropout ANNs with 2D and 3D autocorrelation descriptors outperform conventional ANNs as well as optimized fingerprint similarity search methods. PMID:26830599

Local gradient Gabor pattern (LGGP) with applications in face recognition, cross-spectral matching, and soft biometrics

NASA Astrophysics Data System (ADS)

Chen, Cunjian; Ross, Arun

2013-05-01

Researchers in face recognition have been using Gabor filters for image representation due to their robustness to complex variations in expression and illumination. Numerous methods have been proposed to model the output of filter responses by employing either local or global descriptors. In this work, we propose a novel but simple approach for encoding Gradient information on Gabor-transformed images to represent the face, which can be used for identity, gender and ethnicity assessment. Extensive experiments on the standard face benchmark FERET (Visible versus Visible), as well as the heterogeneous face dataset HFB (Near-infrared versus Visible), suggest that the matching performance due to the proposed descriptor is comparable against state-of-the-art descriptor-based approaches in face recognition applications. Furthermore, the same feature set is used in the framework of a Collaborative Representation Classification (CRC) scheme for deducing soft biometric traits such as gender and ethnicity from face images in the AR, Morph and CAS-PEAL databases.

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.

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.

The effect of cigarillo packaging elements on young adult perceptions of product flavor, taste, smell, and appeal.

PubMed

Meernik, Clare; Ranney, Leah M; Lazard, Allison J; Kim, KyungSu; Queen, Tara L; Avishai, Aya; Boynton, Marcella H; Sheeran, Paschal J; Goldstein, Adam O

2018-01-01

Product packaging has long been used by the tobacco industry to target consumers and manipulate product perceptions. This study examines the extent to which cigarillo packaging influences perceptions of product flavor, taste, smell, and appeal. A web-based experiment was conducted among young adults. Participants viewed three randomly selected cigarillo packs, varying on pack flavor descriptor, color, type, branding, and warning-totaling 180 pack images. Mixed-effects models were used to estimate the effect of pack elements on product perceptions. A total of 2,664 current, ever, and never little cigar and cigarillo users participated. Cigarillo packs with a flavor descriptor were perceived as having a more favorable taste (β = 0.21, p < .001) and smell (β = 0.14, p < .001) compared to packs with no flavor descriptor. Compared to packs with no color, pink and purple packs were more likely to be perceived as containing a flavor (β = 0.11, p < .001), and were rated more favorably on taste (β = 0.17, p < .001), smell (β = 0.15, p < .001), and appeal (β = 0.16, p < .001). While warnings on packs decreased favorable perceptions of product taste (pictorial: β = -0.07, p = .03) and smell (text-only: β = -0.08, p = .01; pictorial: β = -0.09, p = .007), warnings did not moderate the effects of flavor descriptor or color. To our knowledge, this study provides the first quantitative evidence that cigarillo packaging alters consumers' cognitive responses, and warnings on packs do not suffice to overcome the effects of product packaging. The findings support efforts at federal, state, and local levels to prohibit flavor descriptors and their associated product flavoring in non-cigarette products such as cigarillos, along with new data that supports restrictions on flavor cues and colors.

MIND: modality independent neighbourhood descriptor for multi-modal deformable registration.

PubMed

Heinrich, Mattias P; Jenkinson, Mark; Bhushan, Manav; Matin, Tahreema; Gleeson, Fergus V; Brady, Sir Michael; Schnabel, Julia A

2012-10-01

Deformable registration of images obtained from different modalities remains a challenging task in medical image analysis. This paper addresses this important problem and proposes a modality independent neighbourhood descriptor (MIND) for both linear and deformable multi-modal registration. Based on the similarity of small image patches within one image, it aims to extract the distinctive structure in a local neighbourhood, which is preserved across modalities. The descriptor is based on the concept of image self-similarity, which has been introduced for non-local means filtering for image denoising. It is able to distinguish between different types of features such as corners, edges and homogeneously textured regions. MIND is robust to the most considerable differences between modalities: non-functional intensity relations, image noise and non-uniform bias fields. The multi-dimensional descriptor can be efficiently computed in a dense fashion across the whole image and provides point-wise local similarity across modalities based on the absolute or squared difference between descriptors, making it applicable for a wide range of transformation models and optimisation algorithms. We use the sum of squared differences of the MIND representations of the images as a similarity metric within a symmetric non-parametric Gauss-Newton registration framework. In principle, MIND would be applicable to the registration of arbitrary modalities. In this work, we apply and validate it for the registration of clinical 3D thoracic CT scans between inhale and exhale as well as the alignment of 3D CT and MRI scans. Experimental results show the advantages of MIND over state-of-the-art techniques such as conditional mutual information and entropy images, with respect to clinically annotated landmark locations. Copyright © 2012 Elsevier B.V. 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].

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.

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.

Identifying Early Childhood Personality Dimensions Using the California Child Q-Set and Prospective Associations With Behavioral and Psychosocial Development.

PubMed

Wilson, Sylia; Schalet, Benjamin D; Hicks, Brian M; Zucker, Robert A

2013-08-01

The present study used an empirical, "bottom-up" approach to delineate the structure of the California Child Q-Set (CCQ), a comprehensive set of personality descriptors, in a sample of 373 preschool-aged children. This approach yielded two broad trait dimensions, Adaptive Socialization (emotional stability, compliance, intelligence) and Anxious Inhibition (emotional/behavioral introversion). Results demonstrate the value of using empirical derivation to investigate the structure of personality in young children, speak to the importance of early-evident personality traits for adaptive development, and are consistent with a growing body of evidence indicating that personality structure in young children is similar, but not identical to, that in adults, suggesting a model of broad personality dimensions in childhood that evolve into narrower traits in adulthood.

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.

Universal Fragment Descriptors for Predicting Electronic and Mechanical Properties of Inorganic Crystals

NASA Astrophysics Data System (ADS)

Oses, Corey; Isayev, Olexandr; Toher, Cormac; Curtarolo, Stefano; Tropsha, Alexander

Historically, materials discovery is driven by a laborious trial-and-error process. The growth of materials databases and emerging informatics approaches finally offer the opportunity to transform this practice into data- and knowledge-driven rational design-accelerating discovery of novel materials exhibiting desired properties. By using data from the AFLOW repository for high-throughput, ab-initio calculations, we have generated Quantitative Materials Structure-Property Relationship (QMSPR) models to predict critical materials properties, including the metal/insulator classification, band gap energy, and bulk modulus. The prediction accuracy obtained with these QMSPR models approaches training data for virtually any stoichiometric inorganic crystalline material. We attribute the success and universality of these models to the construction of new materials descriptors-referred to as the universal Property-Labeled Material Fragments (PLMF). This representation affords straightforward model interpretation in terms of simple heuristic design rules that could guide rational materials design. This proof-of-concept study demonstrates the power of materials informatics to dramatically accelerate the search for new materials.

