A combinatorial feature selection approach to describe the QSAR of dual site inhibitors of acetylcholinesterase.

PubMed

Asadabadi, Ebrahim Barzegari; Abdolmaleki, Parviz; Barkooie, Seyyed Mohsen Hosseini; Jahandideh, Samad; Rezaei, Mohammad Ali

2009-12-01

Regarding the great potential of dual binding site inhibitors of acetylcholinesterase as the future potent drugs of Alzheimer's disease, this study was devoted to extraction of the most effective structural features of these inhibitors from among a large number of quantitative descriptors. To do this, we adopted a unique approach in quantitative structure-activity relationships. An efficient feature selection method was emphasized in such an approach, using the confirmative results of different routine and novel feature selection methods. The proposed methods generated quite consistent results ensuring the effectiveness of the selected structural features.

A structural SVM approach for reference parsing.

PubMed

Zhang, Xiaoli; Zou, Jie; Le, Daniel X; Thoma, George R

2011-06-09

Automated extraction of bibliographic data, such as article titles, author names, abstracts, and references is essential to the affordable creation of large citation databases. References, typically appearing at the end of journal articles, can also provide valuable information for extracting other bibliographic data. Therefore, parsing individual reference to extract author, title, journal, year, etc. is sometimes a necessary preprocessing step in building citation-indexing systems. The regular structure in references enables us to consider reference parsing a sequence learning problem and to study structural Support Vector Machine (structural SVM), a newly developed structured learning algorithm on parsing references. In this study, we implemented structural SVM and used two types of contextual features to compare structural SVM with conventional SVM. Both methods achieve above 98% token classification accuracy and above 95% overall chunk-level accuracy for reference parsing. We also compared SVM and structural SVM to Conditional Random Field (CRF). The experimental results show that structural SVM and CRF achieve similar accuracies at token- and chunk-levels. When only basic observation features are used for each token, structural SVM achieves higher performance compared to SVM since it utilizes the contextual label features. However, when the contextual observation features from neighboring tokens are combined, SVM performance improves greatly, and is close to that of structural SVM after adding the second order contextual observation features. The comparison of these two methods with CRF using the same set of binary features show that both structural SVM and CRF perform better than SVM, indicating their stronger sequence learning ability in reference parsing.

Subsurface structures of buried features in the lunar Procellarum region

NASA Astrophysics Data System (ADS)

Wang, Wenrui; Heki, Kosuke

2017-07-01

The Gravity Recovery and Interior Laboratory (GRAIL) mission unraveled numbers of features showing strong gravity anomalies without prominent topographic signatures in the lunar Procellarum region. These features, located in different geologic units, are considered to have complex subsurface structures reflecting different evolution processes. By using the GRAIL level-1 data, we estimated the free-air and Bouguer gravity anomalies in several selected regions including such intriguing features. With the three-dimensional inversion technique, we recovered subsurface density structures in these regions.

Accurate facade feature extraction method for buildings from three-dimensional point cloud data considering structural information

NASA Astrophysics Data System (ADS)

Wang, Yongzhi; Ma, Yuqing; Zhu, A.-xing; Zhao, Hui; Liao, Lixia

2018-05-01

Facade features represent segmentations of building surfaces and can serve as a building framework. Extracting facade features from three-dimensional (3D) point cloud data (3D PCD) is an efficient method for 3D building modeling. By combining the advantages of 3D PCD and two-dimensional optical images, this study describes the creation of a highly accurate building facade feature extraction method from 3D PCD with a focus on structural information. The new extraction method involves three major steps: image feature extraction, exploration of the mapping method between the image features and 3D PCD, and optimization of the initial 3D PCD facade features considering structural information. Results show that the new method can extract the 3D PCD facade features of buildings more accurately and continuously. The new method is validated using a case study. In addition, the effectiveness of the new method is demonstrated by comparing it with the range image-extraction method and the optical image-extraction method in the absence of structural information. The 3D PCD facade features extracted by the new method can be applied in many fields, such as 3D building modeling and building information modeling.

High-resolution AFM structure of DNA G-wires in aqueous solution.

PubMed

Bose, Krishnashish; Lech, Christopher J; Heddi, Brahim; Phan, Anh Tuân

2018-05-17

We investigate the self-assembly of short pieces of the Tetrahymena telomeric DNA sequence d[G 4 T 2 G 4 ] in physiologically relevant aqueous solution using atomic force microscopy (AFM). Wire-like structures (G-wires) of 3.0 nm height with well-defined surface periodic features were observed. Analysis of high-resolution AFM images allowed their classification based on the periodicity of these features. A major species is identified with periodic features of 4.3 nm displaying left-handed ridges or zigzag features on the molecular surface. A minor species shows primarily left-handed periodic features of 2.2 nm. In addition to 4.3 and 2.2 nm ridges, background features with periodicity of 0.9 nm are also observed. Using molecular modeling and simulation, we identify a molecular structure that can explain both the periodicity and handedness of the major G-wire species. Our results demonstrate the potential structural diversity of G-wire formation and provide valuable insight into the structure of higher-order intermolecular G-quadruplexes. Our results also demonstrate how AFM can be combined with simulation to gain insight into biomolecular structure.

Geological and Structural Patterns on Titan Enhanced Through Cassini's SAR PCA and High-Resolution Radiometry

NASA Astrophysics Data System (ADS)

Paganelli, F.; Schubert, G.; Lopes, R. M. C.; Malaska, M.; Le Gall, A. A.; Kirk, R. L.

2016-12-01

The current SAR data coverage on Titan encompasses several areas in which multiple radar passes are present and overlapping, providing additional information to aid the interpretation of geological and structural features. We exploit the different combinations of look direction and variable incidence angle to examine Cassini Synthetic Aperture RADAR (SAR) data using the Principal Component Analysis (PCA) technique and high-resolution radiometry, as a tool to aid in the interpretation of geological and structural features. Look direction and variable incidence angle is of particular importance in the analysis of variance in the images, which aid in the perception and identification of geological and structural features, as extensively demonstrated in Earth and planetary examples. The PCA enhancement technique uses projected non-ortho-rectified SAR imagery in order to maintain the inherent differences in scattering and geometric properties due to the different look directions, while enhancing the geometry of surface features. The PC2 component provides a stereo view of the areas in which complex surface features and structural patterns can be enhanced and outlined. We focus on several areas of interest, in older and recently acquired flybys, in which evidence of geological and structural features can be enhanced and outlined in the PC1 and PC2 components. Results of this technique provide enhanced geometry and insights into the interpretation of the observed geological and structural features, thus allowing a better understanding towards the geology and tectonics on Titan.

How Structure Defines Affinity in Protein-Protein Interactions

PubMed Central

Erijman, Ariel; Rosenthal, Eran; Shifman, Julia M.

2014-01-01

Protein-protein interactions (PPI) in nature are conveyed by a multitude of binding modes involving various surfaces, secondary structure elements and intermolecular interactions. This diversity results in PPI binding affinities that span more than nine orders of magnitude. Several early studies attempted to correlate PPI binding affinities to various structure-derived features with limited success. The growing number of high-resolution structures, the appearance of more precise methods for measuring binding affinities and the development of new computational algorithms enable more thorough investigations in this direction. Here, we use a large dataset of PPI structures with the documented binding affinities to calculate a number of structure-based features that could potentially define binding energetics. We explore how well each calculated biophysical feature alone correlates with binding affinity and determine the features that could be used to distinguish between high-, medium- and low- affinity PPIs. Furthermore, we test how various combinations of features could be applied to predict binding affinity and observe a slow improvement in correlation as more features are incorporated into the equation. In addition, we observe a considerable improvement in predictions if we exclude from our analysis low-resolution and NMR structures, revealing the importance of capturing exact intermolecular interactions in our calculations. Our analysis should facilitate prediction of new interactions on the genome scale, better characterization of signaling networks and design of novel binding partners for various target proteins. PMID:25329579

PSSP-RFE: accurate prediction of protein structural class by recursive feature extraction from PSI-BLAST profile, physical-chemical property and functional annotations.

PubMed

Li, Liqi; Cui, Xiang; Yu, Sanjiu; Zhang, Yuan; Luo, Zhong; Yang, Hua; Zhou, Yue; Zheng, Xiaoqi

2014-01-01

Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific score matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets.

Medical X-ray Image Hierarchical Classification Using a Merging and Splitting Scheme in Feature Space.

PubMed

Fesharaki, Nooshin Jafari; Pourghassem, Hossein

2013-07-01

Due to the daily mass production and the widespread variation of medical X-ray images, it is necessary to classify these for searching and retrieving proposes, especially for content-based medical image retrieval systems. In this paper, a medical X-ray image hierarchical classification structure based on a novel merging and splitting scheme and using shape and texture features is proposed. In the first level of the proposed structure, to improve the classification performance, similar classes with regard to shape contents are grouped based on merging measures and shape features into the general overlapped classes. In the next levels of this structure, the overlapped classes split in smaller classes based on the classification performance of combination of shape and texture features or texture features only. Ultimately, in the last levels, this procedure is also continued forming all the classes, separately. Moreover, to optimize the feature vector in the proposed structure, we use orthogonal forward selection algorithm according to Mahalanobis class separability measure as a feature selection and reduction algorithm. In other words, according to the complexity and inter-class distance of each class, a sub-space of the feature space is selected in each level and then a supervised merging and splitting scheme is applied to form the hierarchical classification. The proposed structure is evaluated on a database consisting of 2158 medical X-ray images of 18 classes (IMAGECLEF 2005 database) and accuracy rate of 93.6% in the last level of the hierarchical structure for an 18-class classification problem is obtained.

Structure function analysis of two-scale Scalar Ramps. Part II: Coherent structure scaling and surface renewal applications

USDA-ARS?s Scientific Manuscript database

Structure functions are used to study the dissipation and inertial range scales of turbulent energy, to parameterize remote turbulence measurements, and to characterize ramp features in the turbulent field. The ramp features are associated with turbulent coherent structures, which dominate energy a...

Contour mapping of relic structures in the Precambrian basement of the Reelfoot rift, North American midcontinent

USGS Publications Warehouse

Dart, R.L.; Swolfs, H.S.

1998-01-01

A new contour map of the basement of the Reelfoot rift constructed from drill hole and seismic reflection data shows the general surface configuration as well as several major and minor structural features. The major features are two asymmetric intrarift basins, bounded by three structural highs, and the rift margins. The basins are oriented normal to the northeast trend of the rift. Two of the highs appear to be ridges of undetermined width that extend across the rift. The third high is an isolated dome or platform located between the basins. The minor features are three linear structures of low relief oriented subparallel to the trend of the rift. Two of these, located within the rift basins, may divide the rift basins into paired subbasins. These mapped features may be the remnants of initial extensional rifting, half graben faulting, and basement subsidence. The rift basins are interpreted as having formed as opposing half graben, and the structural highs are interpreted as having formed as associated accommodation zones. Some of these features appear to be reactivated seismogenic structures within the modem midcontinent compressional stress regime. A detailed knowledge of the geometries of the Reelfoot rift's basement features, therefore, is essential when evaluating their seismic risk potential.

Feature Selection Using Information Gain for Improved Structural-Based Alert Correlation

PubMed Central

Siraj, Maheyzah Md; Zainal, Anazida; Elshoush, Huwaida Tagelsir; Elhaj, Fatin

2016-01-01

Grouping and clustering alerts for intrusion detection based on the similarity of features is referred to as structurally base alert correlation and can discover a list of attack steps. Previous researchers selected different features and data sources manually based on their knowledge and experience, which lead to the less accurate identification of attack steps and inconsistent performance of clustering accuracy. Furthermore, the existing alert correlation systems deal with a huge amount of data that contains null values, incomplete information, and irrelevant features causing the analysis of the alerts to be tedious, time-consuming and error-prone. Therefore, this paper focuses on selecting accurate and significant features of alerts that are appropriate to represent the attack steps, thus, enhancing the structural-based alert correlation model. A two-tier feature selection method is proposed to obtain the significant features. The first tier aims at ranking the subset of features based on high information gain entropy in decreasing order. The‏ second tier extends additional features with a better discriminative ability than the initially ranked features. Performance analysis results show the significance of the selected features in terms of the clustering accuracy using 2000 DARPA intrusion detection scenario-specific dataset. PMID:27893821

UbSRD: The Ubiquitin Structural Relational Database.

