Log-Gabor Weber descriptor for face recognition

NASA Astrophysics Data System (ADS)

Li, Jing; Sang, Nong; Gao, Changxin

2015-09-01

The Log-Gabor transform, which is suitable for analyzing gradually changing data such as in iris and face images, has been widely used in image processing, pattern recognition, and computer vision. In most cases, only the magnitude or phase information of the Log-Gabor transform is considered. However, the complementary effect taken by combining magnitude and phase information simultaneously for an image-feature extraction problem has not been systematically explored in the existing works. We propose a local image descriptor for face recognition, called Log-Gabor Weber descriptor (LGWD). The novelty of our LGWD is twofold: (1) to fully utilize the information from the magnitude or phase feature of multiscale and orientation Log-Gabor transform, we apply the Weber local binary pattern operator to each transform response. (2) The encoded Log-Gabor magnitude and phase information are fused at the feature level by utilizing kernel canonical correlation analysis strategy, considering that feature level information fusion is effective when the modalities are correlated. Experimental results on the AR, Extended Yale B, and UMIST face databases, compared with those available from recent experiments reported in the literature, show that our descriptor yields a better performance than state-of-the art methods.

Parametric classification of handvein patterns based on texture features

NASA Astrophysics Data System (ADS)

Al Mahafzah, Harbi; Imran, Mohammad; Supreetha Gowda H., D.

2018-04-01

In this paper, we have developed Biometric recognition system adopting hand based modality Handvein,which has the unique pattern for each individual and it is impossible to counterfeit and fabricate as it is an internal feature. We have opted in choosing feature extraction algorithms such as LBP-visual descriptor, LPQ-blur insensitive texture operator, Log-Gabor-Texture descriptor. We have chosen well known classifiers such as KNN and SVM for classification. We have experimented and tabulated results of single algorithm recognition rate for Handvein under different distance measures and kernel options. The feature level fusion is carried out which increased the performance level.

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.

[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.

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.

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.

Introducing a new methodology for the calculation of local philicity and multiphilic descriptor: an alternative to the finite difference approximation

NASA Astrophysics Data System (ADS)

Sánchez-Márquez, Jesús; Zorrilla, David; García, Víctor; Fernández, Manuel

2018-07-01

This work presents a new development based on the condensation scheme proposed by Chamorro and Pérez, in which new terms to correct the frozen molecular orbital approximation have been introduced (improved frontier molecular orbital approximation). The changes performed on the original development allow taking into account the orbital relaxation effects, providing equivalent results to those achieved by the finite difference approximation and leading also to a methodology with great advantages. Local reactivity indices based on this new development have been obtained for a sample set of molecules and they have been compared with those indices based on the frontier molecular orbital and finite difference approximations. A new definition based on the improved frontier molecular orbital methodology for the dual descriptor index is also shown. In addition, taking advantage of the characteristics of the definitions obtained with the new condensation scheme, the descriptor local philicity is analysed by separating the components corresponding to the frontier molecular orbital approximation and orbital relaxation effects, analysing also the local parameter multiphilic descriptor in the same way. Finally, the effect of using the basis set is studied and calculations using DFT, CI and Möller-Plesset methodologies are performed to analyse the consequence of different electronic-correlation levels.

An Effective 3D Shape Descriptor for Object Recognition with RGB-D Sensors

PubMed Central

Liu, Zhong; Zhao, Changchen; Wu, Xingming; Chen, Weihai

2017-01-01

RGB-D sensors have been widely used in various areas of computer vision and graphics. A good descriptor will effectively improve the performance of operation. This article further analyzes the recognition performance of shape features extracted from multi-modality source data using RGB-D sensors. A hybrid shape descriptor is proposed as a representation of objects for recognition. We first extracted five 2D shape features from contour-based images and five 3D shape features over point cloud data to capture the global and local shape characteristics of an object. The recognition performance was tested for category recognition and instance recognition. Experimental results show that the proposed shape descriptor outperforms several common global-to-global shape descriptors and is comparable to some partial-to-global shape descriptors that achieved the best accuracies in category and instance recognition. Contribution of partial features and computational complexity were also analyzed. The results indicate that the proposed shape features are strong cues for object recognition and can be combined with other features to boost accuracy. PMID:28245553

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.

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.

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%).

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.

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.

Development of structure-activity relationship for metal oxide nanoparticles

NASA Astrophysics Data System (ADS)

Liu, Rong; Zhang, Hai Yuan; Ji, Zhao Xia; Rallo, Robert; Xia, Tian; Chang, Chong Hyun; Nel, Andre; Cohen, Yoram

2013-05-01

Nanomaterial structure-activity relationships (nano-SARs) for metal oxide nanoparticles (NPs) toxicity were investigated using metrics based on dose-response analysis and consensus self-organizing map clustering. The NP cellular toxicity dataset included toxicity profiles consisting of seven different assays for human bronchial epithelial (BEAS-2B) and murine myeloid (RAW 264.7) cells, over a concentration range of 0.39-100 mg L-1 and exposure time up to 24 h, for twenty-four different metal oxide NPs. Various nano-SAR building models were evaluated, based on an initial pool of thirty NP descriptors. The conduction band energy and ionic index (often correlated with the hydration enthalpy) were identified as suitable NP descriptors that are consistent with suggested toxicity mechanisms for metal oxide NPs and metal ions. The best performing nano-SAR with the above two descriptors, built with support vector machine (SVM) model and of validated robustness, had a balanced classification accuracy of ~94%. An applicability domain for the present data was established with a reasonable confidence level of 80%. Given the potential role of nano-SARs in decision making, regarding the environmental impact of NPs, the class probabilities provided by the SVM nano-SAR enabled the construction of decision boundaries with respect to toxicity classification under different acceptance levels of false negative relative to false positive predictions.Nanomaterial structure-activity relationships (nano-SARs) for metal oxide nanoparticles (NPs) toxicity were investigated using metrics based on dose-response analysis and consensus self-organizing map clustering. The NP cellular toxicity dataset included toxicity profiles consisting of seven different assays for human bronchial epithelial (BEAS-2B) and murine myeloid (RAW 264.7) cells, over a concentration range of 0.39-100 mg L-1 and exposure time up to 24 h, for twenty-four different metal oxide NPs. Various nano-SAR building models were evaluated, based on an initial pool of thirty NP descriptors. The conduction band energy and ionic index (often correlated with the hydration enthalpy) were identified as suitable NP descriptors that are consistent with suggested toxicity mechanisms for metal oxide NPs and metal ions. The best performing nano-SAR with the above two descriptors, built with support vector machine (SVM) model and of validated robustness, had a balanced classification accuracy of ~94%. An applicability domain for the present data was established with a reasonable confidence level of 80%. Given the potential role of nano-SARs in decision making, regarding the environmental impact of NPs, the class probabilities provided by the SVM nano-SAR enabled the construction of decision boundaries with respect to toxicity classification under different acceptance levels of false negative relative to false positive predictions. Electronic supplementary information (ESI) available. See DOI: 10.1039/c3nr01533e

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%.

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

PubMed

Husain, Syed Sameed; Bober, Miroslaw

2017-09-01

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

Effect of noise spectra and a listening task upon passenger annoyance in a helicopter interior noise environment

NASA Technical Reports Server (NTRS)

Clevenson, S. A.; Leatherwood, J. D.

1979-01-01

The effects of helicopter interior noise on passenger annoyance were studied. Both reverie and listening situations were studied as well as the relative effectiveness of several descriptors (i.e., overall sound pressure level, A-weighted sound pressure level, and speech interference level) for quantifying annoyance response for these situations. The noise stimuli were based upon recordings of the interior noise of a civil helicopter research aircraft. These noises were presented at levels ranging from approximately 68 to 86 dB(A) with various gear clash tones selectively attenuated to give a range of spectra. Results indicated that annoyance during a listening condition is generally higher than annoyance during a reverie condition for corresponding interior noise environments. Attenuation of the planetary gear clash tone results in increases in listening performance but has negligible effect upon annoyance for a given noise level. The noise descriptor most effective for estimating annoyance response under conditions of reverie and listening situations is shown to be the A-weighted sound pressure level.

Optimizing ROOT’s Performance Using C++ Modules

NASA Astrophysics Data System (ADS)

Vassilev, Vassil

2017-10-01

ROOT comes with a C++ compliant interpreter cling. Cling needs to understand the content of the libraries in order to interact with them. Exposing the full shared library descriptors to the interpreter at runtime translates into increased memory footprint. ROOT’s exploratory programming concepts allow implicit and explicit runtime shared library loading. It requires the interpreter to load the library descriptor. Re-parsing of descriptors’ content has a noticeable effect on the runtime performance. Present state-of-art lazy parsing technique brings the runtime performance to reasonable levels but proves to be fragile and can introduce correctness issues. An elegant solution is to load information from the descriptor lazily and in a non-recursive way. The LLVM community advances its C++ Modules technology providing an io-efficient, on-disk representation capable to reduce build times and peak memory usage. The feature is standardized as a C++ technical specification. C++ Modules are a flexible concept, which can be employed to match CMS and other experiments’ requirement for ROOT: to optimize both runtime memory usage and performance. Cling technically “inherits” the feature, however tweaking it to ROOT scale and beyond is a complex endeavor. The paper discusses the status of the C++ Modules in the context of ROOT, supported by few preliminary performance results. It shows a step-by-step migration plan and describes potential challenges which could appear.

Detection of illegal transfer of videos over the Internet

NASA Astrophysics Data System (ADS)

Chaisorn, Lekha; Sainui, Janya; Manders, Corey

2010-07-01

In this paper, a method for detecting infringements or modifications of a video in real-time is proposed. The method first segments a video stream into shots, after which it extracts some reference frames as keyframes. This process is performed employing a Singular Value Decomposition (SVD) technique developed in this work. Next, for each input video (represented by its keyframes), ordinal-based signature and SIFT (Scale Invariant Feature Transform) descriptors are generated. The ordinal-based method employs a two-level bitmap indexing scheme to construct the index for each video signature. The first level clusters all input keyframes into k clusters while the second level converts the ordinal-based signatures into bitmap vectors. On the other hand, the SIFT-based method directly uses the descriptors as the index. Given a suspect video (being streamed or transferred on the Internet), we generate the signature (ordinal and SIFT descriptors) then we compute similarity between its signature and those signatures in the database based on ordinal signature and SIFT descriptors separately. For similarity measure, besides the Euclidean distance, Boolean operators are also utilized during the matching process. We have tested our system by performing several experiments on 50 videos (each about 1/2 hour in duration) obtained from the TRECVID 2006 data set. For experiments set up, we refer to the conditions provided by TRECVID 2009 on "Content-based copy detection" task. In addition, we also refer to the requirements issued in the call for proposals by MPEG standard on the similar task. Initial result shows that our framework is effective and robust. As compared to our previous work, on top of the achievement we obtained by reducing the storage space and time taken in the ordinal based method, by introducing the SIFT features, we could achieve an overall accuracy in F1 measure of about 96% (improved about 8%).

Learning to assign binary weights to binary descriptor

NASA Astrophysics Data System (ADS)

Huang, Zhoudi; Wei, Zhenzhong; Zhang, Guangjun

2016-10-01

Constructing robust binary local feature descriptors are receiving increasing interest due to their binary nature, which can enable fast processing while requiring significantly less memory than their floating-point competitors. To bridge the performance gap between the binary and floating-point descriptors without increasing the computational cost of computing and matching, optimal binary weights are learning to assign to binary descriptor for considering each bit might contribute differently to the distinctiveness and robustness. Technically, a large-scale regularized optimization method is applied to learn float weights for each bit of the binary descriptor. Furthermore, binary approximation for the float weights is performed by utilizing an efficient alternatively greedy strategy, which can significantly improve the discriminative power while preserve fast matching advantage. Extensive experimental results on two challenging datasets (Brown dataset and Oxford dataset) demonstrate the effectiveness and efficiency of the proposed method.

Local Multi-Grouped Binary Descriptor With Ring-Based Pooling Configuration and Optimization.

PubMed

Gao, Yongqiang; Huang, Weilin; Qiao, Yu

2015-12-01

Local binary descriptors are attracting increasingly attention due to their great advantages in computational speed, which are able to achieve real-time performance in numerous image/vision applications. Various methods have been proposed to learn data-dependent binary descriptors. However, most existing binary descriptors aim overly at computational simplicity at the expense of significant information loss which causes ambiguity in similarity measure using Hamming distance. In this paper, by considering multiple features might share complementary information, we present a novel local binary descriptor, referred as ring-based multi-grouped descriptor (RMGD), to successfully bridge the performance gap between current binary and floated-point descriptors. Our contributions are twofold. First, we introduce a new pooling configuration based on spatial ring-region sampling, allowing for involving binary tests on the full set of pairwise regions with different shapes, scales, and distances. This leads to a more meaningful description than the existing methods which normally apply a limited set of pooling configurations. Then, an extended Adaboost is proposed for an efficient bit selection by emphasizing high variance and low correlation, achieving a highly compact representation. Second, the RMGD is computed from multiple image properties where binary strings are extracted. We cast multi-grouped features integration as rankSVM or sparse support vector machine learning problem, so that different features can compensate strongly for each other, which is the key to discriminativeness and robustness. The performance of the RMGD was evaluated on a number of publicly available benchmarks, where the RMGD outperforms the state-of-the-art binary descriptors significantly.

PROMIS (Procurement Management Information System)

NASA Technical Reports Server (NTRS)

1987-01-01

The PROcurement Management Information System (PROMIS) provides both detailed and summary level information on all procurement actions performed within NASA's procurement offices at Marshall Space Flight Center (MSFC). It provides not only on-line access, but also schedules procurement actions, monitors their progress, and updates Forecast Award Dates. Except for a few computational routines coded in FORTRAN, the majority of the systems is coded in a high level language called NATURAL. A relational Data Base Management System called ADABAS is utilized. Certain fields, called descriptors, are set up on each file to allow the selection of records based on a specified value or range of values. The use of like descriptors on different files serves as the link between the falls, thus producing a relational data base. Twenty related files are currently being maintained on PROMIS.

Expanding Preschoolers' Use of Object Descriptions and Comparisons by Teaching "Category-Descriptor" Statements.

ERIC Educational Resources Information Center

Weisberg, Paul

2003-01-01

Six preschool children, mostly from poverty-level backgrounds, were taught to make descriptive statements about objects. The category-descriptor statements were organized and sequenced into four clusters. As sets of new statements were successively taught and evaluated, the number and diversity of probed category and descriptor terms steadily and…

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.

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.

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.

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.

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.

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

PubMed

Higgins, R; Hogg, P; Robinson, L

2017-09-01

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

Reference Performance Level Descriptors: Outcome of a National Working Session on Defining an "English Proficient" Performance Standard

ERIC Educational Resources Information Center

Cook, H. Gary; MacDonald, Rita

2014-01-01

This document is the second in a series of working papers that elaborate on a framework of four key stages in moving toward a common definition of English learner (EL), as described in the Council of Chief State School Officers (CCSSO) publication, "Toward a 'Common Definition of English Learner': Guidance for States and State Assessment…

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.

Learning moment-based fast local binary descriptor

NASA Astrophysics Data System (ADS)

Bellarbi, Abdelkader; Zenati, Nadia; Otmane, Samir; Belghit, Hayet

2017-03-01

Recently, binary descriptors have attracted significant attention due to their speed and low memory consumption; however, using intensity differences to calculate the binary descriptive vector is not efficient enough. We propose an approach to binary description called POLAR_MOBIL, in which we perform binary tests between geometrical and statistical information using moments in the patch instead of the classical intensity binary test. In addition, we introduce a learning technique used to select an optimized set of binary tests with low correlation and high variance. This approach offers high distinctiveness against affine transformations and appearance changes. An extensive evaluation on well-known benchmark datasets reveals the robustness and the effectiveness of the proposed descriptor, as well as its good performance in terms of low computation complexity when compared with state-of-the-art real-time local descriptors.

Analysis and Overview of NQF Level Descriptors in European Countries. Working Paper No 19

ERIC Educational Resources Information Center

Bjørnåvold, Jens; Grm, Slava Pevec

2013-01-01

Level descriptors are essential elements of national qualifications frameworks being established across Europe. They define what is meant by learning outcomes, describing what an individual is expected to know, be able to do and understand, having acquired a qualification at a particular level. This Cedefop working paper shows that most of the 36…

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.

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.

Variation of the apparent viscosity of thickened drinks.

PubMed

O'Leary, Mark; Hanson, Ben; Smith, Christina H

2011-01-01

In dysphagia care, thickening powders are widely added to drinks to slow their flow speed by increasing their viscosity. Current practice relies on subjective evaluation of viscosity using verbal descriptors. Several brands of thickener are available, with differences in constituent ingredients and instructions for use. Some thickened fluids have previously been shown to exhibit time-varying non-Newtonian flow behaviour, which may complicate attempts at subjective viscosity judgement. The aims were to quantify the apparent viscosity over time produced by thickeners having a range of constituent ingredients, and to relate the results to clinical practice. A comparative evaluation of currently available thickener products, including two which have recently been reformulated, was performed. Their subjective compliance to the National Descriptors standards was assessed, and their apparent viscosity was measured using a rheometer at shear rates representative of situations from slow tipping in a beaker (0.1 s⁻¹) to a fast swallow (100 s⁻¹). Testing was performed repeatedly up to 3 h from mixing. When mixed with water, it was found that most products compared well with subjective National Descriptors at three thickness levels. The fluids were all highly non-Newtonian; their apparent viscosity was strongly dependent on the rate of testing, typically decreasing by a factor of almost 100 as shear rate increased. All fluids showed some change in viscosity with time from mixing; this varied between products from -34% to 37% in the tests. This magnitude was less than the difference between thickness levels specified by the National Descriptors. The apparent viscosity of thickened fluids depends strongly on the shear rate at which it is examined. This inherent behaviour is likely to hinder subjective evaluation of viscosity. If quantitative measures of viscosity are required (for example, for standardization purposes), they must therefore be qualified with information of the test conditions. © 2010 Royal College of Speech & Language Therapists.

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

PubMed

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

2012-01-01

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

Automated detection of feeding strikes by larval fish using continuous high-speed digital video: a novel method to extract quantitative data from fast, sparse kinematic events.

PubMed

Shamur, Eyal; Zilka, Miri; Hassner, Tal; China, Victor; Liberzon, Alex; Holzman, Roi

2016-06-01

Using videography to extract quantitative data on animal movement and kinematics constitutes a major tool in biomechanics and behavioral ecology. Advanced recording technologies now enable acquisition of long video sequences encompassing sparse and unpredictable events. Although such events may be ecologically important, analysis of sparse data can be extremely time-consuming and potentially biased; data quality is often strongly dependent on the training level of the observer and subject to contamination by observer-dependent biases. These constraints often limit our ability to study animal performance and fitness. Using long videos of foraging fish larvae, we provide a framework for the automated detection of prey acquisition strikes, a behavior that is infrequent yet critical for larval survival. We compared the performance of four video descriptors and their combinations against manually identified feeding events. For our data, the best single descriptor provided a classification accuracy of 77-95% and detection accuracy of 88-98%, depending on fish species and size. Using a combination of descriptors improved the accuracy of classification by ∼2%, but did not improve detection accuracy. Our results indicate that the effort required by an expert to manually label videos can be greatly reduced to examining only the potential feeding detections in order to filter false detections. Thus, using automated descriptors reduces the amount of manual work needed to identify events of interest from weeks to hours, enabling the assembly of an unbiased large dataset of ecologically relevant behaviors. © 2016. Published by The Company of Biologists Ltd.

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.

Breast ultrasound image segmentation: an optimization approach based on super-pixels and high-level descriptors

NASA Astrophysics Data System (ADS)

Massich, Joan; Lemaître, Guillaume; Martí, Joan; Mériaudeau, Fabrice

2015-04-01

Breast cancer is the second most common cancer and the leading cause of cancer death among women. Medical imaging has become an indispensable tool for its diagnosis and follow up. During the last decade, the medical community has promoted to incorporate Ultra-Sound (US) screening as part of the standard routine. The main reason for using US imaging is its capability to differentiate benign from malignant masses, when compared to other imaging techniques. The increasing usage of US imaging encourages the development of Computer Aided Diagnosis (CAD) systems applied to Breast Ultra-Sound (BUS) images. However accurate delineations of the lesions and structures of the breast are essential for CAD systems in order to extract information needed to perform diagnosis. This article proposes a highly modular and flexible framework for segmenting lesions and tissues present in BUS images. The proposal takes advantage of optimization strategies using super-pixels and high-level descriptors, which are analogous to the visual cues used by radiologists. Qualitative and quantitative results are provided stating a performance within the range of the state-of-the-art.

Conceptual DFT Descriptors of Amino Acids with Potential Corrosion Inhibition Properties Calculated with the Latest Minnesota Density Functionals.

PubMed

Frau, Juan; Glossman-Mitnik, Daniel

2017-01-01

Amino acids and peptides have the potential to perform as corrosion inhibitors. The chemical reactivity descriptors that arise from Conceptual DFT for the twenty natural amino acids have been calculated by using the latest Minnesota family of density functionals. In order to verify the validity of the calculation of the descriptors directly from the HOMO and LUMO, a comparison has been performed with those obtained through ΔSCF results. Moreover, the active sites for nucleophilic and electrophilic attacks have been identified through Fukui function indices, the dual descriptor Δf( r ) and the electrophilic and nucleophilic Parr functions. The results could be of interest as a starting point for the study of large peptides where the calculation of the radical cation and anion of each system may be computationally harder and costly.

A short feature vector for image matching: The Log-Polar Magnitude feature descriptor

PubMed Central

Hast, Anders; Wählby, Carolina; Sintorn, Ida-Maria

2017-01-01

The choice of an optimal feature detector-descriptor combination for image matching often depends on the application and the image type. In this paper, we propose the Log-Polar Magnitude feature descriptor—a rotation, scale, and illumination invariant descriptor that achieves comparable performance to SIFT on a large variety of image registration problems but with much shorter feature vectors. The descriptor is based on the Log-Polar Transform followed by a Fourier Transform and selection of the magnitude spectrum components. Selecting different frequency components allows optimizing for image patterns specific for a particular application. In addition, by relying only on coordinates of the found features and (optionally) feature sizes our descriptor is completely detector independent. We propose 48- or 56-long feature vectors that potentially can be shortened even further depending on the application. Shorter feature vectors result in better memory usage and faster matching. This combined with the fact that the descriptor does not require a time-consuming feature orientation estimation (the rotation invariance is achieved solely by using the magnitude spectrum of the Log-Polar Transform) makes it particularly attractive to applications with limited hardware capacity. Evaluation is performed on the standard Oxford dataset and two different microscopy datasets; one with fluorescence and one with transmission electron microscopy images. Our method performs better than SURF and comparable to SIFT on the Oxford dataset, and better than SIFT on both microscopy datasets indicating that it is particularly useful in applications with microscopy images. PMID:29190737

Multi-Directional Multi-Level Dual-Cross Patterns for Robust Face Recognition.

PubMed

Ding, Changxing; Choi, Jonghyun; Tao, Dacheng; Davis, Larry S

2016-03-01

To perform unconstrained face recognition robust to variations in illumination, pose and expression, this paper presents a new scheme to extract "Multi-Directional Multi-Level Dual-Cross Patterns" (MDML-DCPs) from face images. Specifically, the MDML-DCPs scheme exploits the first derivative of Gaussian operator to reduce the impact of differences in illumination and then computes the DCP feature at both the holistic and component levels. DCP is a novel face image descriptor inspired by the unique textural structure of human faces. It is computationally efficient and only doubles the cost of computing local binary patterns, yet is extremely robust to pose and expression variations. MDML-DCPs comprehensively yet efficiently encodes the invariant characteristics of a face image from multiple levels into patterns that are highly discriminative of inter-personal differences but robust to intra-personal variations. Experimental results on the FERET, CAS-PERL-R1, FRGC 2.0, and LFW databases indicate that DCP outperforms the state-of-the-art local descriptors (e.g., LBP, LTP, LPQ, POEM, tLBP, and LGXP) for both face identification and face verification tasks. More impressively, the best performance is achieved on the challenging LFW and FRGC 2.0 databases by deploying MDML-DCPs in a simple recognition scheme.

Classifying Measures of Biological Variation

PubMed Central

Gregorius, Hans-Rolf; Gillet, Elizabeth M.

2015-01-01

Biological variation is commonly measured at two basic levels: variation within individual communities, and the distribution of variation over communities or within a metacommunity. We develop a classification for the measurement of biological variation on both levels: Within communities into the categories of dispersion and diversity, and within metacommunities into the categories of compositional differentiation and partitioning of variation. There are essentially two approaches to characterizing the distribution of trait variation over communities in that individuals with the same trait state or type tend to occur in the same community (describes differentiation tendencies), and individuals with different types tend to occur in different communities (describes apportionment tendencies). Both approaches can be viewed from the dual perspectives of trait variation distributed over communities (CT perspective) and community membership distributed over trait states (TC perspective). This classification covers most of the relevant descriptors (qualified measures) of biological variation, as is demonstrated with the help of major families of descriptors. Moreover, the classification is shown to open ways to develop new descriptors that meet current needs. Yet the classification also reveals the misclassification of some prominent and widely applied descriptors: Dispersion is often misclassified as diversity, particularly in cases where dispersion descriptor allow for the computation of effective numbers; the descriptor GST of population genetics is commonly misclassified as compositional differentiation and confused with partitioning-oriented differentiation, whereas it actually measures partitioning-oriented apportionment; descriptors of β-diversity are ambiguous about the differentiation effects they are supposed to represent and therefore require conceptual reconsideration. PMID:25807558

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.

Locator-Checker-Scaler Object Tracking Using Spatially Ordered and Weighted Patch Descriptor.

PubMed

Kim, Han-Ul; Kim, Chang-Su

2017-08-01

In this paper, we propose a simple yet effective object descriptor and a novel tracking algorithm to track a target object accurately. For the object description, we divide the bounding box of a target object into multiple patches and describe them with color and gradient histograms. Then, we determine the foreground weight of each patch to alleviate the impacts of background information in the bounding box. To this end, we perform random walk with restart (RWR) simulation. We then concatenate the weighted patch descriptors to yield the spatially ordered and weighted patch (SOWP) descriptor. For the object tracking, we incorporate the proposed SOWP descriptor into a novel tracking algorithm, which has three components: locator, checker, and scaler (LCS). The locator and the scaler estimate the center location and the size of a target, respectively. The checker determines whether it is safe to adjust the target scale in a current frame. These three components cooperate with one another to achieve robust tracking. Experimental results demonstrate that the proposed LCS tracker achieves excellent performance on recent benchmarks.

Quantitative structure-retention relationships of polycyclic aromatic hydrocarbons gas-chromatographic retention indices.

PubMed

Drosos, Juan Carlos; Viola-Rhenals, Maricela; Vivas-Reyes, Ricardo

2010-06-25

Polycyclic aromatic compounds (PAHs) are of concern in environmental chemistry and toxicology. In the present work, a QSRR study was performed for 209 previously reported PAHs using quantum mechanics and other sources descriptors estimated by different approaches. The B3LYP/6-31G* level of theory was used for geometrical optimization and quantum mechanics related variables. A good linear relationship between gas-chromatographic retention index and electronic or topologic descriptors was found by stepwise linear regression analysis. The molecular polarizability (alpha) and the second order molecular connectivity Kier and Hall index ((2)chi) showed evidence of significant correlation with retention index by means of important squared coefficient of determination, (R(2)), values (R(2)=0.950 and 0.962, respectively). A one variable QSRR model is presented for each descriptor and both models demonstrates a significant predictive capacity established using the leave-many-out LMO (excluding 25% of rows) cross validation method's q(2) cross-validation coefficients q(2)(CV-LMO25%), (obtained q(2)(CV-LMO25%) 0.947 and 0.960, respectively). Furthermore, the physicochemical interpretation of selected descriptors allowed detailed explanation of the source of the observed statistical correlation. The model analysis suggests that only one descriptor is sufficient to establish a consistent retention index-structure relationship. Moderate or non-significant improve was observed for quantitative results or statistical validation parameters when introducing more terms in predictive equation. The one parameter QSRR proposed model offers a consistent scheme to predict chromatographic properties of PAHs compounds. Copyright 2010 Elsevier B.V. All rights reserved.

Artificial intelligence systems based on texture descriptors for vaccine development.

PubMed

Nanni, Loris; Brahnam, Sheryl; Lumini, Alessandra

2011-02-01

The aim of this work is to analyze and compare several feature extraction methods for peptide classification that are based on the calculation of texture descriptors starting from a matrix representation of the peptide. This texture-based representation of the peptide is then used to train a support vector machine classifier. In our experiments, the best results are obtained using local binary patterns variants and the discrete cosine transform with selected coefficients. These results are better than those previously reported that employed texture descriptors for peptide representation. In addition, we perform experiments that combine standard approaches based on amino acid sequence. The experimental section reports several tests performed on a vaccine dataset for the prediction of peptides that bind human leukocyte antigens and on a human immunodeficiency virus (HIV-1). Experimental results confirm the usefulness of our novel descriptors. The matlab implementation of our approaches is available at http://bias.csr.unibo.it/nanni/TexturePeptide.zip.

Conceptual DFT Descriptors of Amino Acids with Potential Corrosion Inhibition Properties Calculated with the Latest Minnesota Density Functionals

PubMed Central

Frau, Juan; Glossman-Mitnik, Daniel

2017-01-01

Amino acids and peptides have the potential to perform as corrosion inhibitors. The chemical reactivity descriptors that arise from Conceptual DFT for the twenty natural amino acids have been calculated by using the latest Minnesota family of density functionals. In order to verify the validity of the calculation of the descriptors directly from the HOMO and LUMO, a comparison has been performed with those obtained through ΔSCF results. Moreover, the active sites for nucleophilic and electrophilic attacks have been identified through Fukui function indices, the dual descriptor Δf(r) and the electrophilic and nucleophilic Parr functions. The results could be of interest as a starting point for the study of large peptides where the calculation of the radical cation and anion of each system may be computationally harder and costly. PMID:28361050

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.

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.

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.

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.

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.

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.

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.

Context-Aware Local Binary Feature Learning for Face Recognition.

PubMed

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

2018-05-01

In this paper, we propose a context-aware local binary feature learning (CA-LBFL) method for face recognition. Unlike existing learning-based local face descriptors such as discriminant face descriptor (DFD) and compact binary face descriptor (CBFD) which learn each feature code individually, our CA-LBFL exploits the contextual information of adjacent bits by constraining the number of shifts from different binary bits, so that more robust information can be exploited for face representation. Given a face image, we first extract pixel difference vectors (PDV) in local patches, and learn a discriminative mapping in an unsupervised manner to project each pixel difference vector into a context-aware binary vector. Then, we perform clustering on the learned binary codes to construct a codebook, and extract a histogram feature for each face image with the learned codebook as the final representation. In order to exploit local information from different scales, we propose a context-aware local binary multi-scale feature learning (CA-LBMFL) method to jointly learn multiple projection matrices for face representation. To make the proposed methods applicable for heterogeneous face recognition, we present a coupled CA-LBFL (C-CA-LBFL) method and a coupled CA-LBMFL (C-CA-LBMFL) method to reduce the modality gap of corresponding heterogeneous faces in the feature level, respectively. Extensive experimental results on four widely used face datasets clearly show that our methods outperform most state-of-the-art face descriptors.

RPBS: Rotational Projected Binary Structure for point cloud representation

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Branch length similarity entropy-based descriptors for shape representation

NASA Astrophysics Data System (ADS)

Kwon, Ohsung; Lee, Sang-Hee

2017-11-01

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

Sonic boom (human response and atmospheric effects) outdoor-to-indoor response to minimized sonic booms

NASA Technical Reports Server (NTRS)

Brown, David; Sutherland, Louis C.

1992-01-01

The preferred descriptor to define the spectral content of sonic booms is the Sound Exposure Spectrum Level, LE(f). This descriptor represents the spectral content of the basic noise descriptors used for describing any single event--the Sound Exposure Level, LE. The latter is equal to ten times the logarithms, to the base ten, of the integral, over the duration of the event, of the square of the instantaneous acoustic pressure, divided by the square of the reference pressure, 20 micro-Pa. When applied to the evaluation of community response to sonic booms, it is customary to use the so-called C-Weighted Sound Exposure Level, LCE, for which the frequency content of the instantaneous acoustic pressure is modified by the C-Weighting curve.

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.

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.

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.

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.

Emodnet Med Sea Check-Point - Indicators for decision- maker

NASA Astrophysics Data System (ADS)

Besnard, Sophie; Claverie, Vincent; Blanc, Frédérique

2015-04-01

The Emodnet Checkpoint projects aim is to assess the cost-effectiveness, reliability and utility of the existing monitoring at the sea basin level. This involves the development of monitoring system indicators and a GIS Platform to perform the assessment and make it available. Assessment or production of Check-Point information is made by developing targeted products based on the monitoring data and determining whether the products are meeting the needs of industry and public authorities. Check-point users are the research community, the 'institutional' policy makers for IMP and MSFD implementation, the 'intermediate users', i.e., users capable to understand basic raw data but that benefit from seeing the Checkpoint targeted products and the assessment of the fitness for purpose. We define assessment criteria aimed to characterize/depict the input datasets in terms of 3 territories capable to show performance and gaps of the present monitoring system, appropriateness, availability and fitness for purpose. • Appropriateness: What is made available to users? What motivate/decide them to select this observation rather than this one. • Availability: How this is made available to the user? Place to understand the readiness and service performance of the EU infrastructure • Fitness for use / fitness for purpose: Ability for non-expert user to appreciate the data exploitability (feedback on efficiency & reliability of marine data) For each territory (appropriateness, Availability and Fitness for purpose / for use), we define several indicators. For example, for Availability we define Visibility, Accessibility and Performance. And Visibility is itself defined by "Easily found" and "EU service". So these indicators can be classified according to their territory and sub-territory as seen above, but also according to the complexity to build them. Indicators are built from raw descriptors in 3 stages:  Stage 1: to give a neutral and basic status directly computed from the raw checkpoint descriptors.  Stage 2: to get a more sectorial status, aggregating level 1 results for instance depending on processing level.  Stage 3: to synthesize and focus view at characteristic level for decision making and actions plan. They are computed from stage 2 indicators. To produce this checkpoint information, we describe upstream data as input data sets which are uniquely identified as a combination of (variable, dataset, intended use) or of (geographical feature, dataset, intended use) depending on their nature. The information is called descriptors. The descriptors cover 8 sections: 1. Characteristics (= What) 2. Data sources (= From) 3. Overview elements (= Why for) 4. Spatial coverage (= Where) 5. Temporal coverage (= When) 6. Accessibility (= How) 7. Quality elements (= ISO 19113 quality elements) 8. Other information (= for administration/management needs) Check-Point services should be permanent services, because: • Monitoring systems will evolve and every few years there is need to re-assess; • Different/more use cases of monitoring are required to really show gaps and complementarities in the monitoring system components; • Monitoring system evolution for the European Sea and the global ocean require constant upgrade of the assessment indicators and descriptors; • Need to establish strong & permanent links with intermediate and end users from industry to public authorities and 'regional' approach is appropriate and feasible. • Need to maintain the process, methodology used and to be applied.

The implementation of aerial object recognition algorithm based on contour descriptor in FPGA-based on-board vision system

NASA Astrophysics Data System (ADS)

Babayan, Pavel; Smirnov, Sergey; Strotov, Valery

2017-10-01

This paper describes the aerial object recognition algorithm for on-board and stationary vision system. Suggested algorithm is intended to recognize the objects of a specific kind using the set of the reference objects defined by 3D models. The proposed algorithm based on the outer contour descriptor building. The algorithm consists of two stages: learning and recognition. Learning stage is devoted to the exploring of reference objects. Using 3D models we can build the database containing training images by rendering the 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. Gathered training image set is used for calculating descriptors, which will be used in the recognition stage of the algorithm. The recognition stage is focusing on estimating the similarity of the captured object and the reference objects by matching an observed image descriptor and the reference object descriptors. The experimental research was performed using a set of the models of the aircraft of the different types (airplanes, helicopters, UAVs). The proposed orientation estimation algorithm showed good accuracy in all case studies. The real-time performance of the algorithm in FPGA-based vision system was demonstrated.