Student-Valued Measurable Teaching Behaviors of Award-Winning Pharmacy Preceptors.

PubMed

O'Sullivan, Teresa A; Lau, Carmen; Patel, Mitul; Mac, Chi; Krueger, Janelle; Danielson, Jennifer; Weber, Stanley S

2015-12-25

To identify specific preceptor teaching-coaching, role modeling, and facilitating behaviors valued by pharmacy students and to develop measures of those behaviors that can be used for an experiential education quality assurance program. Using a qualitative research approach, we conducted a thematic analysis of student comments about excellent preceptors to identify behaviors exhibited by those preceptors. Identified behaviors were sorted according to the preceptor's role as role model, teacher/coach, or learning facilitator; measurable descriptors for each behavior were then developed. Data analysis resulted in identification of 15 measurable behavior themes, the most frequent being: having an interest in student learning and success, making time for students, and displaying a positive preceptor attitude. Measureable descriptors were developed for 5 role-modeling behaviors, 6 teaching-coaching behaviors, and 4 facilitating behaviors. Preceptors may need to be evaluated in their separate roles as teacher-coach, role model, and learning facilitator. The developed measures in this report could be used in site quality evaluation.

Encoding the local connectivity patterns of fMRI for cognitive task and state classification.

PubMed

Onal Ertugrul, Itir; Ozay, Mete; Yarman Vural, Fatos T

2018-06-15

In this work, we propose a novel framework to encode the local connectivity patterns of brain, using Fisher vectors (FV), vector of locally aggregated descriptors (VLAD) and bag-of-words (BoW) methods. We first obtain local descriptors, called mesh arc descriptors (MADs) from fMRI data, by forming local meshes around anatomical regions, and estimating their relationship within a neighborhood. Then, we extract a dictionary of relationships, called brain connectivity dictionary by fitting a generative Gaussian mixture model (GMM) to a set of MADs, and selecting codewords at the mean of each component of the mixture. Codewords represent connectivity patterns among anatomical regions. We also encode MADs by VLAD and BoW methods using k-Means clustering. We classify cognitive tasks using the Human Connectome Project (HCP) task fMRI dataset and cognitive states using the Emotional Memory Retrieval (EMR). We train support vector machines (SVMs) using the encoded MADs. Results demonstrate that, FV encoding of MADs can be successfully employed for classification of cognitive tasks, and outperform VLAD and BoW representations. Moreover, we identify the significant Gaussians in mixture models by computing energy of their corresponding FV parts, and analyze their effect on classification accuracy. Finally, we suggest a new method to visualize the codewords of the learned brain connectivity dictionary.

Approaching Pharmacological Space: Events and Components.

PubMed

Vistoli, Giulio; Pedretti, Alessandro; Mazzolari, Angelica; Testa, Bernard

2018-01-01

With a view to introducing the concept of pharmacological space and its potential applications in investigating and predicting the toxic mechanisms of xenobiotics, this opening chapter describes the logical relations between conformational behavior, physicochemical properties and binding spaces, which are seen as the three key elements composing the pharmacological space. While the concept of conformational space is routinely used to encode molecular flexibility, the concepts of property spaces and, particularly, of binding spaces are more innovative. Indeed, their descriptors can find fruitful applications (a) in describing the dynamic adaptability a given ligand experiences when inserted into a specific environment, and (b) in parameterizing the flexibility a ligand retains when bound to a biological target. Overall, these descriptors can conveniently account for the often disregarded entropic factors and as such they prove successful when inserted in ligand- or structure-based predictive models. Notably, and although binding space parameters can clearly be derived from MD simulations, the chapter will illustrate how docking calculations, despite their static nature, are able to evaluate ligand's flexibility by analyzing several poses for each ligand. Such an approach, which represents the founding core of the binding space concept, can find various applications in which the related descriptors show an impressive enhancing effect on the statistical performances of the resulting predictive models.

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.

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.

Inductive electronegativity scale. Iterative calculation of inductive partial charges.

PubMed

Cherkasov, Artem

2003-01-01

A number of novel QSAR descriptors have been introduced on the basis of the previously elaborated models for steric and inductive effects. The developed "inductive" parameters include absolute and effective electronegativity, atomic partial charges, and local and global chemical hardness and softness. Being based on traditional inductive and steric substituent constants these 3D descriptors provide a valuable insight into intramolecular steric and electronic interactions and can find broad application in structure-activity studies. Possible interpretation of physical meaning of the inductive descriptors has been suggested by considering a neutral molecule as an electrical capacitor formed by charged atomic spheres. This approximation relates inductive chemical softness and hardness of bound atom(s) with the total area of the facings of electrical capacitor formed by the atom(s) and the rest of the molecule. The derived full electronegativity equalization scheme allows iterative calculation of inductive partial charges on the basis of atomic electronegativities, covalent radii, and intramolecular distances. A range of inductive descriptors has been computed for a variety of organic compounds. The calculated inductive charges in the studied molecules have been validated by experimental C-1s Electron Core Binding Energies and molecular dipole moments. Several semiempirical chemical rules, such as equalized electronegativity's arithmetic mean, principle of maximum hardness, and principle of hardness borrowing could be explicitly illustrated in the framework of the developed approach.

How to refer to people with disease in research outputs: The disconnection between academic practise and that preferred by people with multiple sclerosis.

PubMed

Baker, David; Anandhakrishnan, Ananthi; Tuite-Dalton, Katie A; Lockart-Jones, Hazel; Middleton, Rodden M; Ford, David V; Crowe, Christina; Giovannoni, Gavin

2016-11-01

Increasingly, Government and Charity funders require public engagement in research. Invariably these research outputs describe the condition of someone with the disease of interest. We therefore sought to identify the preferred descriptor of someone with a disease, such as multiple sclerosis (MS) and to determine what descriptors are currently used by academics. Several surveys were undertaken: one from the Research Network of the MS Society (MSSRN), a major MS Charity within the United Kingdom, who are involved in reviewing grant applications, priority setting and research governance (n=146), and surveys from both the United Kingdom MS register (MSR; n=1713) and the North American Research Committee on Multiple Sclerosis (NARCOMS) registry (n=518). People were asked to rate descriptors of someone affected with MS. These were compared to that used by academic experimenters in basic science and clinical science research papers. Although the frequency of responses varied between surveys the overall findings showed many consistencies. This included use of person/people with MS (pwMS) as the preferred descriptor for someone with MS for social media and scientific publications. This was the preferred choice in about 55-60% people from the MRS and in over 70% in the NARCOMS and the MSSRN, respectively. Although MSer was the second preferred-choice for use in social media, there was as a large range of preferences from the 'most-preferred' to the 'most-disliked.' This reflected an earlier survey by UK-based research blogs using the term MSer (n=173). In contrast, pwMS had few 'dislikes' and results were skewed towards the 'liked' and 'most-preferred' choices. Client and sufferer were generally disliked terms, although there was some regional variation in levels of choice. Patient was generally seen as a neutral term that was neither strongly liked nor disliked. However, patient gained more public support for use within scientific publications (~20-25%) compared to social media (~10-15%). This descriptor was however most commonly used (98-99%) within both pre-clinical (searched in 6-month output of preclinical autoimmune MS models; n=161) and in clinical publications (specialist MS journals; n=220), whereas pwMS was not reported in over 75% of papers published in some specialised MS journals, and did not appear in the pre-clinical animal studies examined. There is a clear disconnection between preferences by individuals living with MS and current academic practise. As pwMS are increasingly reading primary research publications and are involved in patient and public involvement in research and grant review activities, the sensitivities of lay readers should be considered when writing research outputs. This issue may affect other diseases and a change in writing style could be adopted to show that we respect the wishes of the people that we study and wish to help. Copyright © 2016 Elsevier B.V. All rights reserved.