PubMed

Harrison, Joseph S; Jacobs, Tim M; Houlihan, Kevin; Van Doorslaer, Koenraad; Kuhlman, Brian

2016-02-22

The structurally defined ubiquitin-like homology fold (UBL) can engage in several unique protein-protein interactions and many of these complexes have been characterized with high-resolution techniques. Using Rosetta's structural classification tools, we have created the Ubiquitin Structural Relational Database (UbSRD), an SQL database of features for all 509 UBL-containing structures in the PDB, allowing users to browse these structures by protein-protein interaction and providing a platform for quantitative analysis of structural features. We used UbSRD to define the recognition features of ubiquitin (UBQ) and SUMO observed in the PDB and the orientation of the UBQ tail while interacting with certain types of proteins. While some of the interaction surfaces on UBQ and SUMO overlap, each molecule has distinct features that aid in molecular discrimination. Additionally, we find that the UBQ tail is malleable and can adopt a variety of conformations upon binding. UbSRD is accessible as an online resource at rosettadesign.med.unc.edu/ubsrd. Copyright © 2015 Elsevier Ltd. All rights reserved.

Individual MRI and radiographic features of knee OA in subjects with unilateral knee pain: Health ABC study

PubMed Central

Javaid, MK; Kiran, A; Guermazi, A; Kwoh, K; Zaim, S; Carbone, L; Harris, T.; McCulloch, C.E.; Arden, NK; Lane, NE; Felson, D; Nevitt, M

2012-01-01

Strong associations between radiographic features of knee OA and pain have been demonstrated in persons with unilateral knee symptoms. Our objectives were to compare radiographic with MRI features of knee OA and assess the discrimination between painful and non-painful knees in persons with unilateral symptoms. 283 individuals with unilateral knee pain aged 71 to 80 years from Health ABC, a study of weight-related diseases and mobility, had bilateral knee radiographs, read for KL grade and individual radiographic features, and 1.5T MRIs, read using WORMS. The association of structural features with pain was assessed using a within-person case/control design and conditional logistic regression. Receiver operator characteristics (ROC) were then used to test the discriminatory performance of structural features. In conditional logistic analyses, knee pain was significantly associated with both radiographic (any JSN grade >=1: OR 3.20 (1.79 – 5.71) and MRI (any cartilage defect:>=2: OR 3.67 (1.49 – 9.04)) features. However, most subjects had MR detected osteophytes, cartilage and bone marrow lesions in both knees and no individual structural feature discriminated well between painful and non-painful knees using ROC. The best performing MRI feature (synovitis/effusion) was not significantly more informative than KL grade >=2 (p=0.42). In persons with unilateral knee pain, MR and radiographic features were associated with knee pain confirming an important role in the etiology of pain. However, no single MRI or radiographic finding performed well in discriminating painful from non-painful knees. Further work is needed to examine how structural and non-structural factors influence knee pain. PMID:22736267

Characteristics of circular features on comet 67P/Churyumov-Gerasimenko

NASA Astrophysics Data System (ADS)

Deller, J. F.; Güttler, C.; Tubiana, C.; Hofmann, M.; Sierks, H.

2017-09-01

Comet 67P/Churyumov-Gerasimenko shows a large variety of circular structures such as pits, elevated roundish features in Imhotep, and even a single occurrence of a plausible fresh impact crater. Imaging the pits in the Ma'at region, aiming to understand their structure and origin drove the design of the final descent trajectory of the Rosetta spacecraft. The high-resolution images obtained during the last mission phase allow us to study these pits as exemplary circular features. A complete catalogue of circular features gives us the possibility to compare and classify these structures systematically.

Reducing Sweeping Frequencies in Microwave NDT Employing Machine Learning Feature Selection

PubMed Central

Moomen, Abdelniser; Ali, Abdulbaset; Ramahi, Omar M.

2016-01-01

Nondestructive Testing (NDT) assessment of materials’ health condition is useful for classifying healthy from unhealthy structures or detecting flaws in metallic or dielectric structures. Performing structural health testing for coated/uncoated metallic or dielectric materials with the same testing equipment requires a testing method that can work on metallics and dielectrics such as microwave testing. Reducing complexity and expenses associated with current diagnostic practices of microwave NDT of structural health requires an effective and intelligent approach based on feature selection and classification techniques of machine learning. Current microwave NDT methods in general based on measuring variation in the S-matrix over the entire operating frequency ranges of the sensors. For instance, assessing the health of metallic structures using a microwave sensor depends on the reflection or/and transmission coefficient measurements as a function of the sweeping frequencies of the operating band. The aim of this work is reducing sweeping frequencies using machine learning feature selection techniques. By treating sweeping frequencies as features, the number of top important features can be identified, then only the most influential features (frequencies) are considered when building the microwave NDT equipment. The proposed method of reducing sweeping frequencies was validated experimentally using a waveguide sensor and a metallic plate with different cracks. Among the investigated feature selection techniques are information gain, gain ratio, relief, chi-squared. The effectiveness of the selected features were validated through performance evaluations of various classification models; namely, Nearest Neighbor, Neural Networks, Random Forest, and Support Vector Machine. Results showed good crack classification accuracy rates after employing feature selection algorithms. PMID:27104533

Nucleic Acid Database (NDB)

Science.gov Websites

the NDB archive or in the Non-Redundant list Advanced Search Search for structures based on structural features, chemical features, binding modes, citation and experimental information Featured Tools RNA 3D Motif Atlas, a representative collection of RNA 3D internal and hairpin loop motifs Non-redundant Lists

On the structural context and identification of enzyme catalytic residues.

PubMed

Chien, Yu-Tung; Huang, Shao-Wei

2013-01-01

Enzymes play important roles in most of the biological processes. Although only a small fraction of residues are directly involved in catalytic reactions, these catalytic residues are the most crucial parts in enzymes. The study of the fundamental and unique features of catalytic residues benefits the understanding of enzyme functions and catalytic mechanisms. In this work, we analyze the structural context of catalytic residues based on theoretical and experimental structure flexibility. The results show that catalytic residues have distinct structural features and context. Their neighboring residues, whether sequence or structure neighbors within specific range, are usually structurally more rigid than those of noncatalytic residues. The structural context feature is combined with support vector machine to identify catalytic residues from enzyme structure. The prediction results are better or comparable to those of recent structure-based prediction methods.

Evaluation of physical structural features on influencing enzymatic hydrolysis efficiency of micronized wood

Treesearch

Jinxue Jiang; Jinwu Wang; Xiao Zhang; Michael Wolcott

2016-01-01

Enzymatic hydrolysis of lignocellulosic biomass is highly dependent on the changes in structural features after pretreatment. Mechanical milling pretreatment is an effective approach to alter the physical structure of biomass and thus improve enzymatic hydrolysis. This study examined the influence of structural characteristics on the enzymatic hydrolysis of micronized...

TRANSAT-- method for detecting the conserved helices of functional RNA structures, including transient, pseudo-knotted and alternative structures.

PubMed

Wiebe, Nicholas J P; Meyer, Irmtraud M

2010-06-24

The prediction of functional RNA structures has attracted increased interest, as it allows us to study the potential functional roles of many genes. RNA structure prediction methods, however, assume that there is a unique functional RNA structure and also do not predict functional features required for in vivo folding. In order to understand how functional RNA structures form in vivo, we require sophisticated experiments or reliable prediction methods. So far, there exist only a few, experimentally validated transient RNA structures. On the computational side, there exist several computer programs which aim to predict the co-transcriptional folding pathway in vivo, but these make a range of simplifying assumptions and do not capture all features known to influence RNA folding in vivo. We want to investigate if evolutionarily related RNA genes fold in a similar way in vivo. To this end, we have developed a new computational method, Transat, which detects conserved helices of high statistical significance. We introduce the method, present a comprehensive performance evaluation and show that Transat is able to predict the structural features of known reference structures including pseudo-knotted ones as well as those of known alternative structural configurations. Transat can also identify unstructured sub-sequences bound by other molecules and provides evidence for new helices which may define folding pathways, supporting the notion that homologous RNA sequence not only assume a similar reference RNA structure, but also fold similarly. Finally, we show that the structural features predicted by Transat differ from those assuming thermodynamic equilibrium. Unlike the existing methods for predicting folding pathways, our method works in a comparative way. This has the disadvantage of not being able to predict features as function of time, but has the considerable advantage of highlighting conserved features and of not requiring a detailed knowledge of the cellular environment.

Structural health monitoring feature design by genetic programming

NASA Astrophysics Data System (ADS)

Harvey, Dustin Y.; Todd, Michael D.

2014-09-01

Structural health monitoring (SHM) systems provide real-time damage and performance information for civil, aerospace, and other high-capital or life-safety critical structures. Conventional data processing involves pre-processing and extraction of low-dimensional features from in situ time series measurements. The features are then input to a statistical pattern recognition algorithm to perform the relevant classification or regression task necessary to facilitate decisions by the SHM system. Traditional design of signal processing and feature extraction algorithms can be an expensive and time-consuming process requiring extensive system knowledge and domain expertise. Genetic programming, a heuristic program search method from evolutionary computation, was recently adapted by the authors to perform automated, data-driven design of signal processing and feature extraction algorithms for statistical pattern recognition applications. The proposed method, called Autofead, is particularly suitable to handle the challenges inherent in algorithm design for SHM problems where the manifestation of damage in structural response measurements is often unclear or unknown. Autofead mines a training database of response measurements to discover information-rich features specific to the problem at hand. This study provides experimental validation on three SHM applications including ultrasonic damage detection, bearing damage classification for rotating machinery, and vibration-based structural health monitoring. Performance comparisons with common feature choices for each problem area are provided demonstrating the versatility of Autofead to produce significant algorithm improvements on a wide range of problems.

Attentional Bias in Human Category Learning: The Case of Deep Learning.

PubMed

Hanson, Catherine; Caglar, Leyla Roskan; Hanson, Stephen José

2018-01-01

Category learning performance is influenced by both the nature of the category's structure and the way category features are processed during learning. Shepard (1964, 1987) showed that stimuli can have structures with features that are statistically uncorrelated (separable) or statistically correlated (integral) within categories. Humans find it much easier to learn categories having separable features, especially when attention to only a subset of relevant features is required, and harder to learn categories having integral features, which require consideration of all of the available features and integration of all the relevant category features satisfying the category rule (Garner, 1974). In contrast to humans, a single hidden layer backpropagation (BP) neural network has been shown to learn both separable and integral categories equally easily, independent of the category rule (Kruschke, 1993). This "failure" to replicate human category performance appeared to be strong evidence that connectionist networks were incapable of modeling human attentional bias. We tested the presumed limitations of attentional bias in networks in two ways: (1) by having networks learn categories with exemplars that have high feature complexity in contrast to the low dimensional stimuli previously used, and (2) by investigating whether a Deep Learning (DL) network, which has demonstrated humanlike performance in many different kinds of tasks (language translation, autonomous driving, etc.), would display human-like attentional bias during category learning. We were able to show a number of interesting results. First, we replicated the failure of BP to differentially process integral and separable category structures when low dimensional stimuli are used (Garner, 1974; Kruschke, 1993). Second, we show that using the same low dimensional stimuli, Deep Learning (DL), unlike BP but similar to humans, learns separable category structures more quickly than integral category structures. Third, we show that even BP can exhibit human like learning differences between integral and separable category structures when high dimensional stimuli (face exemplars) are used. We conclude, after visualizing the hidden unit representations, that DL appears to extend initial learning due to feature development thereby reducing destructive feature competition by incrementally refining feature detectors throughout later layers until a tipping point (in terms of error) is reached resulting in rapid asymptotic learning.

SU-E-J-264: Using Magnetic Resonance Imaging-Derived Features to Quantify Radiotherapy-Induced Normal Tissue Morbidity

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

Thor, M; Tyagi, N; Deasy, J

2015-06-15

Purpose: The aim of this study was to explore the use of Magnetic Resonance Imaging (MRI)-derived features as indicators of Radiotherapy (RT)-induced normal tissue morbidity. We also investigate the relationship between these features and RT dose in four critical structures. Methods: We demonstrate our approach for four patients treated with RT for base of tongue cancer in 2005–2007. For each patient, two MRI scans (T1-weighted pre (T1pre) and post (T1post) gadolinium contrast-enhancement) were acquired within the first six months after RT. The assessed morbidity endpoint observed in 2/4 patients was Grade 2+ CTCAEv.3 trismus. Four ipsilateral masticatory-related structures (masseter, lateralmore » and medial pterygoid, and the temporal muscles) were delineated on both T1pre and T1post and these scans were co-registered to the treatment planning CT using a deformable demons algorithm. For each structure, the maximum and mean RT dose, and six MRI-derived features (the second order texture features entropy and homogeneity, and the first order mean, median, kurtosis, and skewness) were extracted and compared structure-wise between patients with and without trismus. All MRI-derived features were calculated as the difference between T1pre and T1post, ΔS. Results: For 5/6 features and all structures, ΔS diverged between trismus and non-trismus patients particularly for the masseter, lateral pterygoid, and temporal muscles using the kurtosis feature (−0.2 vs. 6.4 for lateral pterygoid). Both the maximum and mean RT dose in all four muscles were higher amongst the trismus patients (with the maximum dose being up to 25 Gy higher). Conclusion: Using MRI-derived features to quantify RT-induced normal tissue complications is feasible. We showed that several features are different between patients with and without morbidity and that the RT dose in all investigated structures are higher amongst patients with morbidity. MRI-derived features, therefore, has the potential to improve predictions of normal tissue morbidity.« less

Labyrinths, columns and cavities: new internal features of pollen grain walls in the Acanthaceae detected by FIB-SEM.