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.

Evaluation of Yogurt Microstructure Using Confocal Laser Scanning Microscopy and Image Analysis.

PubMed

Skytte, Jacob L; Ghita, Ovidiu; Whelan, Paul F; Andersen, Ulf; Møller, Flemming; Dahl, Anders B; Larsen, Rasmus

2015-06-01

The microstructure of protein networks in yogurts defines important physical properties of the yogurt and hereby partly its quality. Imaging this protein network using confocal scanning laser microscopy (CSLM) has shown good results, and CSLM has become a standard measuring technique for fermented dairy products. When studying such networks, hundreds of images can be obtained, and here image analysis methods are essential for using the images in statistical analysis. Previously, methods including gray level co-occurrence matrix analysis and fractal analysis have been used with success. However, a range of other image texture characterization methods exists. These methods describe an image by a frequency distribution of predefined image features (denoted textons). Our contribution is an investigation of the choice of image analysis methods by performing a comparative study of 7 major approaches to image texture description. Here, CSLM images from a yogurt fermentation study are investigated, where production factors including fat content, protein content, heat treatment, and incubation temperature are varied. The descriptors are evaluated through nearest neighbor classification, variance analysis, and cluster analysis. Our investigation suggests that the texton-based descriptors provide a fuller description of the images compared to gray-level co-occurrence matrix descriptors and fractal analysis, while still being as applicable and in some cases as easy to tune. © 2015 Institute of Food Technologists®

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

PubMed

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

2017-06-01

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

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.

Direct memory access transfer completion notification

DOEpatents

Archer, Charles J.; Blocksome, Michael A.; Parker, Jeffrey J.

2010-08-17

Methods, apparatus, and products are disclosed for DMA transfer completion notification that include: inserting, by an origin DMA engine on an origin compute node in an injection FIFO buffer, a data descriptor for an application message to be transferred to a target compute node on behalf of an application on the origin compute node; inserting, by the origin DMA engine, a completion notification descriptor in the injection FIFO buffer after the data descriptor for the message, the completion notification descriptor specifying an address of a completion notification field in application storage for the application; transferring, by the origin DMA engine to the target compute node, the message in dependence upon the data descriptor; and notifying, by the origin DMA engine, the application that the transfer of the message is complete, including performing a local direct put operation to store predesignated notification data at the address of the completion notification field.

Investigation of anticancer properties of caffeinated complexes via computational chemistry methods

NASA Astrophysics Data System (ADS)

Sayin, Koray; Üngördü, Ayhan

2018-03-01

Computational investigations were performed for 1,3,7-trimethylpurine-2,6-dione, 3,7-dimethylpurine-2,6-dione, their Ru(II) and Os(III) complexes. B3LYP/6-311 ++G(d,p)(LANL2DZ) level was used in numerical calculations. Geometric parameters, IR spectrum, 1H-, 13C and 15N NMR spectrum were examined in detail. Additionally, contour diagram of frontier molecular orbitals (FMOs), molecular electrostatic potential (MEP) maps, MEP contour and some quantum chemical descriptors were used in the determination of reactivity rankings and active sites. The electron density on the surface was similar to each other in studied complexes. Quantum chemical descriptors were investigated and the anticancer activity of complexes were more than cisplatin and their ligands. Additionally, molecular docking calculations were performed in water between related complexes and a protein (ID: 3WZE). The most interact complex was found as Os complex. The interaction energy was calculated as 342.9 kJ/mol.

Texture classification using non-Euclidean Minkowski dilation

NASA Astrophysics Data System (ADS)

Florindo, Joao B.; Bruno, Odemir M.

2018-03-01

This study presents a new method to extract meaningful descriptors of gray-scale texture images using Minkowski morphological dilation based on the Lp metric. The proposed approach is motivated by the success previously achieved by Bouligand-Minkowski fractal descriptors on texture classification. In essence, such descriptors are directly derived from the morphological dilation of a three-dimensional representation of the gray-level pixels using the classical Euclidean metric. In this way, we generalize the dilation for different values of p in the Lp metric (Euclidean is a particular case when p = 2) and obtain the descriptors from the cumulated distribution of the distance transform computed over the texture image. The proposed method is compared to other state-of-the-art approaches (such as local binary patterns and textons for example) in the classification of two benchmark data sets (UIUC and Outex). The proposed descriptors outperformed all the other approaches in terms of rate of images correctly classified. The interesting results suggest the potential of these descriptors in this type of task, with a wide range of possible applications to real-world problems.

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

PubMed Central

Anouar, El Hassane

2014-01-01

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

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.

Levels-of-processing effects on a task of olfactory naming.

PubMed

Royet, Jean-Pierre; Koenig, Olivier; Paugam-Moisy, Helene; Puzenat, Didier; Chasse, Jean-Luc

2004-02-01

The effects of odor processing were investigated at various analytical levels, from simple sensory analysis to deep or semantic analysis, on a subsequent task of odor naming. Students (106 women, 23.6 +/- 5.5 yr. old; 65 men, 25.1 +/- 7.1 yr. old) were tested. The experimental procedure included two successive sessions, a first session to characterize a set of 30 odors with criteria that used various depths of processing and a second session to name the odors as quickly as possible. Four processing conditions rated the odors using descriptors before naming the odor. The control condition did not rate the odors before naming. The processing conditions were based on lower-level olfactory judgments (superficial processing), higher-level olfactory-gustatory-somesthetic judgments (deep processing), and higher-level nonolfactory judgments (Deep-Control processing, with subjects rating odors with auditory and visual descriptors). One experimental condition successively grouped lower- and higher-level olfactory judgments (Superficial-Deep processing). A naming index which depended on response accuracy and the subjects' response time were calculated. Odor naming was modified for 18 out of 30 odorants as a function of the level of processing required. For 94.5% of significant variations, the scores for odor naming were higher following those tasks for which it was hypothesized that the necessary olfactory processing was carried out at a deeper level. Performance in the naming task was progressively improved as follows: no rating of odors, then superficial, deep-control, deep, and superficial-deep processings. These data show that the deepest olfactory encoding was later associated with progressively higher performance in naming.

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.

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.

Bayesian screening for active compounds in high-dimensional chemical spaces combining property descriptors and molecular fingerprints.

PubMed

Vogt, Martin; Bajorath, Jürgen

2008-01-01

Bayesian classifiers are increasingly being used to distinguish active from inactive compounds and search large databases for novel active molecules. We introduce an approach to directly combine the contributions of property descriptors and molecular fingerprints in the search for active compounds that is based on a Bayesian framework. Conventionally, property descriptors and fingerprints are used as alternative features for virtual screening methods. Following the approach introduced here, probability distributions of descriptor values and fingerprint bit settings are calculated for active and database molecules and the divergence between the resulting combined distributions is determined as a measure of biological activity. In test calculations on a large number of compound activity classes, this methodology was found to consistently perform better than similarity searching using fingerprints and multiple reference compounds or Bayesian screening calculations using probability distributions calculated only from property descriptors. These findings demonstrate that there is considerable synergy between different types of property descriptors and fingerprints in recognizing diverse structure-activity relationships, at least in the context of Bayesian modeling.

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.

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.

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.

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.

Shadow Areas Robust Matching Among Image Sequence in Planetary Landing

NASA Astrophysics Data System (ADS)

Ruoyan, Wei; Xiaogang, Ruan; Naigong, Yu; Xiaoqing, Zhu; Jia, Lin

2017-01-01

In this paper, an approach for robust matching shadow areas in autonomous visual navigation and planetary landing is proposed. The approach begins with detecting shadow areas, which are extracted by Maximally Stable Extremal Regions (MSER). Then, an affine normalization algorithm is applied to normalize the areas. Thirdly, a descriptor called Multiple Angles-SIFT (MA-SIFT) that coming from SIFT is proposed, the descriptor can extract more features of an area. Finally, for eliminating the influence of outliers, a method of improved RANSAC based on Skinner Operation Condition is proposed to extract inliers. At last, series of experiments are conducted to test the performance of the approach this paper proposed, the results show that the approach can maintain the matching accuracy at a high level even the differences among the images are obvious with no attitude measurements supplied.

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 laboratory study of subjective annoyance response to sonic booms and aircraft flyovers

NASA Technical Reports Server (NTRS)

Leatherwood, Jack D.; Sullivan, Brenda M.

1994-01-01

Three experiments were conducted to determine subjective equivalence of aircraft subsonic flyover noise and sonic booms. Two of the experiments were conducted in a loudspeaker-driven sonic boom simulator, and the third in a large room containing conventional loudspeakers. The sound generation system of the boom simulator had a frequency response extending to very low frequencies (about 1 Hz) whereas the large room loudspeakers were limited to about 20 Hz. Subjective equivalence between booms and flyovers was quantified in terms of the difference between the noise level of a boom and that of a flyover when the two were judged equally annoying. Noise levels were quantified in terms of the following noise descriptors: Perceived Level (PL), Perceived Noise Level (PNL), C-weighted sound exposure level (SELC), and A-weighted sound exposure level (SELA). Results from the present study were compared, where possible, to similar results obtained in other studies. Results showed that noise level differences depended upon the descriptor used, specific boom and aircraft noise events being compared and, except for the PNL descriptor, varied between the simulator and large room. Comparison of noise level differences obtained in the present study with those of other studies indicated good agreement across studies only for the PNL and SELA descriptors. Comparison of the present results with assessments of community response to high-energy impulsive sounds made by Working Group 84 of the National Research Council's Committee on Hearing, Bioacoustics, and Biomechanics (CHABA) showed good agreement when boom/flyover noise level differences were based on SELA. However, noise level differences obtained by CHABA using SELA for aircraft flyovers and SELC for booms were not in agreement with results obtained in the present study.

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.

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.

Symbol recognition via statistical integration of pixel-level constraint histograms: a new descriptor.

PubMed

Yang, Su

2005-02-01

A new descriptor for symbol recognition is proposed. 1) A histogram is constructed for every pixel to figure out the distribution of the constraints among the other pixels. 2) All the histograms are statistically integrated to form a feature vector with fixed dimension. The robustness and invariance were experimentally confirmed.

Fingerprint Identification Using SIFT-Based Minutia Descriptors and Improved All Descriptor-Pair Matching

PubMed Central

Zhou, Ru; Zhong, Dexing; Han, Jiuqiang

2013-01-01

The performance of conventional minutiae-based fingerprint authentication algorithms degrades significantly when dealing with low quality fingerprints with lots of cuts or scratches. A similar degradation of the minutiae-based algorithms is observed when small overlapping areas appear because of the quite narrow width of the sensors. Based on the detection of minutiae, Scale Invariant Feature Transformation (SIFT) descriptors are employed to fulfill verification tasks in the above difficult scenarios. However, the original SIFT algorithm is not suitable for fingerprint because of: (1) the similar patterns of parallel ridges; and (2) high computational resource consumption. To enhance the efficiency and effectiveness of the algorithm for fingerprint verification, we propose a SIFT-based Minutia Descriptor (SMD) to improve the SIFT algorithm through image processing, descriptor extraction and matcher. A two-step fast matcher, named improved All Descriptor-Pair Matching (iADM), is also proposed to implement the 1:N verifications in real-time. Fingerprint Identification using SMD and iADM (FISiA) achieved a significant improvement with respect to accuracy in representative databases compared with the conventional minutiae-based method. The speed of FISiA also can meet real-time requirements. PMID:23467056

ANN expert system screening for illicit amphetamines using molecular descriptors

NASA Astrophysics Data System (ADS)

Gosav, S.; Praisler, M.; Dorohoi, D. O.

2007-05-01

The goal of this study was to develop and an artificial neural network (ANN) based on computed descriptors, which would be able to classify the molecular structures of potential illicit amphetamines and to derive their biological activity according to the similarity of their molecular structure with amphetamines of known toxicity. The system is necessary for testing new molecular structures for epidemiological, clinical, and forensic purposes. It was built using a database formed by 146 compounds representing drugs of abuse (mainly central stimulants, hallucinogens, sympathomimetic amines, narcotics and other potent analgesics), precursors, or derivatized counterparts. Their molecular structures were characterized by computing three types of descriptors: 38 constitutional descriptors (CDs), 69 topological descriptors (TDs) and 160 3D-MoRSE descriptors (3DDs). An ANN system was built for each category of variables. All three networks (CD-NN, TD-NN and 3DD-NN) were trained to distinguish between stimulant amphetamines, hallucinogenic amphetamines, and nonamphetamines. A selection of variables was performed when necessary. The efficiency with which each network identifies the class identity of an unknown sample was evaluated by calculating several figures of merit. The results of the comparative analysis are presented.

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.

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.

Shuffling cross-validation-bee algorithm as a new descriptor selection method for retention studies of pesticides in biopartitioning micellar chromatography.

PubMed

Zarei, Kobra; Atabati, Morteza; Ahmadi, Monire

2017-05-04

Bee algorithm (BA) is an optimization algorithm inspired by the natural foraging behaviour of honey bees to find the optimal solution which can be proposed to feature selection. In this paper, shuffling cross-validation-BA (CV-BA) was applied to select the best descriptors that could describe the retention factor (log k) in the biopartitioning micellar chromatography (BMC) of 79 heterogeneous pesticides. Six descriptors were obtained using BA and then the selected descriptors were applied for model development using multiple linear regression (MLR). The descriptor selection was also performed using stepwise, genetic algorithm and simulated annealing methods and MLR was applied to model development and then the results were compared with those obtained from shuffling CV-BA. The results showed that shuffling CV-BA can be applied as a powerful descriptor selection method. Support vector machine (SVM) was also applied for model development using six selected descriptors by BA. The obtained statistical results using SVM were better than those obtained using MLR, as the root mean square error (RMSE) and correlation coefficient (R) for whole data set (training and test), using shuffling CV-BA-MLR, were obtained as 0.1863 and 0.9426, respectively, while these amounts for the shuffling CV-BA-SVM method were obtained as 0.0704 and 0.9922, respectively.

Using probabilistic model as feature descriptor on a smartphone device for autonomous navigation of unmanned ground vehicles

NASA Astrophysics Data System (ADS)

Desai, Alok; Lee, Dah-Jye

2013-12-01

There has been significant research on the development of feature descriptors in the past few years. Most of them do not emphasize real-time applications. This paper presents the development of an affine invariant feature descriptor for low resource applications such as UAV and UGV that are equipped with an embedded system with a small microprocessor, a field programmable gate array (FPGA), or a smart phone device. UAV and UGV have proven suitable for many promising applications such as unknown environment exploration, search and rescue operations. These applications required on board image processing for obstacle detection, avoidance and navigation. All these real-time vision applications require a camera to grab images and match features using a feature descriptor. A good feature descriptor will uniquely describe a feature point thus allowing it to be correctly identified and matched with its corresponding feature point in another image. A few feature description algorithms are available for a resource limited system. They either require too much of the device's resource or too much simplification on the algorithm, which results in reduction in performance. This research is aimed at meeting the needs of these systems without sacrificing accuracy. This paper introduces a new feature descriptor called PRObabilistic model (PRO) for UGV navigation applications. It is a compact and efficient binary descriptor that is hardware-friendly and easy for implementation.

Electrodermal responses to words in chronic low back pain patients: a comparison between pain descriptors, other emotional words, and neutral words.

PubMed

Bonnet, Adeline; Naveteur, Janick

2006-10-01

This study examines the electrodermal reactivity of chronic sufferers to emotional words. The hypothesis that patients are over-sensitive to pain descriptors is tested. Electrodermal activity was recorded in 12 chronic low back pain patients and 12 healthy controls during passive viewing of words on a video monitor. These words were pain descriptors, other emotional words, and neutral words, in a pseudo-randomized order. A jingle was associated with the word occurrence. In chronic low back pain patients, skin conductance responses (SCRs) induced by pain descriptors or other emotional words were larger than SCRs induced by neutral words but they did not differ from each other. Patients presented SCRs, which were both larger and faster than those of controls, including following neutral words. There was no significant effect of word type in controls. Skin conductance level and the number of nonspecific fluctuations were larger in patients as compared with controls. The present electrodermal study suggests that chronic pain is linked to an increased reactivity to a wide range of stimuli. Emotional load amplifies the effect. This leads to recommend broad therapeutic management in chronic sufferers. Contrary to expectation derived from classical conditioning, patients did not prove over-sensitive to pain descriptors. This negative finding is discussed at methodologic, physiologic, and psychologic levels.

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.

Neural network-based feature point descriptors for registration of optical and SAR images

NASA Astrophysics Data System (ADS)

Abulkhanov, Dmitry; Konovalenko, Ivan; Nikolaev, Dmitry; Savchik, Alexey; Shvets, Evgeny; Sidorchuk, Dmitry

2018-04-01

Registration of images of different nature is an important technique used in image fusion, change detection, efficient information representation and other problems of computer vision. Solving this task using feature-based approaches is usually more complex than registration of several optical images because traditional feature descriptors (SIFT, SURF, etc.) perform poorly when images have different nature. In this paper we consider the problem of registration of SAR and optical images. We train neural network to build feature point descriptors and use RANSAC algorithm to align found matches. Experimental results are presented that confirm the method's effectiveness.

RANZCR Body Systems Framework of diagnostic imaging examination descriptors.

PubMed

Pitman, Alexander G; Penlington, Lisa; Doromal, Darren; Slater, Gregory; Vukolova, Natalia

2014-08-01

A unified and logical system of descriptors for diagnostic imaging examinations and procedures is a desirable resource for radiology in Australia and New Zealand and is needed to support core activities of RANZCR. Existing descriptor systems available in Australia and New Zealand (including the Medicare DIST and the ACC Schedule) have significant limitations and are inappropriate for broader clinical application. An anatomically based grid was constructed, with anatomical structures arranged in rows and diagnostic imaging modalities arranged in columns (including nuclear medicine and positron emission tomography). The grid was segregated into five body systems. The cells at the intersection of an anatomical structure row and an imaging modality column were populated with short, formulaic descriptors of the applicable diagnostic imaging examinations. Clinically illogical or physically impossible combinations were 'greyed out'. Where the same examination applied to different anatomical structures, the descriptor was kept identical for the purposes of streamlining. The resulting Body Systems Framework of diagnostic imaging examination descriptors lists all the reasonably common diagnostic imaging examinations currently performed in Australia and New Zealand using a unified grid structure allowing navigation by both referrers and radiologists. The Framework has been placed on the RANZCR website and is available for access free of charge by registered users. The Body Systems Framework of diagnostic imaging examination descriptors is a system of descriptors based on relationships between anatomical structures and imaging modalities. The Framework is now available as a resource and reference point for the radiology profession and to support core College activities. © 2014 The Royal Australian and New Zealand College of Radiologists.

Improving virtual screening predictive accuracy of Human kallikrein 5 inhibitors using machine learning models.

PubMed

Fang, Xingang; Bagui, Sikha; Bagui, Subhash

2017-08-01

The readily available high throughput screening (HTS) data from the PubChem database provides an opportunity for mining of small molecules in a variety of biological systems using machine learning techniques. From the thousands of available molecular descriptors developed to encode useful chemical information representing the characteristics of molecules, descriptor selection is an essential step in building an optimal quantitative structural-activity relationship (QSAR) model. For the development of a systematic descriptor selection strategy, we need the understanding of the relationship between: (i) the descriptor selection; (ii) the choice of the machine learning model; and (iii) the characteristics of the target bio-molecule. In this work, we employed the Signature descriptor to generate a dataset on the Human kallikrein 5 (hK 5) inhibition confirmatory assay data and compared multiple classification models including logistic regression, support vector machine, random forest and k-nearest neighbor. Under optimal conditions, the logistic regression model provided extremely high overall accuracy (98%) and precision (90%), with good sensitivity (65%) in the cross validation test. In testing the primary HTS screening data with more than 200K molecular structures, the logistic regression model exhibited the capability of eliminating more than 99.9% of the inactive structures. As part of our exploration of the descriptor-model-target relationship, the excellent predictive performance of the combination of the Signature descriptor and the logistic regression model on the assay data of the Human kallikrein 5 (hK 5) target suggested a feasible descriptor/model selection strategy on similar targets. Copyright © 2017 Elsevier Ltd. All rights reserved.

Modular Chemical Descriptor Language (MCDL): Stereochemical modules

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

Gakh, Andrei A; Burnett, Michael N; Trepalin, Sergei V.

2011-01-01

In our previous papers we introduced the Modular Chemical Descriptor Language (MCDL) for providing a linear representation of chemical information. A subsequent development was the MCDL Java Chemical Structure Editor which is capable of drawing chemical structures from linear representations and generating MCDL descriptors from structures. In this paper we present MCDL modules and accompanying software that incorporate unique representation of molecular stereochemistry based on Cahn-Ingold-Prelog and Fischer ideas in constructing stereoisomer descriptors. The paper also contains additional discussions regarding canonical representation of stereochemical isomers, and brief algorithm descriptions of the open source LINDES, Java applet, and Open Babel MCDLmore » processing module software packages. Testing of the upgraded MCDL Java Chemical Structure Editor on compounds taken from several large and diverse chemical databases demonstrated satisfactory performance for storage and processing of stereochemical information in MCDL format.« less

Reducing Current Spread using Current Focusing in Cochlear Implant Users

PubMed Central

Landsberger, David M.; Padilla, Monica; Srinivasan, Arthi G.

2012-01-01

Cochlear implant performance in difficult listening situations is limited by channel interactions. It is known that partial tripolar (PTP) stimulation reduces the spread of excitation (SOE). However, the greater the degree of current focusing, the greater the absolute current required to maintain a fixed loudness. As current increases, so does SOE. In experiment 1, the SOE for equally loud stimuli with different degrees of current focusing is measured via a forward-masking procedure. Results suggest that at a fixed loudness, some but not all patients have a reduced SOE with PTP stimulation. Therefore, it seems likely that a PTP speech processing strategy could improve spectral resolution for only those patients with a reduced SOE. In experiment 2, the ability to discriminate different levels of current focusing was measured. In experiment 3, patients subjectively scaled verbal descriptors of stimuli of various levels of current focusing. Both discrimination and scaling of verbal descriptors correlated well with SOE reduction, suggesting that either technique have the potential to be used clinically to quickly predict which patients would receive benefit from a current focusing strategy. PMID:22230370

Using machine learning and quantum chemistry descriptors to predict the toxicity of ionic liquids.

PubMed

Cao, Lingdi; Zhu, Peng; Zhao, Yongsheng; Zhao, Jihong

2018-06-15

Large-scale application of ionic liquids (ILs) hinges on the advancement of designable and eco-friendly nature. Research of the potential toxicity of ILs towards different organisms and trophic levels is insufficient. Quantitative structure-activity relationships (QSAR) model is applied to evaluate the toxicity of ILs towards the leukemia rat cell line (ICP-81). The structures of 57 cations and 21 anions were optimized by quantum chemistry. The electrostatic potential surface area (S EP ) and charge distribution area (S σ-profile ) descriptors are calculated and used to predict the toxicity of ILs. The performance and predictive aptitude of extreme learning machine (ELM) model are analyzed and compared with those of multiple linear regression (MLR) and support vector machine (SVM) models. The highest R 2 and the lowest AARD% and RMSE of the training set, test set and total set for the ELM are observed, which validates the superior performance of the ELM than that of obtained by the MLR and SVM. The applicability domain of the model is assessed by the Williams plot. Copyright © 2018 Elsevier B.V. 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 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

Building Scientific Confidence in the Development and ...

EPA Pesticide Factsheets

Read-across remains a popular data gap filling technique within category and analogue approaches for regulatory purposes. Acceptance of read-across is an ongoing challenge with several efforts underway for identifying and addressing uncertainties. Here we demonstrate an algorithmic approach to facilitate read-across using ToxCast in vitro bioactivity data in conjunction with chemical descriptor information to predict in vivo outcomes in guideline testing studies from ToxRefDB. Over 3400 different chemical structure descriptors were generated for a set of 976 chemicals and supplemented with the outcomes from 821 in vitro assays. The read-across prediction for a given chemical was based on the similarity weighted endpoint outcomes of its nearest neighbors calculated using in vitro bioactivity and chemical structure descriptors, called GenRA. GenRA is based on a computational approach for: (i) defining local validity domains using chemical and bioactivity descriptors, (ii) systematically deriving endpoint read-across predictions within these domains using similarity weighted activity of nearest neighbours, (iii) objectively evaluating predicted performance using tested chemicals, and (iv) assigning read-across predictions to untested chemicals along with estimates of uncertainty. We found in vitro bioactivity descriptors were often found to be more predictive of in vivo toxicity outcomes than chemical structure descriptors. We believe GenRA is an important first st

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.

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.

Nevada Science Content Standards for Grades 2, 3, 5, 8 and 12 and Indicators of Progress for Kindergarten and Grades 1, 4, 6 and 7 [and] Performance Level Descriptors.

ERIC Educational Resources Information Center

Nevada State Dept. of Education, Carson City.

The Nevada Science Standards represent a common core for curriculum throughout Nevada's schools. The Nevada Science Standards are intended to provide Nevada students with a rich, thorough, and varied science education to prepare them for the challenges, discoveries, and demands of life in the 21st century. The two main sections of the document…

Predicting the outcomes of organic reactions via machine learning: are current descriptors sufficient?

PubMed

Skoraczyński, G; Dittwald, P; Miasojedow, B; Szymkuć, S; Gajewska, E P; Grzybowski, B A; Gambin, A

2017-06-15

As machine learning/artificial intelligence algorithms are defeating chess masters and, most recently, GO champions, there is interest - and hope - that they will prove equally useful in assisting chemists in predicting outcomes of organic reactions. This paper demonstrates, however, that the applicability of machine learning to the problems of chemical reactivity over diverse types of chemistries remains limited - in particular, with the currently available chemical descriptors, fundamental mathematical theorems impose upper bounds on the accuracy with which raction yields and times can be predicted. Improving the performance of machine-learning methods calls for the development of fundamentally new chemical descriptors.

Using biological effects tools to define Good Environmental Status under the European Union Marine Strategy Framework Directive.

PubMed

Lyons, B P; Thain, J E; Stentiford, G D; Hylland, K; Davies, I M; Vethaak, A D

2010-10-01

The use of biological effects tools offer enormous potential to meet the challenges outlined by the European Union Marine Strategy Framework Directive (MSFD) whereby Member States are required to develop a robust set of tools for defining 11 qualitative descriptors of Good Environmental Status (GES), such as demonstrating that "Concentrations of contaminants are at levels not giving rise to pollution effects" (GES Descriptor 8). This paper discusses the combined approach of monitoring chemical contaminant levels, along side biological effect measurements relating to the effect of pollutants, for undertaking assessments of GES across European marine regions. We outline the minimum standards that biological effects tools should meet if they are to be used for defining GES in relation to Descriptor 8 and describe the current international initiatives underway to develop assessment criteria for these biological effects techniques. Crown Copyright © 2010. Published by Elsevier Ltd. All rights reserved.

A Novel Approach for Weed Type Classification Based on Shape Descriptors and a Fuzzy Decision-Making Method

PubMed Central

Herrera, Pedro Javier; Dorado, José.; Ribeiro, Ángela.

2014-01-01

An important objective in weed management is the discrimination between grasses (monocots) and broad-leaved weeds (dicots), because these two weed groups can be appropriately controlled by specific herbicides. In fact, efficiency is higher if selective treatment is performed for each type of infestation instead of using a broadcast herbicide on the whole surface. This work proposes a strategy where weeds are characterised by a set of shape descriptors (the seven Hu moments and six geometric shape descriptors). Weeds appear in outdoor field images which display real situations obtained from a RGB camera. Thus, images present a mixture of both weed species under varying conditions of lighting. In the presented approach, four decision-making methods were adapted to use the best shape descriptors as attributes and a choice was taken. This proposal establishes a novel methodology with a high success rate in weed species discrimination. PMID:25195854

Image characterization by fractal descriptors in variational mode decomposition domain: Application to brain magnetic resonance

NASA Astrophysics Data System (ADS)

Lahmiri, Salim

2016-08-01

The main purpose of this work is to explore the usefulness of fractal descriptors estimated in multi-resolution domains to characterize biomedical digital image texture. In this regard, three multi-resolution techniques are considered: the well-known discrete wavelet transform (DWT) and the empirical mode decomposition (EMD), and; the newly introduced; variational mode decomposition mode (VMD). The original image is decomposed by the DWT, EMD, and VMD into different scales. Then, Fourier spectrum based fractal descriptors is estimated at specific scales and directions to characterize the image. The support vector machine (SVM) was used to perform supervised classification. The empirical study was applied to the problem of distinguishing between normal and abnormal brain magnetic resonance images (MRI) affected with Alzheimer disease (AD). Our results demonstrate that fractal descriptors estimated in VMD domain outperform those estimated in DWT and EMD domains; and also those directly estimated from the original image.

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.

Effect of number of probes and their orientation on the calculation of several compressor face distortion descriptors

NASA Technical Reports Server (NTRS)

Stoll, F.; Tremback, J. W.; Arnaiz, H. H.

1979-01-01

A study was performed to determine the effects of the number and position of total pressure probes on the calculation of five compressor face distortion descriptors. This study used three sets of 320 steady state total pressure measurements that were obtained with a special rotating rake apparatus in wind tunnel tests of a mixed-compression inlet. The inlet was a one third scale model of the inlet on a YF-12 airplane, and it was tested in the wind tunnel at representative flight conditions at Mach numbers above 2.0. The study shows that large errors resulted in the calculation of the distortion descriptors even with a number of probes that were considered adequate in the past. There were errors as large as 30 and -50 percent in several distortion descriptors for a configuration consisting of eight rakes with five equal-area-weighted probes on each rake.

Observer synthesis for a class of Takagi-Sugeno descriptor system with unmeasurable premise variable. Application to fault diagnosis

NASA Astrophysics Data System (ADS)

López-Estrada, F. R.; Astorga-Zaragoza, C. M.; Theilliol, D.; Ponsart, J. C.; Valencia-Palomo, G.; Torres, L.

2017-12-01

This paper proposes a methodology to design a Takagi-Sugeno (TS) descriptor observer for a class of TS descriptor systems. Unlike the popular approach that considers measurable premise variables, this paper considers the premise variables depending on unmeasurable vectors, e.g. the system states. This consideration covers a large class of nonlinear systems and represents a real challenge for the observer synthesis. Sufficient conditions to guarantee robustness against the unmeasurable premise variables and asymptotic convergence of the TS descriptor observer are obtained based on the H∞ approach together with the Lyapunov method. As a result, the designing conditions are given in terms of linear matrix inequalities (LMIs). In addition, sensor fault detection and isolation are performed by means of a generalised observer bank. Two numerical experiments, an electrical circuit and a rolling disc system, are presented in order to illustrate the effectiveness of the proposed method.

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

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.

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.

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

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.

lazar: a modular predictive toxicology framework

PubMed Central

Maunz, Andreas; Gütlein, Martin; Rautenberg, Micha; Vorgrimmler, David; Gebele, Denis; Helma, Christoph

2013-01-01

lazar (lazy structure–activity relationships) is a modular framework for predictive toxicology. Similar to the read across procedure in toxicological risk assessment, lazar creates local QSAR (quantitative structure–activity relationship) models for each compound to be predicted. Model developers can choose between a large variety of algorithms for descriptor calculation and selection, chemical similarity indices, and model building. This paper presents a high level description of the lazar framework and discusses the performance of example classification and regression models. PMID:23761761

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.

Principal component analysis on molecular descriptors as an alternative point of view in the search of new Hsp90 inhibitors.

PubMed

Lauria, Antonino; Ippolito, Mario; Almerico, Anna Maria

2009-10-01

Inhibiting a protein that regulates multiple signal transduction pathways in cancer cells is an attractive goal for cancer therapy. Heat shock protein 90 (Hsp90) is one of the most promising molecular targets for such an approach. In fact, Hsp90 is a ubiquitous molecular chaperone protein that is involved in folding, activating and assembling of many key mediators of signal transduction, cellular growth, differentiation, stress-response and apoptothic pathways. With the aim to analyze which molecular descriptors have the higher importance in the binding interactions of these classes, we first performed molecular docking experiments on the 187 Hsp90 inhibitors included in the BindingDB, a public database of measured binding affinities. Further, for each frozen conformation obtained from the docking, a set of 250 molecular descriptors was calculated, and the resulting Structure/Descriptors matrix was submitted to Principal Component Analysis. From the factor scores it emerged a good clusterization among similar compounds both in terms of structural class and activity spectrum, while examination of the loadings of the first two factors also allowed to study the classes of descriptors which mainly contribute to each one.

Object Tracking Using Adaptive Covariance Descriptor and Clustering-Based Model Updating for Visual Surveillance

PubMed Central

Qin, Lei; Snoussi, Hichem; Abdallah, Fahed

2014-01-01

We propose a novel approach for tracking an arbitrary object in video sequences for visual surveillance. The first contribution of this work is an automatic feature extraction method that is able to extract compact discriminative features from a feature pool before computing the region covariance descriptor. As the feature extraction method is adaptive to a specific object of interest, we refer to the region covariance descriptor computed using the extracted features as the adaptive covariance descriptor. The second contribution is to propose a weakly supervised method for updating the object appearance model during tracking. The method performs a mean-shift clustering procedure among the tracking result samples accumulated during a period of time and selects a group of reliable samples for updating the object appearance model. As such, the object appearance model is kept up-to-date and is prevented from contamination even in case of tracking mistakes. We conducted comparing experiments on real-world video sequences, which confirmed the effectiveness of the proposed approaches. The tracking system that integrates the adaptive covariance descriptor and the clustering-based model updating method accomplished stable object tracking on challenging video sequences. PMID:24865883

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

PubMed

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

2017-01-01

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

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.

The link between descriptors 8 and 9 of the Marine Strategy Framework Directive: lessons learnt in Spain.

PubMed

Gago, J; Viñas, L; Besada, V; Bellas, J

2014-12-01

The aim of this note is to discuss the relevance of the interaction/integration of monitoring of contaminants for the protection of the marine environment and for human health safety (descriptors 8 and 9, respectively) within the Marine Strategy Framework Directive (MSFD). The identification of possible relations between contaminant levels in sediments and tissues of fish and other seafood, as well as the association of those levels to pollution sources, are major challenges for marine researchers. The Spanish initial assessment in the North-East Atlantic marine region was used as an example to show some gaps and loopholes when dealing with the relationship between descriptors 8 and 9. The main problem to deal with is that monitoring programmes intended for the assessment of marine environmental quality and for human health safety usually apply different approaches and methodologies, and even different tissues are analysed in some species (mainly fish). It is therefore recommended to make a profound revision of current sampling strategies, procedures and methodologies, including the selection of target species and tissues and to improve the traceability of samples of fish and other seafood for human consumption. On the other hand, despite the scope of descriptor 9 which is limited to commercially relevant species, this fact should not be an obstacle in the application of the 'ecosystem approach' within the MSFD. In order to appropriately solve these shortcomings, an information exchange system between authorities dealing with descriptors 8 and 9 should be strongly encouraged for the next steps of the MSFD's implementation.

A fast and automatic fusion algorithm for unregistered multi-exposure image sequence

NASA Astrophysics Data System (ADS)

Liu, Yan; Yu, Feihong

2014-09-01

Human visual system (HVS) can visualize all the brightness levels of the scene through visual adaptation. However, the dynamic range of most commercial digital cameras and display devices are smaller than the dynamic range of human eye. This implies low dynamic range (LDR) images captured by normal digital camera may lose image details. We propose an efficient approach to high dynamic (HDR) image fusion that copes with image displacement and image blur degradation in a computationally efficient manner, which is suitable for implementation on mobile devices. The various image registration algorithms proposed in the previous literatures are unable to meet the efficiency and performance requirements in the application of mobile devices. In this paper, we selected Oriented Brief (ORB) detector to extract local image structures. The descriptor selected in multi-exposure image fusion algorithm has to be fast and robust to illumination variations and geometric deformations. ORB descriptor is the best candidate in our algorithm. Further, we perform an improved RANdom Sample Consensus (RANSAC) algorithm to reject incorrect matches. For the fusion of images, a new approach based on Stationary Wavelet Transform (SWT) is used. The experimental results demonstrate that the proposed algorithm generates high quality images at low computational cost. Comparisons with a number of other feature matching methods show that our method gets better performance.