Sparse Coding of Natural Human Motion Yields Eigenmotions Consistent Across People

NASA Astrophysics Data System (ADS)

Thomik, Andreas; Faisal, A. Aldo

2015-03-01

Providing a precise mathematical description of the structure of natural human movement is a challenging problem. We use a data-driven approach to seek a generative model of movement capturing the underlying simplicity of spatial and temporal structure of behaviour observed in daily life. In perception, the analysis of natural scenes has shown that sparse codes of such scenes are information theoretic efficient descriptors with direct neuronal correlates. Translating from perception to action, we identify a generative model of movement generation by the human motor system. Using wearable full-hand motion capture, we measure the digit movement of the human hand in daily life. We learn a dictionary of ``eigenmotions'' which we use for sparse encoding of the movement data. We show that the dictionaries are generally well preserved across subjects with small deviations accounting for individuality of the person and variability in tasks. Further, the dictionary elements represent motions which can naturally describe hand movements. Our findings suggest the motor system can compose complex movement behaviours out of the spatially and temporally sparse activation of ``eigenmotion'' neurons, and is consistent with data on grasp-type specificity of specialised neurons in the premotor cortex. Andreas is supported by the Luxemburg Research Fund (1229297).

A Data Driven Model for Predicting RNA-Protein Interactions based on Gradient Boosting Machine.

PubMed

Jain, Dharm Skandh; Gupte, Sanket Rajan; Aduri, Raviprasad

2018-06-22

RNA protein interactions (RPI) play a pivotal role in the regulation of various biological processes. Experimental validation of RPI has been time-consuming, paving the way for computational prediction methods. The major limiting factor of these methods has been the accuracy and confidence of the predictions, and our in-house experiments show that they fail to accurately predict RPI involving short RNA sequences such as TERRA RNA. Here, we present a data-driven model for RPI prediction using a gradient boosting classifier. Amino acids and nucleotides are classified based on the high-resolution structural data of RNA protein complexes. The minimum structural unit consisting of five residues is used as the descriptor. Comparative analysis of existing methods shows the consistently higher performance of our method irrespective of the length of RNA present in the RPI. The method has been successfully applied to map RPI networks involving both long noncoding RNA as well as TERRA RNA. The method is also shown to successfully predict RNA and protein hubs present in RPI networks of four different organisms. The robustness of this method will provide a way for predicting RPI networks of yet unknown interactions for both long noncoding RNA and microRNA.

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.

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.

Modeling the drugs' passive transfer in the body based on their chromatographic behavior.

PubMed

Kouskoura, Maria G; Kachrimanis, Kyriakos G; Markopoulou, Catherine K

2014-11-01

One of the most challenging aims in modern analytical chemistry and pharmaceutical analysis is to create models for drugs' behavior based on simulation experiments. Since drugs' effects are closely related to their molecular properties, numerous characteristics of drugs are used in order to acquire a model of passive absorption and transfer in the human body. Importantly, such direction in innovative bioanalytical methodologies is also of stressful need in the area of personalized medicine to implement nanotechnological and genomics advancements. Simulation experiments were carried out by examining and interpreting the chromatographic behavior of 113 analytes/drugs (400 observations) in RP-HPLC. The dataset employed for this purpose included 73 descriptors which are referring to the physicochemical properties of the mobile phase mixture in different proportions, the physicochemical properties of the analytes and the structural characteristics of their molecules. A series of different software packages was used to calculate all the descriptors apart from those referring to the structure of analytes. The correlation of the descriptors with the retention time of the analytes eluted from a C4 column with an aqueous mobile phase was employed as dataset to introduce the behavior models in the human body. Their evaluation with a Partial Least Squares (PLS) software proved that the chromatographic behavior of a drug on a lipophilic stationary and a polar mobile phase is directly related to its drug-ability. At the same time, the behavior of an unknown drug in the human body can be predicted with reliability via the Artificial Neural Networks (ANNs) software. Copyright © 2014 Elsevier B.V. All rights reserved.

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.

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

Modelling the effect of mixture components on permeation through skin.

PubMed

Ghafourian, T; Samaras, E G; Brooks, J D; Riviere, J E

2010-10-15

A vehicle influences the concentration of penetrant within the membrane, affecting its diffusivity in the skin and rate of transport. Despite the huge amount of effort made for the understanding and modelling of the skin absorption of chemicals, a reliable estimation of the skin penetration potential from formulations remains a challenging objective. In this investigation, quantitative structure-activity relationship (QSAR) was employed to relate the skin permeation of compounds to the chemical properties of the mixture ingredients and the molecular structures of the penetrants. The skin permeability dataset consisted of permeability coefficients of 12 different penetrants each blended in 24 different solvent mixtures measured from finite-dose diffusion cell studies using porcine skin. Stepwise regression analysis resulted in a QSAR employing two penetrant descriptors and one solvent property. The penetrant descriptors were octanol/water partition coefficient, logP and the ninth order path molecular connectivity index, and the solvent property was the difference between boiling and melting points. The negative relationship between skin permeability coefficient and logP was attributed to the fact that most of the drugs in this particular dataset are extremely lipophilic in comparison with the compounds in the common skin permeability datasets used in QSAR. The findings show that compounds formulated in vehicles with small boiling and melting point gaps will be expected to have higher permeation through skin. The QSAR was validated internally, using a leave-many-out procedure, giving a mean absolute error of 0.396. The chemical space of the dataset was compared with that of the known skin permeability datasets and gaps were identified for future skin permeability measurements. Copyright 2010 Elsevier B.V. All rights reserved.

3D QSAR studies on binding affinities of coumarin natural products for glycosomal GAPDH of Trypanosoma cruzi

NASA Astrophysics Data System (ADS)

Menezes, Irwin R. A.; Lopes, Julio C. D.; Montanari, Carlos A.; Oliva, Glaucius; Pavão, Fernando; Castilho, Marcelo S.; Vieira, Paulo C.; Pupo, M.^onica T.