PubMed

House, Alisoun; Balkwill, Kevin

2016-03-01

External pollen grain morphology has been widely used in the taxonomy and systematics of flowering plants, especially the Acanthaceae which are noted for pollen diversity. However internal pollen wall features have received far less attention due to the difficulty of examining the wall structure. Advancing technology in the field of microscopy has made it possible, with the use of a focused ion beam-scanning electron microscope (FIB-SEM), to view the structure of pollen grain walls in far greater detail and in three dimensions. In this study the wall structures of 13 species from the Acanthaceae were investigated for features of potential systematic relevance. FIB-SEM was applied to obtain precise cross sections of pollen grains at selected positions for examining the wall ultrastructure. Exploratory studies of the exine have thus far identified five basic structural types. The investigations also show that similar external pollen wall features may have a distinctly different internal structure. FIB-SEM studies have revealed diverse internal pollen wall features which may now be investigated for their systematic and functional significance.

Feature Grouping and Selection Over an Undirected Graph.

PubMed

Yang, Sen; Yuan, Lei; Lai, Ying-Cheng; Shen, Xiaotong; Wonka, Peter; Ye, Jieping

2012-01-01

High-dimensional regression/classification continues to be an important and challenging problem, especially when features are highly correlated. Feature selection, combined with additional structure information on the features has been considered to be promising in promoting regression/classification performance. Graph-guided fused lasso (GFlasso) has recently been proposed to facilitate feature selection and graph structure exploitation, when features exhibit certain graph structures. However, the formulation in GFlasso relies on pairwise sample correlations to perform feature grouping, which could introduce additional estimation bias. In this paper, we propose three new feature grouping and selection methods to resolve this issue. The first method employs a convex function to penalize the pairwise l ∞ norm of connected regression/classification coefficients, achieving simultaneous feature grouping and selection. The second method improves the first one by utilizing a non-convex function to reduce the estimation bias. The third one is the extension of the second method using a truncated l 1 regularization to further reduce the estimation bias. The proposed methods combine feature grouping and feature selection to enhance estimation accuracy. We employ the alternating direction method of multipliers (ADMM) and difference of convex functions (DC) programming to solve the proposed formulations. Our experimental results on synthetic data and two real datasets demonstrate the effectiveness of the proposed methods.

Texture feature extraction based on a uniformity estimation method for local brightness and structure in chest CT images.

PubMed

Peng, Shao-Hu; Kim, Deok-Hwan; Lee, Seok-Lyong; Lim, Myung-Kwan

2010-01-01

Texture feature is one of most important feature analysis methods in the computer-aided diagnosis (CAD) systems for disease diagnosis. In this paper, we propose a Uniformity Estimation Method (UEM) for local brightness and structure to detect the pathological change in the chest CT images. Based on the characteristics of the chest CT images, we extract texture features by proposing an extension of rotation invariant LBP (ELBP(riu4)) and the gradient orientation difference so as to represent a uniform pattern of the brightness and structure in the image. The utilization of the ELBP(riu4) and the gradient orientation difference allows us to extract rotation invariant texture features in multiple directions. Beyond this, we propose to employ the integral image technique to speed up the texture feature computation of the spatial gray level dependent method (SGLDM). Copyright © 2010 Elsevier Ltd. All rights reserved.

Some volcanic and structural features of Mare Serenitatis. [as determined by low angle lighting in Apollo 17 photography

NASA Technical Reports Server (NTRS)

Bryan, W. B.; Adams, M.

1973-01-01

Relationships between volcanic and structural features along the southern edge of Mare Serenitatis as determined from low angle lighting in Apollo 17 photographs are discussed. Observational summaries are given of: (1) contact relations between the dark border material and the central mare fill, (2) a late stage lava flow with associated cinder cones, and (3) certain structural features related to the development of the mare basin and its associated volcanic landforms. A chronologic summary is given of volcanic and structural events believed to be critical to understanding the development of Mare Serenitatis.

Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis.

PubMed

Sun, Wenqing; Zheng, Bin; Qian, Wei

2017-10-01

This study aimed to analyze the ability of extracting automatically generated features using deep structured algorithms in lung nodule CT image diagnosis, and compare its performance with traditional computer aided diagnosis (CADx) systems using hand-crafted features. All of the 1018 cases were acquired from Lung Image Database Consortium (LIDC) public lung cancer database. The nodules were segmented according to four radiologists' markings, and 13,668 samples were generated by rotating every slice of nodule images. Three multichannel ROI based deep structured algorithms were designed and implemented in this study: convolutional neural network (CNN), deep belief network (DBN), and stacked denoising autoencoder (SDAE). For the comparison purpose, we also implemented a CADx system using hand-crafted features including density features, texture features and morphological features. The performance of every scheme was evaluated by using a 10-fold cross-validation method and an assessment index of the area under the receiver operating characteristic curve (AUC). The observed highest area under the curve (AUC) was 0.899±0.018 achieved by CNN, which was significantly higher than traditional CADx with the AUC=0.848±0.026. The results from DBN was also slightly higher than CADx, while SDAE was slightly lower. By visualizing the automatic generated features, we found some meaningful detectors like curvy stroke detectors from deep structured schemes. The study results showed the deep structured algorithms with automatically generated features can achieve desirable performance in lung nodule diagnosis. With well-tuned parameters and large enough dataset, the deep learning algorithms can have better performance than current popular CADx. We believe the deep learning algorithms with similar data preprocessing procedure can be used in other medical image analysis areas as well. Copyright © 2017. Published by Elsevier Ltd.

Structure function analysis of two-scale Scalar Ramps. Part I: Theory and Modeling

USDA-ARS?s Scientific Manuscript database

Structure functions are used to study the dissipation and inertial range scales of turbulent energy, to parameterize remote turbulence measurements, and to characterize ramp features in the turbulent field. The ramp features are associated with turbulent coherent structures, which dominate energy an...

Rejoice in unexpected gifts from parrots and butterflies

NASA Astrophysics Data System (ADS)

Lakhtakia, Akhlesh

2016-04-01

New biological structures usually evolve from gradual modifications of old structures. Sometimes, biological structures contain hidden features, possibly vestigial. In addition to learning about functionalities, mechanisms, and structures readily apparent in nature, one must be alive to hidden features that could be useful. This aspect of engineered biomimicry is exemplified by two optical structures of psittacine and lepidopteran provenances. In both examples, a schemochrome is hidden by pigments.

Detection of fibrils associated with Rickettsia rickettsii.

PubMed

Todd, W J; Burgdorfer, W; Wray, G P

1983-09-01

The ultrastructural appearance of the "halozone" formed at the interface between the spotted fever agent Rickettsia rickettsii and the cytoplasm of persistently infected cultured vole cells (Microtus pennsylvanicus) was studied by transmission electron microscopy. In sections of epoxy-embedded specimens stained with uranyl acetate and lead citrate, the halozone appeared clear and devoid of ultrastructural features. However, when unembedded preparations of whole infected cells were examined at 1,000 kV, fine structural features were observed within the halozone. These features, associated with the rickettsial outer membrane, were more clearly detectable when the infected cells were extracted with the detergent Triton X-100 before fixation. Under such conditions, long extensions of the rickettsial outer membrane, microfilament-like structures attached to that membrane, and extensive attachments between adjacent rickettsiae were seen. The fine structural features within the rickettsial halozone were also seen at 75 kV when unembedded sections were prepared from polyethylene glycol-embedded specimens. Thus, epoxy-embedding medium obscures the fine structural features within the halozone surrounding the rickettsiae in infected cells.

Detection of fibrils associated with Rickettsia rickettsii.

PubMed Central

Todd, W J; Burgdorfer, W; Wray, G P

1983-01-01

The ultrastructural appearance of the "halozone" formed at the interface between the spotted fever agent Rickettsia rickettsii and the cytoplasm of persistently infected cultured vole cells (Microtus pennsylvanicus) was studied by transmission electron microscopy. In sections of epoxy-embedded specimens stained with uranyl acetate and lead citrate, the halozone appeared clear and devoid of ultrastructural features. However, when unembedded preparations of whole infected cells were examined at 1,000 kV, fine structural features were observed within the halozone. These features, associated with the rickettsial outer membrane, were more clearly detectable when the infected cells were extracted with the detergent Triton X-100 before fixation. Under such conditions, long extensions of the rickettsial outer membrane, microfilament-like structures attached to that membrane, and extensive attachments between adjacent rickettsiae were seen. The fine structural features within the rickettsial halozone were also seen at 75 kV when unembedded sections were prepared from polyethylene glycol-embedded specimens. Thus, epoxy-embedding medium obscures the fine structural features within the halozone surrounding the rickettsiae in infected cells. Images PMID:6411620

Local kernel nonparametric discriminant analysis for adaptive extraction of complex structures

NASA Astrophysics Data System (ADS)

Li, Quanbao; Wei, Fajie; Zhou, Shenghan

2017-05-01

The linear discriminant analysis (LDA) is one of popular means for linear feature extraction. It usually performs well when the global data structure is consistent with the local data structure. Other frequently-used approaches of feature extraction usually require linear, independence, or large sample condition. However, in real world applications, these assumptions are not always satisfied or cannot be tested. In this paper, we introduce an adaptive method, local kernel nonparametric discriminant analysis (LKNDA), which integrates conventional discriminant analysis with nonparametric statistics. LKNDA is adept in identifying both complex nonlinear structures and the ad hoc rule. Six simulation cases demonstrate that LKNDA have both parametric and nonparametric algorithm advantages and higher classification accuracy. Quartic unilateral kernel function may provide better robustness of prediction than other functions. LKNDA gives an alternative solution for discriminant cases of complex nonlinear feature extraction or unknown feature extraction. At last, the application of LKNDA in the complex feature extraction of financial market activities is proposed.

Innovations in individual feature history management - The significance of feature-based temporal model

USGS Publications Warehouse

Choi, J.; Seong, J.C.; Kim, B.; Usery, E.L.

2008-01-01

A feature relies on three dimensions (space, theme, and time) for its representation. Even though spatiotemporal models have been proposed, they have principally focused on the spatial changes of a feature. In this paper, a feature-based temporal model is proposed to represent the changes of both space and theme independently. The proposed model modifies the ISO's temporal schema and adds new explicit temporal relationship structure that stores temporal topological relationship with the ISO's temporal primitives of a feature in order to keep track feature history. The explicit temporal relationship can enhance query performance on feature history by removing topological comparison during query process. Further, a prototype system has been developed to test a proposed feature-based temporal model by querying land parcel history in Athens, Georgia. The result of temporal query on individual feature history shows the efficiency of the explicit temporal relationship structure. ?? Springer Science+Business Media, LLC 2007.

Structural and Organisational Features of Sensorimotor Intelligence among Retarded Infants and Toddlers.

ERIC Educational Resources Information Center

Dunst, C. J.; And Others

1981-01-01

The structural features of sensorimotor intelligence were assessed among three groups of retarded infants and toddlers. Hierarchical cluster analysis (HCA) was performed on two measures of relationship (stage congruence and intercorrelations). The potential utility of HCA for studying Piaget's "structure d'ensemble" stage criteria is…

Does skull shape mediate the relationship between objective features and subjective impressions about the face?