Use of Biodescriptors and Chemodescriptors in Predictive Toxicology: A Mathematical/Computational Approach

DTIC Science & Technology

2005-01-01

proteomic gel analyses. The research group has explored the use of chemodescriptors calculated using high-level ab initio quantum chemical basis sets...descriptors that characterize the entire proteomics map, local descriptors that characterize a subset of the proteins present in the gel, and spectrum...techniques for analyzing the full set of proteins present in a proteomics map. 14. SUBJECT TERMS 1S. NUMBER OF PAGES Topological indices

Object tracking on mobile devices using binary descriptors

NASA Astrophysics Data System (ADS)

Savakis, Andreas; Quraishi, Mohammad Faiz; Minnehan, Breton

2015-03-01

With the growing ubiquity of mobile devices, advanced applications are relying on computer vision techniques to provide novel experiences for users. Currently, few tracking approaches take into consideration the resource constraints on mobile devices. Designing efficient tracking algorithms and optimizing performance for mobile devices can result in better and more efficient tracking for applications, such as augmented reality. In this paper, we use binary descriptors, including Fast Retina Keypoint (FREAK), Oriented FAST and Rotated BRIEF (ORB), Binary Robust Independent Features (BRIEF), and Binary Robust Invariant Scalable Keypoints (BRISK) to obtain real time tracking performance on mobile devices. We consider both Google's Android and Apple's iOS operating systems to implement our tracking approach. The Android implementation is done using Android's Native Development Kit (NDK), which gives the performance benefits of using native code as well as access to legacy libraries. The iOS implementation was created using both the native Objective-C and the C++ programing languages. We also introduce simplified versions of the BRIEF and BRISK descriptors that improve processing speed without compromising tracking accuracy.

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.

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.

wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials

NASA Astrophysics Data System (ADS)

Gastegger, M.; Schwiedrzik, L.; Bittermann, M.; Berzsenyi, F.; Marquetand, P.

2018-06-01

We introduce weighted atom-centered symmetry functions (wACSFs) as descriptors of a chemical system's geometry for use in the prediction of chemical properties such as enthalpies or potential energies via machine learning. The wACSFs are based on conventional atom-centered symmetry functions (ACSFs) but overcome the undesirable scaling of the latter with an increasing number of different elements in a chemical system. The performance of these two descriptors is compared using them as inputs in high-dimensional neural network potentials (HDNNPs), employing the molecular structures and associated enthalpies of the 133 855 molecules containing up to five different elements reported in the QM9 database as reference data. A substantially smaller number of wACSFs than ACSFs is needed to obtain a comparable spatial resolution of the molecular structures. At the same time, this smaller set of wACSFs leads to a significantly better generalization performance in the machine learning potential than the large set of conventional ACSFs. Furthermore, we show that the intrinsic parameters of the descriptors can in principle be optimized with a genetic algorithm in a highly automated manner. For the wACSFs employed here, we find however that using a simple empirical parametrization scheme is sufficient in order to obtain HDNNPs with high accuracy.

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

PubMed

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

2017-07-14

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

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%.

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

Predicting Drug-induced Hepatotoxicity Using QSAR and Toxicogenomics Approaches

PubMed Central

Low, Yen; Uehara, Takeki; Minowa, Yohsuke; Yamada, Hiroshi; Ohno, Yasuo; Urushidani, Tetsuro; Sedykh, Alexander; Muratov, Eugene; Fourches, Denis; Zhu, Hao; Rusyn, Ivan; Tropsha, Alexander

2014-01-01

Quantitative Structure-Activity Relationship (QSAR) modeling and toxicogenomics are used independently as predictive tools in toxicology. In this study, we evaluated the power of several statistical models for predicting drug hepatotoxicity in rats using different descriptors of drug molecules, namely their chemical descriptors and toxicogenomic profiles. The records were taken from the Toxicogenomics Project rat liver microarray database containing information on 127 drugs (http://toxico.nibio.go.jp/datalist.html). The model endpoint was hepatotoxicity in the rat following 28 days of exposure, established by liver histopathology and serum chemistry. First, we developed multiple conventional QSAR classification models using a comprehensive set of chemical descriptors and several classification methods (k nearest neighbor, support vector machines, random forests, and distance weighted discrimination). With chemical descriptors alone, external predictivity (Correct Classification Rate, CCR) from 5-fold external cross-validation was 61%. Next, the same classification methods were employed to build models using only toxicogenomic data (24h after a single exposure) treated as biological descriptors. The optimized models used only 85 selected toxicogenomic descriptors and had CCR as high as 76%. Finally, hybrid models combining both chemical descriptors and transcripts were developed; their CCRs were between 68 and 77%. Although the accuracy of hybrid models did not exceed that of the models based on toxicogenomic data alone, the use of both chemical and biological descriptors enriched the interpretation of the models. In addition to finding 85 transcripts that were predictive and highly relevant to the mechanisms of drug-induced liver injury, chemical structural alerts for hepatotoxicity were also identified. These results suggest that concurrent exploration of the chemical features and acute treatment-induced changes in transcript levels will both enrich the mechanistic understanding of sub-chronic liver injury and afford models capable of accurate prediction of hepatotoxicity from chemical structure and short-term assay results. PMID:21699217

Relationship between Dyspnea Descriptors and Underlying Causes of the Symptom; a Cross-sectional Study

PubMed Central

Sajadi, Seyyed Mohammad Ali; Majidi, Alireza; Abdollahimajd, Fahimeh; jalali, Fatemeh

2017-01-01

Introduction: History taking and physical examination help clinicians identify the patient’s problem and effectively treat it. This study aimed to evaluate the descriptors of dyspnea in patients presenting to emergency department (ED) with asthma, congestive heart failure (CHF), and chronic obstructive pulmonary disease (COPD). Method: This cross-sectional study was conducted on all patients presenting to ED with chief complaint of dyspnea, during 2 years. The patients were asked to describe their dyspnea by choosing three items from the valid and reliable questionnaire or articulating their sensation. The relationship between dyspnea descriptors and underlying cause of symptom was evaluated using SPSS version 16. Results: 312 patients with the mean age of 60.96±17.01 years were evaluated (53.2% male). Most of the patients were > 65 years old (48.7%) and had basic level of education (76.9%). "My breath doesn’t go out all the way" with 83.1%, “My chest feels tight " with 45.8%, and "I feel that my airway is obstructed" with 40.7%, were the most frequent dyspnea descriptors in asthma patients. "My breathing requires work" with 46.3%, "I feel that I am suffocating" with 31.5%, and "My breath doesn’t go out all the way" with 29.6%, were the most frequent dyspnea descriptors in COPD patients. "My breathing is heavy" with 74.4%, "A hunger for more air” with 24.4%, and "I cannot get enough air" with 23.2%, were the most frequent dyspnea descriptors in CHF patients. Except for “My breath does not go in all the way”, there was significant correlation between studied dyspnea descriptors and underlying disease (p = 0.001 for all analyses). Conclusion: It seems that dyspnea descriptors along with other findings from history and physical examination could be helpful in differentiating the causes of the symptom in patients presenting to ED suffering from dyspnea. PMID:28894777

Relationship between Dyspnea Descriptors and Underlying Causes of the Symptom; a Cross-sectional Study.

PubMed

Sajadi, Seyyed Mohammad Ali; Majidi, Alireza; Abdollahimajd, Fahimeh; Jalali, Fatemeh

2017-01-01

History taking and physical examination help clinicians identify the patient's problem and effectively treat it. This study aimed to evaluate the descriptors of dyspnea in patients presenting to emergency department (ED) with asthma, congestive heart failure (CHF), and chronic obstructive pulmonary disease (COPD). This cross-sectional study was conducted on all patients presenting to ED with chief complaint of dyspnea, during 2 years. The patients were asked to describe their dyspnea by choosing three items from the valid and reliable questionnaire or articulating their sensation. The relationship between dyspnea descriptors and underlying cause of symptom was evaluated using SPSS version 16. 312 patients with the mean age of 60.96±17.01 years were evaluated (53.2% male). Most of the patients were > 65 years old (48.7%) and had basic level of education (76.9%). "My breath doesn't go out all the way" with 83.1%, "My chest feels tight " with 45.8%, and "I feel that my airway is obstructed" with 40.7%, were the most frequent dyspnea descriptors in asthma patients. "My breathing requires work" with 46.3%, "I feel that I am suffocating" with 31.5%, and "My breath doesn't go out all the way" with 29.6%, were the most frequent dyspnea descriptors in COPD patients. "My breathing is heavy" with 74.4%, "A hunger for more air" with 24.4%, and "I cannot get enough air" with 23.2%, were the most frequent dyspnea descriptors in CHF patients. Except for "My breath does not go in all the way", there was significant correlation between studied dyspnea descriptors and underlying disease (p = 0.001 for all analyses). It seems that dyspnea descriptors along with other findings from history and physical examination could be helpful in differentiating the causes of the symptom in patients presenting to ED suffering from dyspnea.

Reciprocal Modulation of Cognitive and Emotional Aspects in Pianistic Performances

PubMed Central

Higuchi, Marcia K. Kodama; Fornari, José; Del Ben, Cristina M.; Graeff, Frederico G.; Leite, João Pereira

2011-01-01

Background High level piano performance requires complex integration of perceptual, motor, cognitive and emotive skills. Observations in psychology and neuroscience studies have suggested reciprocal inhibitory modulation of the cognition by emotion and emotion by cognition. However, it is still unclear how cognitive states may influence the pianistic performance. The aim of the present study is to verify the influence of cognitive and affective attention in the piano performances. Methods and Findings Nine pianists were instructed to play the same piece of music, firstly focusing only on cognitive aspects of musical structure (cognitive performances), and secondly, paying attention solely on affective aspects (affective performances). Audio files from pianistic performances were examined using a computational model that retrieves nine specific musical features (descriptors) – loudness, articulation, brightness, harmonic complexity, event detection, key clarity, mode detection, pulse clarity and repetition. In addition, the number of volunteers' errors in the recording sessions was counted. Comments from pianists about their thoughts during performances were also evaluated. The analyses of audio files throughout musical descriptors indicated that the affective performances have more: agogics, legatos, pianos phrasing, and less perception of event density when compared to the cognitive ones. Error analysis demonstrated that volunteers misplayed more left hand notes in the cognitive performances than in the affective ones. Volunteers also played more wrong notes in affective than in cognitive performances. These results correspond to the volunteers' comments that in the affective performances, the cognitive aspects of piano execution are inhibited, whereas in the cognitive performances, the expressiveness is inhibited. Conclusions Therefore, the present results indicate that attention to the emotional aspects of performance enhances expressiveness, but constrains cognitive and motor skills in the piano execution. In contrast, attention to the cognitive aspects may constrain the expressivity and automatism of piano performances. PMID:21931716

Descriptors of natural thermal regimes in streams and their responsiveness to change in the Pacific Northwest of North America

USGS Publications Warehouse

Arismendi, Ivan; Johnson, Sherri L.; Dunham, Jason B.; Haggerty, Roy

2013-01-01

1. Temperature is a major driver of ecological processes in stream ecosystems, yet the dynamics of thermal regimes remain poorly described. Most work has focused on relatively simple descriptors that fail to capture the full range of conditions that characterise thermal regimes of streams across seasons or throughout the year. 2. To more completely describe thermal regimes, we developed several descriptors of magnitude, variability, frequency, duration and timing of thermal events throughout a year. We evaluated how these descriptors change over time using long-term (1979–2009), continuous temperature data from five relatively undisturbed cold-water streams in western Oregon, U.S.A. In addition to trends for each descriptor, we evaluated similarities among them, as well as patterns of spatial coherence, and temporal synchrony. 3. Using different groups of descriptors, we were able to more fully capture distinct aspects of the full range of variability in thermal regimes across space and time. A subset of descriptors showed both higher coherence and synchrony and, thus, an appropriate level of responsiveness to examine evidence of regional climatic influences on thermal regimes. Most notably, daily minimum values during winter–spring were the most responsive descriptors to potential climatic influences. 4. Overall, thermal regimes in streams we studied showed high frequency and low variability of cold temperatures during the cold-water period in winter and spring, and high frequency and high variability of warm temperatures during the warm-water period in summer and autumn. The cold and warm periods differed in the distribution of events with a higher frequency and longer duration of warm events in summer than cold events in winter. The cold period exhibited lower variability in the duration of events, but showed more variability in timing. 5. In conclusion, our results highlight the importance of a year-round perspective in identifying the most responsive characteristics or descriptors of thermal regimes in streams. The descriptors we provide herein can be applied across hydro-ecological regions to evaluate spatial and temporal patterns in thermal regimes. Evaluation of coherence and synchrony of different components of thermal regimes can facilitate identification of impacts of regional climate variability or local human or natural influences.

Assigning Main Orientation to an EOH Descriptor on Multispectral Images.

PubMed

Li, Yong; Shi, Xiang; Wei, Lijun; Zou, Junwei; Chen, Fang

2015-07-01

This paper proposes an approach to compute an EOH (edge-oriented histogram) descriptor with main orientation. EOH has a better matching ability than SIFT (scale-invariant feature transform) on multispectral images, but does not assign a main orientation to keypoints. Alternatively, it tends to assign the same main orientation to every keypoint, e.g., zero degrees. This limits EOH to matching keypoints between images of translation misalignment only. Observing this limitation, we propose assigning to keypoints the main orientation that is computed with PIIFD (partial intensity invariant feature descriptor). In the proposed method, SIFT keypoints are detected from images as the extrema of difference of Gaussians, and every keypoint is assigned to the main orientation computed with PIIFD. Then, EOH is computed for every keypoint with respect to its main orientation. In addition, an implementation variant is proposed for fast computation of the EOH descriptor. Experimental results show that the proposed approach performs more robustly than the original EOH on image pairs that have a rotation misalignment.

Lagrangian Descriptors: A Method for Revealing Phase Space Structures of General Time Dependent Dynamical Systems

NASA Astrophysics Data System (ADS)

Mancho, Ana M.; Wiggins, Stephen; Curbelo, Jezabel; Mendoza, Carolina

2013-11-01

Lagrangian descriptors are a recent technique which reveals geometrical structures in phase space and which are valid for aperiodically time dependent dynamical systems. We discuss a general methodology for constructing them and we discuss a ``heuristic argument'' that explains why this method is successful. We support this argument by explicit calculations on a benchmark problem. Several other benchmark examples are considered that allow us to assess the performance of Lagrangian descriptors with both finite time Lyapunov exponents (FTLEs) and finite time averages of certain components of the vector field (``time averages''). In all cases Lagrangian descriptors are shown to be both more accurate and computationally efficient than these methods. We thank CESGA for computing facilities. This research was supported by MINECO grants: MTM2011-26696, I-Math C3-0104, ICMAT Severo Ochoa project SEV-2011-0087, and CSIC grant OCEANTECH. SW acknowledges the support of the ONR (Grant No. N00014-01-1-0769).

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

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

PubMed

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

2010-11-01

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

Towards interoperable and reproducible QSAR analyses: Exchange of datasets.

PubMed

Spjuth, Ola; Willighagen, Egon L; Guha, Rajarshi; Eklund, Martin; Wikberg, Jarl Es

2010-06-30

QSAR is a widely used method to relate chemical structures to responses or properties based on experimental observations. Much effort has been made to evaluate and validate the statistical modeling in QSAR, but these analyses treat the dataset as fixed. An overlooked but highly important issue is the validation of the setup of the dataset, which comprises addition of chemical structures as well as selection of descriptors and software implementations prior to calculations. This process is hampered by the lack of standards and exchange formats in the field, making it virtually impossible to reproduce and validate analyses and drastically constrain collaborations and re-use of data. We present a step towards standardizing QSAR analyses by defining interoperable and reproducible QSAR datasets, consisting of an open XML format (QSAR-ML) which builds on an open and extensible descriptor ontology. The ontology provides an extensible way of uniquely defining descriptors for use in QSAR experiments, and the exchange format supports multiple versioned implementations of these descriptors. Hence, a dataset described by QSAR-ML makes its setup completely reproducible. We also provide a reference implementation as a set of plugins for Bioclipse which simplifies setup of QSAR datasets, and allows for exporting in QSAR-ML as well as old-fashioned CSV formats. The implementation facilitates addition of new descriptor implementations from locally installed software and remote Web services; the latter is demonstrated with REST and XMPP Web services. Standardized QSAR datasets open up new ways to store, query, and exchange data for subsequent analyses. QSAR-ML supports completely reproducible creation of datasets, solving the problems of defining which software components were used and their versions, and the descriptor ontology eliminates confusions regarding descriptors by defining them crisply. This makes is easy to join, extend, combine datasets and hence work collectively, but also allows for analyzing the effect descriptors have on the statistical model's performance. The presented Bioclipse plugins equip scientists with graphical tools that make QSAR-ML easily accessible for the community.

Towards interoperable and reproducible QSAR analyses: Exchange of datasets

PubMed Central

2010-01-01

Background QSAR is a widely used method to relate chemical structures to responses or properties based on experimental observations. Much effort has been made to evaluate and validate the statistical modeling in QSAR, but these analyses treat the dataset as fixed. An overlooked but highly important issue is the validation of the setup of the dataset, which comprises addition of chemical structures as well as selection of descriptors and software implementations prior to calculations. This process is hampered by the lack of standards and exchange formats in the field, making it virtually impossible to reproduce and validate analyses and drastically constrain collaborations and re-use of data. Results We present a step towards standardizing QSAR analyses by defining interoperable and reproducible QSAR datasets, consisting of an open XML format (QSAR-ML) which builds on an open and extensible descriptor ontology. The ontology provides an extensible way of uniquely defining descriptors for use in QSAR experiments, and the exchange format supports multiple versioned implementations of these descriptors. Hence, a dataset described by QSAR-ML makes its setup completely reproducible. We also provide a reference implementation as a set of plugins for Bioclipse which simplifies setup of QSAR datasets, and allows for exporting in QSAR-ML as well as old-fashioned CSV formats. The implementation facilitates addition of new descriptor implementations from locally installed software and remote Web services; the latter is demonstrated with REST and XMPP Web services. Conclusions Standardized QSAR datasets open up new ways to store, query, and exchange data for subsequent analyses. QSAR-ML supports completely reproducible creation of datasets, solving the problems of defining which software components were used and their versions, and the descriptor ontology eliminates confusions regarding descriptors by defining them crisply. This makes is easy to join, extend, combine datasets and hence work collectively, but also allows for analyzing the effect descriptors have on the statistical model's performance. The presented Bioclipse plugins equip scientists with graphical tools that make QSAR-ML easily accessible for the community. PMID:20591161

Microscopic structural descriptor of liquid water

NASA Astrophysics Data System (ADS)

Shi, Rui; Tanaka, Hajime

2018-03-01

The microscopic structure of liquid water has been believed to be the key to the understanding of the unique properties of this extremely important substance. Many structural descriptors have been developed for revealing local structural order in water, but their properties are still not well understood. The essential difficulty comes from structural fluctuations due to thermal noise, which are intrinsic to the liquid state. The most popular and widely used descriptors are the local structure index (LSI) and d5. Recently, Russo and Tanaka [Nat. Commun. 3, 3556 (2014)] introduced a new descriptor ζ which measures the translational order between the first and second shells considering hydrogen bonding (H-bonding) in the first shell. In this work, we compare the performance of these three structural descriptors for a popular water model known as TIP5P water. We show that local structural ordering can be properly captured only by the structural descriptor ζ, but not by the other two descriptors particularly at a high temperature, where thermal noise effects are severe. The key difference of ζ from LSI and d5 is that only ζ considers H-bonding which is crucial to detect high translational and tetrahedral order of not only oxygen but also hydrogen atoms. The importance of H-bonding is very natural, considering the fact that the locally favored structures are stabilized by energy gain due to the formation of four hydrogen bonds between the central water molecule and its neighboring ones in the first shell. Our analysis of the water structure by using ζ strongly supports the two-state model of water: water is a dynamic mixture of locally favored (ordered) and normal-liquid (disordered) structures. This work demonstrates the importance of H-bonding in the characterization of water's structures and provides a useful structural descriptor for water-type tetrahedral liquids to study their structure and dynamics.

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.

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

Cache Hardware Approaches to Multiple Independent Levels of Security (MILS)

DTIC Science & Technology

2012-10-01

systems that require that several multicore processors be connected together in a single system. However, no such boards were available on the market ...available concerning each module. However, the availability of modules seems to significantly lag the time when the corresponding hardware hits the market ...version of real mode often referred to as “Unreal mode” can be entered by loading a Local Descriptor Table (LDT) and Global Descriptor Table (GDT

Learning Optimized Local Difference Binaries for Scalable Augmented Reality on Mobile Devices.

PubMed

Xin Yang; Kwang-Ting Cheng

2014-06-01

The efficiency, robustness and distinctiveness of a feature descriptor are critical to the user experience and scalability of a mobile augmented reality (AR) system. However, existing descriptors are either too computationally expensive to achieve real-time performance on a mobile device such as a smartphone or tablet, or not sufficiently robust and distinctive to identify correct matches from a large database. As a result, current mobile AR systems still only have limited capabilities, which greatly restrict their deployment in practice. In this paper, we propose a highly efficient, robust and distinctive binary descriptor, called Learning-based Local Difference Binary (LLDB). LLDB directly computes a binary string for an image patch using simple intensity and gradient difference tests on pairwise grid cells within the patch. To select an optimized set of grid cell pairs, we densely sample grid cells from an image patch and then leverage a modified AdaBoost algorithm to automatically extract a small set of critical ones with the goal of maximizing the Hamming distance between mismatches while minimizing it between matches. Experimental results demonstrate that LLDB is extremely fast to compute and to match against a large database due to its high robustness and distinctiveness. Compared to the state-of-the-art binary descriptors, primarily designed for speed, LLDB has similar efficiency for descriptor construction, while achieving a greater accuracy and faster matching speed when matching over a large database with 2.3M descriptors on mobile devices.

Improved nucleic acid descriptors for siRNA efficacy prediction.

PubMed

Sciabola, Simone; Cao, Qing; Orozco, Modesto; Faustino, Ignacio; Stanton, Robert V

2013-02-01

Although considerable progress has been made recently in understanding how gene silencing is mediated by the RNAi pathway, the rational design of effective sequences is still a challenging task. In this article, we demonstrate that including three-dimensional descriptors improved the discrimination between active and inactive small interfering RNAs (siRNAs) in a statistical model. Five descriptor types were used: (i) nucleotide position along the siRNA sequence, (ii) nucleotide composition in terms of presence/absence of specific combinations of di- and trinucleotides, (iii) nucleotide interactions by means of a modified auto- and cross-covariance function, (iv) nucleotide thermodynamic stability derived by the nearest neighbor model representation and (v) nucleic acid structure flexibility. The duplex flexibility descriptors are derived from extended molecular dynamics simulations, which are able to describe the sequence-dependent elastic properties of RNA duplexes, even for non-standard oligonucleotides. The matrix of descriptors was analysed using three statistical packages in R (partial least squares, random forest, and support vector machine), and the most predictive model was implemented in a modeling tool we have made publicly available through SourceForge. Our implementation of new RNA descriptors coupled with appropriate statistical algorithms resulted in improved model performance for the selection of siRNA candidates when compared with publicly available siRNA prediction tools and previously published test sets. Additional validation studies based on in-house RNA interference projects confirmed the robustness of the scoring procedure in prospective studies.

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.

Prediction of the Fate of Organic Compounds in the Environment From Their Molecular Properties: A Review

PubMed Central

Mamy, Laure; Patureau, Dominique; Barriuso, Enrique; Bedos, Carole; Bessac, Fabienne; Louchart, Xavier; Martin-laurent, Fabrice; Miege, Cecile; Benoit, Pierre

2015-01-01

A comprehensive review of quantitative structure-activity relationships (QSAR) allowing the prediction of the fate of organic compounds in the environment from their molecular properties was done. The considered processes were water dissolution, dissociation, volatilization, retention on soils and sediments (mainly adsorption and desorption), degradation (biotic and abiotic), and absorption by plants. A total of 790 equations involving 686 structural molecular descriptors are reported to estimate 90 environmental parameters related to these processes. A significant number of equations was found for dissociation process (pKa), water dissolution or hydrophobic behavior (especially through the KOW parameter), adsorption to soils and biodegradation. A lack of QSAR was observed to estimate desorption or potential of transfer to water. Among the 686 molecular descriptors, five were found to be dominant in the 790 collected equations and the most generic ones: four quantum-chemical descriptors, the energy of the highest occupied molecular orbital (EHOMO) and the energy of the lowest unoccupied molecular orbital (ELUMO), polarizability (α) and dipole moment (μ), and one constitutional descriptor, the molecular weight. Keeping in mind that the combination of descriptors belonging to different categories (constitutional, topological, quantum-chemical) led to improve QSAR performances, these descriptors should be considered for the development of new QSAR, for further predictions of environmental parameters. This review also allows finding of the relevant QSAR equations to predict the fate of a wide diversity of compounds in the environment. PMID:25866458

Prediction of the Fate of Organic Compounds in the Environment From Their Molecular Properties: A Review.

PubMed

Mamy, Laure; Patureau, Dominique; Barriuso, Enrique; Bedos, Carole; Bessac, Fabienne; Louchart, Xavier; Martin-Laurent, Fabrice; Miege, Cecile; Benoit, Pierre

2015-06-18

A comprehensive review of quantitative structure-activity relationships (QSAR) allowing the prediction of the fate of organic compounds in the environment from their molecular properties was done. The considered processes were water dissolution, dissociation, volatilization, retention on soils and sediments (mainly adsorption and desorption), degradation (biotic and abiotic), and absorption by plants. A total of 790 equations involving 686 structural molecular descriptors are reported to estimate 90 environmental parameters related to these processes. A significant number of equations was found for dissociation process (pK a ), water dissolution or hydrophobic behavior (especially through the K OW parameter), adsorption to soils and biodegradation. A lack of QSAR was observed to estimate desorption or potential of transfer to water. Among the 686 molecular descriptors, five were found to be dominant in the 790 collected equations and the most generic ones: four quantum-chemical descriptors, the energy of the highest occupied molecular orbital (E HOMO ) and the energy of the lowest unoccupied molecular orbital (E LUMO ), polarizability (α) and dipole moment (μ), and one constitutional descriptor, the molecular weight. Keeping in mind that the combination of descriptors belonging to different categories (constitutional, topological, quantum-chemical) led to improve QSAR performances, these descriptors should be considered for the development of new QSAR, for further predictions of environmental parameters. This review also allows finding of the relevant QSAR equations to predict the fate of a wide diversity of compounds in the environment.

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.

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.

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).

Learning structure-property relationship in crystalline materials: A study of lanthanide-transition metal alloys

NASA Astrophysics Data System (ADS)

Pham, Tien-Lam; Nguyen, Nguyen-Duong; Nguyen, Van-Doan; Kino, Hiori; Miyake, Takashi; Dam, Hieu-Chi

2018-05-01

We have developed a descriptor named Orbital Field Matrix (OFM) for representing material structures in datasets of multi-element materials. The descriptor is based on the information regarding atomic valence shell electrons and their coordination. In this work, we develop an extension of OFM called OFM1. We have shown that these descriptors are highly applicable in predicting the physical properties of materials and in providing insights on the materials space by mapping into a low embedded dimensional space. Our experiments with transition metal/lanthanide metal alloys show that the local magnetic moments and formation energies can be accurately reproduced using simple nearest-neighbor regression, thus confirming the relevance of our descriptors. Using kernel ridge regressions, we could accurately reproduce formation energies and local magnetic moments calculated based on first-principles, with mean absolute errors of 0.03 μB and 0.10 eV/atom, respectively. We show that meaningful low-dimensional representations can be extracted from the original descriptor using descriptive learning algorithms. Intuitive prehension on the materials space, qualitative evaluation on the similarities in local structures or crystalline materials, and inference in the designing of new materials by element substitution can be performed effectively based on these low-dimensional representations.

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.

A rotation-translation invariant molecular descriptor of partial charges and its use in ligand-based virtual screening

PubMed Central

2014-01-01

Background Measures of similarity for chemical molecules have been developed since the dawn of chemoinformatics. Molecular similarity has been measured by a variety of methods including molecular descriptor based similarity, common molecular fragments, graph matching and 3D methods such as shape matching. Similarity measures are widespread in practice and have proven to be useful in drug discovery. Because of our interest in electrostatics and high throughput ligand-based virtual screening, we sought to exploit the information contained in atomic coordinates and partial charges of a molecule. Results A new molecular descriptor based on partial charges is proposed. It uses the autocorrelation function and linear binning to encode all atoms of a molecule into two rotation-translation invariant vectors. Combined with a scoring function, the descriptor allows to rank-order a database of compounds versus a query molecule. The proposed implementation is called ACPC (AutoCorrelation of Partial Charges) and released in open source. Extensive retrospective ligand-based virtual screening experiments were performed and other methods were compared with in order to validate the method and associated protocol. Conclusions While it is a simple method, it performed remarkably well in experiments. At an average speed of 1649 molecules per second, it reached an average median area under the curve of 0.81 on 40 different targets; hence validating the proposed protocol and implementation. PMID:24887178

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

PubMed

Giordano, Bruno L; McAdams, Stephen

2006-02-01

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

Target recognition of ladar range images using slice image: comparison of four improved algorithms

NASA Astrophysics Data System (ADS)

Xia, Wenze; Han, Shaokun; Cao, Jingya; Wang, Liang; Zhai, Yu; Cheng, Yang

2017-07-01

Compared with traditional 3-D shape data, ladar range images possess properties of strong noise, shape degeneracy, and sparsity, which make feature extraction and representation difficult. The slice image is an effective feature descriptor to resolve this problem. We propose four improved algorithms on target recognition of ladar range images using slice image. In order to improve resolution invariance of the slice image, mean value detection instead of maximum value detection is applied in these four improved algorithms. In order to improve rotation invariance of the slice image, three new improved feature descriptors-which are feature slice image, slice-Zernike moments, and slice-Fourier moments-are applied to the last three improved algorithms, respectively. Backpropagation neural networks are used as feature classifiers in the last two improved algorithms. The performance of these four improved recognition systems is analyzed comprehensively in the aspects of the three invariances, recognition rate, and execution time. The final experiment results show that the improvements for these four algorithms reach the desired effect, the three invariances of feature descriptors are not directly related to the final recognition performance of recognition systems, and these four improved recognition systems have different performances under different conditions.

The use of language to express thermal sensation suggests heat acclimatization by Indonesian people

NASA Astrophysics Data System (ADS)

Tochihara, Yutaka; Lee, Joo-Young; Wakabayashi, Hitoshi; Wijayanto, Titis; Bakri, Ilham; Parsons, Ken

2012-11-01

The purpose of this study was to explore whether there is evidence of heat acclimatization in the words used to express thermal sensation. A total of 458 urban Japanese and 601 Indonesians participated in a questionnaire. In addition, in a preliminary survey, 39 native English speakers in the UK participated. Our results showed that (1) for Indonesians, the closest thermal descriptor of a feeling of thermal comfort was `cool' (75%) followed by `slightly cool' (7%), `slightly cold' (5%) and `cold' (5%), while Japanese responses were distributed uniformly among descriptors `cool', `slightly cool', `neither', `slightly warm', and `warm'; (2) the closest thermal descriptors of a feeling of discomfort for Indonesians were less affected by individual thermal susceptibility (vulnerability) than those for Japanese; (3) in the cases where `cool' and `slightly cold' were imagined in the mind, the descriptors were cognized as a thermal comfortable feeling by 97% and 57% of Indonesians, respectively; (4) the most frequently voted choice endorsing hot weather was `higher than 32°C' for Indonesians and `higher than 29°C' for Japanese respondents; for cold weather, `lower than 15°C' for Japanese and `lower than 20°C' for Indonesians. In summary, the descriptor `cool' in Indonesians connotes a thermally comfortable feeling, but the inter-zone between hot and cold weather that was judged in the mind showed a upward shift when compared to that of Japanese. It is suggested that linguistic heat acclimatization exists on a cognitive level for Indonesians and is preserved in the words of thermal descriptors.

Mining chemical reactions using neighborhood behavior and condensed graphs of reactions approaches.

PubMed

de Luca, Aurélie; Horvath, Dragos; Marcou, Gilles; Solov'ev, Vitaly; Varnek, Alexandre

2012-09-24

This work addresses the problem of similarity search and classification of chemical reactions using Neighborhood Behavior (NB) and Condensed Graphs of Reaction (CGR) approaches. The CGR formalism represents chemical reactions as a classical molecular graph with dynamic bonds, enabling descriptor calculations on this graph. Different types of the ISIDA fragment descriptors generated for CGRs in combination with two metrics--Tanimoto and Euclidean--were considered as chemical spaces, to serve for reaction dissimilarity scoring. The NB method has been used to select an optimal combination of descriptors which distinguish different types of chemical reactions in a database containing 8544 reactions of 9 classes. Relevance of NB analysis has been validated in generic (multiclass) similarity search and in clustering with Self-Organizing Maps (SOM). NB-compliant sets of descriptors were shown to display enhanced mapping propensities, allowing the construction of better Self-Organizing Maps and similarity searches (NB and classical similarity search criteria--AUC ROC--correlate at a level of 0.7). The analysis of the SOM clusters proved chemically meaningful CGR substructures representing specific reaction signatures.

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.

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

PubMed

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

2004-01-01

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

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

DOEpatents

Archer, Charles J.; Blocksome, Michael A.

2010-09-28

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

Replenishing data descriptors in a DMA injection FIFO buffer

DOEpatents

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

2011-10-11

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

Robust and efficient method for matching features in omnidirectional images

NASA Astrophysics Data System (ADS)

Zhu, Qinyi; Zhang, Zhijiang; Zeng, Dan

2018-04-01

Binary descriptors have been widely used in many real-time applications due to their efficiency. These descriptors are commonly designed for perspective images but perform poorly on omnidirectional images, which are severely distorted. To address this issue, this paper proposes tangent plane BRIEF (TPBRIEF) and adapted log polar grid-based motion statistics (ALPGMS). TPBRIEF projects keypoints to a unit sphere and applies the fixed test set in BRIEF descriptor on the tangent plane of the unit sphere. The fixed test set is then backprojected onto the original distorted images to construct the distortion invariant descriptor. TPBRIEF directly enables keypoint detecting and feature describing on original distorted images, whereas other approaches correct the distortion through image resampling, which introduces artifacts and adds time cost. With ALPGMS, omnidirectional images are divided into circular arches named adapted log polar grids. Whether a match is true or false is then determined by simply thresholding the match numbers in a grid pair where the two matched points located. Experiments show that TPBRIEF greatly improves the feature matching accuracy and ALPGMS robustly removes wrong matches. Our proposed method outperforms the state-of-the-art methods.

Bias-Free Chemically Diverse Test Sets from Machine Learning.

PubMed

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

2017-08-14

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

Statistical Analysis of Interactive Surgical Planning Using Shape Descriptors in Mandibular Reconstruction with Fibular Segments

PubMed Central

2016-01-01

This study was performed to quantitatively analyze medical knowledge of, and experience with, decision-making in preoperative virtual planning of mandibular reconstruction. Three shape descriptors were designed to evaluate local differences between reconstructed mandibles and patients’ original mandibles. We targeted an asymmetrical, wide range of cutting areas including the mandibular sidepiece, and defined a unique three-dimensional coordinate system for each mandibular image. The generalized algorithms for computing the shape descriptors were integrated into interactive planning software, where the user can refine the preoperative plan using the spatial map of the local shape distance as a visual guide. A retrospective study was conducted with two oral surgeons and two dental technicians using the developed software. The obtained 120 reconstruction plans show that the participants preferred a moderate shape distance rather than optimization to the smallest. We observed that a visually plausible shape could be obtained when considering specific anatomical features (e.g., mental foramen. mandibular midline). The proposed descriptors can be used to multilaterally evaluate reconstruction plans and systematically learn surgical procedures. PMID:27583465

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

PubMed

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

2017-03-01

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

Learning Compact Binary Face Descriptor for Face Recognition.