2003-05-01

Drug design strategies based on Comparative Molecular Field Analysis (CoMFA) have been used to predict the activity of new compounds. The major advantage of this approach is that it permits the analysis of a large number of quantitative descriptors and uses chemometric methods such as partial least squares (PLS) to correlate changes in bioactivity with changes in chemical structure. Because it is often difficult to rationalize all variables affecting the binding affinity of compounds using CoMFA solely, the program GRID was used to describe ligands in terms of their molecular interaction fields, MIFs. The program VolSurf that is able to compress the relevant information present in 3D maps into a few descriptors can treat these GRID fields. The binding affinities of a new set of compounds consisting of 13 coumarins, for one of which the three-dimensional ligand-enzyme bound structure is known, were studied. A final model based on the mentioned programs was independently validated by synthesizing and testing new coumarin derivatives. By relying on our knowledge of the real physical data (i.e., combining crystallographic and binding affinity results), it is also shown that ligand-based design agrees with structure-based design. The compound with the highest binding affinity was the coumarin chalepin, isolated from Rutaceae species, with an IC50 value of 55.5 μM towards the enzyme glyceraldehyde-3-phosphate dehydrogenase (gGAPDH) from glycosomes of the parasite Trypanosoma cruzi, the causative agent of Chagas' disease. The proposed models from GRID MIFs have revealed the importance of lipophilic interactions in modulating the inhibition, but without excluding the dependence on stereo-electronic properties as found from CoMFA fields.

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.

ToxiM: A Toxicity Prediction Tool for Small Molecules Developed Using Machine Learning and Chemoinformatics Approaches.

PubMed

Sharma, Ashok K; Srivastava, Gopal N; Roy, Ankita; Sharma, Vineet K

2017-01-01

The experimental methods for the prediction of molecular toxicity are tedious and time-consuming tasks. Thus, the computational approaches could be used to develop alternative methods for toxicity prediction. We have developed a tool for the prediction of molecular toxicity along with the aqueous solubility and permeability of any molecule/metabolite. Using a comprehensive and curated set of toxin molecules as a training set, the different chemical and structural based features such as descriptors and fingerprints were exploited for feature selection, optimization and development of machine learning based classification and regression models. The compositional differences in the distribution of atoms were apparent between toxins and non-toxins, and hence, the molecular features were used for the classification and regression. On 10-fold cross-validation, the descriptor-based, fingerprint-based and hybrid-based classification models showed similar accuracy (93%) and Matthews's correlation coefficient (0.84). The performances of all the three models were comparable (Matthews's correlation coefficient = 0.84-0.87) on the blind dataset. In addition, the regression-based models using descriptors as input features were also compared and evaluated on the blind dataset. Random forest based regression model for the prediction of solubility performed better ( R 2 = 0.84) than the multi-linear regression (MLR) and partial least square regression (PLSR) models, whereas, the partial least squares based regression model for the prediction of permeability (caco-2) performed better ( R 2 = 0.68) in comparison to the random forest and MLR based regression models. The performance of final classification and regression models was evaluated using the two validation datasets including the known toxins and commonly used constituents of health products, which attests to its accuracy. The ToxiM web server would be a highly useful and reliable tool for the prediction of toxicity, solubility, and permeability of small molecules.

ToxiM: A Toxicity Prediction Tool for Small Molecules Developed Using Machine Learning and Chemoinformatics Approaches

PubMed Central

Sharma, Ashok K.; Srivastava, Gopal N.; Roy, Ankita; Sharma, Vineet K.

2017-01-01

The experimental methods for the prediction of molecular toxicity are tedious and time-consuming tasks. Thus, the computational approaches could be used to develop alternative methods for toxicity prediction. We have developed a tool for the prediction of molecular toxicity along with the aqueous solubility and permeability of any molecule/metabolite. Using a comprehensive and curated set of toxin molecules as a training set, the different chemical and structural based features such as descriptors and fingerprints were exploited for feature selection, optimization and development of machine learning based classification and regression models. The compositional differences in the distribution of atoms were apparent between toxins and non-toxins, and hence, the molecular features were used for the classification and regression. On 10-fold cross-validation, the descriptor-based, fingerprint-based and hybrid-based classification models showed similar accuracy (93%) and Matthews's correlation coefficient (0.84). The performances of all the three models were comparable (Matthews's correlation coefficient = 0.84–0.87) on the blind dataset. In addition, the regression-based models using descriptors as input features were also compared and evaluated on the blind dataset. Random forest based regression model for the prediction of solubility performed better (R2 = 0.84) than the multi-linear regression (MLR) and partial least square regression (PLSR) models, whereas, the partial least squares based regression model for the prediction of permeability (caco-2) performed better (R2 = 0.68) in comparison to the random forest and MLR based regression models. The performance of final classification and regression models was evaluated using the two validation datasets including the known toxins and commonly used constituents of health products, which attests to its accuracy. The ToxiM web server would be a highly useful and reliable tool for the prediction of toxicity, solubility, and permeability of small molecules. PMID:29249969

Novel 3D bio-macromolecular bilinear descriptors for protein science: Predicting protein structural classes.

PubMed

Marrero-Ponce, Yovani; Contreras-Torres, Ernesto; García-Jacas, César R; Barigye, Stephen J; Cubillán, Néstor; Alvarado, Ysaías J

2015-06-07

In the present study, we introduce novel 3D protein descriptors based on the bilinear algebraic form in the ℝ(n) space on the coulombic matrix. For the calculation of these descriptors, macromolecular vectors belonging to ℝ(n) space, whose components represent certain amino acid side-chain properties, were used as weighting schemes. Generalization approaches for the calculation of inter-amino acidic residue spatial distances based on Minkowski metrics are proposed. The simple- and double-stochastic schemes were defined as approaches to normalize the coulombic matrix. The local-fragment indices for both amino acid-types and amino acid-groups are presented in order to permit characterizing fragments of interest in proteins. On the other hand, with the objective of taking into account specific interactions among amino acids in global or local indices, geometric and topological cut-offs are defined. To assess the utility of global and local indices a classification model for the prediction of the major four protein structural classes, was built with the Linear Discriminant Analysis (LDA) technique. The developed LDA-model correctly classifies the 92.6% and 92.7% of the proteins on the training and test sets, respectively. The obtained model showed high values of the generalized square correlation coefficient (GC(2)) on both the training and test series. The statistical parameters derived from the internal and external validation procedures demonstrate the robustness, stability and the high predictive power of the proposed model. The performance of the LDA-model demonstrates the capability of the proposed indices not only to codify relevant biochemical information related to the structural classes of proteins, but also to yield suitable interpretability. It is anticipated that the current method will benefit the prediction of other protein attributes or functions. Copyright © 2015 Elsevier Ltd. All rights reserved.

Novel dimer based descriptors with solvational computation for QSAR study of oxadiazoylbenzoyl-ureas as novel insect-growth regulators.