PubMed

Marečková, Klára; Chakravarty, M Mallar; Huang, Mei; Lawrence, Claire; Leonard, Gabriel; Perron, Michel; Pike, Bruce G; Richer, Louis; Veillette, Suzanne; Pausova, Zdenka; Paus, Tomáš

2013-10-01

In our previous work, we described facial features associated with a successful recognition of the sex of the face (Marečková et al., 2011). These features were based on landmarks placed on the surface of faces reconstructed from magnetic resonance (MR) images; their position was therefore influenced by both soft tissue (fat and muscle) and bone structure of the skull. Here, we ask whether bone structure has dissociable influences on observers' identification of the sex of the face. To answer this question, we used a novel method of studying skull morphology using MR images and explored the relationship between skull features, facial features, and sex recognition in a large sample of adolescents (n=876; including 475 adolescents from our original report). To determine whether skull features mediate the relationship between facial features and identification accuracy, we performed mediation analysis using bootstrapping. In males, skull features mediated fully the relationship between facial features and sex judgments. In females, the skull mediated this relationship only after adjusting facial features for the amount of body fat (estimated with bioimpedance). While body fat had a very slight positive influence on correct sex judgments about male faces, there was a robust negative influence of body fat on the correct sex judgments about female faces. Overall, these results suggest that craniofacial bone structure is essential for correct sex judgments about a male face. In females, body fat influences negatively the accuracy of sex judgments, and craniofacial bone structure alone cannot explain the relationship between facial features and identification of a face as female. Copyright © 2013 Elsevier Inc. All rights reserved.

External and internal structure of weevils (Insecta: Coleoptera) investigated with phase-contrast X-ray imaging

NASA Astrophysics Data System (ADS)

Hönnicke, M. G.; Cusatis, C.; Rigon, L.; Menk, R.-H.; Arfelli, F.; Foerster, L. A.; Rosado-Neto, G. H.

2010-08-01

Weevils (Coleoptera: Curculionidae) are identified by the external structure (dorsal, ventral and lateral features) and also by internal structure. The genitalia can be used to distinguish the sex and to identify the insects when the external structure appears identical. For this purpose, a destructive dissecting microscopy procedure is usually employed. In this paper, phase contrast X-ray imaging (radiography and tomography) is employed to investigate the internal structure (genitalia) of two entire species of weevils that presents very similar external structures ( Sitophilus oryzae and Sitophilus zeamais). The detection of features, which looks like the genital structure, shows that such non-destructive technique could be used as an alternative method for identification of insects. This method is especially useful in examining the internal features of precious species from museum collections, as already described in the recent literature.

SCPRED: accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences.

PubMed

Kurgan, Lukasz; Cios, Krzysztof; Chen, Ke

2008-05-01

Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction. SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors. The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the design of the features, which are capable of separating the structural classes in spite of their low dimensionality. We also demonstrate that the SCPRED's predictions can be successfully used as a post-processing filter to improve performance of modern fold classification methods.

SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences

PubMed Central

Kurgan, Lukasz; Cios, Krzysztof; Chen, Ke

2008-01-01

Background Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight-zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight-zone pairwise similarity with sequences used for the prediction. Results SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight-zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors. Conclusion The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the design of the features, which are capable of separating the structural classes in spite of their low dimensionality. We also demonstrate that the SCPRED's predictions can be successfully used as a post-processing filter to improve performance of modern fold classification methods. PMID:18452616

Origin of the Hadži ABC structure: An ab initio study

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

Van Hoozen, Brian L.; Petersen, Poul B.

2015-11-14

Medium and strong hydrogen bonds are well known to give rise to broad features in the vibrational spectrum often spanning several hundred wavenumbers. In some cases, these features can span over 1000 cm{sup −1} and even contain multiple broad peaks. One class of strongly hydrogen-bonded dimers that includes many different phosphinic, phosphoric, sulfinic, and selenic acid homodimers exhibits a three-peaked structure over 1500 cm{sup −1} broad. This unusual feature is often referred to as the Hadži ABC structure. The origin of this feature has been debated since its discovery in the 1950s. Only a couple of theoretical studies have attemptedmore » to interpret the origin of this feature; however, no previous study has been able to reproduce this feature from first principles. Here, we present the first ab initio calculation of the Hadži ABC structure. Using a reduced dimensionality calculation that includes four vibrational modes, we are able to reproduce the three-peak structure and much of the broadness of the feature. Our results indicate that Fermi resonances of the in-plane bend, out-of-plane bend, and combination of these bends play significant roles in explaining this feature. Much of the broadness of the feature and the ability of the OH stretch mode to couple with many overtone bending modes are captured by including an adiabatically separated dimer stretch mode in the model. This mode modulates the distance between the monomer units and accordingly the strength of the hydrogen-bonds causing the OH stretch frequency to shift from 2000 to 3000 cm{sup −1}. Using this model, we were also able to reproduce the vibrational spectrum of the deuterated isotopologue which consists of a single 500 cm{sup −1} broad feature. Whereas previous empirical studies have asserted that Fermi resonances contribute very little to this feature, our study indicates that while not appearing as a separate peak, a Fermi resonance of the in-plane bend contributes substantially to the feature.« less

Face recognition algorithm using extended vector quantization histogram features.

PubMed

Yan, Yan; Lee, Feifei; Wu, Xueqian; Chen, Qiu

2018-01-01

In this paper, we propose a face recognition algorithm based on a combination of vector quantization (VQ) and Markov stationary features (MSF). The VQ algorithm has been shown to be an effective method for generating features; it extracts a codevector histogram as a facial feature representation for face recognition. Still, the VQ histogram features are unable to convey spatial structural information, which to some extent limits their usefulness in discrimination. To alleviate this limitation of VQ histograms, we utilize Markov stationary features (MSF) to extend the VQ histogram-based features so as to add spatial structural information. We demonstrate the effectiveness of our proposed algorithm by achieving recognition results superior to those of several state-of-the-art methods on publicly available face databases.

Therapeutic approaches against common structural features of toxic oligomers shared by multiple amyloidogenic proteins.

PubMed

Guerrero-Muñoz, Marcos J; Castillo-Carranza, Diana L; Kayed, Rakez

2014-04-15

Impaired proteostasis is one of the main features of all amyloid diseases, which are associated with the formation of insoluble aggregates from amyloidogenic proteins. The aggregation process can be caused by overproduction or poor clearance of these proteins. However, numerous reports suggest that amyloid oligomers are the most toxic species, rather than insoluble fibrillar material, in Alzheimer's, Parkinson's, and Prion diseases, among others. Although the exact protein that aggregates varies between amyloid disorders, they all share common structural features that can be used as therapeutic targets. In this review, we focus on therapeutic approaches against shared features of toxic oligomeric structures and future directions. Copyright © 2014 Elsevier Inc. All rights reserved.

Computing and visualizing time-varying merge trees for high-dimensional data

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

Oesterling, Patrick; Heine, Christian; Weber, Gunther H.

2017-06-03

We introduce a new method that identifies and tracks features in arbitrary dimensions using the merge tree -- a structure for identifying topological features based on thresholding in scalar fields. This method analyzes the evolution of features of the function by tracking changes in the merge tree and relates features by matching subtrees between consecutive time steps. Using the time-varying merge tree, we present a structural visualization of the changing function that illustrates both features and their temporal evolution. We demonstrate the utility of our approach by applying it to temporal cluster analysis of high-dimensional point clouds.

A sampling-based method for ranking protein structural models by integrating multiple scores and features.

PubMed

Shi, Xiaohu; Zhang, Jingfen; He, Zhiquan; Shang, Yi; Xu, Dong

2011-09-01

One of the major challenges in protein tertiary structure prediction is structure quality assessment. In many cases, protein structure prediction tools generate good structural models, but fail to select the best models from a huge number of candidates as the final output. In this study, we developed a sampling-based machine-learning method to rank protein structural models by integrating multiple scores and features. First, features such as predicted secondary structure, solvent accessibility and residue-residue contact information are integrated by two Radial Basis Function (RBF) models trained from different datasets. Then, the two RBF scores and five selected scoring functions developed by others, i.e., Opus-CA, Opus-PSP, DFIRE, RAPDF, and Cheng Score are synthesized by a sampling method. At last, another integrated RBF model ranks the structural models according to the features of sampling distribution. We tested the proposed method by using two different datasets, including the CASP server prediction models of all CASP8 targets and a set of models generated by our in-house software MUFOLD. The test result shows that our method outperforms any individual scoring function on both best model selection, and overall correlation between the predicted ranking and the actual ranking of structural quality.

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

PubMed Central

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

2018-01-01

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

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

PubMed

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

2018-01-01

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

regSNPs-splicing: a tool for prioritizing synonymous single-nucleotide substitution.

PubMed

Zhang, Xinjun; Li, Meng; Lin, Hai; Rao, Xi; Feng, Weixing; Yang, Yuedong; Mort, Matthew; Cooper, David N; Wang, Yue; Wang, Yadong; Wells, Clark; Zhou, Yaoqi; Liu, Yunlong

2017-09-01

While synonymous single-nucleotide variants (sSNVs) have largely been unstudied, since they do not alter protein sequence, mounting evidence suggests that they may affect RNA conformation, splicing, and the stability of nascent-mRNAs to promote various diseases. Accurately prioritizing deleterious sSNVs from a pool of neutral ones can significantly improve our ability of selecting functional genetic variants identified from various genome-sequencing projects, and, therefore, advance our understanding of disease etiology. In this study, we develop a computational algorithm to prioritize sSNVs based on their impact on mRNA splicing and protein function. In addition to genomic features that potentially affect splicing regulation, our proposed algorithm also includes dozens structural features that characterize the functions of alternatively spliced exons on protein function. Our systematical evaluation on thousands of sSNVs suggests that several structural features, including intrinsic disorder protein scores, solvent accessible surface areas, protein secondary structures, and known and predicted protein family domains, show significant differences between disease-causing and neutral sSNVs. Our result suggests that the protein structure features offer an added dimension of information while distinguishing disease-causing and neutral synonymous variants. The inclusion of structural features increases the predictive accuracy for functional sSNV prioritization.

Seeing Deep Structure from the Interactions of Surface Features

ERIC Educational Resources Information Center

Chi, Michelene T. H.; VanLehn, Kurt A.

2012-01-01

Transfer is typically thought of as requiring individuals to "see" what is the same in the deep structure between a new target problem and a previously encountered source problem, even though the surface features may be dissimilar. We propose that experts can "see" the deep structure by considering the first-order interactions…

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.

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

Baghram, Shant; Abolhasani, Ali Akbar; Firouzjahi, Hassan

We study the predictions of anomalous inflationary models on the abundance of structures in large scale structure observations. The anomalous features encoded in primordial curvature perturbation power spectrum are (a): localized feature in momentum space, (b): hemispherical asymmetry and (c): statistical anisotropies. We present a model-independent expression relating the number density of structures to the changes in the matter density variance. Models with localized feature can alleviate the tension between observations and numerical simulations of cold dark matter structures on galactic scales as a possible solution to the missing satellite problem. In models with hemispherical asymmetry we show that themore » abundance of structures becomes asymmetric depending on the direction of observation to sky. In addition, we study the effects of scale-dependent dipole amplitude on the abundance of structures. Using the quasars data and adopting the power-law scaling k{sup n{sub A}-1} for the amplitude of dipole we find the upper bound n{sub A} < 0.6 for the spectral index of the dipole asymmetry. In all cases there is a critical mass scale M{sub c} in which for M M{sub c}) the enhancement in variance induced from anomalous feature decreases (increases) the abundance of dark matter structures in Universe.« less

Feature-Based Morphometry: Discovering Group-related Anatomical Patterns

PubMed Central

Toews, Matthew; Wells, William; Collins, D. Louis; Arbel, Tal

2015-01-01

This paper presents feature-based morphometry (FBM), a new, fully data-driven technique for discovering patterns of group-related anatomical structure in volumetric imagery. In contrast to most morphometry methods which assume one-to-one correspondence between subjects, FBM explicitly aims to identify distinctive anatomical patterns that may only be present in subsets of subjects, due to disease or anatomical variability. The image is modeled as a collage of generic, localized image features that need not be present in all subjects. Scale-space theory is applied to analyze image features at the characteristic scale of underlying anatomical structures, instead of at arbitrary scales such as global or voxel-level. A probabilistic model describes features in terms of their appearance, geometry, and relationship to subject groups, and is automatically learned from a set of subject images and group labels. Features resulting from learning correspond to group-related anatomical structures that can potentially be used as image biomarkers of disease or as a basis for computer-aided diagnosis. The relationship between features and groups is quantified by the likelihood of feature occurrence within a specific group vs. the rest of the population, and feature significance is quantified in terms of the false discovery rate. Experiments validate FBM clinically in the analysis of normal (NC) and Alzheimer's (AD) brain images using the freely available OASIS database. FBM automatically identifies known structural differences between NC and AD subjects in a fully data-driven fashion, and an equal error classification rate of 0.80 is achieved for subjects aged 60-80 years exhibiting mild AD (CDR=1). PMID:19853047

Prediction of Protein-Protein Interaction Sites by Random Forest Algorithm with mRMR and IFS

PubMed Central

Li, Bi-Qing; Feng, Kai-Yan; Chen, Lei; Huang, Tao; Cai, Yu-Dong

2012-01-01

Prediction of protein-protein interaction (PPI) sites is one of the most challenging problems in computational biology. Although great progress has been made by employing various machine learning approaches with numerous characteristic features, the problem is still far from being solved. In this study, we developed a novel predictor based on Random Forest (RF) algorithm with the Minimum Redundancy Maximal Relevance (mRMR) method followed by incremental feature selection (IFS). We incorporated features of physicochemical/biochemical properties, sequence conservation, residual disorder, secondary structure and solvent accessibility. We also included five 3D structural features to predict protein-protein interaction sites and achieved an overall accuracy of 0.672997 and MCC of 0.347977. Feature analysis showed that 3D structural features such as Depth Index (DPX) and surface curvature (SC) contributed most to the prediction of protein-protein interaction sites. It was also shown via site-specific feature analysis that the features of individual residues from PPI sites contribute most to the determination of protein-protein interaction sites. It is anticipated that our prediction method will become a useful tool for identifying PPI sites, and that the feature analysis described in this paper will provide useful insights into the mechanisms of interaction. PMID:22937126

Image segmentation using association rule features.