PubMed

Lu, Jiwen; Liong, Venice Erin; Zhou, Xiuzhuang; Zhou, Jie

2015-10-01

Binary feature descriptors such as local binary patterns (LBP) and its variations have been widely used in many face recognition systems due to their excellent robustness and strong discriminative power. However, most existing binary face descriptors are hand-crafted, which require strong prior knowledge to engineer them by hand. In this paper, we propose a compact binary face descriptor (CBFD) feature learning method for face representation and recognition. Given each face image, we first extract pixel difference vectors (PDVs) in local patches by computing the difference between each pixel and its neighboring pixels. Then, we learn a feature mapping to project these pixel difference vectors into low-dimensional binary vectors in an unsupervised manner, where 1) the variance of all binary codes in the training set is maximized, 2) the loss between the original real-valued codes and the learned binary codes is minimized, and 3) binary codes evenly distribute at each learned bin, so that the redundancy information in PDVs is removed and compact binary codes are obtained. Lastly, we cluster and pool these binary codes into a histogram feature as the final representation for each face image. Moreover, we propose a coupled CBFD (C-CBFD) method by reducing the modality gap of heterogeneous faces at the feature level to make our method applicable to heterogeneous face recognition. Extensive experimental results on five widely used face datasets show that our methods outperform state-of-the-art face descriptors.

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.'

Evaluating the Energetic Driving Force for Cocrystal Formation

PubMed Central

2017-01-01

We present a periodic density functional theory study of the stability of 350 organic cocrystals relative to their pure single-component structures, the largest study of cocrystals yet performed with high-level computational methods. Our calculations demonstrate that cocrystals are on average 8 kJ mol–1 more stable than their constituent single-component structures and are very rarely (<5% of cases) less stable; cocrystallization is almost always a thermodynamically favorable process. We consider the variation in stability between different categories of systems—hydrogen-bonded, halogen-bonded, and weakly bound cocrystals—finding that, contrary to chemical intuition, the presence of hydrogen or halogen bond interactions is not necessarily a good predictor of stability. Finally, we investigate the correlation of the relative stability with simple chemical descriptors: changes in packing efficiency and hydrogen bond strength. We find some broad qualitative agreement with chemical intuition—more densely packed cocrystals with stronger hydrogen bonding tend to be more stable—but the relationship is weak, suggesting that such simple descriptors do not capture the complex balance of interactions driving cocrystallization. Our conclusions suggest that while cocrystallization is often a thermodynamically favorable process, it remains difficult to formulate general rules to guide synthesis, highlighting the continued importance of high-level computation in predicting and rationalizing such systems. PMID:29445316

Evaluating the Energetic Driving Force for Cocrystal Formation.

PubMed

Taylor, Christopher R; Day, Graeme M

2018-02-07

We present a periodic density functional theory study of the stability of 350 organic cocrystals relative to their pure single-component structures, the largest study of cocrystals yet performed with high-level computational methods. Our calculations demonstrate that cocrystals are on average 8 kJ mol -1 more stable than their constituent single-component structures and are very rarely (<5% of cases) less stable; cocrystallization is almost always a thermodynamically favorable process. We consider the variation in stability between different categories of systems-hydrogen-bonded, halogen-bonded, and weakly bound cocrystals-finding that, contrary to chemical intuition, the presence of hydrogen or halogen bond interactions is not necessarily a good predictor of stability. Finally, we investigate the correlation of the relative stability with simple chemical descriptors: changes in packing efficiency and hydrogen bond strength. We find some broad qualitative agreement with chemical intuition-more densely packed cocrystals with stronger hydrogen bonding tend to be more stable-but the relationship is weak, suggesting that such simple descriptors do not capture the complex balance of interactions driving cocrystallization. Our conclusions suggest that while cocrystallization is often a thermodynamically favorable process, it remains difficult to formulate general rules to guide synthesis, highlighting the continued importance of high-level computation in predicting and rationalizing such systems.

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

Local functional descriptors for surface comparison based binding prediction

PubMed Central

2012-01-01

Background Molecular recognition in proteins occurs due to appropriate arrangements of physical, chemical, and geometric properties of an atomic surface. Similar surface regions should create similar binding interfaces. Effective methods for comparing surface regions can be used in identifying similar regions, and to predict interactions without regard to the underlying structural scaffold that creates the surface. Results We present a new descriptor for protein functional surfaces and algorithms for using these descriptors to compare protein surface regions to identify ligand binding interfaces. Our approach uses descriptors of local regions of the surface, and assembles collections of matches to compare larger regions. Our approach uses a variety of physical, chemical, and geometric properties, adaptively weighting these properties as appropriate for different regions of the interface. Our approach builds a classifier based on a training corpus of examples of binding sites of the target ligand. The constructed classifiers can be applied to a query protein providing a probability for each position on the protein that the position is part of a binding interface. We demonstrate the effectiveness of the approach on a number of benchmarks, demonstrating performance that is comparable to the state-of-the-art, with an approach with more generality than these prior methods. Conclusions Local functional descriptors offer a new method for protein surface comparison that is sufficiently flexible to serve in a variety of applications. PMID:23176080

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.

Phenotypic characterization of glioblastoma identified through shape descriptors

NASA Astrophysics Data System (ADS)

Chaddad, Ahmad; Desrosiers, Christian; Toews, Matthew

2016-03-01

This paper proposes quantitatively describing the shape of glioblastoma (GBM) tissue phenotypes as a set of shape features derived from segmentations, for the purposes of discriminating between GBM phenotypes and monitoring tumor progression. GBM patients were identified from the Cancer Genome Atlas, and quantitative MR imaging data were obtained from the Cancer Imaging Archive. Three GBM tissue phenotypes are considered including necrosis, active tumor and edema/invasion. Volumetric tissue segmentations are obtained from registered T1˗weighted (T1˗WI) postcontrast and fluid-attenuated inversion recovery (FLAIR) MRI modalities. Shape features are computed from respective tissue phenotype segmentations, and a Kruskal-Wallis test was employed to select features capable of classification with a significance level of p < 0.05. Several classifier models are employed to distinguish phenotypes, where a leave-one-out cross-validation was performed. Eight features were found statistically significant for classifying GBM phenotypes with p <0.05, orientation is uninformative. Quantitative evaluations show the SVM results in the highest classification accuracy of 87.50%, sensitivity of 94.59% and specificity of 92.77%. In summary, the shape descriptors proposed in this work show high performance in predicting GBM tissue phenotypes. They are thus closely linked to morphological characteristics of GBM phenotypes and could potentially be used in a computer assisted labeling system.

The implementation of contour-based object orientation estimation algorithm in FPGA-based on-board vision system

NASA Astrophysics Data System (ADS)

Alpatov, Boris; Babayan, Pavel; Ershov, Maksim; Strotov, Valery

2016-10-01

This paper describes the implementation of the orientation estimation algorithm in FPGA-based vision system. An approach to estimate an orientation of objects lacking axial symmetry is proposed. Suggested algorithm is intended to estimate orientation of a specific known 3D object based on object 3D model. The proposed orientation estimation algorithm consists of two 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. 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 in all case studies. The real-time performance of the algorithm in FPGA-based vision system was demonstrated.

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.

Integrative Approaches for Predicting in vivo Effects of Chemicals from their Structural Descriptors and the Results of Short-term Biological Assays

PubMed Central

Low, Yen S.; Sedykh, Alexander; Rusyn, Ivan; Tropsha, Alexander

2017-01-01

Cheminformatics approaches such as Quantitative Structure Activity Relationship (QSAR) modeling have been used traditionally for predicting chemical toxicity. In recent years, high throughput biological assays have been increasingly employed to elucidate mechanisms of chemical toxicity and predict toxic effects of chemicals in vivo. The data generated in such assays can be considered as biological descriptors of chemicals that can be combined with molecular descriptors and employed in QSAR modeling to improve the accuracy of toxicity prediction. In this review, we discuss several approaches for integrating chemical and biological data for predicting biological effects of chemicals in vivo and compare their performance across several data sets. We conclude that while no method consistently shows superior performance, the integrative approaches rank consistently among the best yet offer enriched interpretation of models over those built with either chemical or biological data alone. We discuss the outlook for such interdisciplinary methods and offer recommendations to further improve the accuracy and interpretability of computational models that predict chemical toxicity. PMID:24805064

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.

Macrophytes, epipelic biofilm, and invertebrates as biotic indicators of physical habitat degradation of lowland streams (Argentina).

PubMed

Cortelezzi, Agustina; Sierra, María Victoria; Gómez, Nora; Marinelli, Claudia; Rodrigues Capítulo, Alberto

2013-07-01

Our objective was to assess the effect of the physical habitat degradation in three lowland streams of Argentina that are subject to different land uses. To address this matter, we looked into some physical habitat alterations, mainly the water quality and channel changes, the impact on macrophytes' community, and the structural and functional descriptors of the epipelic biofilm and invertebrate assemblages. As a consequence of physical and chemical perturbations, we differentiated sampling sites with different degradation levels. The low degraded sites were affected mainly for the suburban land use, the moderately degraded sites for the rural land use, and the highly degraded sites for the urban land use. The data shows that the biotic descriptors that best reflected the environmental degradation were vegetation cover and macrophytes richness, the dominance of tolerant species (epipelic biofilm and invertebrates), algal biomass, O2 consumption by the epipelic biofilm, and invertebrates' richness and diversity. Furthermore, the results obtained highlight the importance of the macrophytes in the lowland streams, where there is a poor diversification of abiotic substrates and where the macrophytes not only provide shelter but also a food source for invertebrates and other trophic levels such as fish. We also noted that both in benthic communities, invertebrates and epipelic biofilm supplied different information: the habitat's physical structure provided by the macrophytes influenced mainly the invertebrate descriptors; meanwhile, the water quality mainly influenced most of the epipelic biofilm descriptors.

Exploration of the Medicinal Peptide Space.

PubMed

Gevaert, Bert; Stalmans, Sofie; Wynendaele, Evelien; Taevernier, Lien; Bracke, Nathalie; D'Hondt, Matthias; De Spiegeleer, Bart

2016-01-01

The chemical properties of peptide medicines, known as the 'medicinal peptide space' is considered a multi-dimensional subset of the global peptide space, where each dimension represents a chemical descriptor. These descriptors can be linked to biofunctional, medicinal properties to varying degrees. Knowledge of this space can increase the efficiency of the peptide-drug discovery and development process, as well as advance our understanding and classification of peptide medicines. For 245 peptide drugs, already available on the market or in clinical development, multivariate dataexploration was performed using peptide relevant physicochemical descriptors, their specific peptidedrug target and their clinical use. Our retrospective analysis indicates that clusters in the medicinal peptide space are located in a relatively narrow range of the physicochemical space: dense and empty regions were found, which can be explored for the discovery of novel peptide drugs.

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.

Has the tobacco industry evaded the FDA's ban on ‘Light’ cigarette descriptors?

PubMed Central

Connolly, Gregory N; Alpert, Hillel R

2014-01-01

Background Under the Family Smoking Prevention and Tobacco Control Act (FSPTCA), the Food and Drug Administration (FDA) banned the use of “Lights” descriptors or similar terms on tobacco products that convey messages of reduced risk. Manufacturers eliminated terms explicitly stated and substituted colour name descriptors corresponding to the banned terms. This paper examines whether the tobacco industry complied with or circumvented the law and potential FDA regulatory actions. Methods Philip Morris retailer manuals, manufacturers' annual reports filed with the Massachusetts Department of Public Health, a national public opinion survey, and market-wide cigarette sales data were examined. Results Manufacturers substituted “Gold” for “Light” and “Silver” for “Ultra-light” in the names of Marlboro sub-brands, and “Blue”, “Gold”, and “Silver” for banned descriptors in sub-brand names. Percent filter ventilation levels, used to generate the smoke yield ranges associated with “Lights” categories, appear to have been reassigned to the new colour brand name descriptors. Following the ban, 92% of smokers reported they could easily identify their usual brands, and 68% correctly named the package colour associated with their usual brand, while sales for “Lights” cigarettes remained unchanged. Conclusions Tobacco manufacturers appear to have evaded a critical element of the FSPTCA, the ban on misleading descriptors that convey reduced health risk messages. The FPSTCA provides regulatory mechanisms, including banning these products as adulterated (Section 902). Manufacturers could then apply for pre-market approval as new products and produce evidence for FDA evaluation and determination whether or not sales of these products are in the public health interest. PMID:23485704

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

PubMed

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

2013-01-01

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

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.

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.

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

PubMed

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

2011-09-10

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

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.

Significance of MPEG-7 textural features for improved mass detection in mammography.

PubMed

Eltonsy, Nevine H; Tourassi, Georgia D; Fadeev, Aleksey; Elmaghraby, Adel S

2006-01-01

The purpose of the study is to investigate the significance of MPEG-7 textural features for improving the detection of masses in screening mammograms. The detection scheme was originally based on morphological directional neighborhood features extracted from mammographic regions of interest (ROIs). Receiver Operating Characteristics (ROC) was performed to evaluate the performance of each set of features independently and merged into a back-propagation artificial neural network (BPANN) using the leave-one-out sampling scheme (LOOSS). The study was based on a database of 668 mammographic ROIs (340 depicting cancer regions and 328 depicting normal parenchyma). Overall, the ROC area index of the BPANN using the directional morphological features was Az=0.85+/-0.01. The MPEG-7 edge histogram descriptor-based BPNN showed an ROC area index of Az=0.71+/-0.01 while homogeneous textural descriptors using 30 and 120 channels helped the BPNN achieve similar ROC area indexes of Az=0.882+/-0.02 and Az=0.877+/-0.01 respectively. After merging the MPEG-7 homogeneous textural features with the directional neighborhood features the performance of the BPANN increased providing an ROC area index of Az=0.91+/-0.01. MPEG-7 homogeneous textural descriptor significantly improved the morphology-based detection scheme.

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®.

Synthesis, crystal structure investigation, spectroscopic characterizations and DFT computations on a novel 1-(2-chloro-4-phenylquinolin-3-yl)ethanone

NASA Astrophysics Data System (ADS)

Murugavel, S.; Stephen, C. S. Jacob Prasanna; Subashini, R.; Reddy, H. Raveendranatha; AnanthaKrishnan, Dhanabalan

2016-10-01

The title compound 1-(2-chloro-4-phenylquinolin-3-yl)ethanone (CPQE) was synthesised effectively by chlorination of 3-acetyl-4-phenylquinolin-2(1H)-one (APQ) using POCl3 reagent. Structural and vibrational spectroscopic studies were performed by utilizing single crystal X-ray diffraction, FTIR and NMR spectral analysis along with DFT method utilizing GAUSSIAN‧ 03 software. Veda program has been employed to perform a detailed interpretation of vibrational spectra. Mulliken population analyses on atomic charges, MEP, HOMO-LUMO, NBO, Global chemical reactivity descriptors and thermodynamic properties have been examined by (DFT/B3LYP) method with the 6-311G(d,p) basis set level.

Texture Descriptors Ensembles Enable Image-Based Classification of Maturation of Human Stem Cell-Derived Retinal Pigmented Epithelium

PubMed Central

Caetano dos Santos, Florentino Luciano; Skottman, Heli; Juuti-Uusitalo, Kati; Hyttinen, Jari

2016-01-01

Aims A fast, non-invasive and observer-independent method to analyze the homogeneity and maturity of human pluripotent stem cell (hPSC) derived retinal pigment epithelial (RPE) cells is warranted to assess the suitability of hPSC-RPE cells for implantation or in vitro use. The aim of this work was to develop and validate methods to create ensembles of state-of-the-art texture descriptors and to provide a robust classification tool to separate three different maturation stages of RPE cells by using phase contrast microscopy images. The same methods were also validated on a wide variety of biological image classification problems, such as histological or virus image classification. Methods For image classification we used different texture descriptors, descriptor ensembles and preprocessing techniques. Also, three new methods were tested. The first approach was an ensemble of preprocessing methods, to create an additional set of images. The second was the region-based approach, where saliency detection and wavelet decomposition divide each image in two different regions, from which features were extracted through different descriptors. The third method was an ensemble of Binarized Statistical Image Features, based on different sizes and thresholds. A Support Vector Machine (SVM) was trained for each descriptor histogram and the set of SVMs combined by sum rule. The accuracy of the computer vision tool was verified in classifying the hPSC-RPE cell maturation level. Dataset and Results The RPE dataset contains 1862 subwindows from 195 phase contrast images. The final descriptor ensemble outperformed the most recent stand-alone texture descriptors, obtaining, for the RPE dataset, an area under ROC curve (AUC) of 86.49% with the 10-fold cross validation and 91.98% with the leave-one-image-out protocol. The generality of the three proposed approaches was ascertained with 10 more biological image datasets, obtaining an average AUC greater than 97%. Conclusions Here we showed that the developed ensembles of texture descriptors are able to classify the RPE cell maturation stage. Moreover, we proved that preprocessing and region-based decomposition improves many descriptors’ accuracy in biological dataset classification. Finally, we built the first public dataset of stem cell-derived RPE cells, which is publicly available to the scientific community for classification studies. The proposed tool is available at https://www.dei.unipd.it/node/2357 and the RPE dataset at http://www.biomeditech.fi/data/RPE_dataset/. Both are available at https://figshare.com/s/d6fb591f1beb4f8efa6f. PMID:26895509

A comparative study of personality descriptors attributed to the deaf, the blind, and individuals with no sensory disability.

PubMed

Cambra, C

1996-03-01

During the building of self-concept, one's self-perception is influenced by the attitudes and levels of acceptance of significant individuals in one's immediate environment and in society as a whole. This study explores the social image of the deaf, beginning with an analysis of personality characteristics attributed to this group. The resulting profile is then compared to those of two other previously assessed groups: the blind and those with no sensory disability. A sample of 222 university students evaluated personality descriptors as applied to the three groups by means of a semantic differential. For certain personality descriptors, the students had different impressions of the three groups. Results show that certain negative stereotypes still mark the social representation of deafness. Instances of familiarity or friendship between haring people and deaf people serve generally to mitigate such stereotypes.

LSAH: a fast and efficient local surface feature for point cloud registration

NASA Astrophysics Data System (ADS)

Lu, Rongrong; Zhu, Feng; Wu, Qingxiao; Kong, Yanzi

2018-04-01

Point cloud registration is a fundamental task in high level three dimensional applications. Noise, uneven point density and varying point cloud resolutions are the three main challenges for point cloud registration. In this paper, we design a robust and compact local surface descriptor called Local Surface Angles Histogram (LSAH) and propose an effectively coarse to fine algorithm for point cloud registration. The LSAH descriptor is formed by concatenating five normalized sub-histograms into one histogram. The five sub-histograms are created by accumulating a different type of angle from a local surface patch respectively. The experimental results show that our LSAH is more robust to uneven point density and point cloud resolutions than four state-of-the-art local descriptors in terms of feature matching. Moreover, we tested our LSAH based coarse to fine algorithm for point cloud registration. The experimental results demonstrate that our algorithm is robust and efficient as well.

Photoperiodic controls on ecosystem-level photosynthetic capacity

NASA Astrophysics Data System (ADS)

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

2012-12-01

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

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.

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.

Supporting statement for community study of human response to aircraft noise

NASA Technical Reports Server (NTRS)

Dempsey, T. K.; Deloach, R.; Stephens, D. G.

1980-01-01

A study plan for quantifying the relationship between human annoyance and the noise level of individual aircraft events is studied. The validity of various noise descriptors or noise metrics for quantifying aircraft noise levels are assessed.

Review of measured vibration and noise environments experienced by passengers in aircraft and in ground transportation systems

NASA Technical Reports Server (NTRS)

Stephens, D. G.

1975-01-01

Measured vibration and interior noise data are presented for a number of air and surface vehicles. Consideration is given to the importance of direction effects; of vehicle operations such as take-off, cruise, and landing; and of measurement location on the level and frequency of the measurements. Various physical measurement units or descriptors are used to quantify and compare the data. Results suggest the range of vibration and noise associated with a particular mode of transportation and illustrate the comparative levels in terms of each of the descriptors. Collectively, the results form a data base which may be useful in assessing the ride of existing or future systems relative to vehicles in current operation.

Learned Compact Local Feature Descriptor for Tls-Based Geodetic Monitoring of Natural Outdoor Scenes

NASA Astrophysics Data System (ADS)

Gojcic, Z.; Zhou, C.; Wieser, A.

2018-05-01

The advantages of terrestrial laser scanning (TLS) for geodetic monitoring of man-made and natural objects are not yet fully exploited. Herein we address one of the open challenges by proposing feature-based methods for identification of corresponding points in point clouds of two or more epochs. We propose a learned compact feature descriptor tailored for point clouds of natural outdoor scenes obtained using TLS. We evaluate our method both on a benchmark data set and on a specially acquired outdoor dataset resembling a simplified monitoring scenario where we successfully estimate 3D displacement vectors of a rock that has been displaced between the scans. We show that the proposed descriptor has the capacity to generalize to unseen data and achieves state-of-the-art performance while being time efficient at the matching step due the low dimension.

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.

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.

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.

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

Use of transcranial direct current stimulation for the treatment of auditory hallucinations of schizophrenia – a systematic review

PubMed Central

Pondé, Pedro H; de Sena, Eduardo P; Camprodon, Joan A; de Araújo, Arão Nogueira; Neto, Mário F; DiBiasi, Melany; Baptista, Abrahão Fontes; Moura, Lidia MVR; Cosmo, Camila

2017-01-01

Introduction Auditory hallucinations are defined as experiences of auditory perceptions in the absence of a provoking external stimulus. They are the most prevalent symptoms of schizophrenia with high capacity for chronicity and refractoriness during the course of disease. The transcranial direct current stimulation (tDCS) – a safe, portable, and inexpensive neuromodulation technique – has emerged as a promising treatment for the management of auditory hallucinations. Objective The aim of this study is to analyze the level of evidence in the literature available for the use of tDCS as a treatment for auditory hallucinations in schizophrenia. Methods A systematic review was performed, searching in the main electronic databases including the Cochrane Library and MEDLINE/PubMed. The searches were performed by combining descriptors, applying terms of the Medical Subject Headings (MeSH) of Descriptors of Health Sciences and descriptors contractions. PRISMA protocol was used as a guide and the terms used were the clinical outcomes (“Schizophrenia” OR “Auditory Hallucinations” OR “Auditory Verbal Hallucinations” OR “Psychosis”) searched together (“AND”) with interventions (“transcranial Direct Current Stimulation” OR “tDCS” OR “Brain Polarization”). Results Six randomized controlled trials that evaluated the effects of tDCS on the severity of auditory hallucinations in schizophrenic patients were selected. Analysis of the clinical results of these studies pointed toward incongruence in the information with regard to the therapeutic use of tDCS with a view to reducing the severity of auditory hallucinations in schizophrenia. Only three studies revealed a therapeutic benefit, manifested by reductions in severity and frequency of auditory verbal hallucinations in schizophrenic patients. Conclusion Although tDCS has shown promising results in reducing the severity of auditory hallucinations in schizophrenic patients, this technique cannot yet be used as a therapeutic alternative due to lack of studies with large sample sizes that portray the positive effects that have been described. PMID:28203084

Comparison of k-means related clustering methods for nuclear medicine images segmentation

NASA Astrophysics Data System (ADS)

Borys, Damian; Bzowski, Pawel; Danch-Wierzchowska, Marta; Psiuk-Maksymowicz, Krzysztof

2017-03-01

In this paper, we evaluate the performance of SURF descriptor for high resolution satellite imagery (HRSI) retrieval through a BoVW model on a land-use/land-cover (LULC) dataset. Local feature approaches such as SIFT and SURF descriptors can deal with a large variation of scale, rotation and illumination of the images, providing, therefore, a better discriminative power and retrieval efficiency than global features, especially for HRSI which contain a great range of objects and spatial patterns. Moreover, we combine SURF and color features to improve the retrieval accuracy, and we propose to learn a category-specific dictionary for each image category which results in a more discriminative image representation and boosts the image retrieval performance.

High resolution satellite image indexing and retrieval using SURF features and bag of visual words

NASA Astrophysics Data System (ADS)

Bouteldja, Samia; Kourgli, Assia

2017-03-01

In this paper, we evaluate the performance of SURF descriptor for high resolution satellite imagery (HRSI) retrieval through a BoVW model on a land-use/land-cover (LULC) dataset. Local feature approaches such as SIFT and SURF descriptors can deal with a large variation of scale, rotation and illumination of the images, providing, therefore, a better discriminative power and retrieval efficiency than global features, especially for HRSI which contain a great range of objects and spatial patterns. Moreover, we combine SURF and color features to improve the retrieval accuracy, and we propose to learn a category-specific dictionary for each image category which results in a more discriminative image representation and boosts the image retrieval performance.

Combined invariants to similarity transformation and to blur using orthogonal Zernike moments

PubMed Central

Beijing, Chen; Shu, Huazhong; Zhang, Hui; Coatrieux, Gouenou; Luo, Limin; Coatrieux, Jean-Louis

2011-01-01

The derivation of moment invariants has been extensively investigated in the past decades. In this paper, we construct a set of invariants derived from Zernike moments which is simultaneously invariant to similarity transformation and to convolution with circularly symmetric point spread function (PSF). Two main contributions are provided: the theoretical framework for deriving the Zernike moments of a blurred image and the way to construct the combined geometric-blur invariants. The performance of the proposed descriptors is evaluated with various PSFs and similarity transformations. The comparison of the proposed method with the existing ones is also provided in terms of pattern recognition accuracy, template matching and robustness to noise. Experimental results show that the proposed descriptors perform on the overall better. PMID:20679028

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.

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.

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.

Using novel descriptor accounting for ligand-receptor interactions to define and visually explore biologically relevant chemical space.

PubMed

Rabal, Obdulia; Oyarzabal, Julen

2012-05-25

The definition and pragmatic implementation of biologically relevant chemical space is critical in addressing navigation strategies in the overlapping regions where chemistry and therapeutically relevant targets reside and, therefore, also key to performing an efficient drug discovery project. Here, we describe the development and implementation of a simple and robust method for representing biologically relevant chemical space as a general reference according to current knowledge, independently of any reference space, and analyzing chemical structures accordingly. Underlying our method is the generation of a novel descriptor (LiRIf) that converts structural information into a one-dimensional string accounting for the plausible ligand-receptor interactions as well as for topological information. Capitalizing on ligand-receptor interactions as a descriptor enables the clustering, profiling, and comparison of libraries of compounds from a chemical biology and medicinal chemistry perspective. In addition, as a case study, R-groups analysis is performed to identify the most populated ligand-receptor interactions according to different target families (GPCR, kinases, etc.), as well as to evaluate the coverage of biologically relevant chemical space by structures annotated in different databases (ChEMBL, Glida, etc.).

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 ).

A Competency-Based Framework for Graduate-Level Health Educators.

ERIC Educational Resources Information Center

American Alliance for Health, Physical Education, Recreation and Dance, Reston, VA. American Association for Health Education.

This document builds on the 1997 Standards for the Preparation of Graduate-Level Health Educators by including expanded content descriptors and objectives for the graduate level competencies. It begins by discussing evolution of health education competencies; chronology of the graduate standard development process; benefits of graduate level…

Contrast-enhanced spectral mammography: Impact of the qualitative morphology descriptors on the diagnosis of breast lesions.

PubMed

Mohamed Kamal, Rasha; Hussien Helal, Maha; Wessam, Rasha; Mahmoud Mansour, Sahar; Godda, Iman; Alieldin, Nelly

2015-06-01

To analyze the morphology and enhancement characteristics of breast lesions on contrast-enhanced spectral mammography (CESM) and to assess their impact on the differentiation between benign and malignant lesions. This ethics committee approved study included 168 consecutive patients with 211 breast lesions over 18 months. Lesions classified as non-enhancing and enhancing and then the latter group was subdivided into mass and non-mass. Mass lesions descriptors included: shape, margins, pattern and degree of internal enhancement. Non-mass lesions descriptors included: distribution, pattern and degree of internal enhancement. The impact of each descriptor on diagnosis individually assessed using Chi test and the validity compared in both benign and malignant lesions. The overall performance of CESM were also calculated. The study included 102 benign (48.3%) and 109 malignant (51.7%) lesions. Enhancement was encountered in 145/211 (68.7%) lesions. They further classified into enhancing mass (99/145, 68.3%) and non-mass lesions (46/145, 31.7%). Contrast uptake was significantly more frequent in malignant breast lesions (p value ≤ 0.001). Irregular mass lesions with intense and heterogeneous enhancement patterns correlated with a malignant pathology (p value ≤ 0.001). CESM showed an overall sensitivity of 88.99% and specificity of 83.33%. The positive and negative likelihood ratios were 5.34 and 0.13 respectively. The assessment of the morphology and enhancement characteristics of breast lesions on CESM enhances the performance of digital mammography in the differentiation between benign and malignant breast lesions. Copyright © 2015 Elsevier Ireland Ltd. 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.

Discriminative latent models for recognizing contextual group activities.

PubMed

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

2012-08-01

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

Discriminative Latent Models for Recognizing Contextual Group Activities

PubMed Central

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

2012-01-01

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

From basic physics to mechanisms of toxicity: the "liquid drop" approach applied to develop predictive classification models for toxicity of metal oxide nanoparticles.

PubMed

Sizochenko, Natalia; Rasulev, Bakhtiyor; Gajewicz, Agnieszka; Kuz'min, Victor; Puzyn, Tomasz; Leszczynski, Jerzy

2014-11-21

Many metal oxide nanoparticles are able to cause persistent stress to live organisms, including humans, when discharged to the environment. To understand the mechanism of metal oxide nanoparticles' toxicity and reduce the number of experiments, the development of predictive toxicity models is important. In this study, performed on a series of nanoparticles, the comparative quantitative-structure activity relationship (nano-QSAR) analyses of their toxicity towards E. coli and HaCaT cells were established. A new approach for representation of nanoparticles' structure is presented. For description of the supramolecular structure of nanoparticles the "liquid drop" model was applied. It is expected that a novel, proposed approach could be of general use for predictions related to nanomaterials. In addition, in our study fragmental simplex descriptors and several ligand-metal binding characteristics were calculated. The developed nano-QSAR models were validated and reliably predict the toxicity of all studied metal oxide nanoparticles. Based on the comparative analysis of contributed properties in both models the LDM-based descriptors were revealed to have an almost similar level of contribution to toxicity in both cases, while other parameters (van der Waals interactions, electronegativity and metal-ligand binding characteristics) have unequal contribution levels. In addition, the models developed here suggest different mechanisms of nanotoxicity for these two types of cells.

Electronic structure and physicochemical properties of selected penicillins

NASA Astrophysics Data System (ADS)

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

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

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

PubMed

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

2009-08-01

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

Rough Set Approach to Incomplete Multiscale Information System

PubMed Central

Yang, Xibei; Qi, Yong; Yu, Dongjun; Yu, Hualong; Song, Xiaoning; Yang, Jingyu

2014-01-01

Multiscale information system is a new knowledge representation system for expressing the knowledge with different levels of granulations. In this paper, by considering the unknown values, which can be seen everywhere in real world applications, the incomplete multiscale information system is firstly investigated. The descriptor technique is employed to construct rough sets at different scales for analyzing the hierarchically structured data. The problem of unravelling decision rules at different scales is also addressed. Finally, the reduct descriptors are formulated to simplify decision rules, which can be derived from different scales. Some numerical examples are employed to substantiate the conceptual arguments. PMID:25276852

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

PubMed

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

2013-12-06

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

Effect of cultivar and roasting technique on sensory quality of Bierzo roasted pepper.

PubMed

Guerra, Marcos; Sanz, Miguel A; Valenciano, José B; Casquero, Pedro A

2011-10-01

Pepper (Capsicum annuum L.) is one of the main horticultural products in the world. Roasted pepper is a high quality transformed product in the Iberian Peninsula, and obtained the recognition of 'Protected Geographical Indication' (PGI) of 'Pimiento Asado del Bierzo' in 2002. Roasted pepper has been traditionally processed with a steel-sheet hob. However, there are no data available about the effect of roasting technique in the quality of roasted pepper. The objective of this work was to compare the sensory quality of roasted pepper using industrial roasting techniques. Sensory properties that showed significant differences between roasting techniques were colour, thickness and charred remains (appearance descriptors), bitterness (taste descriptor) and smokiness (after-taste descriptor). Higher value of descriptors such as colour, charred remains and smokiness for peppers elaborated in a rotary oven, helped roasted pepper to reach a higher level of overall quality, although rotary oven samples reached the lowest roast yield. Roasting technique, rather than landrace, had the greatest effect on the sensory quality of roasted pepper, so the rotary oven was the roasting technique that achieved the highest quality score. This will contribute to improve sensory quality and marketing of PGI 'Pimiento Asado del Bierzo' in high quality markets. Copyright © 2011 Society of Chemical Industry.

Developing a clinical academic career pathway for nursing.

PubMed

Coombs, Maureen; Latter, Sue; Richardson, Alison

Since the publication of the UK Clinical Research Collaboration's (UKRC, 2007) recommendations on careers in clinical research, interest has grown in the concept of clinical academic nursing careers, with increased debate on how such roles might be developed and sustained (Department of Health, 2012). To embed clinical academic nursing roles in the NHS and universities, a clear understanding and appreciation of the contribution that such posts might make to organisational objectives and outcomes must be developed. This paper outlines an initiative to define the potential practice and research contribution of clinical academic roles through setting out role descriptors. This exercise was based on our experience of a clinical academic career initiative at the University of Southampton run in partnership with NHS organisations. Role descriptors were developed by a group of service providers, academics and two clinical academic award-holders from the local programme. This paper outlines clinical academic roles from novice to professor and describes examples of role descriptors at the different levels of a career pathway. These descriptors are informed by clinical academic posts in place at Southampton as well as others at the planning stage. Understanding the nature of clinical academic posts and the contribution that these roles can make to healthcare will enable them to become embedded into organisational structures and career pathways.

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.

Image segmentation via foreground and background semantic descriptors

NASA Astrophysics Data System (ADS)

Yuan, Ding; Qiang, Jingjing; Yin, Jihao

2017-09-01

In the field of image processing, it has been a challenging task to obtain a complete foreground that is not uniform in color or texture. Unlike other methods, which segment the image by only using low-level features, we present a segmentation framework, in which high-level visual features, such as semantic information, are used. First, the initial semantic labels were obtained by using the nonparametric method. Then, a subset of the training images, with a similar foreground to the input image, was selected. Consequently, the semantic labels could be further refined according to the subset. Finally, the input image was segmented by integrating the object affinity and refined semantic labels. State-of-the-art performance was achieved in experiments with the challenging MSRC 21 dataset.

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.

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.

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.

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.

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.

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

NASA Astrophysics Data System (ADS)

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

2018-06-01

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

Improved parallel image reconstruction using feature refinement.

PubMed

Cheng, Jing; Jia, Sen; Ying, Leslie; Liu, Yuanyuan; Wang, Shanshan; Zhu, Yanjie; Li, Ye; Zou, Chao; Liu, Xin; Liang, Dong

2018-07-01

The aim of this study was to develop a novel feature refinement MR reconstruction method from highly undersampled multichannel acquisitions for improving the image quality and preserve more detail information. The feature refinement technique, which uses a feature descriptor to pick up useful features from residual image discarded by sparsity constrains, is applied to preserve the details of the image in compressed sensing and parallel imaging in MRI (CS-pMRI). The texture descriptor and structure descriptor recognizing different types of features are required for forming the feature descriptor. Feasibility of the feature refinement was validated using three different multicoil reconstruction methods on in vivo data. Experimental results show that reconstruction methods with feature refinement improve the quality of reconstructed image and restore the image details more accurately than the original methods, which is also verified by the lower values of the root mean square error and high frequency error norm. A simple and effective way to preserve more useful detailed information in CS-pMRI is proposed. This technique can effectively improve the reconstruction quality and has superior performance in terms of detail preservation compared with the original version without feature refinement. Magn Reson Med 80:211-223, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Systematically evaluating read-across prediction and ...