PubMed

Fan, Feng; Cheng, Jiagao; Li, Zhong; Xu, Xiaoyong; Qian, Xuhong

2010-02-01

Molecular aggregation state of bioactive compounds plays a key role in its bio-interactive procedure. In this article, based on the structure information of dimers, the simplest model of molecular aggregation state, and combined with solvational computation, total four descriptors (DeltaV, MR2, DeltaE(1), and DeltaE(2)) were calculated for QSAR study of a novel insect-growth regulator, N-(5-phenyl-1,3,4-oxadiazol-2-yl)-N'-benzoyl urea. Two QSAR models were constructed with r(2) = 0.671, q(2) = 0.516 and r(2) = 0.816, q(2) = 0.695, respectively. It implicates that the bioactivity may strongly depend on the characters of molecular aggregation state, especially on the dimeric transport ability from oil phase to water phase. Copyright 2009 Wiley Periodicals, Inc.

Discriminatively learning for representing local image features with quadruplet model

NASA Astrophysics Data System (ADS)

Zhang, Da-long; Zhao, Lei; Xu, Duan-qing; Lu, Dong-ming

2017-11-01

Traditional hand-crafted features for representing local image patches are evolving into current data-driven and learning-based image feature, but learning a robust and discriminative descriptor which is capable of controlling various patch-level computer vision tasks is still an open problem. In this work, we propose a novel deep convolutional neural network (CNN) to learn local feature descriptors. We utilize the quadruplets with positive and negative training samples, together with a constraint to restrict the intra-class variance, to learn good discriminative CNN representations. Compared with previous works, our model reduces the overlap in feature space between corresponding and non-corresponding patch pairs, and mitigates margin varying problem caused by commonly used triplet loss. We demonstrate that our method achieves better embedding result than some latest works, like PN-Net and TN-TG, on benchmark dataset.

Prediction of atmospheric degradation data for POPs by gene expression programming.

PubMed

Luan, F; Si, H Z; Liu, H T; Wen, Y Y; Zhang, X Y

2008-01-01

Quantitative structure-activity relationship models for the prediction of the mean and the maximum atmospheric degradation half-life values of persistent organic pollutants were developed based on the linear heuristic method (HM) and non-linear gene expression programming (GEP). Molecular descriptors, calculated from the structures alone, were used to represent the characteristics of the compounds. HM was used both to pre-select the whole descriptor sets and to build the linear model. GEP yielded satisfactory prediction results: the square of the correlation coefficient r(2) was 0.80 and 0.81 for the mean and maximum half-life values of the test set, and the root mean square errors were 0.448 and 0.426, respectively. The results of this work indicate that the GEP is a very promising tool for non-linear approximations.

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.

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.

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

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.

A structural equation model analysis of relationships among ENSO, seasonal descriptors and wildfires.

PubMed

Slocum, Matthew G; Orzell, Steve L

2013-01-01

Seasonality drives ecological processes through networks of forcings, and the resultant complexity requires creative approaches for modeling to be successful. Recently ecologists and climatologists have developed sophisticated methods for fully describing seasons. However, to date the relationships among the variables produced by these methods have not been analyzed as networks, but rather with simple univariate statistics. In this manuscript we used structural equation modeling (SEM) to analyze a proposed causal network describing seasonality of rainfall for a site in south-central Florida. We also described how this network was influenced by the El Niño-Southern Oscillation (ENSO), and how the network in turn affected the site's wildfire regime. Our models indicated that wet and dry seasons starting later in the year (or ending earlier) were shorter and had less rainfall. El Niño conditions increased dry season rainfall, and via this effect decreased the consistency of that season's drying trend. El Niño conditions also negatively influenced how consistent the moistening trend was during the wet season, but in this case the effect was direct and did not route through rainfall. In modeling wildfires, our models showed that area burned was indirectly influenced by ENSO via its effect on dry season rainfall. Area burned was also indirectly reduced when the wet season had consistent rainfall, as such wet seasons allowed fewer wildfires in subsequent fire seasons. Overall area burned at the study site was estimated with high accuracy (R (2) score = 0.63). In summary, we found that by using SEMs, we were able to clearly describe causal patterns involving seasonal climate, ENSO and wildfire. We propose that similar approaches could be effectively applied to other sites where seasonality exerts strong and complex forcings on ecological processes.

A Structural Equation Model Analysis of Relationships among ENSO, Seasonal Descriptors and Wildfires

PubMed Central

Slocum, Matthew G.; Orzell, Steve L.

2013-01-01

Seasonality drives ecological processes through networks of forcings, and the resultant complexity requires creative approaches for modeling to be successful. Recently ecologists and climatologists have developed sophisticated methods for fully describing seasons. However, to date the relationships among the variables produced by these methods have not been analyzed as networks, but rather with simple univariate statistics. In this manuscript we used structural equation modeling (SEM) to analyze a proposed causal network describing seasonality of rainfall for a site in south-central Florida. We also described how this network was influenced by the El Niño-Southern Oscillation (ENSO), and how the network in turn affected the site’s wildfire regime. Our models indicated that wet and dry seasons starting later in the year (or ending earlier) were shorter and had less rainfall. El Niño conditions increased dry season rainfall, and via this effect decreased the consistency of that season’s drying trend. El Niño conditions also negatively influenced how consistent the moistening trend was during the wet season, but in this case the effect was direct and did not route through rainfall. In modeling wildfires, our models showed that area burned was indirectly influenced by ENSO via its effect on dry season rainfall. Area burned was also indirectly reduced when the wet season had consistent rainfall, as such wet seasons allowed fewer wildfires in subsequent fire seasons. Overall area burned at the study site was estimated with high accuracy (R 2 score = 0.63). In summary, we found that by using SEMs, we were able to clearly describe causal patterns involving seasonal climate, ENSO and wildfire. We propose that similar approaches could be effectively applied to other sites where seasonality exerts strong and complex forcings on ecological processes. PMID:24086670

Searching for Global Descriptors of Engineered Nanomaterial Fate and Transport in the Environment