PubMed

Rushing, John A; Ranganath, Heggere; Hinke, Thomas H; Graves, Sara J

2002-01-01

A new type of texture feature based on association rules is described. Association rules have been used in applications such as market basket analysis to capture relationships present among items in large data sets. It is shown that association rules can be adapted to capture frequently occurring local structures in images. The frequency of occurrence of these structures can be used to characterize texture. Methods for segmentation of textured images based on association rule features are described. Simulation results using images consisting of man made and natural textures show that association rule features perform well compared to other widely used texture features. Association rule features are used to detect cumulus cloud fields in GOES satellite images and are found to achieve higher accuracy than other statistical texture features for this problem.

Changes in quantitative 3D shape features of the optic nerve head associated with age

NASA Astrophysics Data System (ADS)

Christopher, Mark; Tang, Li; Fingert, John H.; Scheetz, Todd E.; Abramoff, Michael D.

2013-02-01

Optic nerve head (ONH) structure is an important biological feature of the eye used by clinicians to diagnose and monitor progression of diseases such as glaucoma. ONH structure is commonly examined using stereo fundus imaging or optical coherence tomography. Stereo fundus imaging provides stereo views of the ONH that retain 3D information useful for characterizing structure. In order to quantify 3D ONH structure, we applied a stereo correspondence algorithm to a set of stereo fundus images. Using these quantitative 3D ONH structure measurements, eigen structures were derived using principal component analysis from stereo images of 565 subjects from the Ocular Hypertension Treatment Study (OHTS). To evaluate the usefulness of the eigen structures, we explored associations with the demographic variables age, gender, and race. Using regression analysis, the eigen structures were found to have significant (p < 0.05) associations with both age and race after Bonferroni correction. In addition, classifiers were constructed to predict the demographic variables based solely on the eigen structures. These classifiers achieved an area under receiver operating characteristic curve of 0.62 in predicting a binary age variable, 0.52 in predicting gender, and 0.67 in predicting race. The use of objective, quantitative features or eigen structures can reveal hidden relationships between ONH structure and demographics. The use of these features could similarly allow specific aspects of ONH structure to be isolated and associated with the diagnosis of glaucoma, disease progression and outcomes, and genetic factors.

SU-E-QI-16: Reproducibility of Computed Tomography Quantitative Structural Features Using the FDA Thoracic Phantom Image Database

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

Budzevich, M; Grove, O; Balagurunathan, Y

Purpose: To assess the reproducibility of quantitative structural features using images from the computed tomography thoracic FDA phantom database under different scanning conditions. Methods: Development of quantitative image features to describe lesion shape and size, beyond conventional RECIST measures, is an evolving area of research in need of benchmarking standards. Gavrielides et al. (2010) scanned a FDA-developed thoracic phantom with nodules of various Hounsfield units (HU) values, shapes and sizes close to vascular structures using several scanners and varying scanning conditions/parameters; these images are in the public domain. We tested six structural features, namely, Convexity, Perimeter, Major Axis, Minor Axis,more » Extent Mean and Eccentricity, to characterize lung nodules. Convexity measures lesion irregularity referenced to a convex surface. Previously, we showed it to have prognostic value in lung adenocarcinoma. The above metrics and RECIST measures were evaluated on three spiculated (8mm/-300HU, 12mm/+30HU and 15mm/+30HU) and two non-spiculated (8mm/+100HU and 10mm/+100HU) nodules (from layout 2) imaged at three different mAs values: 25, 100 and 200 mAs; on a Phillips scanner (16-slice Mx8000-IDT; 3mm slice thickness). The nodules were segmented semi-automatically using a commercial software tool; the same HU range was used for all nodules. Results: Analysis showed convexity having the lowest maximum coefficient of variation (MCV): 1.1% and 0.6% for spiculated and non-spiculated nodules, respectively, much lower compared to RECIST Major and Minor axes whose MCV were 10.1% and 13.4% for spiculated, and 1.9% and 2.3% for non-spiculated nodules, respectively, across the various mAs. MCVs were consistently larger for speculated nodules. In general, the dependence of structural features on mAs (noise) was low. Conclusion: The FDA phantom CT database may be used for benchmarking of structural features for various scanners and scanning conditions; we used only a small fraction of available data. Our feature convexity outperformed other structural features including RECIST measures.« less

A Feature and Algorithm Selection Method for Improving the Prediction of Protein Structural Class.

PubMed

Ni, Qianwu; Chen, Lei

2017-01-01

Correct prediction of protein structural class is beneficial to investigation on protein functions, regulations and interactions. In recent years, several computational methods have been proposed in this regard. However, based on various features, it is still a great challenge to select proper classification algorithm and extract essential features to participate in classification. In this study, a feature and algorithm selection method was presented for improving the accuracy of protein structural class prediction. The amino acid compositions and physiochemical features were adopted to represent features and thirty-eight machine learning algorithms collected in Weka were employed. All features were first analyzed by a feature selection method, minimum redundancy maximum relevance (mRMR), producing a feature list. Then, several feature sets were constructed by adding features in the list one by one. For each feature set, thirtyeight algorithms were executed on a dataset, in which proteins were represented by features in the set. The predicted classes yielded by these algorithms and true class of each protein were collected to construct a dataset, which were analyzed by mRMR method, yielding an algorithm list. From the algorithm list, the algorithm was taken one by one to build an ensemble prediction model. Finally, we selected the ensemble prediction model with the best performance as the optimal ensemble prediction model. Experimental results indicate that the constructed model is much superior to models using single algorithm and other models that only adopt feature selection procedure or algorithm selection procedure. The feature selection procedure or algorithm selection procedure are really helpful for building an ensemble prediction model that can yield a better performance. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

The structure and rainfall features of Tropical Cyclone Rammasun (2002)

NASA Astrophysics Data System (ADS)

Ma, Leiming; Duan, Yihong; Zhu, Yongti

2004-12-01

Tropical Rainfall Measuring Mission (TRMM) data [TRMM Microwave Imager/Precipitation Radar/Visible and Infrared Scanner (TMI/PR/VIRS)] and a numerical model are used to investigate the structure and rainfall features of Tropical Cyclone (TC) Rammasun (2002). Based on the analysis of TRMM data, which are diagnosed together with NCEP/AVN [Aviation (global model)] analysis data, some typical features of TC structure and rainfall are preliminary discovered. Since the limitations of TRMM data are considered for their time resolution and coverage, the world observed by TRMM at several moments cannot be taken as the representation of the whole period of the TC lifecycle, therefore the picture should be reproduced by a numerical model of high quality. To better understand the structure and rainfall features of TC Rammasun, a numerical simulation is carried out with mesoscale model MM5 in which the validations have been made with the data of TRMM and NCEP/AVN analysis.

Influence of culture medium growth variables on Ganoderma lucidum exopolysaccharides structural features.

PubMed

Fraga, Irene; Coutinho, João; Bezerra, Rui M; Dias, Albino A; Marques, Guilhermina; Nunes, Fernando M

2014-10-13

In this work the effect of carbon and nitrogen levels and initial pH of the wheat extract culture medium of submerged culture of Ganoderma lucidum on the amount, purity and structural features of exopolysaccharides (EPS) were studied. A low peptone level (1.65 g L(-1)) favored mycelium biomass, EPS purity, but a higher supply of peptone (4.80 g L(-1)) is needed for maximum EPS production. The carbohydrate composition of the EPS and structural features also changed significantly according to the different growing conditions, being observed significant differences in the (1 → 3)/(1 → 4)-Glcp ratio and also on the branching degree of EPS. As the biological activities of EPS are highly dependent on the polysaccharide structural features, this variability can have implications on the EPS biological activities, but can also be used advantageously to produce tailor made polysaccharides with specific applications. Copyright © 2014 Elsevier Ltd. All rights reserved.

Process Features in Writing: Internal Structure and Incremental Value over Product Features. Research Report. ETS RR-15-27

ERIC Educational Resources Information Center

Zhang, Mo; Deane, Paul

2015-01-01

In educational measurement contexts, essays have been evaluated and formative feedback has been given based on the end product. In this study, we used a large sample collected from middle school students in the United States to investigate the factor structure of the writing process features gathered from keystroke logs and the association of that…

Craters of elevation / forced folds: more examples of shallow magma accumulation and its consequences

NASA Astrophysics Data System (ADS)

van Wyk de Vries, Benjamin; Marquez, Alvaro; Craig, Magee; Valdislav, Rapprich; Hetherington, Rachel; Bastow, Ian

2016-04-01

Craters of elevation are uplifts with apical depressions that are caused by shallow magma intrusion. Forced folds are dome-like folds caused by magma intrusion that also have apical extensional structures. They are the same feature described from the different viewpoints of the volcanologist and the structural geologist. While working on such features in the Chaîne des Puys (Central France), and Ethiopia we have been searching for other examples in the world. This is our most up to date review of such phenomena taken from a global search in the world of volcanology where some stunning examples are seen in the landscape, and in outcrop. We also show such features from tectonics data and literature, where such features are superbly displayed in seismic data. We take three examples, the Puy de Gouttes, in the Chaîne des Puys, the Montana Encantada in Lanzarote, which we have mapped in the field, and the Diamond Craters National Monument in Oregon to show the different structures and possible evolutionary trends that such features can follow. We use the observations to integrate the possible eruptive, deformational and structural events that can combine in a forced fold to create the surface features observed at such craters of elevation. The hazard implications of the growth and destruction of such features are assessed.

Visualizing bacterial tRNA identity determinants and antideterminants using function logos and inverse function logos

PubMed Central

Freyhult, Eva; Moulton, Vincent; Ardell, David H.

2006-01-01

Sequence logos are stacked bar graphs that generalize the notion of consensus sequence. They employ entropy statistics very effectively to display variation in a structural alignment of sequences of a common function, while emphasizing its over-represented features. Yet sequence logos cannot display features that distinguish functional subclasses within a structurally related superfamily nor do they display under-represented features. We introduce two extensions to address these needs: function logos and inverse logos. Function logos display subfunctions that are over-represented among sequences carrying a specific feature. Inverse logos generalize both sequence logos and function logos by displaying under-represented, rather than over-represented, features or functions in structural alignments. To make inverse logos, a compositional inverse is applied to the feature or function frequency distributions before logo construction, where a compositional inverse is a mathematical transform that makes common features or functions rare and vice versa. We applied these methods to a database of structurally aligned bacterial tDNAs to create highly condensed, birds-eye views of potentially all so-called identity determinants and antideterminants that confer specific amino acid charging or initiator function on tRNAs in bacteria. We recovered both known and a few potentially novel identity elements. Function logos and inverse logos are useful tools for exploratory bioinformatic analysis of structure–function relationships in sequence families and superfamilies. PMID:16473848

Multiple co-clustering based on nonparametric mixture models with heterogeneous marginal distributions

PubMed Central

Yoshimoto, Junichiro; Shimizu, Yu; Okada, Go; Takamura, Masahiro; Okamoto, Yasumasa; Yamawaki, Shigeto; Doya, Kenji

2017-01-01

We propose a novel method for multiple clustering, which is useful for analysis of high-dimensional data containing heterogeneous types of features. Our method is based on nonparametric Bayesian mixture models in which features are automatically partitioned (into views) for each clustering solution. This feature partition works as feature selection for a particular clustering solution, which screens out irrelevant features. To make our method applicable to high-dimensional data, a co-clustering structure is newly introduced for each view. Further, the outstanding novelty of our method is that we simultaneously model different distribution families, such as Gaussian, Poisson, and multinomial distributions in each cluster block, which widens areas of application to real data. We apply the proposed method to synthetic and real data, and show that our method outperforms other multiple clustering methods both in recovering true cluster structures and in computation time. Finally, we apply our method to a depression dataset with no true cluster structure available, from which useful inferences are drawn about possible clustering structures of the data. PMID:29049392

An evaluation of the suitability of ERTS data for the purposes of petroleum exploration

NASA Technical Reports Server (NTRS)

Collins, R. J., Jr. (Principal Investigator); Mccown, F. P.; Stonis, L. P.; Petzel, G.