EPA Pesticide Factsheets

Read-across is a popular data gap filling technique within category and analogue approaches for regulatory purposes. Acceptance of read-across remains an ongoing challenge with several efforts underway for identifying and addressing uncertainties. Here we demonstrate an algorithmic, automated approach to evaluate the utility of using in vitro bioactivity data (“bioactivity descriptors”, from EPA’s ToxCast program) in conjunction with chemical descriptor information to derive local validity domains (specific sets of nearest neighbors) to facilitate read-across for a number of in vivo repeated dose toxicity study types. Over 3400 different chemical structure descriptors were generated for a set of 976 chemicals and supplemented with the outcomes from 821 in vitro assays. The read-across prediction for a given chemical was based on the similarity weighted endpoint outcomes of its nearest neighbors. The approach enabled a performance baseline for read-across predictions of specific study outcomes to be established. Bioactivity descriptors were often found to be more predictive of in vivo toxicity outcomes than chemical descriptors or a combination of both. The approach shows promise as part of a screening assessment in the absence of prior knowledge. Future work will investigate to what extent encoding expert knowledge leads to an improvement in read-across prediction. Read-across is a popular data gap filling technique within category and analogue approaches

Ship detection based on rotation-invariant HOG descriptors for airborne infrared images

NASA Astrophysics Data System (ADS)

Xu, Guojing; Wang, Jinyan; Qi, Shengxiang

2018-03-01

Infrared thermal imagery is widely used in various kinds of aircraft because of its all-time application. Meanwhile, detecting ships from infrared images attract lots of research interests in recent years. In the case of downward-looking infrared imagery, in order to overcome the uncertainty of target imaging attitude due to the unknown position relationship between the aircraft and the target, we propose a new infrared ship detection method which integrates rotation invariant gradient direction histogram (Circle Histogram of Oriented Gradient, C-HOG) descriptors and the support vector machine (SVM) classifier. In details, the proposed method uses HOG descriptors to express the local feature of infrared images to adapt to changes in illumination and to overcome sea clutter effects. Different from traditional computation of HOG descriptor, we subdivide the image into annular spatial bins instead of rectangle sub-regions, and then Radial Gradient Transform (RGT) on the gradient is applied to achieve rotation invariant histogram information. Considering the engineering application of airborne and real-time requirements, we use SVM for training ship target and non-target background infrared sample images to discriminate real ships from false targets. Experimental results show that the proposed method has good performance in both the robustness and run-time for infrared ship target detection with different rotation angles.

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.

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.

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.

Arterial stiffness, body fat compartments, central hemodynamics, renal function and left atrial size.

PubMed

Katulska, Katarzyna; Milewska, Agata; Wykretowicz, Mateusz; Krauze, Tomasz; Przymuszala, Dagmara; Piskorski, Jaroslaw; Stajgis, Marek; Guzik, Przemyslaw; Wysocki, Henryk; Wykrętowicz, Andrzej

2013-10-01

Left atrial (LA) size is an important predictor of stroke, death, and atrial fibrillation. It was demonstrated recently that body fat, arterial stiffness and renal functions are associated with LA diameter. However, data are lacking for comprehensive assessments of all these risk factors in a single population. Therefore, the aim of the present study was to investigate the association between LA size and different fat descriptors, central hemodynamics, arterial stiffness, and renal function in healthy subjects. To this end, body fat percentage, abdominal, subcutaneous fat, and general descriptors of body fat were estimated in 162 healthy subjects (mean age 51 years). Echocardiography was performed to assess LA diameter. Arterial stiffness and peripheral and central hemodynamics were estimated by digital volume pulse analysis and pulse wave analysis. Glomerular filtration rate was estimated by MDRD formula. There were significant (p < 0.05) bivariate correlations between LA diameter and all descriptors of body fat (except subcutaneous fat). Arterial stiffness and estimated glomerular filtration rate (eGFR) were also significantly correlated with LA size. Multiple regression analysis including all significant confounders, such as sex, mean arterial pressure, arterial stiffness, eGFR and body fat descriptors, explained 35% of variance in LA diameter. In conclusion, the present study reveals significant, independent relationships between body fat, arterial stiffness, and LA size.

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.

Fast protein tertiary structure retrieval based on global surface shape similarity.

PubMed

Sael, Lee; Li, Bin; La, David; Fang, Yi; Ramani, Karthik; Rustamov, Raif; Kihara, Daisuke

2008-09-01

Characterization and identification of similar tertiary structure of proteins provides rich information for investigating function and evolution. The importance of structure similarity searches is increasing as structure databases continue to expand, partly due to the structural genomics projects. A crucial drawback of conventional protein structure comparison methods, which compare structures by their main-chain orientation or the spatial arrangement of secondary structure, is that a database search is too slow to be done in real-time. Here we introduce a global surface shape representation by three-dimensional (3D) Zernike descriptors, which represent a protein structure compactly as a series expansion of 3D functions. With this simplified representation, the search speed against a few thousand structures takes less than a minute. To investigate the agreement between surface representation defined by 3D Zernike descriptor and conventional main-chain based representation, a benchmark was performed against a protein classification generated by the combinatorial extension algorithm. Despite the different representation, 3D Zernike descriptor retrieved proteins of the same conformation defined by combinatorial extension in 89.6% of the cases within the top five closest structures. The real-time protein structure search by 3D Zernike descriptor will open up new possibility of large-scale global and local protein surface shape comparison. 2008 Wiley-Liss, Inc.

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 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.

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

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.

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.

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.

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

PubMed

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

2011-01-01

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

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

PubMed

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

2016-07-01

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

Multi person detection and tracking based on hierarchical level-set method

NASA Astrophysics Data System (ADS)

Khraief, Chadia; Benzarti, Faouzi; Amiri, Hamid

2018-04-01

In this paper, we propose an efficient unsupervised method for mutli-person tracking based on hierarchical level-set approach. The proposed method uses both edge and region information in order to effectively detect objects. The persons are tracked on each frame of the sequence by minimizing an energy functional that combines color, texture and shape information. These features are enrolled in covariance matrix as region descriptor. The present method is fully automated without the need to manually specify the initial contour of Level-set. It is based on combined person detection and background subtraction methods. The edge-based is employed to maintain a stable evolution, guide the segmentation towards apparent boundaries and inhibit regions fusion. The computational cost of level-set is reduced by using narrow band technique. Many experimental results are performed on challenging video sequences and show the effectiveness of the proposed method.

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.

Composite multi-lobe descriptor for cross spectral face recognition: matching active IR to visible light images

NASA Astrophysics Data System (ADS)

Cao, Zhicheng; Schmid, Natalia A.

2015-05-01

Matching facial images across electromagnetic spectrum presents a challenging problem in the field of biometrics and identity management. An example of this problem includes cross spectral matching of active infrared (IR) face images or thermal IR face images against a dataset of visible light images. This paper describes a new operator named Composite Multi-Lobe Descriptor (CMLD) for facial feature extraction in cross spectral matching of near-infrared (NIR) or short-wave infrared (SWIR) against visible light images. The new operator is inspired by the design of ordinal measures. The operator combines Gaussian-based multi-lobe kernel functions, Local Binary Pattern (LBP), generalized LBP (GLBP) and Weber Local Descriptor (WLD) and modifies them into multi-lobe functions with smoothed neighborhoods. The new operator encodes both the magnitude and phase responses of Gabor filters. The combining of LBP and WLD utilizes both the orientation and intensity information of edges. Introduction of multi-lobe functions with smoothed neighborhoods further makes the proposed operator robust against noise and poor image quality. Output templates are transformed into histograms and then compared by means of a symmetric Kullback-Leibler metric resulting in a matching score. The performance of the multi-lobe descriptor is compared with that of other operators such as LBP, Histogram of Oriented Gradients (HOG), ordinal measures, and their combinations. The experimental results show that in many cases the proposed method, CMLD, outperforms the other operators and their combinations. In addition to different infrared spectra, various standoff distances from close-up (1.5 m) to intermediate (50 m) and long (106 m) are also investigated in this paper. Performance of CMLD is evaluated for of each of the three cases of distances.

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.

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

PubMed

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

2001-01-01

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

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.

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

PubMed Central

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

2014-01-01

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

Chemical and sensorial aroma characterization of freshly distilled Calvados. 1. Evaluation of quality and defects on the basis of key odorants by olfactometry and sensory analysis.

PubMed

Guichard, Hugues; Lemesle, Stéphane; Ledauphin, Jérôme; Barillier, Daniel; Picoche, Bernard

2003-01-15

Eight freshly distilled samples of Calvados, a fermented and distilled apple juice, were analyzed by sensory evaluation and direct injection GC to determine the composition of higher alcohols, esters, and aldehydes. The composition determined by direct injection was tentatively related to sensory descriptors. Esters have a probable maximum level around 500 g/hl of pure alcohol (PA). This level also corresponds to the threshold of the main ester constituent, ethyl acetate. A high ratio of esters to ethyl acetate seems to be of prime importance for good quality. Total aldehydes, with a maximum level between 8 and 11 g/hl of PA and mainly comprising acetal (maximum between 5 and 9 g/hl of PA), were related to a "green" descriptor. Higher alcohols do not have a direct impact on quality, but other volatile compounds with a positive impact on flavor should probably be present at a high level. As overall quality was not well related to sensory quality, it was necessary to perform more a precise analysis to determine the key odorants. The Calvados samples were thus extracted using pentane. Gas chromatography, employing both a flame ionization detector and an olfactometry port, was used to analyze the obtained extracts. Seventy-one odors were detected and distributed according to Calvados quality determined by sensory evaluation. Nineteen odors common to all Calvados samples constituted the "skeleton" of the aroma. Twenty-eight odors were specific to a quality class: 6 for good quality, 4 for neutral, and 18 for defective. Twenty-four other odors had either too low an odor impact or no evident specificity.

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

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

A neurally inspired musical instrument classification system based upon the sound onset.

PubMed

Newton, Michael J; Smith, Leslie S

2012-06-01

Physiological evidence suggests that sound onset detection in the auditory system may be performed by specialized neurons as early as the cochlear nucleus. Psychoacoustic evidence shows that the sound onset can be important for the recognition of musical sounds. Here the sound onset is used in isolation to form tone descriptors for a musical instrument classification task. The task involves 2085 isolated musical tones from the McGill dataset across five instrument categories. A neurally inspired tone descriptor is created using a model of the auditory system's response to sound onset. A gammatone filterbank and spiking onset detectors, built from dynamic synapses and leaky integrate-and-fire neurons, create parallel spike trains that emphasize the sound onset. These are coded as a descriptor called the onset fingerprint. Classification uses a time-domain neural network, the echo state network. Reference strategies, based upon mel-frequency cepstral coefficients, evaluated either over the whole tone or only during the sound onset, provide context to the method. Classification success rates for the neurally-inspired method are around 75%. The cepstral methods perform between 73% and 76%. Further testing with tones from the Iowa MIS collection shows that the neurally inspired method is considerably more robust when tested with data from an unrelated dataset.

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.

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

PubMed

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

2015-01-01

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

Morphological classification of odontogenic keratocysts using Bouligand-Minkowski fractal descriptors.

PubMed

Florindo, Joao B; Bruno, Odemir M; Landini, Gabriel

2017-02-01

The Odontogenic keratocyst (OKC) is a cystic lesion of the jaws, which has high growth and recurrence rates compared to other cysts of the jaws (for instance, radicular cyst, which is the most common jaw cyst type). For this reason OKCs are considered by some to be benign neoplasms. There exist two sub-types of OKCs (sporadic and syndromic) and the ability to discriminate between these sub-types, as well as other jaw cysts, is an important task in terms of disease diagnosis and prognosis. With the development of digital pathology, computational algorithms have become central to addressing this type of problem. Considering that only basic feature-based methods have been investigated in this problem before, we propose to use a different approach (the Bouligand-Minkowski descriptors) to assess the success rates achieved on the classification of a database of histological images of the epithelial lining of these cysts. This does not require the level of abstraction necessary to extract histologically-relevant features and therefore has the potential of being more robust than previous approaches. The descriptors were obtained by mapping pixel intensities into a three dimensional cloud of points in discrete space and applying morphological dilations with spheres of increasing radii. The descriptors were computed from the volume of the dilated set and submitted to a machine learning algorithm to classify the samples into diagnostic groups. This approach was capable of discriminating between OKCs and radicular cysts in 98% of images (100% of cases) and between the two sub-types of OKCs in 68% of images (71% of cases). These results improve over previously reported classification rates reported elsewhere and suggest that Bouligand-Minkowski descriptors are useful features to be used in histopathological images of these cysts. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

The effect of phenol composition on the sensory profile of smoke affected wines.

PubMed

Kelly, David; Zerihun, Ayalsew

2015-05-26

Vineyards exposed to wildfire generated smoke can produce wines with elevated levels of lignin derived phenols that have acrid, metallic and smoky aromas and flavour attributes. While a large number of phenols are present in smoke affected wines, the effect of smoke vegetation source on the sensory descriptors has not been reported. Here we report on a descriptive sensory analysis of wines made from grapes exposed to different vegetation sources of smoke to examine: (1) the effect vegetation source has on wine sensory attribute ratings and; (2) associations between volatile and glycoconjugated phenol composition and sensory attributes. Sensory attribute ratings were determined by a trained sensory panel and phenol concentrations determined by gas chromatography-mass spectroscopy. Analysis of variance, principal component analysis and partial least squares regressions were used to evaluate the interrelationships between the phenol composition and sensory attributes. The results showed that vegetation source of smoke significantly affected sensory attribute intensity, especially the taste descriptors. Differences in aroma and taste from smoke exposure were not limited to an elevation in a range of detractive descriptors but also a masking of positive fruit descriptors. Sensory differences due to vegetation type were driven by phenol composition and concentration. In particular, the glycoconjugates of 4-hydroxy-3-methoxybenzaldehyde (vanillin), 1-(4-hydroxy-3-methoxyphenyl)ethanone (acetovanillone), 4-hydroxy-3,5-dimethoxybenzaldehyde (syringaldehyde) and 1-(4-hydroxy-3,5-dimethoxyphenyl)ethanone (acetosyringone) concentrations were influential in separating the vegetation sources of smoke. It is concluded that the detractive aroma attributes of smoke affected wine, especially of smoke and ash, were associated with volatile phenols while the detractive flavour descriptors were correlated with glycoconjugated phenols.

Synthesis and DFT calculations of some 2-aminothiazoles

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

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.

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

PubMed

Ariyasena, Thiloka C; Poole, Colin F

2014-09-26

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

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.

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

NASA Astrophysics Data System (ADS)

Xiao, Qinhan; Li, Yanjun

2009-12-01

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

The DSFPN, a new neural network for optical character recognition.

PubMed

Morns, L P; Dlay, S S

1999-01-01

A new type of neural network for recognition tasks is presented in this paper. The network, called the dynamic supervised forward-propagation network (DSFPN), is based on the forward only version of the counterpropagation network (CPN). The DSFPN, trains using a supervised algorithm and can grow dynamically during training, allowing subclasses in the training data to be learnt in an unsupervised manner. It is shown to train in times comparable to the CPN while giving better classification accuracies than the popular backpropagation network. Both Fourier descriptors and wavelet descriptors are used for image preprocessing and the wavelets are proven to give a far better performance.

Hand-Based Biometric Analysis

NASA Technical Reports Server (NTRS)

Bebis, George (Inventor); Amayeh, Gholamreza (Inventor)

2015-01-01

Hand-based biometric analysis systems and techniques are described which provide robust hand-based identification and verification. An image of a hand is obtained, which is then segmented into a palm region and separate finger regions. Acquisition of the image is performed without requiring particular orientation or placement restrictions. Segmentation is performed without the use of reference points on the images. Each segment is analyzed by calculating a set of Zernike moment descriptors for the segment. The feature parameters thus obtained are then fused and compared to stored sets of descriptors in enrollment templates to arrive at an identity decision. By using Zernike moments, and through additional manipulation, the biometric analysis is invariant to rotation, scale, or translation or an in put image. Additionally, the analysis utilizes re-use of commonly-seen terms in Zernike calculations to achieve additional efficiencies over traditional Zernike moment calculation.

Hand-Based Biometric Analysis

NASA Technical Reports Server (NTRS)

Bebis, George

2013-01-01

Hand-based biometric analysis systems and techniques provide robust hand-based identification and verification. An image of a hand is obtained, which is then segmented into a palm region and separate finger regions. Acquisition of the image is performed without requiring particular orientation or placement restrictions. Segmentation is performed without the use of reference points on the images. Each segment is analyzed by calculating a set of Zernike moment descriptors for the segment. The feature parameters thus obtained are then fused and compared to stored sets of descriptors in enrollment templates to arrive at an identity decision. By using Zernike moments, and through additional manipulation, the biometric analysis is invariant to rotation, scale, or translation or an input image. Additionally, the analysis uses re-use of commonly seen terms in Zernike calculations to achieve additional efficiencies over traditional Zernike moment calculation.

Microscopic Description of Le Châtelier's Principle

NASA Astrophysics Data System (ADS)

Novak, Igor

2005-08-01

The analysis based on microscopic descriptors (energy levels and their populations) is given that provides visualization of free energies and conceptual rationalization of Le Châtelier's principle. The misconception "nature favors equilibrium" is highlighted.

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.

Support vector regression-guided unravelling: antioxidant capacity and quantitative structure-activity relationship predict reduction and promotion effects of flavonoids on acrylamide formation

PubMed Central

Huang, Mengmeng; Wei, Yan; Wang, Jun; Zhang, Yu

2016-01-01

We used the support vector regression (SVR) approach to predict and unravel reduction/promotion effect of characteristic flavonoids on the acrylamide formation under a low-moisture Maillard reaction system. Results demonstrated the reduction/promotion effects by flavonoids at addition levels of 1–10000 μmol/L. The maximal inhibition rates (51.7%, 68.8% and 26.1%) and promote rates (57.7%, 178.8% and 27.5%) caused by flavones, flavonols and isoflavones were observed at addition levels of 100 μmol/L and 10000 μmol/L, respectively. The reduction/promotion effects were closely related to the change of trolox equivalent antioxidant capacity (ΔTEAC) and well predicted by triple ΔTEAC measurements via SVR models (R: 0.633–0.900). Flavonols exhibit stronger effects on the acrylamide formation than flavones and isoflavones as well as their O-glycosides derivatives, which may be attributed to the number and position of phenolic and 3-enolic hydroxyls. The reduction/promotion effects were well predicted by using optimized quantitative structure-activity relationship (QSAR) descriptors and SVR models (R: 0.926–0.994). Compared to artificial neural network and multi-linear regression models, SVR models exhibited better fitting performance for both TEAC-dependent and QSAR descriptor-dependent predicting work. These observations demonstrated that the SVR models are competent for predicting our understanding on the future use of natural antioxidants for decreasing the acrylamide formation. PMID:27586851

Support vector regression-guided unravelling: antioxidant capacity and quantitative structure-activity relationship predict reduction and promotion effects of flavonoids on acrylamide formation

NASA Astrophysics Data System (ADS)

Huang, Mengmeng; Wei, Yan; Wang, Jun; Zhang, Yu

2016-09-01

We used the support vector regression (SVR) approach to predict and unravel reduction/promotion effect of characteristic flavonoids on the acrylamide formation under a low-moisture Maillard reaction system. Results demonstrated the reduction/promotion effects by flavonoids at addition levels of 1-10000 μmol/L. The maximal inhibition rates (51.7%, 68.8% and 26.1%) and promote rates (57.7%, 178.8% and 27.5%) caused by flavones, flavonols and isoflavones were observed at addition levels of 100 μmol/L and 10000 μmol/L, respectively. The reduction/promotion effects were closely related to the change of trolox equivalent antioxidant capacity (ΔTEAC) and well predicted by triple ΔTEAC measurements via SVR models (R: 0.633-0.900). Flavonols exhibit stronger effects on the acrylamide formation than flavones and isoflavones as well as their O-glycosides derivatives, which may be attributed to the number and position of phenolic and 3-enolic hydroxyls. The reduction/promotion effects were well predicted by using optimized quantitative structure-activity relationship (QSAR) descriptors and SVR models (R: 0.926-0.994). Compared to artificial neural network and multi-linear regression models, SVR models exhibited better fitting performance for both TEAC-dependent and QSAR descriptor-dependent predicting work. These observations demonstrated that the SVR models are competent for predicting our understanding on the future use of natural antioxidants for decreasing the acrylamide formation.

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

PubMed

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

2017-08-29

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

Descriptions and identifications of strangers by youth and adult eyewitnesses.

PubMed

Pozzulo, Joanna D; Warren, Kelly L

2003-04-01

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

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.

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.

Collection-Level Surveys for Special Collections: Coalescing Descriptors across Standards

ERIC Educational Resources Information Center

Ascher, James P.; Ferris, Anna M.

2012-01-01

Developing collection-level surveys to expose hidden collections in special collections and archives departments within ARL libraries has received a great deal of scholarly attention in the recent years. Numerous standards have been explored, and each has its strengths and weaknesses. This paper summarizes some of the major initiatives in…

Examining the predictive accuracy of the novel 3D N-linear algebraic molecular codifications on benchmark datasets.

PubMed

García-Jacas, César R; Contreras-Torres, Ernesto; Marrero-Ponce, Yovani; Pupo-Meriño, Mario; Barigye, Stephen J; Cabrera-Leyva, Lisset

2016-01-01

Recently, novel 3D alignment-free molecular descriptors (also known as QuBiLS-MIDAS) based on two-linear, three-linear and four-linear algebraic forms have been introduced. These descriptors codify chemical information for relations between two, three and four atoms by using several (dis-)similarity metrics and multi-metrics. Several studies aimed at assessing the quality of these novel descriptors have been performed. However, a deeper analysis of their performance is necessary. Therefore, in the present manuscript an assessment and statistical validation of the performance of these novel descriptors in QSAR studies is performed. To this end, eight molecular datasets (angiotensin converting enzyme, acetylcholinesterase inhibitors, benzodiazepine receptor, cyclooxygenase-2 inhibitors, dihydrofolate reductase inhibitors, glycogen phosphorylase b, thermolysin inhibitors, thrombin inhibitors) widely used as benchmarks in the evaluation of several procedures are utilized. Three to nine variable QSAR models based on Multiple Linear Regression are built for each chemical dataset according to the original division into training/test sets. Comparisons with respect to leave-one-out cross-validation correlation coefficients[Formula: see text] reveal that the models based on QuBiLS-MIDAS indices possess superior predictive ability in 7 of the 8 datasets analyzed, outperforming methodologies based on similar or more complex techniques such as: Partial Least Square, Neural Networks, Support Vector Machine and others. On the other hand, superior external correlation coefficients[Formula: see text] are attained in 6 of the 8 test sets considered, confirming the good predictive power of the obtained models. For the [Formula: see text] values non-parametric statistic tests were performed, which demonstrated that the models based on QuBiLS-MIDAS indices have the best global performance and yield significantly better predictions in 11 of the 12 QSAR procedures used in the comparison. Lastly, a study concerning to the performance of the indices according to several conformer generation methods was performed. This demonstrated that the quality of predictions of the QSAR models based on QuBiLS-MIDAS indices depend on 3D structure generation method considered, although in this preliminary study the results achieved do not present significant statistical differences among them. As conclusions it can be stated that the QuBiLS-MIDAS indices are suitable for extracting structural information of the molecules and thus, constitute a promissory alternative to build models that contribute to the prediction of pharmacokinetic, pharmacodynamics and toxicological properties on novel compounds.Graphical abstractComparative graphical representation of the performance of the novel QuBiLS-MIDAS 3D-MDs with respect to other methodologies in QSAR modeling of eight chemical datasets.

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.

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.

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.

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

Effective Rating Scale Development for Speaking Tests: Performance Decision Trees

ERIC Educational Resources Information Center

Fulcher, Glenn; Davidson, Fred; Kemp, Jenny

2011-01-01

Rating scale design and development for testing speaking is generally conducted using one of two approaches: the measurement-driven approach or the performance data-driven approach. The measurement-driven approach prioritizes the ordering of descriptors onto a single scale. Meaning is derived from the scaling methodology and the agreement of…

Application of morphological associative memories and Fourier descriptors for classification of noisy subsurface signatures

NASA Astrophysics Data System (ADS)

Ortiz, Jorge L.; Parsiani, Hamed; Tolstoy, Leonid

2004-02-01

This paper presents a method for recognition of Noisy Subsurface Images using Morphological Associative Memories (MAM). MAM are type of associative memories that use a new kind of neural networks based in the algebra system known as semi-ring. The operations performed in this algebraic system are highly nonlinear providing additional strength when compared to other transformations. Morphological associative memories are a new kind of neural networks that provide a robust performance with noisy inputs. Two representations of morphological associative memories are used called M and W matrices. M associative memory provides a robust association with input patterns corrupted by dilative random noise, while the W associative matrix performs a robust recognition in patterns corrupted with erosive random noise. The robust performance of MAM is used in combination of the Fourier descriptors for the recognition of underground objects in Ground Penetrating Radar (GPR) images. Multiple 2-D GPR images of a site are made available by NASA-SSC center. The buried objects in these images appear in the form of hyperbolas which are the results of radar backscatter from the artifacts or objects. The Fourier descriptors of the prototype hyperbola-like and shapes from non-hyperbola shapes in the sub-surface images are used to make these shapes scale-, shift-, and rotation-invariant. Typical hyperbola-like and non-hyperbola shapes are used to calculate the morphological associative memories. The trained MAMs are used to process other noisy images to detect the presence of these underground objects. The outputs from the MAM using the noisy patterns may be equal to the training prototypes, providing a positive identification of the artifacts. The results are images with recognized hyperbolas which indicate the presence of buried artifacts. A model using MATLAB has been developed and results are presented.

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.

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.

Laplace-Beltrami Eigenvalues and Topological Features of Eigenfunctions for Statistical Shape Analysis

PubMed Central

Reuter, Martin; Wolter, Franz-Erich; Shenton, Martha; Niethammer, Marc

2009-01-01

This paper proposes the use of the surface based Laplace-Beltrami and the volumetric Laplace eigenvalues and -functions as shape descriptors for the comparison and analysis of shapes. These spectral measures are isometry invariant and therefore allow for shape comparisons with minimal shape pre-processing. In particular, no registration, mapping, or remeshing is necessary. The discriminatory power of the 2D surface and 3D solid methods is demonstrated on a population of female caudate nuclei (a subcortical gray matter structure of the brain, involved in memory function, emotion processing, and learning) of normal control subjects and of subjects with schizotypal personality disorder. The behavior and properties of the Laplace-Beltrami eigenvalues and -functions are discussed extensively for both the Dirichlet and Neumann boundary condition showing advantages of the Neumann vs. the Dirichlet spectra in 3D. Furthermore, topological analyses employing the Morse-Smale complex (on the surfaces) and the Reeb graph (in the solids) are performed on selected eigenfunctions, yielding shape descriptors, that are capable of localizing geometric properties and detecting shape differences by indirectly registering topological features such as critical points, level sets and integral lines of the gradient field across subjects. The use of these topological features of the Laplace-Beltrami eigenfunctions in 2D and 3D for statistical shape analysis is novel. PMID:20161035

Prediction of Chemical Function: Model Development and ...

EPA Pesticide Factsheets

The United States Environmental Protection Agency’s Exposure Forecaster (ExpoCast) project is developing both statistical and mechanism-based computational models for predicting exposures to thousands of chemicals, including those in consumer products. The high-throughput (HT) screening-level exposures developed under ExpoCast can be combined with HT screening (HTS) bioactivity data for the risk-based prioritization of chemicals for further evaluation. The functional role (e.g. solvent, plasticizer, fragrance) that a chemical performs can drive both the types of products in which it is found and the concentration in which it is present and therefore impacting exposure potential. However, critical chemical use information (including functional role) is lacking for the majority of commercial chemicals for which exposure estimates are needed. A suite of machine-learning based models for classifying chemicals in terms of their likely functional roles in products based on structure were developed. This effort required collection, curation, and harmonization of publically-available data sources of chemical functional use information from government and industry bodies. Physicochemical and structure descriptor data were generated for chemicals with function data. Machine-learning classifier models for function were then built in a cross-validated manner from the descriptor/function data using the method of random forests. The models were applied to: 1) predict chemi

On application of kernel PCA for generating stimulus features for fMRI during continuous music listening.

PubMed

Tsatsishvili, Valeri; Burunat, Iballa; Cong, Fengyu; Toiviainen, Petri; Alluri, Vinoo; Ristaniemi, Tapani

2018-06-01

There has been growing interest towards naturalistic neuroimaging experiments, which deepen our understanding of how human brain processes and integrates incoming streams of multifaceted sensory information, as commonly occurs in real world. Music is a good example of such complex continuous phenomenon. In a few recent fMRI studies examining neural correlates of music in continuous listening settings, multiple perceptual attributes of music stimulus were represented by a set of high-level features, produced as the linear combination of the acoustic descriptors computationally extracted from the stimulus audio. NEW METHOD: fMRI data from naturalistic music listening experiment were employed here. Kernel principal component analysis (KPCA) was applied to acoustic descriptors extracted from the stimulus audio to generate a set of nonlinear stimulus features. Subsequently, perceptual and neural correlates of the generated high-level features were examined. The generated features captured musical percepts that were hidden from the linear PCA features, namely Rhythmic Complexity and Event Synchronicity. Neural correlates of the new features revealed activations associated to processing of complex rhythms, including auditory, motor, and frontal areas. Results were compared with the findings in the previously published study, which analyzed the same fMRI data but applied linear PCA for generating stimulus features. To enable comparison of the results, methodology for finding stimulus-driven functional maps was adopted from the previous study. Exploiting nonlinear relationships among acoustic descriptors can lead to the novel high-level stimulus features, which can in turn reveal new brain structures involved in music processing. Copyright © 2018 Elsevier B.V. All rights reserved.

Divergence between strength indicators in packaging and cigarette engineering: a case study of Marlboro varieties in Australia and the USA.

PubMed

King, Bill; Borland, Ron; Abdul-Salaam, Shadeed; Polzin, Gregory; Ashley, David; Watson, Clifford; O'Connor, Richard J

2010-10-01

To investigate how the tobacco industry is adapting to regulatory action in accordance with provisions of the Framework Convention on Tobacco Control that targets misleading packaging and labelling. To relate the packaging and labelling of new cigarette varieties to their construction and performance. The principal design features and tar, nicotine and carbon monoxide yields of the Marlboro 'brand family' in Australia were measured and compared with those of the US equivalents. Marlboro Red and Blue/Medium, could not be differentiated in preliminary tests in Australia, but were different in the USA. However, yield testing showed Marlboro Blue/Medium did not have lower tar and nicotine yields in either country, indeed being higher in Australia. Colour can be used to market cigarettes as 'milder', independently of ISO yields and 'Light'/'Mild' descriptors. Banning of 'Light' and 'Mild' brand descriptors may be inadequate to end belief in less harmful cigarettes so long as the tobacco industry remains free to engineer 'mildness' and to use colours, other descriptors and design features to characterise varieties it wants to market as 'milder'.

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.

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.

a Clustering-Based Approach for Evaluation of EO Image Indexing

NASA Astrophysics Data System (ADS)

Bahmanyar, R.; Rigoll, G.; Datcu, M.

2013-09-01

The volume of Earth Observation data is increasing immensely in order of several Terabytes a day. Therefore, to explore and investigate the content of this huge amount of data, developing more sophisticated Content-Based Information Retrieval (CBIR) systems are highly demanded. These systems should be able to not only discover unknown structures behind the data, but also provide relevant results to the users' queries. Since in any retrieval system the images are processed based on a discrete set of their features (i.e., feature descriptors), study and assessment of the structure of feature space, build by different feature descriptors, is of high importance. In this paper, we introduce a clustering-based approach to study the content of image collections. In our approach, we claim that using both internal and external evaluation of clusters for different feature descriptors, helps to understand the structure of feature space. Moreover, the semantic understanding of users about the images also can be assessed. To validate the performance of our approach, we used an annotated Synthetic Aperture Radar (SAR) image collection. Quantitative results besides the visualization of feature space demonstrate the applicability of our approach.

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.

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.

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.

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.

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

Learning Rotation-Invariant Local Binary Descriptor.

PubMed

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

2017-08-01

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

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

PubMed

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

2015-01-01

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

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

PubMed

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

2014-06-01

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

Improving predictions of protein-protein interfaces by combining amino acid-specific classifiers based on structural and physicochemical descriptors with their weighted neighbor averages.

PubMed

de Moraes, Fábio R; Neshich, Izabella A P; Mazoni, Ivan; Yano, Inácio H; Pereira, José G C; Salim, José A; Jardine, José G; Neshich, Goran

2014-01-01

Protein-protein interactions are involved in nearly all regulatory processes in the cell and are considered one of the most important issues in molecular biology and pharmaceutical sciences but are still not fully understood. Structural and computational biology contributed greatly to the elucidation of the mechanism of protein interactions. In this paper, we present a collection of the physicochemical and structural characteristics that distinguish interface-forming residues (IFR) from free surface residues (FSR). We formulated a linear discriminative analysis (LDA) classifier to assess whether chosen descriptors from the BlueStar STING database (http://www.cbi.cnptia.embrapa.br/SMS/) are suitable for such a task. Receiver operating characteristic (ROC) analysis indicates that the particular physicochemical and structural descriptors used for building the linear classifier perform much better than a random classifier and in fact, successfully outperform some of the previously published procedures, whose performance indicators were recently compared by other research groups. The results presented here show that the selected set of descriptors can be utilized to predict IFRs, even when homologue proteins are missing (particularly important for orphan proteins where no homologue is available for comparative analysis/indication) or, when certain conformational changes accompany interface formation. The development of amino acid type specific classifiers is shown to increase IFR classification performance. Also, we found that the addition of an amino acid conservation attribute did not improve the classification prediction. This result indicates that the increase in predictive power associated with amino acid conservation is exhausted by adequate use of an extensive list of independent physicochemical and structural parameters that, by themselves, fully describe the nano-environment at protein-protein interfaces. The IFR classifier developed in this study is now integrated into the BlueStar STING suite of programs. Consequently, the prediction of protein-protein interfaces for all proteins available in the PDB is possible through STING_interfaces module, accessible at the following website: (http://www.cbi.cnptia.embrapa.br/SMS/predictions/index.html).

Improving Predictions of Protein-Protein Interfaces by Combining Amino Acid-Specific Classifiers Based on Structural and Physicochemical Descriptors with Their Weighted Neighbor Averages

PubMed Central

de Moraes, Fábio R.; Neshich, Izabella A. P.; Mazoni, Ivan; Yano, Inácio H.; Pereira, José G. C.; Salim, José A.; Jardine, José G.; Neshich, Goran

2014-01-01

Protein-protein interactions are involved in nearly all regulatory processes in the cell and are considered one of the most important issues in molecular biology and pharmaceutical sciences but are still not fully understood. Structural and computational biology contributed greatly to the elucidation of the mechanism of protein interactions. In this paper, we present a collection of the physicochemical and structural characteristics that distinguish interface-forming residues (IFR) from free surface residues (FSR). We formulated a linear discriminative analysis (LDA) classifier to assess whether chosen descriptors from the BlueStar STING database (http://www.cbi.cnptia.embrapa.br/SMS/) are suitable for such a task. Receiver operating characteristic (ROC) analysis indicates that the particular physicochemical and structural descriptors used for building the linear classifier perform much better than a random classifier and in fact, successfully outperform some of the previously published procedures, whose performance indicators were recently compared by other research groups. The results presented here show that the selected set of descriptors can be utilized to predict IFRs, even when homologue proteins are missing (particularly important for orphan proteins where no homologue is available for comparative analysis/indication) or, when certain conformational changes accompany interface formation. The development of amino acid type specific classifiers is shown to increase IFR classification performance. Also, we found that the addition of an amino acid conservation attribute did not improve the classification prediction. This result indicates that the increase in predictive power associated with amino acid conservation is exhausted by adequate use of an extensive list of independent physicochemical and structural parameters that, by themselves, fully describe the nano-environment at protein-protein interfaces. The IFR classifier developed in this study is now integrated into the BlueStar STING suite of programs. Consequently, the prediction of protein-protein interfaces for all proteins available in the PDB is possible through STING_interfaces module, accessible at the following website: (http://www.cbi.cnptia.embrapa.br/SMS/predictions/index.html). PMID:24489849

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.