PubMed Central

Nowack, Bernd

2012-01-01

CONSPECTUS Engineered nanomaterials (ENMs) are a new class of environmental pollutants. Researchers are beginning to debate whether new modeling paradigms and experimental tests to obtain model parameters are required for ENMs or if approaches for existing pollutants are robust enough to predict ENM distribution between environmental compartments. This Account outlines how experimental research can yield quantitative data for use in ENM fate and exposure models. We first review experimental testing approaches that are employed with ENMs. Then we compare and contrast ENMs against other pollutants. Finally, we summarize the findings and identify research needs that may yield global descriptors for ENMs that are suitable for use in fate and transport modeling. Over the past decade, researchers have made significant progress in understanding factors that influence the fate and transport of ENMs. In some cases researchers have developed approaches toward global descriptor models (experimental, conceptual, and quantitative). We suggest the following global descriptors for ENMs: octanol-water partition coefficients, solid-water partition coefficients, attachment coefficients, and rate constants describing reactions such as dissolution, sedimentation, and degradation. ENMs appear to accumulate at the octanol-water interface and readily interact with other interfaces, such as lipid-water interfaces. Batch experiments to investigate factors that influence retention of ENMs on solid phases are very promising. However ENMs probably do not behave in the same way as dissolved chemicals, and therefore researchers need to use measurement techniques and concepts more commonly associated with colloids. Despite several years of research with ENMs in column studies, available summaries tend to discuss the effects of ionic strength, pH, organic matter, ENM type, packing media, or other parameters qualitatively rather than reporting quantitative values, such as attachment efficiencies, that would facilitate comparison across studies. Only a few structure-activity relationships have been developed for ENMs so far, but such evaluations will facilitate the understanding of the reactivities of different forms of a single ENM. The establishment of predictive capabilities for ENMs in the environment would enable accurate exposure assessments that would assist in ENM risk management. Such information is also critical for understanding the ultimate disposition of ENMs and may provide a framework for improved engineering of nanomaterials that are more environmentally benign. PMID:22950943

QSAR models for anti-malarial activity of 4-aminoquinolines.

PubMed

Masand, Vijay H; Toropov, Andrey A; Toropova, Alla P; Mahajan, Devidas T

2014-03-01

In the present study, predictive quantitative structure - activity relationship (QSAR) models for anti-malarial activity of 4-aminoquinolines have been developed. CORAL, which is freely available on internet (http://www.insilico.eu/coral), has been used as a tool of QSAR analysis to establish statistically robust QSAR model of anti-malarial activity of 4-aminoquinolines. Six random splits into the visible sub-system of the training and invisible subsystem of validation were examined. Statistical qualities for these splits vary, but in all these cases, statistical quality of prediction for anti-malarial activity was quite good. The optimal SMILES-based descriptor was used to derive the single descriptor based QSAR model for a data set of 112 aminoquinolones. All the splits had r(2)> 0.85 and r(2)> 0.78 for subtraining and validation sets, respectively. The three parametric multilinear regression (MLR) QSAR model has Q(2) = 0.83, R(2) = 0.84 and F = 190.39. The anti-malarial activity has strong correlation with presence/absence of nitrogen and oxygen at a topological distance of six.

Predictive and mechanistic multivariate linear regression models for reaction development

PubMed Central

Santiago, Celine B.; Guo, Jing-Yao

2018-01-01

Multivariate Linear Regression (MLR) models utilizing computationally-derived and empirically-derived physical organic molecular descriptors are described in this review. Several reports demonstrating the effectiveness of this methodological approach towards reaction optimization and mechanistic interrogation are discussed. A detailed protocol to access quantitative and predictive MLR models is provided as a guide for model development and parameter analysis. PMID:29719711

Prioritization of in silico models and molecular descriptors for the assessment of ready biodegradability

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

Fernández, Alberto; Rallo, Robert; Giralt, Francesc

2015-10-15

Ready biodegradability is a key property for evaluating the long-term effects of chemicals on the environment and human health. As such, it is used as a screening test for the assessment of persistent, bioaccumulative and toxic substances. Regulators encourage the use of non-testing methods, such as in silico models, to save money and time. A dataset of 757 chemicals was collected to assess the performance of four freely available in silico models that predict ready biodegradability. They were applied to develop a new consensus method that prioritizes the use of each individual model according to its performance on chemical subsetsmore » driven by the presence or absence of different molecular descriptors. This consensus method was capable of almost eliminating unpredictable chemicals, while the performance of combined models was substantially improved with respect to that of the individual models. - Highlights: • Consensus method to predict ready biodegradability by prioritizing multiple QSARs. • Consensus reduced the amount of unpredictable chemicals to less than 2%. • Performance increased with the number of QSAR models considered. • The absence of 2D atom pairs contributed significantly to the consensus model.« less

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…

A preliminary evaluation of the relationship of cannabinoid blood concentrations with the analgesic response to vaporized cannabis

PubMed Central

Wilsey, Barth L; Deutsch, Reena; Samara, Emil; Marcotte, Thomas D; Barnes, Allan J; Huestis, Marilyn A; Le, Danny

2016-01-01

A randomized, placebo-controlled crossover trial utilizing vaporized cannabis containing placebo and 6.7% and 2.9% delta-9-tetrahydrocannabinol (THC) was performed in 42 subjects with central neuropathic pain related to spinal cord injury and disease. Subjects received two administrations of the study medication in a 4-hour interval. Blood samples for pharmacokinetic evaluation were collected, and pain assessment tests were performed immediately after the second administration and 3 hours later. Pharmacokinetic data, although limited, were consistent with literature reports, namely dose-dependent increase in systemic exposure followed by rapid disappearance of THC. Dose-dependent improvement in pain score was evident across all pain scale elements. Using mixed model regression, an evaluation of the relationship between plasma concentrations of selected cannabinoids and percent change in items from the Neuropathic Pain Scale was conducted. Changes in the concentration of THC and its nonpsychotropic metabolite, 11-nor-9-carboxy-THC, were related to percent change from baseline of several descriptors (eg, itching, burning, and deep pain). However, given the large number of multiple comparisons, false-discovery-rate-adjusted P-values were not significant. Plans for future work are outlined to explore the relationship of plasma concentrations with the analgesic response to different cannabinoids. Such an appraisal of descriptors might contribute to the identification of distinct pathophysiologic mechanisms and, ultimately, the development of mechanism-based treatment approaches for neuropathic pain, a condition that remains difficult to treat. PMID:27621666

A preliminary evaluation of the relationship of cannabinoid blood concentrations with the analgesic response to vaporized cannabis.

PubMed

Wilsey, Barth L; Deutsch, Reena; Samara, Emil; Marcotte, Thomas D; Barnes, Allan J; Huestis, Marilyn A; Le, Danny

2016-01-01

A randomized, placebo-controlled crossover trial utilizing vaporized cannabis containing placebo and 6.7% and 2.9% delta-9-tetrahydrocannabinol (THC) was performed in 42 subjects with central neuropathic pain related to spinal cord injury and disease. Subjects received two administrations of the study medication in a 4-hour interval. Blood samples for pharmacokinetic evaluation were collected, and pain assessment tests were performed immediately after the second administration and 3 hours later. Pharmacokinetic data, although limited, were consistent with literature reports, namely dose-dependent increase in systemic exposure followed by rapid disappearance of THC. Dose-dependent improvement in pain score was evident across all pain scale elements. Using mixed model regression, an evaluation of the relationship between plasma concentrations of selected cannabinoids and percent change in items from the Neuropathic Pain Scale was conducted. Changes in the concentration of THC and its nonpsychotropic metabolite, 11-nor-9-carboxy-THC, were related to percent change from baseline of several descriptors (eg, itching, burning, and deep pain). However, given the large number of multiple comparisons, false-discovery-rate-adjusted P-values were not significant. Plans for future work are outlined to explore the relationship of plasma concentrations with the analgesic response to different cannabinoids. Such an appraisal of descriptors might contribute to the identification of distinct pathophysiologic mechanisms and, ultimately, the development of mechanism-based treatment approaches for neuropathic pain, a condition that remains difficult to treat.