1973-01-01

The author has identified the following significant results. ERTS-1 imagery seems to be good to excellent for reconnaissance level investigations of large sedimentary basins such as the Anadarko Basin. Many lithologic boundaries, and geomorphic features, and linear features inferred to be indicative of geologic structure are visible in the imagery. This imagery in conjunction with high altitude photography seems to be useful as a tool for intermediate level geologic exploration. Several types of crudely circular anomalous features, such as geomorphic/structural anomalies, hazy areas and tonal anomalies, are identifiable in the imagery. There seems to be a strong correlation between the geomorphic/structural and hazy anomalies and known structurally controlled oil and gas fields. The features recognizable on ERTS-1 imagery and their ease of recognition vary from area to area even in imagery acquired at the same time under essentially uniform atmospheric conditions. Repeated coverage is exceedingly valuable in geologic applications. One time complete coverage even for the various seasons does not reveal all the features that ERTS-1 can reveal.

A neighboring structure reconstructed matching algorithm based on LARK features

NASA Astrophysics Data System (ADS)

Xue, Taobei; Han, Jing; Zhang, Yi; Bai, Lianfa

2015-11-01

Aimed at the low contrast ratio and high noise of infrared images, and the randomness and ambient occlusion of its objects, this paper presents a neighboring structure reconstructed matching (NSRM) algorithm based on LARK features. The neighboring structure relationships of local window are considered based on a non-negative linear reconstruction method to build a neighboring structure relationship matrix. Then the LARK feature matrix and the NSRM matrix are processed separately to get two different similarity images. By fusing and analyzing the two similarity images, those infrared objects are detected and marked by the non-maximum suppression. The NSRM approach is extended to detect infrared objects with incompact structure. High performance is demonstrated on infrared body set, indicating a lower false detecting rate than conventional methods in complex natural scenes.

MEVTV Workshop on Tectonic Features on Mars

NASA Technical Reports Server (NTRS)

Watters, Thomas R. (Editor); Golombek, Matthew P. (Editor)

1989-01-01

The state of knowledge of tectonic features on Mars was determined and kinematic and mechanical models were assessed for their origin. Three sessions were held: wrinkle ridges and compressional structure; strike-slip faults; and extensional structures. Each session began with an overview of the features under discussion. In the case of wrinkle ridges and extensional structures, the overview was followed by keynote addresses by specialists working on similar structures on the Earth. The first session of the workshop focused on the controversy over the relative importance of folding, faulting, and intrusive volcanism in the origin of wrinkle ridges. The session ended with discussions of the origin of compressional flank structures associated with Martian volcanoes and the relationship between the volcanic complexes and the inferred regional stress field. The second day of the workshop began with the presentation and discussion of evidence for strike-slip faults on Mars at various scales. In the last session, the discussion of extensional structures ranged from the origin of grabens, tension cracks, and pit-crater chains to the origin of Valles Marineris canyons. Shear and tensile modes of brittle failure in the formation of extensional features and the role of these failure modes in the formation of pit-crater chains and the canyons of Valles Marineris were debated. The relationship of extensional features to other surface processes, such as carbonate dissolution (karst) were also discussed.

Compact, Two-Sided Structural Cold Plate Configuration

NASA Technical Reports Server (NTRS)

Zaffetti, Mark

2011-01-01

In two-sided structural cold plates, typically there is a structural member, such as a honeycomb panel, that provides the structural strength for the cold plates that cool equipment. The cold plates are located on either side of the structural member and thus need to have the cooling fluid supplied to them. One method of accomplishing this is to route the inlet and outlet tubing to both sides of the structural member. Another method might be to supply the inlet to one side and the outlet to the other. With the latter method, an external feature such as a hose, tube, or manifold must be incorporated to pass the fluid from one side of the structural member to the other. Although this is a more compact design than the first option, since it eliminates the need for a dedicated supply and return line to each side of the structural member, it still poses problems, as these external features can be easily damaged and are now new areas for potential fluid leakage. This invention eliminates the need for an external feature and instead incorporates the feature internally to the structural member. This is accomplished by utilizing a threaded insert that not only connects the cold plate to the structural member, but also allows the cooling fluid to flow through it into the structural member, and then to the cold plate on the opposite side. The insert also employs a cap that acts as a cover to seal the open area needed to install the insert. There are multiple options for location of o-ring style seals, as well as the option to use adhesive for redundant sealing. Another option is to weld the cap to the cold plate after its installation, thus making it an integral part of the structural member. This new configuration allows the fluid to pass from one cold plate to the other without any exposed external features.

In vivo genome-wide profiling of RNA secondary structure reveals novel regulatory features.

PubMed

Ding, Yiliang; Tang, Yin; Kwok, Chun Kit; Zhang, Yu; Bevilacqua, Philip C; Assmann, Sarah M

2014-01-30

RNA structure has critical roles in processes ranging from ligand sensing to the regulation of translation, polyadenylation and splicing. However, a lack of genome-wide in vivo RNA structural data has limited our understanding of how RNA structure regulates gene expression in living cells. Here we present a high-throughput, genome-wide in vivo RNA structure probing method, structure-seq, in which dimethyl sulphate methylation of unprotected adenines and cytosines is identified by next-generation sequencing. Application of this method to Arabidopsis thaliana seedlings yielded the first in vivo genome-wide RNA structure map at nucleotide resolution for any organism, with quantitative structural information across more than 10,000 transcripts. Our analysis reveals a three-nucleotide periodic repeat pattern in the structure of coding regions, as well as a less-structured region immediately upstream of the start codon, and shows that these features are strongly correlated with translation efficiency. We also find patterns of strong and weak secondary structure at sites of alternative polyadenylation, as well as strong secondary structure at 5' splice sites that correlates with unspliced events. Notably, in vivo structures of messenger RNAs annotated for stress responses are poorly predicted in silico, whereas mRNA structures of genes related to cell function maintenance are well predicted. Global comparison of several structural features between these two categories shows that the mRNAs associated with stress responses tend to have more single-strandedness, longer maximal loop length and higher free energy per nucleotide, features that may allow these RNAs to undergo conformational changes in response to environmental conditions. Structure-seq allows the RNA structurome and its biological roles to be interrogated on a genome-wide scale and should be applicable to any organism.

DSSR-enhanced visualization of nucleic acid structures in Jmol

PubMed Central

Hanson, Robert M.

2017-01-01

Abstract Sophisticated and interactive visualizations are essential for making sense of the intricate 3D structures of macromolecules. For proteins, secondary structural components are routinely featured in molecular graphics visualizations. However, the field of RNA structural bioinformatics is still lagging behind; for example, current molecular graphics tools lack built-in support even for base pairs, double helices, or hairpin loops. DSSR (Dissecting the Spatial Structure of RNA) is an integrated and automated command-line tool for the analysis and annotation of RNA tertiary structures. It calculates a comprehensive and unique set of features for characterizing RNA, as well as DNA structures. Jmol is a widely used, open-source Java viewer for 3D structures, with a powerful scripting language. JSmol, its reincarnation based on native JavaScript, has a predominant position in the post Java-applet era for web-based visualization of molecular structures. The DSSR-Jmol integration presented here makes salient features of DSSR readily accessible, either via the Java-based Jmol application itself, or its HTML5-based equivalent, JSmol. The DSSR web service accepts 3D coordinate files (in mmCIF or PDB format) initiated from a Jmol or JSmol session and returns DSSR-derived structural features in JSON format. This seamless combination of DSSR and Jmol/JSmol brings the molecular graphics of 3D RNA structures to a similar level as that for proteins, and enables a much deeper analysis of structural characteristics. It fills a gap in RNA structural bioinformatics, and is freely accessible (via the Jmol application or the JSmol-based website http://jmol.x3dna.org). PMID:28472503

Comparative evaluation of Populus variants total sugar release and structural features following pretreatment and digestion by two distinct biological systems

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

Thomas, Vanessa A.; Kothari, Ninad; Bhagia, Samarthya

Populus natural variants have been shown to realize a broad range of sugar yields during saccharification, however, the structural features responsible for higher sugar release from natural variants are not clear. In addition, the sugar release patterns resulting from digestion with two distinct biological systems, fungal enzymes and Clostridium thermocellum, have yet to be evaluated and compared. This study evaluates the effect of structural features of three natural variant Populus lines, which includes the line BESC standard, with respect to the overall process of sugar release for two different biological systems.

Comparative evaluation of Populus variants total sugar release and structural features following pretreatment and digestion by two distinct biological systems

DOE PAGES

Thomas, Vanessa A.; Kothari, Ninad; Bhagia, Samarthya; ...

2017-11-30

Populus natural variants have been shown to realize a broad range of sugar yields during saccharification, however, the structural features responsible for higher sugar release from natural variants are not clear. In addition, the sugar release patterns resulting from digestion with two distinct biological systems, fungal enzymes and Clostridium thermocellum, have yet to be evaluated and compared. This study evaluates the effect of structural features of three natural variant Populus lines, which includes the line BESC standard, with respect to the overall process of sugar release for two different biological systems.

Clouds on Neptune: Motions, Evolution, and Structure

NASA Technical Reports Server (NTRS)

Sromovsky, Larry A.; Morgan, Thomas (Technical Monitor)

2001-01-01

The aims of our original proposal were these: (1) improving measurements of Neptune's circulation, (2) understanding the spatial distribution of cloud features, (3) discovery of new cloud features and understanding their evolutionary process, (4) understanding the vertical structure of zonal cloud patterns, (5) defining the structure of discrete cloud features, and (6) defining the near IR albedo and light curve of Triton. Towards these aims we proposed analysis of existing 1996 groundbased NSFCAM/IRTF observations and nearly simultaneous WFPC2 observations from the Hubble Space Telescope. We also proposed to acquire new observations from both HST and the IRTF.

iFeature: a python package and web server for features extraction and selection from protein and peptide sequences.

PubMed

Chen, Zhen; Zhao, Pei; Li, Fuyi; Leier, André; Marquez-Lago, Tatiana T; Wang, Yanan; Webb, Geoffrey I; Smith, A Ian; Daly, Roger J; Chou, Kuo-Chen; Song, Jiangning

2018-03-08

Structural and physiochemical descriptors extracted from sequence data have been widely used to represent sequences and predict structural, functional, expression and interaction profiles of proteins and peptides as well as DNAs/RNAs. Here, we present iFeature, a versatile Python-based toolkit for generating various numerical feature representation schemes for both protein and peptide sequences. iFeature is capable of calculating and extracting a comprehensive spectrum of 18 major sequence encoding schemes that encompass 53 different types of feature descriptors. It also allows users to extract specific amino acid properties from the AAindex database. Furthermore, iFeature integrates 12 different types of commonly used feature clustering, selection, and dimensionality reduction algorithms, greatly facilitating training, analysis, and benchmarking of machine-learning models. The functionality of iFeature is made freely available via an online web server and a stand-alone toolkit. http://iFeature.erc.monash.edu/; https://github.com/Superzchen/iFeature/. jiangning.song@monash.edu; kcchou@gordonlifescience.org; roger.daly@monash.edu. Supplementary data are available at Bioinformatics online.

ECG Identification System Using Neural Network with Global and Local Features

ERIC Educational Resources Information Center

Tseng, Kuo-Kun; Lee, Dachao; Chen, Charles

2016-01-01

This paper proposes a human identification system via extracted electrocardiogram (ECG) signals. Two hierarchical classification structures based on global shape feature and local statistical feature is used to extract ECG signals. Global shape feature represents the outline information of ECG signals and local statistical feature extracts the…

Proteins without unique 3D structures: biotechnological applications of intrinsically unstable/disordered proteins.