Application of ant colony optimization in development of models for prediction of anti-HIV-1 activity of HEPT derivatives.

PubMed

Zare-Shahabadi, Vali; Abbasitabar, Fatemeh

2010-09-01

Quantitative structure-activity relationship models were derived for 107 analogs of 1-[(2-hydroxyethoxy) methyl]-6-(phenylthio)thymine, a potent inhibitor of the HIV-1 reverse transcriptase. The activities of these compounds were investigated by means of multiple linear regression (MLR) technique. An ant colony optimization algorithm, called Memorized_ACS, was applied for selecting relevant descriptors and detecting outliers. This algorithm uses an external memory based upon knowledge incorporation from previous iterations. At first, the memory is empty, and then it is filled by running several ACS algorithms. In this respect, after each ACS run, the elite ant is stored in the memory and the process is continued to fill the memory. Here, pheromone updating is performed by all elite ants collected in the memory; this results in improvements in both exploration and exploitation behaviors of the ACS algorithm. The memory is then made empty and is filled again by performing several ACS algorithms using updated pheromone trails. This process is repeated for several iterations. At the end, the memory contains several top solutions for the problem. Number of appearance of each descriptor in the external memory is a good criterion for its importance. Finally, prediction is performed by the elitist ant, and interpretation is carried out by considering the importance of each descriptor. The best MLR model has a training error of 0.47 log (1/EC(50)) units (R(2) = 0.90) and a prediction error of 0.76 log (1/EC(50)) units (R(2) = 0.88). Copyright 2010 Wiley Periodicals, Inc.

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.

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

Content and ratings of mature-rated video games.

PubMed

Thompson, Kimberly M; Tepichin, Karen; Haninger, Kevin

2006-04-01

To quantify the depiction of violence, blood, sexual themes, profanity, substances, and gambling in video games rated M (for "mature") and to measure agreement between the content observed and the rating information provided to consumers on the game box by the Entertainment Software Rating Board. We created a database of M-rated video game titles, selected a random sample, recorded at least 1 hour of game play, quantitatively assessed the content, performed statistical analyses to describe the content, and compared our observations with the Entertainment Software Rating Board content descriptors and results of our prior studies. Harvard University, Boston, Mass. Authors and 1 hired game player. M-rated video games. Percentages of game play depicting violence, blood, sexual themes, gambling, alcohol, tobacco, or other drugs; use of profanity in dialogue, song lyrics, or gestures. Although the Entertainment Software Rating Board content descriptors for violence and blood provide a good indication of such content in the game, we identified 45 observations of content that could warrant a content descriptor in 29 games (81%) that lacked these content descriptors. M-rated video games are significantly more likely to contain blood, profanity, and substances; depict more severe injuries to human and nonhuman characters; and have a higher rate of human deaths than video games rated T (for "teen"). Parents and physicians should recognize that popular M-rated video games contain a wide range of unlabeled content and may expose children and adolescents to messages that may negatively influence their perceptions, attitudes, and behaviors.

Chi-square-based scoring function for categorization of MEDLINE citations.

PubMed

Kastrin, A; Peterlin, B; Hristovski, D

2010-01-01

Text categorization has been used in biomedical informatics for identifying documents containing relevant topics of interest. We developed a simple method that uses a chi-square-based scoring function to determine the likelihood of MEDLINE citations containing genetic relevant topic. Our procedure requires construction of a genetic and a nongenetic domain document corpus. We used MeSH descriptors assigned to MEDLINE citations for this categorization task. We compared frequencies of MeSH descriptors between two corpora applying chi-square test. A MeSH descriptor was considered to be a positive indicator if its relative observed frequency in the genetic domain corpus was greater than its relative observed frequency in the nongenetic domain corpus. The output of the proposed method is a list of scores for all the citations, with the highest score given to those citations containing MeSH descriptors typical for the genetic domain. Validation was done on a set of 734 manually annotated MEDLINE citations. It achieved predictive accuracy of 0.87 with 0.69 recall and 0.64 precision. We evaluated the method by comparing it to three machine-learning algorithms (support vector machines, decision trees, naïve Bayes). Although the differences were not statistically significantly different, results showed that our chi-square scoring performs as good as compared machine-learning algorithms. We suggest that the chi-square scoring is an effective solution to help categorize MEDLINE citations. The algorithm is implemented in the BITOLA literature-based discovery support system as a preprocessor for gene symbol disambiguation process.

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.

Army Officer Job Analysis: Identifying Performance Requirements to Inform Officer Selection and Assignment

DTIC Science & Technology

2011-08-01

ABSTRACT (Maximum 200 words): This report includes lists of leadership and technical duties performed by all Army officers and the skills...proficiency, and we called these Major Duties (MDs). We compiled lists of major duties (MDs) performed by all officers in each of the five positions...26  TABLE 4.1. NUMBER OF SMES WHO REVIEWED DESCRIPTOR LISTS ACROSS ALL SITES

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.

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...

Protein-protein docking using region-based 3D Zernike descriptors

PubMed Central

2009-01-01

Background Protein-protein interactions are a pivotal component of many biological processes and mediate a variety of functions. Knowing the tertiary structure of a protein complex is therefore essential for understanding the interaction mechanism. However, experimental techniques to solve the structure of the complex are often found to be difficult. To this end, computational protein-protein docking approaches can provide a useful alternative to address this issue. Prediction of docking conformations relies on methods that effectively capture shape features of the participating proteins while giving due consideration to conformational changes that may occur. Results We present a novel protein docking algorithm based on the use of 3D Zernike descriptors as regional features of molecular shape. The key motivation of using these descriptors is their invariance to transformation, in addition to a compact representation of local surface shape characteristics. Docking decoys are generated using geometric hashing, which are then ranked by a scoring function that incorporates a buried surface area and a novel geometric complementarity term based on normals associated with the 3D Zernike shape description. Our docking algorithm was tested on both bound and unbound cases in the ZDOCK benchmark 2.0 dataset. In 74% of the bound docking predictions, our method was able to find a near-native solution (interface C-αRMSD ≤ 2.5 Å) within the top 1000 ranks. For unbound docking, among the 60 complexes for which our algorithm returned at least one hit, 60% of the cases were ranked within the top 2000. Comparison with existing shape-based docking algorithms shows that our method has a better performance than the others in unbound docking while remaining competitive for bound docking cases. Conclusion We show for the first time that the 3D Zernike descriptors are adept in capturing shape complementarity at the protein-protein interface and useful for protein docking prediction. Rigorous benchmark studies show that our docking approach has a superior performance compared to existing methods. PMID:20003235

Protein-protein docking using region-based 3D Zernike descriptors.

PubMed

Venkatraman, Vishwesh; Yang, Yifeng D; Sael, Lee; Kihara, Daisuke

2009-12-09

Protein-protein interactions are a pivotal component of many biological processes and mediate a variety of functions. Knowing the tertiary structure of a protein complex is therefore essential for understanding the interaction mechanism. However, experimental techniques to solve the structure of the complex are often found to be difficult. To this end, computational protein-protein docking approaches can provide a useful alternative to address this issue. Prediction of docking conformations relies on methods that effectively capture shape features of the participating proteins while giving due consideration to conformational changes that may occur. We present a novel protein docking algorithm based on the use of 3D Zernike descriptors as regional features of molecular shape. The key motivation of using these descriptors is their invariance to transformation, in addition to a compact representation of local surface shape characteristics. Docking decoys are generated using geometric hashing, which are then ranked by a scoring function that incorporates a buried surface area and a novel geometric complementarity term based on normals associated with the 3D Zernike shape description. Our docking algorithm was tested on both bound and unbound cases in the ZDOCK benchmark 2.0 dataset. In 74% of the bound docking predictions, our method was able to find a near-native solution (interface C-alphaRMSD < or = 2.5 A) within the top 1000 ranks. For unbound docking, among the 60 complexes for which our algorithm returned at least one hit, 60% of the cases were ranked within the top 2000. Comparison with existing shape-based docking algorithms shows that our method has a better performance than the others in unbound docking while remaining competitive for bound docking cases. We show for the first time that the 3D Zernike descriptors are adept in capturing shape complementarity at the protein-protein interface and useful for protein docking prediction. Rigorous benchmark studies show that our docking approach has a superior performance compared to existing methods.

Gun bore flaw image matching based on improved SIFT descriptor

NASA Astrophysics Data System (ADS)

Zeng, Luan; Xiong, Wei; Zhai, You

2013-01-01

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

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

PubMed

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

2004-10-07

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

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

PubMed

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

2015-02-01

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

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.

Feature extraction using gray-level co-occurrence matrix of wavelet coefficients and texture matching for batik motif recognition

NASA Astrophysics Data System (ADS)

Suciati, Nanik; Herumurti, Darlis; Wijaya, Arya Yudhi

2017-02-01

Batik is one of Indonesian's traditional cloth. Motif or pattern drawn on a piece of batik fabric has a specific name and philosopy. Although batik cloths are widely used in everyday life, but only few people understand its motif and philosophy. This research is intended to develop a batik motif recognition system which can be used to identify motif of Batik image automatically. First, a batik image is decomposed into sub-images using wavelet transform. Six texture descriptors, i.e. max probability, correlation, contrast, uniformity, homogenity and entropy, are extracted from gray-level co-occurrence matrix of each sub-image. The texture features are then matched to the template features using canberra distance. The experiment is performed on Batik Dataset consisting of 1088 batik images grouped into seven motifs. The best recognition rate, that is 92,1%, is achieved using feature extraction process with 5 level wavelet decomposition and 4 directional gray-level co-occurrence matrix.

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

PubMed Central

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

Introduction 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. Methods 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. Results 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. Conclusions 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. PMID:29672604

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…

Brand as Relevance.

ERIC Educational Resources Information Center

Sevier, Robert A.

2001-01-01

Discusses seven steps for colleges to create effective brands: identifying essential qualities that your audiences desire; assessing how well your audience perceives you deliver on those qualities; identifying which performance and perception gaps you want to fill; responding strategically; revising and prioritizing your vivid descriptors;…

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-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.

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.

SU-F-303-11: Implementation and Applications of Rapid, SIFT-Based Cine MR Image Binning and Region Tracking

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

Mazur, T; Wang, Y; Fischer-Valuck, B

2015-06-15

Purpose: To develop a novel and rapid, SIFT-based algorithm for assessing feature motion on cine MR images acquired during MRI-guided radiotherapy treatments. In particular, we apply SIFT descriptors toward both partitioning cine images into respiratory states and tracking regions across frames. Methods: Among a training set of images acquired during a fraction, we densely assign SIFT descriptors to pixels within the images. We cluster these descriptors across all frames in order to produce a dictionary of trackable features. Associating the best-matching descriptors at every frame among the training images to these features, we construct motion traces for the features. Wemore » use these traces to define respiratory bins for sorting images in order to facilitate robust pixel-by-pixel tracking. Instead of applying conventional methods for identifying pixel correspondences across frames we utilize a recently-developed algorithm that derives correspondences via a matching objective for SIFT descriptors. Results: We apply these methods to a collection of lung, abdominal, and breast patients. We evaluate the procedure for respiratory binning using target sites exhibiting high-amplitude motion among 20 lung and abdominal patients. In particular, we investigate whether these methods yield minimal variation between images within a bin by perturbing the resulting image distributions among bins. Moreover, we compare the motion between averaged images across respiratory states to 4DCT data for these patients. We evaluate the algorithm for obtaining pixel correspondences between frames by tracking contours among a set of breast patients. As an initial case, we track easily-identifiable edges of lumpectomy cavities that show minimal motion over treatment. Conclusions: These SIFT-based methods reliably extract motion information from cine MR images acquired during patient treatments. While we performed our analysis retrospectively, the algorithm lends itself to prospective motion assessment. Applications of these methods include motion assessment, identifying treatment windows for gating, and determining optimal margins for treatment.« less

QSRR modeling for the chromatographic retention behavior of some β-lactam antibiotics using forward and firefly variable selection algorithms coupled with multiple linear regression.

PubMed

Fouad, Marwa A; Tolba, Enas H; El-Shal, Manal A; El Kerdawy, Ahmed M

2018-05-11

The justified continuous emerging of new β-lactam antibiotics provokes the need for developing suitable analytical methods that accelerate and facilitate their analysis. A face central composite experimental design was adopted using different levels of phosphate buffer pH, acetonitrile percentage at zero time and after 15 min in a gradient program to obtain the optimum chromatographic conditions for the elution of 31 β-lactam antibiotics. Retention factors were used as the target property to build two QSRR models utilizing the conventional forward selection and the advanced nature-inspired firefly algorithm for descriptor selection, coupled with multiple linear regression. The obtained models showed high performance in both internal and external validation indicating their robustness and predictive ability. Williams-Hotelling test and student's t-test showed that there is no statistical significant difference between the models' results. Y-randomization validation showed that the obtained models are due to significant correlation between the selected molecular descriptors and the analytes' chromatographic retention. These results indicate that the generated FS-MLR and FFA-MLR models are showing comparable quality on both the training and validation levels. They also gave comparable information about the molecular features that influence the retention behavior of β-lactams under the current chromatographic conditions. We can conclude that in some cases simple conventional feature selection algorithm can be used to generate robust and predictive models comparable to that are generated using advanced ones. Copyright © 2018 Elsevier B.V. All rights reserved.

Theories of quantum dissipation and nonlinear coupling bath descriptors

NASA Astrophysics Data System (ADS)

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

2018-03-01

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

Classification of melanoma lesions using sparse coded features and random forests

NASA Astrophysics Data System (ADS)

Rastgoo, Mojdeh; Lemaître, Guillaume; Morel, Olivier; Massich, Joan; Garcia, Rafael; Meriaudeau, Fabrice; Marzani, Franck; Sidibé, Désiré

2016-03-01

Malignant melanoma is the most dangerous type of skin cancer, yet it is the most treatable kind of cancer, conditioned by its early diagnosis which is a challenging task for clinicians and dermatologists. In this regard, CAD systems based on machine learning and image processing techniques are developed to differentiate melanoma lesions from benign and dysplastic nevi using dermoscopic images. Generally, these frameworks are composed of sequential processes: pre-processing, segmentation, and classification. This architecture faces mainly two challenges: (i) each process is complex with the need to tune a set of parameters, and is specific to a given dataset; (ii) the performance of each process depends on the previous one, and the errors are accumulated throughout the framework. In this paper, we propose a framework for melanoma classification based on sparse coding which does not rely on any pre-processing or lesion segmentation. Our framework uses Random Forests classifier and sparse representation of three features: SIFT, Hue and Opponent angle histograms, and RGB intensities. The experiments are carried out on the public PH2 dataset using a 10-fold cross-validation. The results show that SIFT sparse-coded feature achieves the highest performance with sensitivity and specificity of 100% and 90.3% respectively, with a dictionary size of 800 atoms and a sparsity level of 2. Furthermore, the descriptor based on RGB intensities achieves similar results with sensitivity and specificity of 100% and 71.3%, respectively for a smaller dictionary size of 100 atoms. In conclusion, dictionary learning techniques encode strong structures of dermoscopic images and provide discriminant descriptors.

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…

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.

DMA engine for repeating communication patterns

DOEpatents

Chen, Dong; Gara, Alan G.; Giampapa, Mark E.; Heidelberger, Philip; Steinmacher-Burow, Burkhard; Vranas, Pavlos

2010-09-21

A parallel computer system is constructed as a network of interconnected compute nodes to operate a global message-passing application for performing communications across the network. Each of the compute nodes includes one or more individual processors with memories which run local instances of the global message-passing application operating at each compute node to carry out local processing operations independent of processing operations carried out at other compute nodes. Each compute node also includes a DMA engine constructed to interact with the application via Injection FIFO Metadata describing multiple Injection FIFOs where each Injection FIFO may containing an arbitrary number of message descriptors in order to process messages with a fixed processing overhead irrespective of the number of message descriptors included in the Injection FIFO.

Reinforcement learning interfaces for biomedical database systems.

PubMed

Rudowsky, I; Kulyba, O; Kunin, M; Parsons, S; Raphan, T

2006-01-01

Studies of neural function that are carried out in different laboratories and that address different questions use a wide range of descriptors for data storage, depending on the laboratory and the individuals that input the data. A common approach to describe non-textual data that are referenced through a relational database is to use metadata descriptors. We have recently designed such a prototype system, but to maintain efficiency and a manageable metadata table, free formatted fields were designed as table entries. The database interface application utilizes an intelligent agent to improve integrity of operation. The purpose of this study was to investigate how reinforcement learning algorithms can assist the user in interacting with the database interface application that has been developed to improve the performance of the system.

THTM: A template matching algorithm based on HOG descriptor and two-stage matching

NASA Astrophysics Data System (ADS)

Jiang, Yuanjie; Ruan, Li; Xiao, Limin; Liu, Xi; Yuan, Feng; Wang, Haitao

2018-04-01

We propose a novel method for template matching named THTM - a template matching algorithm based on HOG (histogram of gradient) and two-stage matching. We rely on the fast construction of HOG and the two-stage matching that jointly lead to a high accuracy approach for matching. TMTM give enough attention on HOG and creatively propose a twice-stage matching while traditional method only matches once. Our contribution is to apply HOG to template matching successfully and present two-stage matching, which is prominent to improve the matching accuracy based on HOG descriptor. We analyze key features of THTM and perform compared to other commonly used alternatives on a challenging real-world datasets. Experiments show that our method outperforms the comparison method.

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

PubMed

Vistoli, Giulio; Pedretti, Alessandro; Testa, Bernard

2011-06-01

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

Indicator Properties of Baltic Zooplankton for Classification of Environmental Status within Marine Strategy Framework Directive.

PubMed

Gorokhova, Elena; Lehtiniemi, Maiju; Postel, Lutz; Rubene, Gunta; Amid, Callis; Lesutiene, Jurate; Uusitalo, Laura; Strake, Solvita; Demereckiene, Natalja

2016-01-01

The European Marine Strategy Framework Directive requires the EU Member States to estimate the level of anthropogenic impacts on their marine systems using 11 Descriptors. Assessing food web response to altered habitats is addressed by Descriptor 4 and its indicators, which are being developed for regional seas. However, the development of simple foodweb indicators able to assess the health of ecologically diverse, spatially variable and complex interactions is challenging. Zooplankton is a key element in marine foodwebs and thus comprise an important part of overall ecosystem health. Here, we review work on zooplankton indicator development using long-term data sets across the Baltic Sea and report the main findings. A suite of zooplankton community metrics were evaluated as putative ecological indicators that track community state in relation to Good Environmental Status (GES) criteria with regard to eutrophication and fish feeding conditions in the Baltic Sea. On the basis of an operational definition of GES, we propose mean body mass of zooplankton in the community in combination with zooplankton stock measured as either abundance or biomass to be applicable as an integrated indicator that could be used within the Descriptor 4 in the Baltic Sea. These metrics performed best in predicting zooplankton being in-GES when considering all datasets evaluated. However, some other metrics, such as copepod biomass, the contribution of copepods to the total zooplankton biomass or biomass-based Cladocera: Copepoda ratio, were equally reliable or even superior in certain basin-specific assessments. Our evaluation suggests that in several basins of the Baltic Sea, zooplankton communities currently appear to be out-of-GES, being comprised by smaller zooplankters and having lower total abundance or biomass compared to the communities during the reference conditions; however, the changes in the taxonomic structure underlying these trends vary widely across the sea basins due to the estuarine character of the Baltic Sea.

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.

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

NASA Astrophysics Data System (ADS)

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

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

New descriptors of homogeneity of the propagation of ventricular repolarization.

PubMed

Batchvarov, V; Dilaveris, P; Färbom, P; Ghuran, A; Acar, B; Hnatkova, K; Camm, A J; Malik, M

2000-11-01

Available descriptors of irregularities of ventricular repolarization are of limited clinical value. We studied the effect of autonomic variations on several new descriptors of the three-dimensional T loop. Twelve-lead digital ECGs were recorded continuously in 40 healthy subjects at baseline in the supine position, during postural changes (supine-->sitting-->standing-->supine-->standing), and during Valsalva maneuver performed three times in the supine and three times in the standing positions. A minimum dimensional space was constructed from the 12-lead ECG, using singular value decomposition, on the basis of median ECG beats constructed from 10-second consecutive ECG recordings. Temporal variations (TLA and PL, which measure the T loop area, and LD, the interlead relationship during repolarization) and wavefront direction descriptors (TCRT, the deviation between the QRS and T vectors) were calculated and expressed as normalized values. Values of TLA, PL, and TCRT were significantly lower in the sitting than in the supine position (-38,139 +/- 9099 vs 47,133 +/- 7511, -0.017 +/- 0.005 vs 0.033 +/- 0.005 and -0.032 +/- 0.019 vs 0.071 +/- 0.015, respectively, P < 0.001 for all) and decreased further in the standing position (-88,288 +/- 14,468, -0.067 +/- 0.013, -0.198 +/- 0.025, respectively, P < 0.001 for all). LD increased from supine to sitting (98.7 +/- 29.4 vs -87.5 +/- 15.2, P < 0.001) and increased further, though nonsignificantly in the standing position (118.3 +/- 35.2). TLA, PL, and TCRT decreased from baseline during Valsalva in the supine (-34,118 +/- 11,424 vs 62,234 +/- 12,215, -0.038 +/- 0.014 vs 0.065 +/- 0.010, -0.08 +/- 0.03 vs 0.10 +/- 0.02, respectively, P < 0.001 for all) and standing positions (-108,263 +/- 21,051 vs -68,909 +/- 10,271, -0.109 +/- 0.014 vs -0.048 +/- 0.009, -0.30 +/- 0.035 vs -015 +/- 0.016, respectively, P < 0.05 for all). LD was significantly increased by Valsalva in the supine position (13 +/- 46 vs -153 +/- 30, P < 0.001) and nonsignificantly in the standing position (99 +/- 50 vs 86 +/- 30, P = NS). There were significant correlations among TLA, PL, and LD, and no significant correlation between TCRT and any of the temporal variation descriptors. These new temporal and wavefront direction descriptors are sensitive and rapid detectors of autonomic effects on ventricular repolarization.

Flavoured cigarettes, sensation seeking and adolescents' perceptions of cigarette brands.

PubMed

Manning, K C; Kelly, K J; Comello, M L

2009-12-01

This study examined the interactive effects of cigarette package flavour descriptors and sensation seeking on adolescents' brand perceptions. High school students (n = 253) were randomly assigned to one of two experimental conditions and sequentially exposed to cigarette package illustrations for three different brands. In the flavour descriptor condition, the packages included a description of the cigarettes as "cherry", while in the traditional descriptor condition the cigarette brands were described with common phrases found on tobacco packages such as "domestic blend." Following exposure to each package participants' hedonic beliefs, brand attitudes and trial intentions were assessed. Sensation seeking was also measured, and participants were categorised as lower or higher sensation seekers. Across hedonic belief, brand attitude and trial intention measures, there were interactions between package descriptor condition and sensation seeking. These interactions revealed that among high (but not low) sensation seekers, exposure to cigarette packages including sweet flavour descriptors led to more favourable brand impressions than did exposure to packages with traditional descriptors. Among high sensation seeking youths, the appeal of cigarette brands is enhanced through the use of flavours and associated descriptions on product packaging.

The Prediction of Task and Contextual Performance by Political Skill: A Meta-Analysis and Moderator Test

ERIC Educational Resources Information Center

Bing, Mark N.; Davison, H. Kristl; Minor, Inneka; Novicevic, Milorad M.; Frink, Dwight D.

2011-01-01

Political skill is a relatively newly articulated construct. Despite its novelty, it has been investigated in a variety of contexts, showing promise not only as a descriptor of several organizational phenomena, but also as a predictor of job performance. Given this status, it seems appropriate to review the empirical literature to this point for…

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

Characterizing region of interest in image using MPEG-7 visual descriptors

NASA Astrophysics Data System (ADS)

Ryu, Min-Sung; Park, Soo-Jun; Won, Chee Sun

2005-08-01

In this paper, we propose a region-based image retrieval system using EHD (Edge Histogram Descriptor) and CLD (Color Layout Descriptor) of MPEG-7 descriptors. The combined descriptor can efficiently describe edge and color features in terms of sub-image regions. That is, the basic unit for the selection of the region-of-interest (ROI) in the image is the sub-image block of the EHD, which corresponds to 16 (i.e., 4x4) non-overlapping image blocks in the image space. This implies that, to have a one-to-one region correspondence between EHD and CLD, we need to take an 8x8 inverse DCT (IDCT) for the CLD. Experimental results show that the proposed retrieval scheme can be used for image retrieval with the ROI based image retrieval for MPEG-7 indexed images.

Central Component Descriptors for Levels of Technological Pedagogical Content Knowledge

ERIC Educational Resources Information Center

Niess, Margaret L.

2013-01-01

Technological pedagogical content knowledge (TPACK) proposes a theoretical framework that incorporates four central components: an overarching conception of what it means to teach with technology, knowledge of students' thinking and understandings of specific topics with technologies, knowledge of curricular materials that incorporate…

Claims, Evidence and Achievement Level Descriptors as a Foundation for Item Design and Test Specifications

ERIC Educational Resources Information Center

Hendrickson, Amy; Huff, Kristen; Luecht, Ric

2009-01-01

[Slides] presented at the Annual Meeting of National Council on Measurement in Education (NCME) in San Diego, CA in April 2009. This presentation describes how the vehicles for gathering student evidence--task models and test specifications--are developed.

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.

Identification of terms to define unconstrained air transportation demands

NASA Technical Reports Server (NTRS)

Jacobson, I. D.; Kuhilhau, A. R.

1982-01-01

The factors involved in the evaluation of unconstrained air transportation systems were carefully analyzed. By definition an unconstrained system is taken to be one in which the design can employ innovative and advanced concepts no longer limited by present environmental, social, political or regulatory settings. Four principal evaluation criteria are involved: (1) service utilization, based on the operating performance characteristics as viewed by potential patrons; (2) community impacts, reflecting decisions based on the perceived impacts of the system; (3) technological feasibility, estimating what is required to reduce the system to practice; and (4) financial feasibility, predicting the ability of the concepts to attract financial support. For each of these criteria, a set of terms or descriptors was identified, which should be used in the evaluation to render it complete. It is also demonstrated that these descriptors have the following properties: (a) their interpretation may be made by different groups of evaluators; (b) their interpretations and the way they are used may depend on the stage of development of the system in which they are used; (c) in formulating the problem, all descriptors should be addressed independent of the evaluation technique selected.

Evaluation of MPEG-7-Based Audio Descriptors for Animal Voice Recognition over Wireless Acoustic Sensor Networks.

PubMed

Luque, Joaquín; Larios, Diego F; Personal, Enrique; Barbancho, Julio; León, Carlos

2016-05-18

Environmental audio monitoring is a huge area of interest for biologists all over the world. This is why some audio monitoring system have been proposed in the literature, which can be classified into two different approaches: acquirement and compression of all audio patterns in order to send them as raw data to a main server; or specific recognition systems based on audio patterns. The first approach presents the drawback of a high amount of information to be stored in a main server. Moreover, this information requires a considerable amount of effort to be analyzed. The second approach has the drawback of its lack of scalability when new patterns need to be detected. To overcome these limitations, this paper proposes an environmental Wireless Acoustic Sensor Network architecture focused on use of generic descriptors based on an MPEG-7 standard. These descriptors demonstrate it to be suitable to be used in the recognition of different patterns, allowing a high scalability. The proposed parameters have been tested to recognize different behaviors of two anuran species that live in Spanish natural parks; the Epidalea calamita and the Alytes obstetricans toads, demonstrating to have a high classification performance.

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.

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.

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

PubMed

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

2013-09-01

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

Hearing aid fine-tuning based on Dutch descriptions.

PubMed

Thielemans, Thijs; Pans, Donné; Chenault, Michelene; Anteunis, Lucien

2017-07-01

The aim of this study was to derive an independent fitting assistant based on expert consensus. Two questions were asked: (1) what (Dutch) terms do hearing impaired listeners use nowadays to describe their specific hearing aid fitting problems? (2) What is the expert consensus on how to resolve these complaints by adjusting hearing aid parameters? Hearing aid dispensers provided descriptors that impaired listeners use to describe their reactions to specific hearing aid fitting problems. Hearing aid fitting experts were asked "How would you adjust the hearing aid if its user reports that the aid sounds…?" with the blank filled with each of the 40 most frequently mentioned descriptors. 112 hearing aid dispensers and 15 hearing aid experts. The expert solution with the highest weight value was considered the best solution for that descriptor. Principal component analysis (PCA) was performed to identify a factor structure in fitting problems. Nine fitting problems could be identified resulting in an expert-based, hearing aid manufacturer independent, fine-tuning fitting assistant for clinical use. The construction of an expert-based, hearing aid manufacturer independent, fine-tuning fitting assistant to be used as an additional tool in the iterative fitting process is feasible.

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.

Evaluation of MPEG-7-Based Audio Descriptors for Animal Voice Recognition over Wireless Acoustic Sensor Networks

PubMed Central

Luque, Joaquín; Larios, Diego F.; Personal, Enrique; Barbancho, Julio; León, Carlos

2016-01-01

Environmental audio monitoring is a huge area of interest for biologists all over the world. This is why some audio monitoring system have been proposed in the literature, which can be classified into two different approaches: acquirement and compression of all audio patterns in order to send them as raw data to a main server; or specific recognition systems based on audio patterns. The first approach presents the drawback of a high amount of information to be stored in a main server. Moreover, this information requires a considerable amount of effort to be analyzed. The second approach has the drawback of its lack of scalability when new patterns need to be detected. To overcome these limitations, this paper proposes an environmental Wireless Acoustic Sensor Network architecture focused on use of generic descriptors based on an MPEG-7 standard. These descriptors demonstrate it to be suitable to be used in the recognition of different patterns, allowing a high scalability. The proposed parameters have been tested to recognize different behaviors of two anuran species that live in Spanish natural parks; the Epidalea calamita and the Alytes obstetricans toads, demonstrating to have a high classification performance. PMID:27213375

QuBiLS-MAS, open source multi-platform software for atom- and bond-based topological (2D) and chiral (2.5D) algebraic molecular descriptors computations.

PubMed

Valdés-Martiní, José R; Marrero-Ponce, Yovani; García-Jacas, César R; Martinez-Mayorga, Karina; Barigye, Stephen J; Vaz d'Almeida, Yasser Silveira; Pham-The, Hai; Pérez-Giménez, Facundo; Morell, Carlos A

2017-06-07

In previous reports, Marrero-Ponce et al. proposed algebraic formalisms for characterizing topological (2D) and chiral (2.5D) molecular features through atom- and bond-based ToMoCoMD-CARDD (acronym for Topological Molecular Computational Design-Computer Aided Rational Drug Design) molecular descriptors. These MDs codify molecular information based on the bilinear, quadratic and linear algebraic forms and the graph-theoretical electronic-density and edge-adjacency matrices in order to consider atom- and bond-based relations, respectively. These MDs have been successfully applied in the screening of chemical compounds of different therapeutic applications ranging from antimalarials, antibacterials, tyrosinase inhibitors and so on. To compute these MDs, a computational program with the same name was initially developed. However, this in house software barely offered the functionalities required in contemporary molecular modeling tasks, in addition to the inherent limitations that made its usability impractical. Therefore, the present manuscript introduces the QuBiLS-MAS (acronym for Quadratic, Bilinear and N-Linear mapS based on graph-theoretic electronic-density Matrices and Atomic weightingS) software designed to compute topological (0-2.5D) molecular descriptors based on bilinear, quadratic and linear algebraic forms for atom- and bond-based relations. The QuBiLS-MAS module was designed as standalone software, in which extensions and generalizations of the former ToMoCoMD-CARDD 2D-algebraic indices are implemented, considering the following aspects: (a) two new matrix normalization approaches based on double-stochastic and mutual probability formalisms; (b) topological constraints (cut-offs) to take into account particular inter-atomic relations; (c) six additional atomic properties to be used as weighting schemes in the calculation of the molecular vectors; (d) four new local-fragments to consider molecular regions of interest; (e) number of lone-pair electrons in chemical structure defined by diagonal coefficients in matrix representations; and (f) several aggregation operators (invariants) applied over atom/bond-level descriptors in order to compute global indices. This software permits the parallel computation of the indices, contains a batch processing module and data curation functionalities. This program was developed in Java v1.7 using the Chemistry Development Kit library (version 1.4.19). The QuBiLS-MAS software consists of two components: a desktop interface (GUI) and an API library allowing for the easy integration of the latter in chemoinformatics applications. The relevance of the novel extensions and generalizations implemented in this software is demonstrated through three studies. Firstly, a comparative Shannon's entropy based variability study for the proposed QuBiLS-MAS and the DRAGON indices demonstrates superior performance for the former. A principal component analysis reveals that the QuBiLS-MAS approach captures chemical information orthogonal to that codified by the DRAGON descriptors. Lastly, a QSAR study for the binding affinity to the corticosteroid-binding globulin using Cramer's steroid dataset is carried out. From these analyses, it is revealed that the QuBiLS-MAS approach for atom-pair relations yields similar-to-superior performance with regard to other QSAR methodologies reported in the literature. Therefore, the QuBiLS-MAS approach constitutes a useful tool for the diversity analysis of chemical compound datasets and high-throughput screening of structure-activity data.

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.

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.

Median Robust Extended Local Binary Pattern for Texture Classification.

PubMed

Liu, Li; Lao, Songyang; Fieguth, Paul W; Guo, Yulan; Wang, Xiaogang; Pietikäinen, Matti

2016-03-01

Local binary patterns (LBP) are considered among the most computationally efficient high-performance texture features. However, the LBP method is very sensitive to image noise and is unable to capture macrostructure information. To best address these disadvantages, in this paper, we introduce a novel descriptor for texture classification, the median robust extended LBP (MRELBP). Different from the traditional LBP and many LBP variants, MRELBP compares regional image medians rather than raw image intensities. A multiscale LBP type descriptor is computed by efficiently comparing image medians over a novel sampling scheme, which can capture both microstructure and macrostructure texture information. A comprehensive evaluation on benchmark data sets reveals MRELBP's high performance-robust to gray scale variations, rotation changes and noise-but at a low computational cost. MRELBP produces the best classification scores of 99.82%, 99.38%, and 99.77% on three popular Outex test suites. More importantly, MRELBP is shown to be highly robust to image noise, including Gaussian noise, Gaussian blur, salt-and-pepper noise, and random pixel corruption.

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

PubMed

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

2017-03-01

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

MULTIMODAL CLASSIFICATION OF DEMENTIA USING FUNCTIONAL DATA, ANATOMICAL FEATURES AND 3D INVARIANT SHAPE DESCRIPTORS

PubMed Central

Mikhno, Arthur; Nuevo, Pablo Martinez; Devanand, Davangere P.; Parsey, Ramin V.; Laine, Andrew F.

2013-01-01

Multimodality classification of Alzheimer’s disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), is of interest to the medical community. We improve on prior classification frameworks by incorporating multiple features from MRI and PET data obtained with multiple radioligands, fluorodeoxyglucose (FDG) and Pittsburg compound B (PIB). We also introduce a new MRI feature, invariant shape descriptors based on 3D Zernike moments applied to the hippocampus region. Classification performance is evaluated on data from 17 healthy controls (CTR), 22 MCI, and 17 AD subjects. Zernike significantly outperforms volume, accuracy (Zernike to volume): CTR/AD (90.7% to 71.6%), CTR/MCI (76.2% to 60.0%), MCI/AD (84.3% to 65.5%). Zernike also provides comparable and complementary performance to PET. Optimal accuracy is achieved when Zernike and PET features are combined (accuracy, specificity, sensitivity), CTR/AD (98.8%, 99.5%, 98.1%), CTR/MCI (84.3%, 82.9%, 85.9%) and MCI/AD (93.3%, 93.6%, 93.3%). PMID:24576927

MULTIMODAL CLASSIFICATION OF DEMENTIA USING FUNCTIONAL DATA, ANATOMICAL FEATURES AND 3D INVARIANT SHAPE DESCRIPTORS.