A Bayesian network model for predicting aquatic toxicity mode of action using two dimensional theoretical molecular descriptors

EPA Science Inventory

The mode of toxic action (MoA) has been recognized as a key determinant of chemical toxicity, but development of predictive MoA classification models in aquatic toxicology has been limited. We developed a Bayesian network model to classify aquatic toxicity MoA using a recently pu...

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…

RGB-D SLAM Combining Visual Odometry and Extended Information Filter

PubMed Central

Zhang, Heng; Liu, Yanli; Tan, Jindong; Xiong, Naixue

2015-01-01

In this paper, we present a novel RGB-D SLAM system based on visual odometry and an extended information filter, which does not require any other sensors or odometry. In contrast to the graph optimization approaches, this is more suitable for online applications. A visual dead reckoning algorithm based on visual residuals is devised, which is used to estimate motion control input. In addition, we use a novel descriptor called binary robust appearance and normals descriptor (BRAND) to extract features from the RGB-D frame and use them as landmarks. Furthermore, considering both the 3D positions and the BRAND descriptors of the landmarks, our observation model avoids explicit data association between the observations and the map by marginalizing the observation likelihood over all possible associations. Experimental validation is provided, which compares the proposed RGB-D SLAM algorithm with just RGB-D visual odometry and a graph-based RGB-D SLAM algorithm using the publicly-available RGB-D dataset. The results of the experiments demonstrate that our system is quicker than the graph-based RGB-D SLAM algorithm. PMID:26263990

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.

Fragment-based prediction of skin sensitization using recursive partitioning

NASA Astrophysics Data System (ADS)

Lu, Jing; Zheng, Mingyue; Wang, Yong; Shen, Qiancheng; Luo, Xiaomin; Jiang, Hualiang; Chen, Kaixian

2011-09-01

Skin sensitization is an important toxic endpoint in the risk assessment of chemicals. In this paper, structure-activity relationships analysis was performed on the skin sensitization potential of 357 compounds with local lymph node assay data. Structural fragments were extracted by GASTON (GrAph/Sequence/Tree extractiON) from the training set. Eight fragments with accuracy significantly higher than 0.73 ( p < 0.1) were retained to make up an indicator descriptor fragment. The fragment descriptor and eight other physicochemical descriptors closely related to the endpoint were calculated to construct the recursive partitioning tree (RP tree) for classification. The balanced accuracy of the training set, test set I, and test set II in the leave-one-out model were 0.846, 0.800, and 0.809, respectively. The results highlight that fragment-based RP tree is a preferable method for identifying skin sensitizers. Moreover, the selected fragments provide useful structural information for exploring sensitization mechanisms, and RP tree creates a graphic tree to identify the most important properties associated with skin sensitization. They can provide some guidance for designing of drugs with lower sensitization level.

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.

Scene text recognition in mobile applications by character descriptor and structure configuration.

PubMed

Yi, Chucai; Tian, Yingli

2014-07-01

Text characters and strings in natural scene can provide valuable information for many applications. Extracting text directly from natural scene images or videos is a challenging task because of diverse text patterns and variant background interferences. This paper proposes a method of scene text recognition from detected text regions. In text detection, our previously proposed algorithms are applied to obtain text regions from scene image. First, we design a discriminative character descriptor by combining several state-of-the-art feature detectors and descriptors. Second, we model character structure at each character class by designing stroke configuration maps. Our algorithm design is compatible with the application of scene text extraction in smart mobile devices. An Android-based demo system is developed to show the effectiveness of our proposed method on scene text information extraction from nearby objects. The demo system also provides us some insight into algorithm design and performance improvement of scene text extraction. The evaluation results on benchmark data sets demonstrate that our proposed scheme of text recognition is comparable with the best existing methods.

Recognizing stationary and locomotion activities using combinational of spectral analysis with statistical descriptors features

NASA Astrophysics Data System (ADS)

Zainudin, M. N. Shah; Sulaiman, Md Nasir; Mustapha, Norwati; Perumal, Thinagaran

2017-10-01

Prior knowledge in pervasive computing recently garnered a lot of attention due to its high demand in various application domains. Human activity recognition (HAR) considered as the applications that are widely explored by the expertise that provides valuable information to the human. Accelerometer sensor-based approach is utilized as devices to undergo the research in HAR since their small in size and this sensor already build-in in the various type of smartphones. However, the existence of high inter-class similarities among the class tends to degrade the recognition performance. Hence, this work presents the method for activity recognition using our proposed features from combinational of spectral analysis with statistical descriptors that able to tackle the issue of differentiating stationary and locomotion activities. The noise signal is filtered using Fourier Transform before it will be extracted using two different groups of features, spectral frequency analysis, and statistical descriptors. Extracted signal later will be classified using random forest ensemble classifier models. The recognition results show the good accuracy performance for stationary and locomotion activities based on USC HAD datasets.

Prototype Procedures to Describe Army Jobs

DTIC Science & Technology

2010-07-01

ratings for the same MOS. Consistent with a multi-trait multi- method framework, high profile similarities (or low mean differences ) among different ...rater types for the same MOS would indicate convergent validity. That is, different methods (i.e., rater types) yield converging results for the same... different methods of data collection depends upon the type of data collected. For example, it could be that data on work-oriented descriptors are most

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.

US Smokers’ Beliefs, Experiences and Perceptions of Different Cigarette Variants Before and After the FSPTCA Ban on Misleading Descriptors Such as “Light,” “Mild,” or “Low”

PubMed Central

Borland, Ron; Cummings, K. Michael; Lindblom, Eric N.; Li, Lin; Bansal-Travers, Maansi; O’Connor, Richard J.; Elton-Marshall, Tara; Thrasher, James F.; Hammond, David; Thompson, Mary E.; Partos, Timea R.