PubMed

Uversky, Vladimir N

2015-03-01

Intrinsically disordered proteins (IDPs) and intrinsically disordered protein regions (IDPRs) are functional proteins or regions that do not have unique 3D structures under functional conditions. Therefore, from the viewpoint of their lack of stable 3D structure, IDPs/IDPRs are inherently unstable. As much as structure and function of normal ordered globular proteins are determined by their amino acid sequences, the lack of unique 3D structure in IDPs/IDPRs and their disorder-based functionality are also encoded in the amino acid sequences. Because of their specific sequence features and distinctive conformational behavior, these intrinsically unstable proteins or regions have several applications in biotechnology. This review introduces some of the most characteristic features of IDPs/IDPRs (such as peculiarities of amino acid sequences of these proteins and regions, their major structural features, and peculiar responses to changes in their environment) and describes how these features can be used in the biotechnology, for example for the proteome-wide analysis of the abundance of extended IDPs, for recombinant protein isolation and purification, as polypeptide nanoparticles for drug delivery, as solubilization tools, and as thermally sensitive carriers of active peptides and proteins. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

What are the structural features that drive partitioning of proteins in aqueous two-phase systems?

PubMed

Wu, Zhonghua; Hu, Gang; Wang, Kui; Zaslavsky, Boris Yu; Kurgan, Lukasz; Uversky, Vladimir N

2017-01-01

Protein partitioning in aqueous two-phase systems (ATPSs) represents a convenient, inexpensive, and easy to scale-up protein separation technique. Since partition behavior of a protein dramatically depends on an ATPS composition, it would be highly beneficial to have reliable means for (even qualitative) prediction of partitioning of a target protein under different conditions. Our aim was to understand which structural features of proteins contribute to partitioning of a query protein in a given ATPS. We undertook a systematic empirical analysis of relations between 57 numerical structural descriptors derived from the corresponding amino acid sequences and crystal structures of 10 well-characterized proteins and the partition behavior of these proteins in 29 different ATPSs. This analysis revealed that just a few structural characteristics of proteins can accurately determine behavior of these proteins in a given ATPS. However, partition behavior of proteins in different ATPSs relies on different structural features. In other words, we could not find a unique set of protein structural features derived from their crystal structures that could be used for the description of the protein partition behavior of all proteins in all ATPSs analyzed in this study. We likely need to gain better insight into relationships between protein-solvent interactions and protein structure peculiarities, in particular given limitations of the used here crystal structures, to be able to construct a model that accurately predicts protein partition behavior across all ATPSs. Copyright © 2016 Elsevier B.V. All rights reserved.

Dermoscopy of accessory nipples in authors’ own study

PubMed Central

Szymszal, Jan; Silny, Wojciech

2014-01-01

Introduction The accessory nipple (AN) is characterised by its network-like structures, which may suggest the diagnosis of a melanocytic lesion. The knowledge about additional dermoscopic features of AN may greatly minimise the risk of unnecessary surgical excisions. Aim To analyse and present different clinical and dermoscopic forms, in which the AN may appear. Material and methods Ninety AN with dermoscopic features were evaluated in the study, detected in 14 patients between the years 2008 and 2014. Results The most common dermoscopic features of the AN were central, scar-like areas (15/19) and peripheral network-like structures (12/19). A number of cleft-like appearances (8/19) and central network-like structures (7/19) had also been observed. Moreover, among the dermoscopic features, white cobblestone-like structures (7/19), a central round dimpling with a plug (6/19) and fisheye-like structures resembling comedo-like openings (9/19) have all also been noted. There is a statistical significance in the occurrence of white cobblestone-like structures with central network-like structures (Fisher's exact test p = 0.0449). The presence of peripheral network-like structures with the occurrence of central scar-like areas was statistically highly significant (p = 0.0091). The central round dimpling was never observed alongside any central network-like structures in any of the lesions (p = 0.0436). Conclusions Accessory nipples are most commonly characterised by the occurrence of a peripheral network-like structure accompanied by the presence of a scar-like area. PMID:25097482

Advances in the REDCAT software package

PubMed Central

2013-01-01

Background Residual Dipolar Couplings (RDCs) have emerged in the past two decades as an informative source of experimental restraints for the study of structure and dynamics of biological macromolecules and complexes. The REDCAT software package was previously introduced for the analysis of molecular structures using RDC data. Here we report additional features that have been included in this software package in order to expand the scope of its analyses. We first discuss the features that enhance REDCATs user-friendly nature, such as the integration of a number of analyses into one single operation and enabling convenient examination of a structural ensemble in order to identify the most suitable structure. We then describe the new features which expand the scope of RDC analyses, performing exercises that utilize both synthetic and experimental data to illustrate and evaluate different features with regard to structure refinement and structure validation. Results We establish the seamless interaction that takes place between REDCAT, VMD, and Xplor-NIH in demonstrations that utilize our newly developed REDCAT-VMD and XplorGUI interfaces. These modules enable visualization of RDC analysis results on the molecular structure displayed in VMD and refinement of structures with Xplor-NIH, respectively. We also highlight REDCAT’s Error-Analysis feature in reporting the localized fitness of a structure to RDC data, which provides a more effective means of recognizing local structural anomalies. This allows for structurally sound regions of a molecule to be identified, and for any refinement efforts to be focused solely on locally distorted regions. Conclusions The newly engineered REDCAT software package, which is available for download via the WWW from http://ifestos.cse.sc.edu, has been developed in the Object Oriented C++ environment. Our most recent enhancements to REDCAT serve to provide a more complete RDC analysis suite, while also accommodating a more user-friendly experience, and will be of great interest to the community of researchers and developers since it hides the complications of software development. PMID:24098943

Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks

NASA Astrophysics Data System (ADS)

Abdeljaber, Osama; Avci, Onur; Kiranyaz, Serkan; Gabbouj, Moncef; Inman, Daniel J.

2017-02-01

Structural health monitoring (SHM) and vibration-based structural damage detection have been a continuous interest for civil, mechanical and aerospace engineers over the decades. Early and meticulous damage detection has always been one of the principal objectives of SHM applications. The performance of a classical damage detection system predominantly depends on the choice of the features and the classifier. While the fixed and hand-crafted features may either be a sub-optimal choice for a particular structure or fail to achieve the same level of performance on another structure, they usually require a large computation power which may hinder their usage for real-time structural damage detection. This paper presents a novel, fast and accurate structural damage detection system using 1D Convolutional Neural Networks (CNNs) that has an inherent adaptive design to fuse both feature extraction and classification blocks into a single and compact learning body. The proposed method performs vibration-based damage detection and localization of the damage in real-time. The advantage of this approach is its ability to extract optimal damage-sensitive features automatically from the raw acceleration signals. Large-scale experiments conducted on a grandstand simulator revealed an outstanding performance and verified the computational efficiency of the proposed real-time damage detection method.

Structural anomalies in undoped Gallium Arsenide observed in high resolution diffraction imaging with monochromatic synchrotron radiation

NASA Technical Reports Server (NTRS)

Steiner, B.; Kuriyama, M.; Dobbyn, R. C.; Laor, U.; Larson, D.; Brown, M.

1988-01-01

Novel, streak-like disruption features restricted to the plane of diffraction have recently been observed in images obtained by synchrotron radiation diffraction from undoped, semi-insulating gallium arsenide crystals. These features were identified as ensembles of very thin platelets or interfaces lying in (110) planes, and a structural model consisting of antiphase domain boundaries was proposed. We report here the other principal features observed in high resolution monochromatic synchrotron radiation diffraction images: (quasi) cellular structure; linear, very low-angle subgrain boundaries in (110) directions, and surface stripes in a (110) direction. In addition, we report systematic differences in the acceptance angle for images involving various diffraction vectors. When these observations are considered together, a unifying picture emerges. The presence of ensembles of thin (110) antiphase platelet regions or boundaries is generally consistent not only with the streak-like diffraction features but with the other features reported here as well. For the formation of such regions we propose two mechanisms, operating in parallel, that appear to be consistent with the various defect features observed by a variety of techniques.

Structural anomalies in undoped gallium arsenide observed in high-resolution diffraction imaging with monochromatic synchrotron radiation

NASA Technical Reports Server (NTRS)

Steiner, B.; Kuriyama, M.; Dobbyn, R. C.; Laor, U.; Larson, D.

1989-01-01

Novel, streak-like disruption features restricted to the plane of diffraction have recently been observed in images obtained by synchrotron radiation diffraction from undoped, semi-insulating gallium arsenide crystals. These features were identified as ensembles of very thin platelets or interfaces lying in (110) planes, and a structural model consisting of antiphase domain boundaries was proposed. We report here the other principal features observed in high resolution monochromatic synchrotron radiation diffraction images: (quasi) cellular structure; linear, very low-angle subgrain boundaries in (110) directions, and surface stripes in a (110) direction. In addition, we report systematic differences in the acceptance angle for images involving various diffraction vectors. When these observations are considered together, a unifying picture emerges. The presence of ensembles of thin (110) antiphase platelet regions or boundaries is generally consistent not only with the streak-like diffraction features but with the other features reported here as well. For the formation of such regions we propose two mechanisms, operating in parallel, that appear to be consistent with the various defect features observed by a variety of techniques.

Structural and compositional features of high-rise buildings: experimental design in Yekaterinburg

NASA Astrophysics Data System (ADS)

Yankovskaya, Yulia; Lobanov, Yuriy; Temnov, Vladimir

2018-03-01

The study looks at the specifics of high-rise development in Yekaterinburg. High-rise buildings are considered in the context of their historical development, structural features, compositional and imaginative design techniques. Experience of Yekaterinburg architects in experimental design is considered and analyzed. Main issues and prospects of high-rise development within the Yekaterinburg structure are studied. The most interesting and significant conceptual approaches to the structural and compositional arrangement of high-rise buildings are discussed.

Structural Mapping of Paterae and Mountains on Io: Implications for Crustal Stresses and Feature Evolution

NASA Astrophysics Data System (ADS)

Ahern, A.; Radebaugh, J.; Christiansen, E. H.; Harris, R. A.

2015-12-01

Paterae and mountains are some of the most distinguishing and well-distributed surface features on Io, and they reveal the role of tectonism in Io's crust. Paterae, similar to calderas, are volcano-tectonic collapse features that often have straight margins. Io's mountains are some of the highest in the solar system and contain linear features that reveal crustal stresses. Paterae and mountains are often found adjacent to one another, suggesting possible genetic relationships. We have produced twelve detailed regional structural maps from high-resolution images of relevant features, where available, as well as a global structural map from the Io Global Color Mosaic. The regional structural maps identify features such as fractures, lineations, folds, faults, and mass wasting scarps, which are then interpreted in the context of global and regional stress regimes. A total of 1048 structural lineations have been identified globally. Preliminary analyses of major thrust and normal fault orientations are dominantly 90° offset from each other, suggesting the maximum contractional stresses leading to large mountain formation are not a direct result of tidal extension. Rather, these results corroborate the model of volcanic loading of the crust and global shortening, leading to thrust faulting and uplift of coherent crustal blocks. Several paterae, such as Hi'iaka and Tohil, are found adjacent to mountains inside extensional basins where lava has migrated up normal faults to erupt onto patera floors. Over time, mass wasting and volcanic resurfacing can change mountains from young, steep, and angular peaks to older, gentler, and more rounded hills. Mass wasting scarps make up 53% of all features identified. The structural maps highlight the significant effect of mass wasting on Io's surface, the evolution of mountains through time, the role of tectonics in the formation of paterae, and the formation of mountains through global contraction due to volcanism.

Prediction of protein structural classes by Chou's pseudo amino acid composition: approached using continuous wavelet transform and principal component analysis.