PubMed

Mikhno, Arthur; Nuevo, Pablo Martinez; Devanand, Davangere P; Parsey, Ramin V; Laine, Andrew F

2012-01-01

Multimodality classification of Alzheimer's disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), is of interest to the medical community. We improve on prior classification frameworks by incorporating multiple features from MRI and PET data obtained with multiple radioligands, fluorodeoxyglucose (FDG) and Pittsburg compound B (PIB). We also introduce a new MRI feature, invariant shape descriptors based on 3D Zernike moments applied to the hippocampus region. Classification performance is evaluated on data from 17 healthy controls (CTR), 22 MCI, and 17 AD subjects. Zernike significantly outperforms volume, accuracy (Zernike to volume): CTR/AD (90.7% to 71.6%), CTR/MCI (76.2% to 60.0%), MCI/AD (84.3% to 65.5%). Zernike also provides comparable and complementary performance to PET. Optimal accuracy is achieved when Zernike and PET features are combined (accuracy, specificity, sensitivity), CTR/AD (98.8%, 99.5%, 98.1%), CTR/MCI (84.3%, 82.9%, 85.9%) and MCI/AD (93.3%, 93.6%, 93.3%).

Quantum chemical investigation of levofloxacin-boron complexes: A computational approach

NASA Astrophysics Data System (ADS)

Sayin, Koray; Karakaş, Duran

2018-04-01

Quantum chemical calculations are performed over some boron complexes with levofloxacin. Boron complex with fluorine atoms are optimized at three different methods (HF, B3LYP and M062X) with 6-31 + G(d) basis set. The best level is determined as M062X/6-31 + G(d) by comparison of experimental and calculated results of complex (1). The other complexes are optimized by using the best level. Structural properties, IR and NMR spectrum are examined in detail. Biological activities of mentioned complexes are investigated by some quantum chemical descriptors and molecular docking analyses. As a result, biological activities of complex (2) and (4) are close to each other and higher than those of other complexes. Additionally, NLO properties of mentioned complexes are investigated by some quantum chemical parameters. It is found that complex (3) is the best candidate for NLO applications.

Infrared and visible images registration with adaptable local-global feature integration for rail inspection

NASA Astrophysics Data System (ADS)

Tang, Chaoqing; Tian, Gui Yun; Chen, Xiaotian; Wu, Jianbo; Li, Kongjing; Meng, Hongying

2017-12-01

Active thermography provides infrared images that contain sub-surface defect information, while visible images only reveal surface information. Mapping infrared information to visible images offers more comprehensive visualization for decision-making in rail inspection. However, the common information for registration is limited due to different modalities in both local and global level. For example, rail track which has low temperature contrast reveals rich details in visible images, but turns blurry in the infrared counterparts. This paper proposes a registration algorithm called Edge-Guided Speeded-Up-Robust-Features (EG-SURF) to address this issue. Rather than sequentially integrating local and global information in matching stage which suffered from buckets effect, this algorithm adaptively integrates local and global information into a descriptor to gather more common information before matching. This adaptability consists of two facets, an adaptable weighting factor between local and global information, and an adaptable main direction accuracy. The local information is extracted using SURF while the global information is represented by shape context from edges. Meanwhile, in shape context generation process, edges are weighted according to local scale and decomposed into bins using a vector decomposition manner to provide more accurate descriptor. The proposed algorithm is qualitatively and quantitatively validated using eddy current pulsed thermography scene in the experiments. In comparison with other algorithms, better performance has been achieved.

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

PubMed

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

2018-04-27

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

Discovering collectively informative descriptors from high-throughput experiments

PubMed Central

2009-01-01

Background Improvements in high-throughput technology and its increasing use have led to the generation of many highly complex datasets that often address similar biological questions. Combining information from these studies can increase the reliability and generalizability of results and also yield new insights that guide future research. Results This paper describes a novel algorithm called BLANKET for symmetric analysis of two experiments that assess informativeness of descriptors. The experiments are required to be related only in that their descriptor sets intersect substantially and their definitions of case and control are consistent. From resulting lists of n descriptors ranked by informativeness, BLANKET determines shortlists of descriptors from each experiment, generally of different lengths p and q. For any pair of shortlists, four numbers are evident: the number of descriptors appearing in both shortlists, in exactly one shortlist, or in neither shortlist. From the associated contingency table, BLANKET computes Right Fisher Exact Test (RFET) values used as scores over a plane of possible pairs of shortlist lengths [1,2]. BLANKET then chooses a pair or pairs with RFET score less than a threshold; the threshold depends upon n and shortlist length limits and represents a quality of intersection achieved by less than 5% of random lists. Conclusions Researchers seek within a universe of descriptors some minimal subset that collectively and efficiently predicts experimental outcomes. Ideally, any smaller subset should be insufficient for reliable prediction and any larger subset should have little additional accuracy. As a method, BLANKET is easy to conceptualize and presents only moderate computational complexity. Many existing databases could be mined using BLANKET to suggest optimal sets of predictive descriptors. PMID:20021653

Health sciences descriptors in the brazilian speech-language and hearing science.

PubMed

Campanatti-Ostiz, Heliane; Andrade, Claudia Regina Furquim de

2010-01-01

Terminology in Speech-Language and Hearing Science. To propose a specific thesaurus about the Speech-Language and Hearing Science, for the English, Portuguese and Spanish languages, based on the existing keywords available on the Health Sciences Descriptors (DeCS). Methodology was based on the pilot study developed by Campanatti-Ostiz and Andrade; that had as a purpose to verify the methodological viability for the creation of a Speech-Language and Hearing Science category in the DeCS. The scientific journals selected for analyses of the titles, abstracts and keywords of all scientific articles were those in the field of the Speech-Language and Hearing Science, indexed on the SciELO. 1. Recovery of the Descriptors in the English language (Medical Subject Headings--MeSH); 2. Recovery and hierarchic organization of the descriptors in the Portuguese language was done (DeCS). The obtained data was analyzed as follows: descriptive analyses and relative relevance analyses of the DeCS areas. Based on the first analyses, we decided to select all 761 descriptors, with all the hierarchic numbers, independently of their occurrence (occurrence number--ON), and based on the second analyses, we decided to propose to exclude the less relevant areas and the exclusive DeCS areas. The proposal was finished with a total of 1676 occurrences of DeCS descriptors, distributed in the following areas: Anatomy; Diseases; Analytical, Diagnostic and Therapeutic Techniques and Equipments; Psychiatry and Psychology; Phenomena and Processes; Health Care. The presented proposal of a thesaurus contains the specific terminology of the Brazilian Speech-Language and Hearing Sciences and reflects the descriptors of the published scientific production. Being the DeCS a trilingual vocabulary (Portuguese, English and Spanish), the present descriptors organization proposition can be used in these three languages, allowing greater cultural interchange between different nations.

Active classifier selection for RGB-D object categorization using a Markov random field ensemble method

NASA Astrophysics Data System (ADS)

Durner, Maximilian; Márton, Zoltán.; Hillenbrand, Ulrich; Ali, Haider; Kleinsteuber, Martin

2017-03-01

In this work, a new ensemble method for the task of category recognition in different environments is presented. The focus is on service robotic perception in an open environment, where the robot's task is to recognize previously unseen objects of predefined categories, based on training on a public dataset. We propose an ensemble learning approach to be able to flexibly combine complementary sources of information (different state-of-the-art descriptors computed on color and depth images), based on a Markov Random Field (MRF). By exploiting its specific characteristics, the MRF ensemble method can also be executed as a Dynamic Classifier Selection (DCS) system. In the experiments, the committee- and topology-dependent performance boost of our ensemble is shown. Despite reduced computational costs and using less information, our strategy performs on the same level as common ensemble approaches. Finally, the impact of large differences between datasets is analyzed.

Evaluation of marine subareas of Europe using life history parameters and trophic levels of selected fish populations.

PubMed

Jayasinghe, R P Prabath K; Amarasinghe, Upali S; Newton, Alice

2015-12-01

European marine waters include four regional seas that provide valuable ecosystem services to humans, including fish and other seafood. However, these marine environments are threatened by pressures from multiple anthropogenic activities and climate change. The European Marine Strategy Framework Directive (MSFD) was adopted in 2008 to achieve good environmental status (GEnS) in European Seas by year 2020, using an Ecosystem Approach. GEnS is to be assessed using 11 descriptors and up to 56 indicators. In the present analysis two descriptors namely "commercially exploited fish and shellfish populations" and "food webs" were used to evaluate the status of subareas of FAO 27 area. Data on life history parameters, trophic levels and fisheries related data of cod, haddock, saithe, herring, plaice, whiting, hake and sprat were obtained from the FishBase online database and advisory reports of International Council for the Exploration of the Sea (ICES). Subareas inhabited by r and K strategists were identified using interrelationships of life history parameters of commercially important fish stocks. Mean trophic level (MTL) of fish community each subarea was calculated and subareas with species of high and low trophic level were identified. The Fish in Balance (FiB) index was computed for each subarea and recent trends of FiB indices were analysed. The overall environmental status of each subarea was evaluated considering life history trends, MTL and FiB Index. The analysis showed that subareas I, II, V, VIII and IX were assessed as "good" whereas subareas III, IV, VI and VII were assessed as "poor". The subareas assessed as "good" were subject to lower environmental pressures, (less fishing pressure, less eutrophication and more water circulation), while the areas with "poor" environment experienced excessive fishing pressure, eutrophication and disturbed seabed. The evaluation was based on two qualitative descriptors ("commercially exploited fish and shellfish populations" and "food webs") is therefore more robust. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

Direct memory access transfer completion notification

DOEpatents

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

2011-02-15

DMA transfer completion notification includes: inserting, by an origin DMA engine on an origin node in an injection first-in-first-out (`FIFO`) buffer, a data descriptor for an application message to be transferred to a target node on behalf of an application on the origin node; inserting, by the origin DMA engine, a completion notification descriptor in the injection FIFO buffer after the data descriptor for the message, the completion notification descriptor specifying a packet header for a completion notification packet; transferring, by the origin DMA engine to the target node, the message in dependence upon the data descriptor; sending, by the origin DMA engine, the completion notification packet to a local reception FIFO buffer using a local memory FIFO transfer operation; and notifying, by the origin DMA engine, the application that transfer of the message is complete in response to receiving the completion notification packet in the local reception FIFO buffer.

Stress Be Not Proud: The Myth of Burnout.

ERIC Educational Resources Information Center

DeMoulin, Donald F.

1991-01-01

Analysis of data from 827 professional educators, using the Career Awareness Index and the Instrument Summary Assessment Program, indicates that "burnout" may be an inappropriate descriptor of low to moderate levels of self-efficacy and personal productivity. Professional development activities should be specifically designed to match individual…

75 FR 60091 - Science and Technology Reinvention Laboratory Personnel Management Demonstration Project...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-09-29

... Engineering Command, Armament Research, Development and Engineering Center (ARDEC); Correction AGENCY: Office... employees at the Army Research, Development and Engineering Command, Armament Research, Development and Engineering Center (ARDEC). Within that notice the descriptors for levels IV and V are incorrect under factor...

Evidence-Centered Design as a Foundation for ALD Development

ERIC Educational Resources Information Center

Plake, Barbara S.; Huff, Kristen; Reshetar, Rosemary

2009-01-01

[Slides] presented at the Annual Meeting of National Council on Measurement in Education (NCME) in San Diego, CA in April 2009. This presentation discusses a methodology for directly connecting evidence-centered assessment design (ECD) to score interpretation and use through the development of Achievement level descriptors.

Explaining reaction mechanisms using the dual descriptor: a complementary tool to the molecular electrostatic potential.

PubMed

Martínez-Araya, Jorge Ignacio

2013-07-01

The intrinsic reactivity of cyanide when interacting with a silver cation was rationalized using the dual descriptor (DD) as a complement to the molecular electrostatic potential (MEP) in order to predict interactions at the local level. It was found that DD accurately explains covalent interactions that cannot be explained by MEP, which focuses on essentially ionic interactions. This allowed the rationalization of the reaction mechanism that yields silver cyanide in the gas phase. Other similar reaction mechanisms involving a silver cation interacting with water, ammonia, and thiosulfate were also explained by the combination of MEP and DD. This analysis provides another example of the usefulness of DD as a tool for gaining a deeper understanding of any reaction mechanism that is mainly governed by covalent interactions.

Automatic loudness control in short-form content for broadcasting.

PubMed

Pires, Leandro da S; Vieira, Maurílio N; Yehia, Hani C

2017-03-01

During the early years of the International Telecommunication Union (ITU) loudness calculation standard for sound broadcasting [ITU-R (2006), Rec. BS Series, 1770], the need for additional loudness descriptors to evaluate short-form content, such as commercials and live inserts, was identified. This work proposes a loudness control scheme to prevent loudness jumps, which can bother audiences. It employs short-form content audio detection and dynamic range processing methods for the maximum loudness level criteria. Detection is achieved by combining principal component analysis for dimensionality reduction and support vector machines for binary classification. Subsequent processing is based on short-term loudness integrators and Hilbert transformers. The performance was assessed using quality classification metrics and demonstrated through a loudness control example.

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

Content Based Image Retrieval by Using Color Descriptor and Discrete Wavelet Transform.

PubMed

Ashraf, Rehan; Ahmed, Mudassar; Jabbar, Sohail; Khalid, Shehzad; Ahmad, Awais; Din, Sadia; Jeon, Gwangil

2018-01-25

Due to recent development in technology, the complexity of multimedia is significantly increased and the retrieval of similar multimedia content is a open research problem. Content-Based Image Retrieval (CBIR) is a process that provides a framework for image search and low-level visual features are commonly used to retrieve the images from the image database. The basic requirement in any image retrieval process is to sort the images with a close similarity in term of visually appearance. The color, shape and texture are the examples of low-level image features. The feature plays a significant role in image processing. The powerful representation of an image is known as feature vector and feature extraction techniques are applied to get features that will be useful in classifying and recognition of images. As features define the behavior of an image, they show its place in terms of storage taken, efficiency in classification and obviously in time consumption also. In this paper, we are going to discuss various types of features, feature extraction techniques and explaining in what scenario, which features extraction technique will be better. The effectiveness of the CBIR approach is fundamentally based on feature extraction. In image processing errands like object recognition and image retrieval feature descriptor is an immense among the most essential step. The main idea of CBIR is that it can search related images to an image passed as query from a dataset got by using distance metrics. The proposed method is explained for image retrieval constructed on YCbCr color with canny edge histogram and discrete wavelet transform. The combination of edge of histogram and discrete wavelet transform increase the performance of image retrieval framework for content based search. The execution of different wavelets is additionally contrasted with discover the suitability of specific wavelet work for image retrieval. The proposed algorithm is prepared and tried to implement for Wang image database. For Image Retrieval Purpose, Artificial Neural Networks (ANN) is used and applied on standard dataset in CBIR domain. The execution of the recommended descriptors is assessed by computing both Precision and Recall values and compared with different other proposed methods with demonstrate the predominance of our method. The efficiency and effectiveness of the proposed approach outperforms the existing research in term of average precision and recall values.

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

Predicted Hematologic and Plasma Volume Responses Following Rapid Ascent to Progressive Altitudes

DTIC Science & Technology

2014-06-01

of these changes, and define baseline demographics and physiologic descriptors that are important in predicting these changes. The overall impact of... physiologic descriptors that are important in predicting these changes. Using general linear mixed models and a comprehensive relational database...accomplished using a comprehensive relational database containing individual ascent profiles, demographics, and physiologic subject descriptors as well as

CINAHL and MEDLINE: a comparison of indexing practices.

PubMed

Brenner, S H; McKinin, E J

1989-10-01

A random sample of fifty nursing articles indexed in both MEDLINE and CINAHL (NURSING & ALLIED HEALTH) during 1986 was used for comparing indexing practices. Indexing was analyzed by counting the number of major descriptors, the number of major and minor descriptors, the number of indexing access points, the number of common indexing access points, and the number and type of unique indexing access points. The study results indicate: there are few differences in the number of major descriptors used, MEDLINE uses almost twice as many descriptors, MEDLINE has almost twice as many indexing access points, and MEDLINE and CINAHL provide few common access points.

CINAHL and MEDLINE: a comparison of indexing practices.

PubMed Central

Brenner, S H; McKinin, E J

1989-01-01

A random sample of fifty nursing articles indexed in both MEDLINE and CINAHL (NURSING & ALLIED HEALTH) during 1986 was used for comparing indexing practices. Indexing was analyzed by counting the number of major descriptors, the number of major and minor descriptors, the number of indexing access points, the number of common indexing access points, and the number and type of unique indexing access points. The study results indicate: there are few differences in the number of major descriptors used, MEDLINE uses almost twice as many descriptors, MEDLINE has almost twice as many indexing access points, and MEDLINE and CINAHL provide few common access points. PMID:2676049

Spherical harmonics coefficients for ligand-based virtual screening of cyclooxygenase inhibitors.

PubMed

Wang, Quan; Birod, Kerstin; Angioni, Carlo; Grösch, Sabine; Geppert, Tim; Schneider, Petra; Rupp, Matthias; Schneider, Gisbert

2011-01-01

Molecular descriptors are essential for many applications in computational chemistry, such as ligand-based similarity searching. Spherical harmonics have previously been suggested as comprehensive descriptors of molecular structure and properties. We investigate a spherical harmonics descriptor for shape-based virtual screening. We introduce and validate a partially rotation-invariant three-dimensional molecular shape descriptor based on the norm of spherical harmonics expansion coefficients. Using this molecular representation, we parameterize molecular surfaces, i.e., isosurfaces of spatial molecular property distributions. We validate the shape descriptor in a comprehensive retrospective virtual screening experiment. In a prospective study, we virtually screen a large compound library for cyclooxygenase inhibitors, using a self-organizing map as a pre-filter and the shape descriptor for candidate prioritization. 12 compounds were tested in vitro for direct enzyme inhibition and in a whole blood assay. Active compounds containing a triazole scaffold were identified as direct cyclooxygenase-1 inhibitors. This outcome corroborates the usefulness of spherical harmonics for representation of molecular shape in virtual screening of large compound collections. The combination of pharmacophore and shape-based filtering of screening candidates proved to be a straightforward approach to finding novel bioactive chemotypes with minimal experimental effort.

A Concise Guide to Feature Histograms with Applications to LIDAR-Based Spacecraft Relative Navigation

NASA Astrophysics Data System (ADS)

Rhodes, Andrew P.; Christian, John A.; Evans, Thomas

2017-12-01

With the availability and popularity of 3D sensors, it is advantageous to re-examine the use of point cloud descriptors for the purpose of pose estimation and spacecraft relative navigation. One popular descriptor is the oriented unique repeatable clustered viewpoint feature histogram (OUR-CVFH), which is most often utilized in personal and industrial robotics to simultaneously recognize and navigate relative to an object. Recent research into using the OUR-CVFH descriptor for spacecraft navigation has produced favorable results. Since OUR-CVFH is the most recent innovation in a large family of feature histogram point cloud descriptors, discussions of parameter settings and insights into its functionality are spread among various publications and online resources. This paper organizes the history of feature histogram point cloud descriptors for a straightforward explanation of their evolution. This article compiles all the requisite information needed to implement OUR-CVFH into one location, as well as providing useful suggestions on how to tune the generation parameters. This work is beneficial for anyone interested in using this histogram descriptor for object recognition or navigation - may it be personal robotics or spacecraft navigation.

Retro-regression--another important multivariate regression improvement.

PubMed

Randić, M

2001-01-01

We review the serious problem associated with instabilities of the coefficients of regression equations, referred to as the MRA (multivariate regression analysis) "nightmare of the first kind". This is manifested when in a stepwise regression a descriptor is included or excluded from a regression. The consequence is an unpredictable change of the coefficients of the descriptors that remain in the regression equation. We follow with consideration of an even more serious problem, referred to as the MRA "nightmare of the second kind", arising when optimal descriptors are selected from a large pool of descriptors. This process typically causes at different steps of the stepwise regression a replacement of several previously used descriptors by new ones. We describe a procedure that resolves these difficulties. The approach is illustrated on boiling points of nonanes which are considered (1) by using an ordered connectivity basis; (2) by using an ordering resulting from application of greedy algorithm; and (3) by using an ordering derived from an exhaustive search for optimal descriptors. A novel variant of multiple regression analysis, called retro-regression (RR), is outlined showing how it resolves the ambiguities associated with both "nightmares" of the first and the second kind of MRA.

Can we apply the MRI BI-RADS lexicon morphology descriptors on contrast-enhanced spectral mammography?

PubMed

Kamal, Rasha M; Helal, Maha H; Mansour, Sahar M; Haggag, Marwa A; Nada, Omniya M; Farahat, Iman G; Alieldin, Nelly H

2016-07-12

To assess the feasibility of using the MRI breast imaging reporting and data system (BI-RADS) lexicon morphology descriptors to characterize enhancing breast lesions identified on contrast-enhanced spectral mammography (CESM). The study is a retrospective analysis of the morphology descriptors of 261 enhancing breast lesions identified on CESM in 239 patients. We presented the morphological categorization of the included lesions into focus, mass and non-mass. Further classifications included (1) the multiplicity for "focus" category, (2) the shape, margin and internal enhancement for "mass" category and (3) the distribution and internal enhancement for "non-mass" category. Each morphology descriptor was evaluated individually (irrespective of all other descriptors) by calculating its sensitivity, specificity, positive-predictive value (PPV) and negative-predictive value (NPV) and likelihood ratios (LRs). The study included 68/261 (26.1%) benign lesions and 193/261 (73.9%) malignant lesions. Intensely enhancing foci, whether single (7/12, 58.3%) or multiple (2/12, 16.7%), were malignant. Descriptors of "irregular"-shape (PPV: 92.4%) and "non-circumscribed" margin (odds ratio: 55.2, LR positive: 4.77; p-value: <0.001) were more compatible with malignancy. Internal mass enhancement patterns showed a very low specificity (58.0%) and NPV (40.0%). Non-mass enhancement (NME) was detected in 81/261 lesions. Asymmetrical NME in 81% (n = 52/81) lesions was malignant lesions and internal enhancement patterns indicative of malignancy were the heterogeneous and clumped ones. We can apply the MRI morphology descriptors to characterize lesions on CESM, but with few expectations. In many situations, irregular-shaped, non-circumscribed masses and NME with focal, ductal or segmental distribution and heterogeneous or clumped enhancement are the most suggestive descriptors of malignant pathologies. (1) The MRI BI-RADS lexicon morphology descriptors can be applied in the characterization of enhancing lesions on CESM with a few exceptions. (2) Multiple bilateral intensely enhancing foci should not be included under the normal background parenchymal enhancement unless they are proved to be benign by biopsy. (3) Mass lesion features that indicated malignancy were irregular-shaped, spiculated and irregular margins and heterogeneous internal enhancement patterns. The rim enhancement pattern should not be considered as a descriptor of malignant lesions unless CESM is coupled with an ultrasound examination.

Can we apply the MRI BI-RADS lexicon morphology descriptors on contrast-enhanced spectral mammography?

PubMed Central

Kamal, Rasha M; Helal, Maha H; Haggag, Marwa A; Nada, Omniya M; Farahat, Iman G; Alieldin, Nelly H

2016-01-01

Objective: To assess the feasibility of using the MRI breast imaging reporting and data system (BI-RADS) lexicon morphology descriptors to characterize enhancing breast lesions identified on contrast-enhanced spectral mammography (CESM). Methods: The study is a retrospective analysis of the morphology descriptors of 261 enhancing breast lesions identified on CESM in 239 patients. We presented the morphological categorization of the included lesions into focus, mass and non-mass. Further classifications included (1) the multiplicity for “focus” category, (2) the shape, margin and internal enhancement for “mass” category and (3) the distribution and internal enhancement for “non-mass” category. Each morphology descriptor was evaluated individually (irrespective of all other descriptors) by calculating its sensitivity, specificity, positive-predictive value (PPV) and negative-predictive value (NPV) and likelihood ratios (LRs). Results: The study included 68/261 (26.1%) benign lesions and 193/261 (73.9%) malignant lesions. Intensely enhancing foci, whether single (7/12, 58.3%) or multiple (2/12, 16.7%), were malignant. Descriptors of “irregular”-shape (PPV: 92.4%) and “non-circumscribed” margin (odds ratio: 55.2, LR positive: 4.77; p-value: <0.001) were more compatible with malignancy. Internal mass enhancement patterns showed a very low specificity (58.0%) and NPV (40.0%). Non-mass enhancement (NME) was detected in 81/261 lesions. Asymmetrical NME in 81% (n = 52/81) lesions was malignant lesions and internal enhancement patterns indicative of malignancy were the heterogeneous and clumped ones. Conclusion: We can apply the MRI morphology descriptors to characterize lesions on CESM, but with few expectations. In many situations, irregular-shaped, non-circumscribed masses and NME with focal, ductal or segmental distribution and heterogeneous or clumped enhancement are the most suggestive descriptors of malignant pathologies. Advances in knowledge: (1) The MRI BI-RADS lexicon morphology descriptors can be applied in the characterization of enhancing lesions on CESM with a few exceptions. (2) Multiple bilateral intensely enhancing foci should not be included under the normal background parenchymal enhancement unless they are proved to be benign by biopsy. (3) Mass lesion features that indicated malignancy were irregular-shaped, spiculated and irregular margins and heterogeneous internal enhancement patterns. The rim enhancement pattern should not be considered as a descriptor of malignant lesions unless CESM is coupled with an ultrasound examination. PMID:27327403

Blurred image recognition by legendre moment invariants

PubMed Central

Zhang, Hui; Shu, Huazhong; Han, Guo-Niu; Coatrieux, Gouenou; Luo, Limin; Coatrieux, Jean-Louis

2010-01-01

Processing blurred images is a key problem in many image applications. Existing methods to obtain blur invariants which are invariant with respect to centrally symmetric blur are based on geometric moments or complex moments. In this paper, we propose a new method to construct a set of blur invariants using the orthogonal Legendre moments. Some important properties of Legendre moments for the blurred image are presented and proved. The performance of the proposed descriptors is evaluated with various point-spread functions and different image noises. The comparison of the present approach with previous methods in terms of pattern recognition accuracy is also provided. The experimental results show that the proposed descriptors are more robust to noise and have better discriminative power than the methods based on geometric or complex moments. PMID:19933003

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.

Chemosensory characteristics of regional Vidal icewines from China and Canada.

PubMed

Huang, Ling; Ma, Yue; Tian, Xin; Li, Ji-Ming; Li, Lan-Xiao; Tang, Ke; Xu, Yan

2018-09-30

This work aimed to compare the flavor characteristics of Vidal icewines from China and Canada and to establish relationships between sensory descriptors and chemical composition. Descriptive analysis was performed with a trained panel to obtain the sensory profiles. Thirty important aroma-active compounds were quantified by four different methodologies. Partial least squares discriminant analysis was used to identify candidate compounds, which were unique to certain sensory descriptors. The sensory profiles of icewines from China were characterized by nut and honey aromas, while icewines from Canada expressed caramel and rose aromas. Nut and honey aromas had a close correlation with 1-hexanol, isoamyl acetate, phenethyl acetate and phenylethyl alcohol. Caramel aroma was correlated with ethyl esters and lactones and rose aroma was correlated with terpenes. Copyright © 2018 Elsevier Ltd. All rights reserved.

Artificial intelligence techniques for embryo and oocyte classification.

PubMed

Manna, Claudio; Nanni, Loris; Lumini, Alessandra; Pappalardo, Sebastiana

2013-01-01

One of the most relevant aspects in assisted reproduction technology is the possibility of characterizing and identifying the most viable oocytes or embryos. In most cases, embryologists select them by visual examination and their evaluation is totally subjective. Recently, due to the rapid growth in the capacity to extract texture descriptors from a given image, a growing interest has been shown in the use of artificial intelligence methods for embryo or oocyte scoring/selection in IVF programmes. This work concentrates the efforts on the possible prediction of the quality of embryos and oocytes in order to improve the performance of assisted reproduction technology, starting from their images. The artificial intelligence system proposed in this work is based on a set of Levenberg-Marquardt neural networks trained using textural descriptors (the local binary patterns). The proposed system was tested on two data sets of 269 oocytes and 269 corresponding embryos from 104 women and compared with other machine learning methods already proposed in the past for similar classification problems. Although the results are only preliminary, they show an interesting classification performance. This technique may be of particular interest in those countries where legislation restricts embryo selection. One of the most relevant aspects in assisted reproduction technology is the possibility of characterizing and identifying the most viable oocytes or embryos. In most cases, embryologists select them by visual examination and their evaluation is totally subjective. Recently, due to the rapid growth in our capacity to extract texture descriptors from a given image, a growing interest has been shown in the use of artificial intelligence methods for embryo or oocyte scoring/selection in IVF programmes. In this work, we concentrate our efforts on the possible prediction of the quality of embryos and oocytes in order to improve the performance of assisted reproduction technology, starting from their images. The artificial intelligence system proposed in this work is based on a set of Levenberg-Marquardt neural networks trained using textural descriptors (the 'local binary patterns'). The proposed system is tested on two data sets, of 269 oocytes and 269 corresponding embryos from 104 women, and compared with other machine learning methods already proposed in the past for similar classification problems. Although the results are only preliminary, they showed an interesting classification performance. This technique may be of particular interest in those countries where legislation restricts embryo selection. Copyright © 2012 Reproductive Healthcare Ltd. Published by Elsevier Ltd. All rights reserved.

Qualifications Frameworks: Implementation and Impact in Botswana

ERIC Educational Resources Information Center

Tau, Daniel; Modesto, Stanslaus T.

2011-01-01

A growing number of countries are introducing qualification frameworks (QFs) following a common definition of outcomes, level descriptors, and a set of occupational or knowledge fields. Botswana has been no exception to this trend. The passing of the Vocational Training Act (1998) led to the creation of the Botswana National Vocational…

Microscopic Description of Le Chatelier's Principle

ERIC Educational Resources Information Center

Novak, Igor

2005-01-01

A simple approach that "demystifies" Le Chatelier's principle (LCP) and simulates students to think about fundamental physical background behind the well-known principles is presented. The approach uses microscopic descriptors of matter like energy levels and populations and does not require any assumption about the fixed amount of substance being…

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,…

Developing and Improving Modified Achievement Level Descriptors: Rationale, Procedures, and Tools

ERIC Educational Resources Information Center

Quenemoen, Rachel; Albus, Debra; Rogers, Chris; Lazarus, Sheryl

2010-01-01

Some states are developing alternate assessments based on modified achievement standards (AA-MAS) to measure the academic achievement of some students with disabilities (Albus, Lazarus, Thurlow, & Cormier, 2009; Lazarus, Thurlow, Christensen, & Cormier, 2007). These assessments measure the same content as the general assessment for a given…

A PROMIS Measure of Neuropathic Pain Quality

PubMed Central

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

2016-01-01

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

Computer-Aided Diagnosis of Solid Breast Lesions Using an Ultrasonic Multi-Feature Analysis Procedure

DTIC Science & Technology

2011-01-01

areas. We quantified morphometric features by geometric and fractal analysis of traced lesion boundaries. Although no single parameter can reliably...These include acoustic descriptors (“echogenicity,” “heterogeneity,” “shadowing”) and morphometric descriptors (“area,” “aspect ratio,” “border...quantitative descriptors; some morphometric features (such as border irregularity) also were particularly effective in lesion classification. Our

Background element content of the lichen Pseudevernia furfuracea: A supra-national state of art implemented by novel field data from Italy.

PubMed

Cecconi, Elva; Incerti, Guido; Capozzi, Fiore; Adamo, Paola; Bargagli, Roberto; Benesperi, Renato; Candotto Carniel, Fabio; Favero-Longo, Sergio Enrico; Giordano, Simonetta; Puntillo, Domenico; Ravera, Sonia; Spagnuolo, Valeria; Tretiach, Mauro

2018-05-01

In biomonitoring, the knowledge of background element content (BEC) values is an essential pre-requisite for the correct assessment of pollution levels. Here, we estimated the BEC values of a highly performing biomonitor, the epiphytic lichen Pseudevernia furfuracea, by means of a careful review of literature data, integrated by an extensive field survey. Methodologically homogeneous element content datasets, reflecting different exposure conditions across European and extra-European countries, were compiled and comparatively analysed. Element content in samples collected in remote areas was compared to that of potentially enriched samples, testing differences between medians for 25 elements. This analysis confirmed that the former samples were substantially unaffected by anthropogenic contributions, and their metrics were therefore proposed as a first overview at supra-national background level. We also showed that bioaccumulation studies suffer a huge methodological variability. Limited to original field data, we investigated the background variability of 43 elements in 62 remote Italian sites, characterized in GIS environment for anthropization, land use, climate and lithology at different scale resolution. The relationships between selected environmental descriptors and BEC were tested using Principal Component Regression (PCR) modelling. Elemental composition resulted significantly dependent on land use, climate and lithology. In the case of lithogenic elements, regression models correctly reproduced the lichen content throughout the country at randomly selected sites. Further descriptors should be identified only for As, Co, and V. Through a multivariate approach we also identified three geographically homogeneous macro-regions for which specific BECs were provided for use as reference in biomonitoring applications. Copyright © 2017 Elsevier B.V. All rights reserved.

What happened to smokers’ beliefs about light cigarettes when “light/mild” brand descriptors were banned in the UK? Findings from the International Tobacco Control (ITC) Four Country Survey

PubMed Central

Borland, R; Fong, G T; Yong, H-H; Cummings, K M; Hammond, D; King, B; Siahpush, M; McNeill, A; Hastings, G; O’Connor, R J; Elton-Marshall, T; Zanna, M P

2015-01-01

Aim This paper examines how beliefs of smokers in the UK were affected by the removal of “light” and “mild” brand descriptors, which came into effect on 30 September 2003 for Member States of the European Union (EU). Participants The data come from the first four waves (2002–2005) of the International Tobacco Control Policy Evaluation (ITC) Four-Country Survey, an annual cohort telephone survey of adult smokers in Canada, USA, UK and Australia (15 450 individual cases). Design The UK ban on misleading descriptors occurred around the second wave of data collection in the ITC survey, permitting us to compare beliefs about light cigarettes among adult smokers in the UK before and after the ban, with beliefs in the three other ITC countries unaffected by the ban. Results There was a substantial decline in reported beliefs about the benefits of light cigarettes in the UK following the policy change and an associated public information campaign, but by 2005 (ie, wave 4), these beliefs rebounded slightly and the change in beliefs was no greater than in the USA, where there was no policy change. Conclusions The findings reveal that high levels of misperceptions about light cigarettes existed among smokers in all four countries before and after the EU ban took effect. We cannot conclude that the policy of removing some aspects of misleading labels has been effective in changing beliefs about light cigarettes. Efforts to correct decades of consumer misperceptions about light cigarettes must extend beyond simply removing “light” and “mild” brand descriptors. PMID:18426868

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.

76 FR 73680 - Proposed Information Collection Request Submitted for Sixty Days' Public Comment; O*NET Data...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-11-29

.... Descriptive information using standardized descriptors for skills, abilities, interests, knowledge, work... which: Evaluate whether the proposed collection of information is necessary for the proper performance...; Evaluate the accuracy of the agency's estimate of the burden of the proposed collection of information...

The Components of Good Acoustics in a High Performance School

ERIC Educational Resources Information Center

Stewart, William

2009-01-01

Acoustics has received greater importance in the learning environment in recent years. In August 2000, The Acoustical Society of America (ASA) published the study "Classroom Acoustics: A Resource for Creating Learning Environments with Desirable Listening Conditions" providing a framework for understanding the qualities, descriptors of the…

Artificial Neural Networks for differential diagnosis of breast lesions in MR-Mammography: a systematic approach addressing the influence of network architecture on diagnostic performance using a large clinical database.