2016-01-01

Introduction: In December 2008, the Federal Trade Commission (FTC) took action that prompted the removal of nicotine and tar listings from cigarette packs and ads. As of June 2010, the US Family Smoking Prevention and Tobacco Control Act prohibited the use of explicit or implicit descriptors on tobacco packaging or in advertising that convey messages of reduced risk or exposure, specifically including terms like “light,” “mild,” and “low” and similar descriptors. This study evaluates the effect of these two policy changes on smokers’ beliefs, experiences and perceptions of different cigarettes. Methods: Using generalized estimating equations models, this study analyzed survey data collected between 2002 and 2013 by the International Tobacco Control Policy Evaluation Study regarding US smokers’ beliefs, experiences, and perceptions of different cigarettes. Results: Between 2002 and 2013, smoker misperceptions about “light” cigarettes being less harmful did not change significantly and remained substantial, especially among those who reported using lower-strength cigarettes. After the two policy changes, reported reliance on pack colors, color terms, and other product descriptors like “smooth” to determine cigarette strength style trended upward. Conclusions: Policies implemented to reduce smoker misperceptions that some cigarettes are safer than others appear to have had little impact. Because of pack colors, color terms, descriptors such as “smooth,” cigarette taste or feel, and possibly other characteristics, millions of smokers continue to believe, inaccurately, that they can reduce their harms and risks by smoking one cigarette brand or sub-brand instead of another, which may be delaying or reducing smoking cessation. Implications: What this study adds: This study confirms that US policies to reduce smoker misperceptions that some cigarettes are less harmful than others have not been successful. Following the removal of light/low descriptors and tar and nicotine numbers from cigarette packs and ads, pack colors, color words, other descriptors (eg, smooth), and sensory experiences of smoother or lighter taste have helped smokers to continue to identify their preferred cigarette brand styles and otherwise distinguish between which brands and styles they consider “lighter” or lower in tar and, mistakenly, less harmful than others. These findings provide additional evidence to support new enforcement or regulatory action to stop cigarettes and their packaging from misleading smokers about relative risk, which may be reducing or delaying quit attempts. PMID:27083215

US Smokers' Beliefs, Experiences and Perceptions of Different Cigarette Variants Before and After the FSPTCA Ban on Misleading Descriptors Such as "Light," "Mild," or "Low".

PubMed

Yong, Hua-Hie; Borland, Ron; Cummings, K Michael; Lindblom, Eric N; Li, Lin; Bansal-Travers, Maansi; O'Connor, Richard J; Elton-Marshall, Tara; Thrasher, James F; Hammond, David; Thompson, Mary E; Partos, Timea R

2016-11-01

In December 2008, the Federal Trade Commission (FTC) took action that prompted the removal of nicotine and tar listings from cigarette packs and ads. As of June 2010, the US Family Smoking Prevention and Tobacco Control Act prohibited the use of explicit or implicit descriptors on tobacco packaging or in advertising that convey messages of reduced risk or exposure, specifically including terms like "light," "mild," and "low" and similar descriptors. This study evaluates the effect of these two policy changes on smokers' beliefs, experiences and perceptions of different cigarettes. Using generalized estimating equations models, this study analyzed survey data collected between 2002 and 2013 by the International Tobacco Control Policy Evaluation Study regarding US smokers' beliefs, experiences, and perceptions of different cigarettes. Between 2002 and 2013, smoker misperceptions about "light" cigarettes being less harmful did not change significantly and remained substantial, especially among those who reported using lower-strength cigarettes. After the two policy changes, reported reliance on pack colors, color terms, and other product descriptors like "smooth" to determine cigarette strength style trended upward. Policies implemented to reduce smoker misperceptions that some cigarettes are safer than others appear to have had little impact. Because of pack colors, color terms, descriptors such as "smooth," cigarette taste or feel, and possibly other characteristics, millions of smokers continue to believe, inaccurately, that they can reduce their harms and risks by smoking one cigarette brand or sub-brand instead of another, which may be delaying or reducing smoking cessation. What this study adds: This study confirms that US policies to reduce smoker misperceptions that some cigarettes are less harmful than others have not been successful. Following the removal of light/low descriptors and tar and nicotine numbers from cigarette packs and ads, pack colors, color words, other descriptors (eg, smooth), and sensory experiences of smoother or lighter taste have helped smokers to continue to identify their preferred cigarette brand styles and otherwise distinguish between which brands and styles they consider "lighter" or lower in tar and, mistakenly, less harmful than others. These findings provide additional evidence to support new enforcement or regulatory action to stop cigarettes and their packaging from misleading smokers about relative risk, which may be reducing or delaying quit attempts. © The Author 2016. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Predicting the aquatic toxicity mode of action using logistic regression and linear discriminant analysis.

PubMed

Ren, Y Y; Zhou, L C; Yang, L; Liu, P Y; Zhao, B W; Liu, H X

2016-09-01

The paper highlights the use of the logistic regression (LR) method in the construction of acceptable statistically significant, robust and predictive models for the classification of chemicals according to their aquatic toxic modes of action. Essentials accounting for a reliable model were all considered carefully. The model predictors were selected by stepwise forward discriminant analysis (LDA) from a combined pool of experimental data and chemical structure-based descriptors calculated by the CODESSA and DRAGON software packages. Model predictive ability was validated both internally and externally. The applicability domain was checked by the leverage approach to verify prediction reliability. The obtained models are simple and easy to interpret. In general, LR performs much better than LDA and seems to be more attractive for the prediction of the more toxic compounds, i.e. compounds that exhibit excess toxicity versus non-polar narcotic compounds and more reactive compounds versus less reactive compounds. In addition, model fit and regression diagnostics was done through the influence plot which reflects the hat-values, studentized residuals, and Cook's distance statistics of each sample. Overdispersion was also checked for the LR model. The relationships between the descriptors and the aquatic toxic behaviour of compounds are also discussed.

Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture

PubMed Central

Meszlényi, Regina J.; Buza, Krisztian; Vidnyánszky, Zoltán

2017-01-01

Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a convolutional neural network architecture for functional connectome classification called connectome-convolutional neural network (CCNN). Our results on simulated datasets and a publicly available dataset for amnestic mild cognitive impairment classification demonstrate that our CCNN model can efficiently distinguish between subject groups. We also show that the connectome-convolutional network is capable to combine information from diverse functional connectivity metrics and that models using a combination of different connectivity descriptors are able to outperform classifiers using only one metric. From this flexibility follows that our proposed CCNN model can be easily adapted to a wide range of connectome based classification or regression tasks, by varying which connectivity descriptor combinations are used to train the network. PMID:29089883

Resting State fMRI Functional Connectivity-Based Classification Using a Convolutional Neural Network Architecture.

PubMed

Meszlényi, Regina J; Buza, Krisztian; Vidnyánszky, Zoltán

2017-01-01

Machine learning techniques have become increasingly popular in the field of resting state fMRI (functional magnetic resonance imaging) network based classification. However, the application of convolutional networks has been proposed only very recently and has remained largely unexplored. In this paper we describe a convolutional neural network architecture for functional connectome classification called connectome-convolutional neural network (CCNN). Our results on simulated datasets and a publicly available dataset for amnestic mild cognitive impairment classification demonstrate that our CCNN model can efficiently distinguish between subject groups. We also show that the connectome-convolutional network is capable to combine information from diverse functional connectivity metrics and that models using a combination of different connectivity descriptors are able to outperform classifiers using only one metric. From this flexibility follows that our proposed CCNN model can be easily adapted to a wide range of connectome based classification or regression tasks, by varying which connectivity descriptor combinations are used to train the network.

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.