PubMed

Dietzel, Matthias; Baltzer, Pascal A T; Dietzel, Andreas; Zoubi, Ramy; Gröschel, Tobias; Burmeister, Hartmut P; Bogdan, Martin; Kaiser, Werner A

2012-07-01

Differential diagnosis of lesions in MR-Mammography (MRM) remains a complex task. The aim of this MRM study was to design and to test robustness of Artificial Neural Network architectures to predict malignancy using a large clinical database. For this IRB-approved investigation standardized protocols and study design were applied (T1w-FLASH; 0.1 mmol/kgBW Gd-DTPA; T2w-TSE; histological verification after MRM). All lesions were evaluated by two experienced (>500 MRM) radiologists in consensus. In every lesion, 18 previously published descriptors were assessed and documented in the database. An Artificial Neural Network (ANN) was developed to process this database (The-MathWorks/Inc., feed-forward-architecture/resilient back-propagation-algorithm). All 18 descriptors were set as input variables, whereas histological results (malignant vs. benign) was defined as classification variable. Initially, the ANN was optimized in terms of "Training Epochs" (TE), "Hidden Layers" (HL), "Learning Rate" (LR) and "Neurons" (N). Robustness of the ANN was addressed by repeated evaluation cycles (n: 9) with receiver operating characteristics (ROC) analysis of the results applying 4-fold Cross Validation. The best network architecture was identified comparing the corresponding Area under the ROC curve (AUC). Histopathology revealed 436 benign and 648 malignant lesions. Enhancing the level of complexity could not increase diagnostic accuracy of the network (P: n.s.). The optimized ANN architecture (TE: 20, HL: 1, N: 5, LR: 1.2) was accurate (mean-AUC 0.888; P: <0.001) and robust (CI: 0.885-0.892; range: 0.880-0.898). The optimized neural network showed robust performance and high diagnostic accuracy for prediction of malignancy on unknown data. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

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.

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.

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.

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.

SigVox - A 3D feature matching algorithm for automatic street object recognition in mobile laser scanning point clouds

NASA Astrophysics Data System (ADS)

Wang, Jinhu; Lindenbergh, Roderik; Menenti, Massimo

2017-06-01

Urban road environments contain a variety of objects including different types of lamp poles and traffic signs. Its monitoring is traditionally conducted by visual inspection, which is time consuming and expensive. Mobile laser scanning (MLS) systems sample the road environment efficiently by acquiring large and accurate point clouds. This work proposes a methodology for urban road object recognition from MLS point clouds. The proposed method uses, for the first time, shape descriptors of complete objects to match repetitive objects in large point clouds. To do so, a novel 3D multi-scale shape descriptor is introduced, that is embedded in a workflow that efficiently and automatically identifies different types of lamp poles and traffic signs. The workflow starts by tiling the raw point clouds along the scanning trajectory and by identifying non-ground points. After voxelization of the non-ground points, connected voxels are clustered to form candidate objects. For automatic recognition of lamp poles and street signs, a 3D significant eigenvector based shape descriptor using voxels (SigVox) is introduced. The 3D SigVox descriptor is constructed by first subdividing the points with an octree into several levels. Next, significant eigenvectors of the points in each voxel are determined by principal component analysis (PCA) and mapped onto the appropriate triangle of a sphere approximating icosahedron. This step is repeated for different scales. By determining the similarity of 3D SigVox descriptors between candidate point clusters and training objects, street furniture is automatically identified. The feasibility and quality of the proposed method is verified on two point clouds obtained in opposite direction of a stretch of road of 4 km. 6 types of lamp pole and 4 types of road sign were selected as objects of interest. Ground truth validation showed that the overall accuracy of the ∼170 automatically recognized objects is approximately 95%. The results demonstrate that the proposed method is able to recognize street furniture in a practical scenario. Remaining difficult cases are touching objects, like a lamp pole close to a tree.

Staging Lung Cancer: Metastasis.

PubMed

Shroff, Girish S; Viswanathan, Chitra; Carter, Brett W; Benveniste, Marcelo F; Truong, Mylene T; Sabloff, Bradley S

2018-05-01

The updated eighth edition of the tumor, node, metastasis (TNM) classification for lung cancer includes revisions to T and M descriptors. In terms of the M descriptor, the classification of intrathoracic metastatic disease as M1a is unchanged from TNM-7. Extrathoracic metastatic disease, which was classified as M1b in TNM-7, is now subdivided into M1b (single metastasis, single organ) and M1c (multiple metastases in one or multiple organs) descriptors. In this article, the rationale for changes in the M descriptors, the utility of preoperative staging with PET/computed tomography, and the treatment options available for patients with oligometastatic disease are discussed. Copyright © 2018 Elsevier Inc. All rights reserved.

A Framework of Temporal-Spatial Descriptors-Based Feature Extraction for Improved Myoelectric Pattern Recognition.

PubMed

Khushaba, Rami N; Al-Timemy, Ali H; Al-Ani, Ahmed; Al-Jumaily, Adel

2017-10-01

The extraction of the accurate and efficient descriptors of muscular activity plays an important role in tackling the challenging problem of myoelectric control of powered prostheses. In this paper, we present a new feature extraction framework that aims to give an enhanced representation of muscular activities through increasing the amount of information that can be extracted from individual and combined electromyogram (EMG) channels. We propose to use time-domain descriptors (TDDs) in estimating the EMG signal power spectrum characteristics; a step that preserves the computational power required for the construction of spectral features. Subsequently, TDD is used in a process that involves: 1) representing the temporal evolution of the EMG signals by progressively tracking the correlation between the TDD extracted from each analysis time window and a nonlinearly mapped version of it across the same EMG channel and 2) representing the spatial coherence between the different EMG channels, which is achieved by calculating the correlation between the TDD extracted from the differences of all possible combinations of pairs of channels and their nonlinearly mapped versions. The proposed temporal-spatial descriptors (TSDs) are validated on multiple sparse and high-density (HD) EMG data sets collected from a number of intact-limbed and amputees performing a large number of hand and finger movements. Classification results showed significant reductions in the achieved error rates in comparison to other methods, with the improvement of at least 8% on average across all subjects. Additionally, the proposed TSDs achieved significantly well in problems with HD-EMG with average classification errors of <5% across all subjects using windows lengths of 50 ms only.

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

PubMed Central

Dyrka, Witold; Nebel, Jean-Christophe

2009-01-01

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

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.

Ligand Electron Density Shape Recognition Using 3D Zernike Descriptors

NASA Astrophysics Data System (ADS)

Gunasekaran, Prasad; Grandison, Scott; Cowtan, Kevin; Mak, Lora; Lawson, David M.; Morris, Richard J.

We present a novel approach to crystallographic ligand density interpretation based on Zernike shape descriptors. Electron density for a bound ligand is expanded in an orthogonal polynomial series (3D Zernike polynomials) and the coefficients from this expansion are employed to construct rotation-invariant descriptors. These descriptors can be compared highly efficiently against large databases of descriptors computed from other molecules. In this manuscript we describe this process and show initial results from an electron density interpretation study on a dataset containing over a hundred OMIT maps. We could identify the correct ligand as the first hit in about 30 % of the cases, within the top five in a further 30 % of the cases, and giving rise to an 80 % probability of getting the correct ligand within the top ten matches. In all but a few examples, the top hit was highly similar to the correct ligand in both shape and chemistry. Further extensions and intrinsic limitations of the method are discussed.

Design and implementation of a high performance network security processor

NASA Astrophysics Data System (ADS)

Wang, Haixin; Bai, Guoqiang; Chen, Hongyi

2010-03-01

The last few years have seen many significant progresses in the field of application-specific processors. One example is network security processors (NSPs) that perform various cryptographic operations specified by network security protocols and help to offload the computation intensive burdens from network processors (NPs). This article presents a high performance NSP system architecture implementation intended for both internet protocol security (IPSec) and secure socket layer (SSL) protocol acceleration, which are widely employed in virtual private network (VPN) and e-commerce applications. The efficient dual one-way pipelined data transfer skeleton and optimised integration scheme of the heterogenous parallel crypto engine arrays lead to a Gbps rate NSP, which is programmable with domain specific descriptor-based instructions. The descriptor-based control flow fragments large data packets and distributes them to the crypto engine arrays, which fully utilises the parallel computation resources and improves the overall system data throughput. A prototyping platform for this NSP design is implemented with a Xilinx XC3S5000 based FPGA chip set. Results show that the design gives a peak throughput for the IPSec ESP tunnel mode of 2.85 Gbps with over 2100 full SSL handshakes per second at a clock rate of 95 MHz.

Using Theoretical Descriptions in Structure Activity Relations. 3. Electronic Descriptors

DTIC Science & Technology

1988-08-01

Activity Relationships (QSAR) have been used successfully in the past to develop predictive equations for several biological and physical properties...Linear Free Energy Relationships (,FF.3) and is based on work by Hammet in which he derived electronic descriptors for the dissociation of substituted...structure of a compound and its activity in a system. Several different structural descriptors have been used in QSAR equations . These range from

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.

A Universal 3D Voxel Descriptor for Solid-State Material Informatics with Deep Convolutional Neural Networks.

PubMed

Kajita, Seiji; Ohba, Nobuko; Jinnouchi, Ryosuke; Asahi, Ryoji

2017-12-05

Material informatics (MI) is a promising approach to liberate us from the time-consuming Edisonian (trial and error) process for material discoveries, driven by machine-learning algorithms. Several descriptors, which are encoded material features to feed computers, were proposed in the last few decades. Especially to solid systems, however, their insufficient representations of three dimensionality of field quantities such as electron distributions and local potentials have critically hindered broad and practical successes of the solid-state MI. We develop a simple, generic 3D voxel descriptor that compacts any field quantities, in such a suitable way to implement convolutional neural networks (CNNs). We examine the 3D voxel descriptor encoded from the electron distribution by a regression test with 680 oxides data. The present scheme outperforms other existing descriptors in the prediction of Hartree energies that are significantly relevant to the long-wavelength distribution of the valence electrons. The results indicate that this scheme can forecast any functionals of field quantities just by learning sufficient amount of data, if there is an explicit correlation between the target properties and field quantities. This 3D descriptor opens a way to import prominent CNNs-based algorithms of supervised, semi-supervised and reinforcement learnings into the solid-state MI.

Developing descriptors to predict mechanical properties of nanotubes.

PubMed

Borders, Tammie L; Fonseca, Alexandre F; Zhang, Hengji; Cho, Kyeongjae; Rusinko, Andrew

2013-04-22

Descriptors and quantitative structure property relationships (QSPR) were investigated for mechanical property prediction of carbon nanotubes (CNTs). 78 molecular dynamics (MD) simulations were carried out, and 20 descriptors were calculated to build quantitative structure property relationships (QSPRs) for Young's modulus and Poisson's ratio in two separate analyses: vacancy only and vacancy plus methyl functionalization. In the first analysis, C(N2)/C(T) (number of non-sp2 hybridized carbons per the total carbons) and chiral angle were identified as critical descriptors for both Young's modulus and Poisson's ratio. Further analysis and literature findings indicate the effect of chiral angle is negligible at larger CNT radii for both properties. Raman spectroscopy can be used to measure C(N2)/C(T), providing a direct link between experimental and computational results. Poisson's ratio approaches two different limiting values as CNT radii increases: 0.23-0.25 for chiral and armchair CNTs and 0.10 for zigzag CNTs (surface defects <3%). In the second analysis, the critical descriptors were C(N2)/C(T), chiral angle, and M(N)/C(T) (number of methyl groups per total carbons). These results imply new types of defects can be represented as a new descriptor in QSPR models. Finally, results are qualified and quantified against experimental data.

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

Establishment of an in silico phototoxicity prediction method by combining descriptors related to photo-absorption and photo-reaction.

PubMed

Haranosono, Yu; Kurata, Masaaki; Sakaki, Hideyuki

2014-08-01

One of the mechanisms of phototoxicity is photo-reaction, such as reactive oxygen species (ROS) generation following photo-absorption. We focused on ROS generation and photo-absorption as key-steps, because these key-steps are able to be described by photochemical properties, and these properties are dependent on chemical structure. Photo-reactivity of a compound is described by HOMO-LUMO Gap (HLG), generally. Herein, we showed that HLG can be used as a descriptor of the generation of reactive oxygen species. Moreover, the maximum-conjugated π electron number (PENMC), which we found as a descriptor of photo-absorption, could also predict in vitro phototoxicity. Each descriptor could predict in vitro phototoxicity with 70.0% concordance, but there was un-predicted area found (gray zone). Interestingly, some compounds in each gray zone were not common, indicating that the combination of two descriptors could improve prediction potential. We reset the cut-off lines to define positive zone, negative zone and gray zone for each descriptor. Thereby we overlapped HLG and PENMC in a graph, and divided the total area to nine zones with cut-off lines of each descriptor. The rules to prediction were decided to achieve the best concordance, and the concordances were improved up to 82.8% for self-validation, 81.6% for cross-validation. We found common properties among false positive or negative compounds, photo-reactive structure and photo-allergenic, respectively. In addition, our method could be adapted to compounds rich in structural diversity using only chemical structure without any statistical analysis and complicated calculation.

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.

GTM-Based QSAR Models and Their Applicability Domains.

PubMed

Gaspar, H A; Baskin, I I; Marcou, G; Horvath, D; Varnek, A

2015-06-01

In this paper we demonstrate that Generative Topographic Mapping (GTM), a machine learning method traditionally used for data visualisation, can be efficiently applied to QSAR modelling using probability distribution functions (PDF) computed in the latent 2-dimensional space. Several different scenarios of the activity assessment were considered: (i) the "activity landscape" approach based on direct use of PDF, (ii) QSAR models involving GTM-generated on descriptors derived from PDF, and, (iii) the k-Nearest Neighbours approach in 2D latent space. Benchmarking calculations were performed on five different datasets: stability constants of metal cations Ca(2+) , Gd(3+) and Lu(3+) complexes with organic ligands in water, aqueous solubility and activity of thrombin inhibitors. It has been shown that the performance of GTM-based regression models is similar to that obtained with some popular machine-learning methods (random forest, k-NN, M5P regression tree and PLS) and ISIDA fragment descriptors. By comparing GTM activity landscapes built both on predicted and experimental activities, we may visually assess the model's performance and identify the areas in the chemical space corresponding to reliable predictions. The applicability domain used in this work is based on data likelihood. Its application has significantly improved the model performances for 4 out of 5 datasets. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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.

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

Accurate prediction of RNA-binding protein residues with two discriminative structural descriptors.

PubMed

Sun, Meijian; Wang, Xia; Zou, Chuanxin; He, Zenghui; Liu, Wei; Li, Honglin

2016-06-07

RNA-binding proteins participate in many important biological processes concerning RNA-mediated gene regulation, and several computational methods have been recently developed to predict the protein-RNA interactions of RNA-binding proteins. Newly developed discriminative descriptors will help to improve the prediction accuracy of these prediction methods and provide further meaningful information for researchers. In this work, we designed two structural features (residue electrostatic surface potential and triplet interface propensity) and according to the statistical and structural analysis of protein-RNA complexes, the two features were powerful for identifying RNA-binding protein residues. Using these two features and other excellent structure- and sequence-based features, a random forest classifier was constructed to predict RNA-binding residues. The area under the receiver operating characteristic curve (AUC) of five-fold cross-validation for our method on training set RBP195 was 0.900, and when applied to the test set RBP68, the prediction accuracy (ACC) was 0.868, and the F-score was 0.631. The good prediction performance of our method revealed that the two newly designed descriptors could be discriminative for inferring protein residues interacting with RNAs. To facilitate the use of our method, a web-server called RNAProSite, which implements the proposed method, was constructed and is freely available at http://lilab.ecust.edu.cn/NABind .

Predicting hepatotoxicity using ToxCast in vitro bioactivity and ...

EPA Pesticide Factsheets

Background: The U.S. EPA ToxCastTM program is screening thousands of environmental chemicals for bioactivity using hundreds of high-throughput in vitro assays to build predictive models of toxicity. We represented chemicals based on bioactivity and chemical structure descriptors then used supervised machine learning to predict their hepatotoxic effects.Results: A set of 677 chemicals were represented by 711 in vitro bioactivity descriptors (from ToxCast assays), 4,376 chemical structure descriptors (from QikProp, OpenBabel, PADEL, and PubChem), and three hepatotoxicity categories (from animal studies). Hepatotoxicants were defined by rat liver histopathology observed after chronic chemical testing and grouped into hypertrophy (161), injury (101) and proliferative lesions (99). Classifiers were built using six machine learning algorithms: linear discriminant analysis (LDA), Naïve Bayes (NB), support vector classification (SVM), classification and regression trees (CART), k-nearest neighbors (KNN) and an ensemble of classifiers (ENSMB). Classifiers of hepatotoxicity were built using chemical structure, ToxCast bioactivity, and a hybrid representation. Predictive performance was evaluated using 10-fold cross-validation testing and in-loop, filter-based, feature subset selection. Hybrid classifiers had the best balanced accuracy for predicting hypertrophy (0.78±0.08), injury (0.73±0.10) and proliferative lesions (0.72±0.09). Though chemical and bioactivity class

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.

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.

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.

Action Spotting and Recognition Based on a Spatiotemporal Orientation Analysis.

PubMed

Derpanis, Konstantinos G; Sizintsev, Mikhail; Cannons, Kevin J; Wildes, Richard P

2013-03-01

This paper provides a unified framework for the interrelated topics of action spotting, the spatiotemporal detection and localization of human actions in video, and action recognition, the classification of a given video into one of several predefined categories. A novel compact local descriptor of video dynamics in the context of action spotting and recognition is introduced based on visual spacetime oriented energy measurements. This descriptor is efficiently computed directly from raw image intensity data and thereby forgoes the problems typically associated with flow-based features. Importantly, the descriptor allows for the comparison of the underlying dynamics of two spacetime video segments irrespective of spatial appearance, such as differences induced by clothing, and with robustness to clutter. An associated similarity measure is introduced that admits efficient exhaustive search for an action template, derived from a single exemplar video, across candidate video sequences. The general approach presented for action spotting and recognition is amenable to efficient implementation, which is deemed critical for many important applications. For action spotting, details of a real-time GPU-based instantiation of the proposed approach are provided. Empirical evaluation of both action spotting and action recognition on challenging datasets suggests the efficacy of the proposed approach, with state-of-the-art performance documented on standard datasets.

Development of an automatic cow body condition scoring using body shape signature and Fourier descriptors.

PubMed

Bercovich, A; Edan, Y; Alchanatis, V; Moallem, U; Parmet, Y; Honig, H; Maltz, E; Antler, A; Halachmi, I

2013-01-01

Body condition evaluation is a common tool to assess energy reserves of dairy cows and to estimate their fatness or thinness. This study presents a computer-vision tool that automatically estimates cow's body condition score. Top-view images of 151 cows were collected on an Israeli research dairy farm using a digital still camera located at the entrance to the milking parlor. The cow's tailhead area and its contour were segmented and extracted automatically. Two types of features of the tailhead contour were extracted: (1) the angles and distances between 5 anatomical points; and (2) the cow signature, which is a 1-dimensional vector of the Euclidean distances from each point in the normalized tailhead contour to the shape center. Two methods were applied to describe the cow's signature and to reduce its dimension: (1) partial least squares regression, and (2) Fourier descriptors of the cow signature. Three prediction models were compared with manual scores of an expert. Results indicate that (1) it is possible to automatically extract and predict body condition from color images without any manual interference; and (2) Fourier descriptors of the cow's signature result in improved performance (R(2)=0.77). Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Face liveness detection using shearlet-based feature descriptors

NASA Astrophysics Data System (ADS)

Feng, Litong; Po, Lai-Man; Li, Yuming; Yuan, Fang

2016-07-01

Face recognition is a widely used biometric technology due to its convenience but it is vulnerable to spoofing attacks made by nonreal faces such as photographs or videos of valid users. The antispoof problem must be well resolved before widely applying face recognition in our daily life. Face liveness detection is a core technology to make sure that the input face is a live person. However, this is still very challenging using conventional liveness detection approaches of texture analysis and motion detection. The aim of this paper is to propose a feature descriptor and an efficient framework that can be used to effectively deal with the face liveness detection problem. In this framework, new feature descriptors are defined using a multiscale directional transform (shearlet transform). Then, stacked autoencoders and a softmax classifier are concatenated to detect face liveness. We evaluated this approach using the CASIA Face antispoofing database and replay-attack database. The experimental results show that our approach performs better than the state-of-the-art techniques following the provided protocols of these databases, and it is possible to significantly enhance the security of the face recognition biometric system. In addition, the experimental results also demonstrate that this framework can be easily extended to classify different spoofing attacks.

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.

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.

GENERALITY OF THE MATCHING LAW AS A DESCRIPTOR OF SHOT SELECTION IN BASKETBALL

PubMed Central

Alferink, Larry A; Critchfield, Thomas S; Hitt, Jennifer L; Higgins, William J

2009-01-01

Based on a small sample of highly successful teams, past studies suggested that shot selection (two- vs. three-point field goals) in basketball corresponds to predictions of the generalized matching law. We examined the generality of this finding by evaluating shot selection of college (Study 1) and professional (Study 3) players. The matching law accounted for the majority of variance in shot selection, with undermatching and a bias for taking three-point shots. Shot-selection matching varied systematically for players who (a) were members of successful versus unsuccessful teams, (b) competed at different levels of collegiate play, and (c) served as regulars versus substitutes (Study 2). These findings suggest that the matching law is a robust descriptor of basketball shot selection, although the mechanism that produces matching is unknown. PMID:20190921

Potential for protein surface shape analysis using spherical harmonics and 3D Zernike descriptors.

PubMed

Venkatraman, Vishwesh; Sael, Lee; Kihara, Daisuke

2009-01-01

With structure databases expanding at a rapid rate, the task at hand is to provide reliable clues to their molecular function and to be able to do so on a large scale. This, however, requires suitable encodings of the molecular structure which are amenable to fast screening. To this end, moment-based representations provide a compact and nonredundant description of molecular shape and other associated properties. In this article, we present an overview of some commonly used representations with specific focus on two schemes namely spherical harmonics and their extension, the 3D Zernike descriptors. Key features and differences of the two are reviewed and selected applications are highlighted. We further discuss recent advances covering aspects of shape and property-based comparison at both global and local levels and demonstrate their applicability through some of our studies.

Speaker gender identification based on majority vote classifiers

NASA Astrophysics Data System (ADS)

Mezghani, Eya; Charfeddine, Maha; Nicolas, Henri; Ben Amar, Chokri

2017-03-01

Speaker gender identification is considered among the most important tools in several multimedia applications namely in automatic speech recognition, interactive voice response systems and audio browsing systems. Gender identification systems performance is closely linked to the selected feature set and the employed classification model. Typical techniques are based on selecting the best performing classification method or searching optimum tuning of one classifier parameters through experimentation. In this paper, we consider a relevant and rich set of features involving pitch, MFCCs as well as other temporal and frequency-domain descriptors. Five classification models including decision tree, discriminant analysis, nave Bayes, support vector machine and k-nearest neighbor was experimented. The three best perming classifiers among the five ones will contribute by majority voting between their scores. Experimentations were performed on three different datasets spoken in three languages: English, German and Arabic in order to validate language independency of the proposed scheme. Results confirm that the presented system has reached a satisfying accuracy rate and promising classification performance thanks to the discriminating abilities and diversity of the used features combined with mid-level statistics.

Advancing Bag-of-Visual-Words Representations for Lesion Classification in Retinal Images

PubMed Central

Pires, Ramon; Jelinek, Herbert F.; Wainer, Jacques; Valle, Eduardo; Rocha, Anderson

2014-01-01

Diabetic Retinopathy (DR) is a complication of diabetes that can lead to blindness if not readily discovered. Automated screening algorithms have the potential to improve identification of patients who need further medical attention. However, the identification of lesions must be accurate to be useful for clinical application. The bag-of-visual-words (BoVW) algorithm employs a maximum-margin classifier in a flexible framework that is able to detect the most common DR-related lesions such as microaneurysms, cotton-wool spots and hard exudates. BoVW allows to bypass the need for pre- and post-processing of the retinographic images, as well as the need of specific ad hoc techniques for identification of each type of lesion. An extensive evaluation of the BoVW model, using three large retinograph datasets (DR1, DR2 and Messidor) with different resolution and collected by different healthcare personnel, was performed. The results demonstrate that the BoVW classification approach can identify different lesions within an image without having to utilize different algorithms for each lesion reducing processing time and providing a more flexible diagnostic system. Our BoVW scheme is based on sparse low-level feature detection with a Speeded-Up Robust Features (SURF) local descriptor, and mid-level features based on semi-soft coding with max pooling. The best BoVW representation for retinal image classification was an area under the receiver operating characteristic curve (AUC-ROC) of 97.8% (exudates) and 93.5% (red lesions), applying a cross-dataset validation protocol. To assess the accuracy for detecting cases that require referral within one year, the sparse extraction technique associated with semi-soft coding and max pooling obtained an AUC of 94.22.0%, outperforming current methods. Those results indicate that, for retinal image classification tasks in clinical practice, BoVW is equal and, in some instances, surpasses results obtained using dense detection (widely believed to be the best choice in many vision problems) for the low-level descriptors. PMID:24886780

Flood Frequency Analysis using different flood descriptors - the Warsaw reach of the river Vistula case study

NASA Astrophysics Data System (ADS)

Karamuz, Emilia; Kochanek, Krzysztof; Romanowicz, Renata

2014-05-01

Flood frequency analysis (FFA) is customarily performed using annual maximum flows. However, there is a number of different flood descriptors that could be used. Among them are water levels, peaks over the threshold, flood-wave duration, flood volume, etc. In this study we compare different approaches to FFA for their suitability for flood risk assessment. The main goal is to obtain the FFA curve with the smallest possible uncertainty limits, in particular for the distribution tail. The extrapolation of FFA curves is crucial in future flood risk assessment in a changing climate. We compare the FFA curves together with their uncertainty limits obtained using flows, water levels, flood inundation area and volumes for the Warsaw reach of the river Vistula. Moreover, we derive the FFA curves obtained using simulated flows. The results are used to derive the error distribution for the maximum simulated and observed values under different modelling techniques and assess its influence on flood risk predictions for ungauged catchments. MIKE11, HEC-RAS and transfer function model are applied in average and extreme conditions to model flow propagation in the Warsaw Vistula reach. The additional questions we want to answer are what is the range of application of different modelling tools under various flow conditions and how can the uncertainty of flood risk assessment be decreased. This work was partly supported by the projects "Stochastic flood forecasting system (The River Vistula reach from Zawichost to Warsaw)" and "Modern statistical models for analysis of flood frequency and features of flood waves", carried by the Institute of Geophysics, Polish Academy of Sciences on the order of the National Science Centre (contracts Nos. 2011/01/B/ST10/06866 and 2012/05/B/ST10/00482, respectively). The water level and flow data were provided by the Institute of Meteorology and Water Management (IMGW), Poland.

Ship recognition for improved persistent tracking with descriptor localization and compact representations

NASA Astrophysics Data System (ADS)

van den Broek, Sebastiaan P.; Bouma, Henri; den Hollander, Richard J. M.; Veerman, Henny E. T.; Benoist, Koen W.; Schwering, Piet B. W.

2014-10-01

For maritime situational awareness, it is important to identify currently observed ships as earlier encounters. For example, past location and behavior analysis are useful to determine whether a ship is of interest in case of piracy and smuggling. It is beneficial to verify this with cameras at a distance, to avoid the costs of bringing an own asset closer to the ship. The focus of this paper is on ship recognition from electro-optical imagery. The main contribution is an analysis of the effect of using the combination of descriptor localization and compact representations. An evaluation is performed to assess the usefulness in persistent tracking, especially for larger intervals (i.e. re-identification of ships). From the evaluation on recordings of imagery, it is estimated how well the system discriminates between different ships.

Recognition of rotated images using the multi-valued neuron and rotation-invariant 2D Fourier descriptors

NASA Astrophysics Data System (ADS)

Aizenberg, Evgeni; Bigio, Irving J.; Rodriguez-Diaz, Eladio

2012-03-01

The Fourier descriptors paradigm is a well-established approach for affine-invariant characterization of shape contours. In the work presented here, we extend this method to images, and obtain a 2D Fourier representation that is invariant to image rotation. The proposed technique retains phase uniqueness, and therefore structural image information is not lost. Rotation-invariant phase coefficients were used to train a single multi-valued neuron (MVN) to recognize satellite and human face images rotated by a wide range of angles. Experiments yielded 100% and 96.43% classification rate for each data set, respectively. Recognition performance was additionally evaluated under effects of lossy JPEG compression and additive Gaussian noise. Preliminary results show that the derived rotation-invariant features combined with the MVN provide a promising scheme for efficient recognition of rotated images.

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.

Measuring the Impact of Varied Instructional Approaches in an Introductory Animal Science Course

ERIC Educational Resources Information Center

Bolt, Brian Grady

2009-01-01

The objectives of this project were to: evaluate the impact of demographic descriptors (gender, class rank and final grade) on student's self perceived level of engagement in classroom activities; measure the impact of varying teaching styles on a student's likelihood of correctly answering a knowledge based question and: assessing the…

New formulae for Zagreb indices

NASA Astrophysics Data System (ADS)

Cangul, Ismail Naci; Yurttas, Aysun; Togan, Muge; Cevik, Ahmet Sinan

2017-07-01

In this paper, we study with some graph descriptors also called topological indices. These descriptors are useful in determination of some properties of chemical structures and preferred to some earlier descriptors as they are more practical. Especially the first and second Zagreb indices together with the first and second multiplicative Zagreb indices are considered and they are calculated in terms of the smallest and largest vertex degrees and vertex number for some well-known classes of graphs.

Analysis of A Drug Target-based Classification System using Molecular Descriptors.

PubMed

Lu, Jing; Zhang, Pin; Bi, Yi; Luo, Xiaomin

2016-01-01

Drug-target interaction is an important topic in drug discovery and drug repositioning. KEGG database offers a drug annotation and classification using a target-based classification system. In this study, we gave an investigation on five target-based classes: (I) G protein-coupled receptors; (II) Nuclear receptors; (III) Ion channels; (IV) Enzymes; (V) Pathogens, using molecular descriptors to represent each drug compound. Two popular feature selection methods, maximum relevance minimum redundancy and incremental feature selection, were adopted to extract the important descriptors. Meanwhile, an optimal prediction model based on nearest neighbor algorithm was constructed, which got the best result in identifying drug target-based classes. Finally, some key descriptors were discussed to uncover their important roles in the identification of drug-target classes.

A blur-invariant local feature for motion blurred image matching

NASA Astrophysics Data System (ADS)

Tong, Qiang; Aoki, Terumasa

2017-07-01

Image matching between a blurred (caused by camera motion, out of focus, etc.) image and a non-blurred image is a critical task for many image/video applications. However, most of the existing local feature schemes fail to achieve this work. This paper presents a blur-invariant descriptor and a novel local feature scheme including the descriptor and the interest point detector based on moment symmetry - the authors' previous work. The descriptor is based on a new concept - center peak moment-like element (CPME) which is robust to blur and boundary effect. Then by constructing CPMEs, the descriptor is also distinctive and suitable for image matching. Experimental results show our scheme outperforms state of the art methods for blurred image matching

Text Extraction from Scene Images by Character Appearance and Structure Modeling

PubMed Central

Yi, Chucai; Tian, Yingli

2012-01-01

In this paper, we propose a novel algorithm to detect text information from natural scene images. Scene text classification and detection are still open research topics. Our proposed algorithm is able to model both character appearance and structure to generate representative and discriminative text descriptors. The contributions of this paper include three aspects: 1) a new character appearance model by a structure correlation algorithm which extracts discriminative appearance features from detected interest points of character samples; 2) a new text descriptor based on structons and correlatons, which model character structure by structure differences among character samples and structure component co-occurrence; and 3) a new text region localization method by combining color decomposition, character contour refinement, and string line alignment to localize character candidates and refine detected text regions. We perform three groups of experiments to evaluate the effectiveness of our proposed algorithm, including text classification, text detection, and character identification. The evaluation results on benchmark datasets demonstrate that our algorithm achieves the state-of-the-art performance on scene text classification and detection, and significantly outperforms the existing algorithms for character identification. PMID:23316111

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

PubMed

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

2017-05-28

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

Nonparametric regression applied to quantitative structure-activity relationships

PubMed

Constans; Hirst

2000-03-01

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

Real-time probabilistic covariance tracking with efficient model update.

PubMed

Wu, Yi; Cheng, Jian; Wang, Jinqiao; Lu, Hanqing; Wang, Jun; Ling, Haibin; Blasch, Erik; Bai, Li

2012-05-01

The recently proposed covariance region descriptor has been proven robust and versatile for a modest computational cost. The covariance matrix enables efficient fusion of different types of features, where the spatial and statistical properties, as well as their correlation, are characterized. The similarity between two covariance descriptors is measured on Riemannian manifolds. Based on the same metric but with a probabilistic framework, we propose a novel tracking approach on Riemannian manifolds with a novel incremental covariance tensor learning (ICTL). To address the appearance variations, ICTL incrementally learns a low-dimensional covariance tensor representation and efficiently adapts online to appearance changes of the target with only O(1) computational complexity, resulting in a real-time performance. The covariance-based representation and the ICTL are then combined with the particle filter framework to allow better handling of background clutter, as well as the temporary occlusions. We test the proposed probabilistic ICTL tracker on numerous benchmark sequences involving different types of challenges including occlusions and variations in illumination, scale, and pose. The proposed approach demonstrates excellent real-time performance, both qualitatively and quantitatively, in comparison with several previously proposed trackers.

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

NASA Astrophysics Data System (ADS)

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

2017-05-01

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

Flexible feature-space-construction architecture and its VLSI implementation for multi-scale object detection

NASA Astrophysics Data System (ADS)

Luo, Aiwen; An, Fengwei; Zhang, Xiangyu; Chen, Lei; Huang, Zunkai; Jürgen Mattausch, Hans

2018-04-01

Feature extraction techniques are a cornerstone of object detection in computer-vision-based applications. The detection performance of vison-based detection systems is often degraded by, e.g., changes in the illumination intensity of the light source, foreground-background contrast variations or automatic gain control from the camera. In order to avoid such degradation effects, we present a block-based L1-norm-circuit architecture which is configurable for different image-cell sizes, cell-based feature descriptors and image resolutions according to customization parameters from the circuit input. The incorporated flexibility in both the image resolution and the cell size for multi-scale image pyramids leads to lower computational complexity and power consumption. Additionally, an object-detection prototype for performance evaluation in 65 nm CMOS implements the proposed L1-norm circuit together with a histogram of oriented gradients (HOG) descriptor and a support vector machine (SVM) classifier. The proposed parallel architecture with high hardware efficiency enables real-time processing, high detection robustness, small chip-core area as well as low power consumption for multi-scale object detection.

Investigations on the charge transfer mechanism at donor/acceptor interfaces in the quest for descriptors of organic solar cell performance.

PubMed

Muraoka, Azusa; Fujii, Mikiya; Mishima, Kenji; Matsunaga, Hiroki; Benten, Hiroaki; Ohkita, Hideo; Ito, Shinzaburo; Yamashita, Koichi

2018-05-07

Herein, we theoretically and experimentally investigated the mechanisms of charge separation processes of organic thin-film solar cells. PTB7, PTB1, and PTBF2 have been chosen as donors and PC 71 BM has been chosen as an acceptor considering that effective charge generation depends on the difference between the material combinations. Experimental results of transient absorption spectroscopy show that the hot process is a key step for determining external quantum efficiency (EQE) in these systems. From the quantum chemistry calculations, it has been found that EQE tends to increase as the transferred charge, charge transfer distance, and variation of dipole moments between the ground and excited states of the donor/acceptor complexes increase; this indicates that these physical quantities are a good descriptor to assess the donor-acceptor charge transfer quality contributing to the solar cell performance. We propose that designing donor/acceptor interfaces with large values of charge transfer distance and variation of dipole moments of the donor/acceptor complexes is a prerequisite for developing high-efficiency polymer/PCBM solar cells.

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