PubMed

Li, Zhan-Chao; Zhou, Xi-Bin; Dai, Zong; Zou, Xiao-Yong

2009-07-01

A prior knowledge of protein structural classes can provide useful information about its overall structure, so it is very important for quick and accurate determination of protein structural class with computation method in protein science. One of the key for computation method is accurate protein sample representation. Here, based on the concept of Chou's pseudo-amino acid composition (AAC, Chou, Proteins: structure, function, and genetics, 43:246-255, 2001), a novel method of feature extraction that combined continuous wavelet transform (CWT) with principal component analysis (PCA) was introduced for the prediction of protein structural classes. Firstly, the digital signal was obtained by mapping each amino acid according to various physicochemical properties. Secondly, CWT was utilized to extract new feature vector based on wavelet power spectrum (WPS), which contains more abundant information of sequence order in frequency domain and time domain, and PCA was then used to reorganize the feature vector to decrease information redundancy and computational complexity. Finally, a pseudo-amino acid composition feature vector was further formed to represent primary sequence by coupling AAC vector with a set of new feature vector of WPS in an orthogonal space by PCA. As a showcase, the rigorous jackknife cross-validation test was performed on the working datasets. The results indicated that prediction quality has been improved, and the current approach of protein representation may serve as a useful complementary vehicle in classifying other attributes of proteins, such as enzyme family class, subcellular localization, membrane protein types and protein secondary structure, etc.

Feature Inference Learning and Eyetracking

ERIC Educational Resources Information Center

Rehder, Bob; Colner, Robert M.; Hoffman, Aaron B.

2009-01-01

Besides traditional supervised classification learning, people can learn categories by inferring the missing features of category members. It has been proposed that feature inference learning promotes learning a category's internal structure (e.g., its typical features and interfeature correlations) whereas classification promotes the learning of…

The discovery of a new infrared emission feature at 1905 wavenumbers (5.25 microns) in the spectrum of BD + 30 deg 3639 and its relation to the polycyclic aromatic hydrocarbon model

NASA Technical Reports Server (NTRS)

Allamandola, L. J.; Bregman, J. D.; Sandford, S. A.; Tielens, A. G. G. M.; Witteborn, F. C.

1989-01-01

A new IR emission feature at 1905/cm (5.25 microns) has been discovered in the spectrum of BD + 30 deg 3639. This feature joins the family of well-known IR emission features at 3040, 2940, 1750, 1610, '1310', 1160, and 890/cm. The origin of this new feature is discussed and it is assigned to an overtone or combination band involving C-H bending modes of polycyclic aromatic hydrocarbons (PAHs). Laboratory work suggests that spectral studies of the 2000-1650/cm region may be very useful in elucidating the molecular structure of interstellar PAHs. The new feature, in conjunction with other recently discovered spectral structures, suggests that the narrow IR emission features originate in PAH molecules rather than large carbon grains.

A Search for Structure in PAH Emission in Extended Sources at 3.3 and 3.4 Microns

NASA Technical Reports Server (NTRS)

Bregman, Jesse; Temi, P.; Rank, D. M.; Sloan, G. C.; Schultz, A. S. B.; Witteborn, Fred C. (Technical Monitor)

1994-01-01

We have observed three extended sources of the infrared emission features associated with polycyclic aromatic hydrocarbons (PAHs), using a 128x128 InSb array mounted on the 1.5 m NASA/Steward telescope on Mt. Lemmon. We used a CVF (1.5% bandpass) to isolate the emission from the 3.29 and 3.40 microns PAH features in NGC 1333 #3, Sharpless 106, and the Orion Bar. In all three sources, the 3.29 and 3.40 microns emission features arise from the same regions, but show decidedly different structure. We are analyzing the images to determine the relationship of the 3.40 microns feature to the main feature at 3.29 microns. The 3.40 microns feature may be a vibrational overtone of the 3.29 microns feature, or it may arise from attached molecular sidegroups.

DSSR-enhanced visualization of nucleic acid structures in Jmol.

PubMed

Hanson, Robert M; Lu, Xiang-Jun

2017-07-03

Sophisticated and interactive visualizations are essential for making sense of the intricate 3D structures of macromolecules. For proteins, secondary structural components are routinely featured in molecular graphics visualizations. However, the field of RNA structural bioinformatics is still lagging behind; for example, current molecular graphics tools lack built-in support even for base pairs, double helices, or hairpin loops. DSSR (Dissecting the Spatial Structure of RNA) is an integrated and automated command-line tool for the analysis and annotation of RNA tertiary structures. It calculates a comprehensive and unique set of features for characterizing RNA, as well as DNA structures. Jmol is a widely used, open-source Java viewer for 3D structures, with a powerful scripting language. JSmol, its reincarnation based on native JavaScript, has a predominant position in the post Java-applet era for web-based visualization of molecular structures. The DSSR-Jmol integration presented here makes salient features of DSSR readily accessible, either via the Java-based Jmol application itself, or its HTML5-based equivalent, JSmol. The DSSR web service accepts 3D coordinate files (in mmCIF or PDB format) initiated from a Jmol or JSmol session and returns DSSR-derived structural features in JSON format. This seamless combination of DSSR and Jmol/JSmol brings the molecular graphics of 3D RNA structures to a similar level as that for proteins, and enables a much deeper analysis of structural characteristics. It fills a gap in RNA structural bioinformatics, and is freely accessible (via the Jmol application or the JSmol-based website http://jmol.x3dna.org). © The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.

Protein functional features are reflected in the patterns of mRNA translation speed.

PubMed

López, Daniel; Pazos, Florencio

2015-07-09

The degeneracy of the genetic code makes it possible for the same amino acid string to be coded by different messenger RNA (mRNA) sequences. These "synonymous mRNAs" may differ largely in a number of aspects related to their overall translational efficiency, such as secondary structure content and availability of the encoded transfer RNAs (tRNAs). Consequently, they may render different yields of the translated polypeptides. These mRNA features related to translation efficiency are also playing a role locally, resulting in a non-uniform translation speed along the mRNA, which has been previously related to some protein structural features and also used to explain some dramatic effects of "silent" single-nucleotide-polymorphisms (SNPs). In this work we perform the first large scale analysis of the relationship between three experimental proxies of mRNA local translation efficiency and the local features of the corresponding encoded proteins. We found that a number of protein functional and structural features are reflected in the patterns of ribosome occupancy, secondary structure and tRNA availability along the mRNA. One or more of these proxies of translation speed have distinctive patterns around the mRNA regions coding for certain protein local features. In some cases the three patterns follow a similar trend. We also show specific examples where these patterns of translation speed point to the protein's important structural and functional features. This support the idea that the genome not only codes the protein functional features as sequences of amino acids, but also as subtle patterns of mRNA properties which, probably through local effects on the translation speed, have some consequence on the final polypeptide. These results open the possibility of predicting a protein's functional regions based on a single genomic sequence, and have implications for heterologous protein expression and fine-tuning protein function.

Structural Integrity and Aging-Related Issues of Helicopters

DTIC Science & Technology

2000-10-01

inherently damage lolerant , any damage- inspection in critical locations where tests have indicated tolerant features in airframe design only enhances...required, so European Rotorcraft Forum. Marseilles, France, 15- that helicopters are equipped with such features as fly- 17 September 1998 . by-wire and...fatigue Evaluation of structural integrity issues of aging helicopters. The Structure," 29 April, 1998 . extended safe-life approach encompasses the best

Decoding the spectroscopic features and time scales of aqueous proton defects

NASA Astrophysics Data System (ADS)

Napoli, Joseph A.; Marsalek, Ondrej; Markland, Thomas E.

2018-06-01

Acid solutions exhibit a variety of complex structural and dynamical features arising from the presence of multiple interacting reactive proton defects and counterions. However, disentangling the transient structural motifs of proton defects in the water hydrogen bond network and the mechanisms for their interconversion remains a formidable challenge. Here, we use simulations treating the quantum nature of both the electrons and nuclei to show how the experimentally observed spectroscopic features and relaxation time scales can be elucidated using a physically transparent coordinate that encodes the overall asymmetry of the solvation environment of the proton defect. We demonstrate that this coordinate can be used both to discriminate the extremities of the features observed in the linear vibrational spectrum and to explain the molecular motions that give rise to the interconversion time scales observed in recent nonlinear experiments. This analysis provides a unified condensed-phase picture of the proton structure and dynamics that, at its extrema, encompasses proton sharing and spectroscopic features resembling the limiting Eigen [H3O(H2O)3]+ and Zundel [H(H2O)2]+ gas-phase structures, while also describing the rich variety of interconverting environments in the liquid phase.

Automated retrieval of forest structure variables based on multi-scale texture analysis of VHR satellite imagery

NASA Astrophysics Data System (ADS)

Beguet, Benoit; Guyon, Dominique; Boukir, Samia; Chehata, Nesrine

2014-10-01

The main goal of this study is to design a method to describe the structure of forest stands from Very High Resolution satellite imagery, relying on some typical variables such as crown diameter, tree height, trunk diameter, tree density and tree spacing. The emphasis is placed on the automatization of the process of identification of the most relevant image features for the forest structure retrieval task, exploiting both spectral and spatial information. Our approach is based on linear regressions between the forest structure variables to be estimated and various spectral and Haralick's texture features. The main drawback of this well-known texture representation is the underlying parameters which are extremely difficult to set due to the spatial complexity of the forest structure. To tackle this major issue, an automated feature selection process is proposed which is based on statistical modeling, exploring a wide range of parameter values. It provides texture measures of diverse spatial parameters hence implicitly inducing a multi-scale texture analysis. A new feature selection technique, we called Random PRiF, is proposed. It relies on random sampling in feature space, carefully addresses the multicollinearity issue in multiple-linear regression while ensuring accurate prediction of forest variables. Our automated forest variable estimation scheme was tested on Quickbird and Pléiades panchromatic and multispectral images, acquired at different periods on the maritime pine stands of two sites in South-Western France. It outperforms two well-established variable subset selection techniques. It has been successfully applied to identify the best texture features in modeling the five considered forest structure variables. The RMSE of all predicted forest variables is improved by combining multispectral and panchromatic texture features, with various parameterizations, highlighting the potential of a multi-resolution approach for retrieving forest structure variables from VHR satellite images. Thus an average prediction error of ˜ 1.1 m is expected on crown diameter, ˜ 0.9 m on tree spacing, ˜ 3 m on height and ˜ 0.06 m on diameter at breast height.

Correlation of HIV protease structure with Indinavir resistance: a data mining and neural networks approach

NASA Astrophysics Data System (ADS)

Draghici, Sorin; Cumberland, Lonnie T., Jr.; Kovari, Ladislau C.

2000-04-01

This paper presents some results of data mining HIV genotypic and structural data. Our aim is to try to relate structural features of HIV enzymes essential to its reproductive abilities to the drug resistance phenomenon. This paper concentrates on the HIV protease enzyme and Indinavir which is one of the FDA approved protease inhibitors. Our starting point was the current list of HIV mutations related to drug resistance. We used the fact that some molecular structures determined through high resolution X-ray crystallography were available for the protease-Indinavir complex. Starting with these structures and the known mutations, we modelled the mutant proteases and studied the pattern of atomic contacts between the protease and the drug. After suitable pre- processing, these patterns have been used as the input of our data mining process. We have used both supervised and unsupervised learning techniques with the aim of understanding the relationship between structural features at a molecular level and resistance to Indinavir. The supervised learning was aimed at predicting IC90 values for arbitrary mutants. The SOFM was aimed at identifying those structural features that are important for drug resistance and discovering a classifier based on such features. We have used validation and cross validation to test the generalization abilities of the learning paradigm we have designed. The straightforward supervised learning was able to learn very successfully but validation results are less than satisfactory. This is due to the insufficient number of patterns in the training set which in turn is due to the scarcity of the available data. The data mining using SOFM was very successful. We have managed to distinguish between resistant and non-resistant mutants using structural features. We have been able to divide all reported HIV mutants into several categories based on their 3- dimensional molecular structures and the pattern of contacts between the mutant protease and Indinavir. Our classifier shows reasonably good prediction performance being able to predict the drug resistance of previously unseen mutants with an accuracy of between 60% and 70%. We believe that this performance can be greatly improved once more data becomes available. The results presented here support the hypothesis that structural features of the molecular structure can be used in antiviral drug treatment selection and drug design.

Extracting physicochemical features to predict protein secondary structure.

PubMed

Huang, Yin-Fu; Chen, Shu-Ying

2013-01-01

We propose a protein secondary structure prediction method based on position-specific scoring matrix (PSSM) profiles and four physicochemical features including conformation parameters, net charges, hydrophobic, and side chain mass. First, the SVM with the optimal window size and the optimal parameters of the kernel function is found. Then, we train the SVM using the PSSM profiles generated from PSI-BLAST and the physicochemical features extracted from the CB513 data set. Finally, we use the filter to refine the predicted results from the trained SVM. For all the performance measures of our method, Q 3 reaches 79.52, SOV94 reaches 86.10, and SOV99 reaches 74.60; all the measures are higher than those of the SVMpsi method and the SVMfreq method. This validates that considering these physicochemical features in predicting protein secondary structure would exhibit better performances.

Extracting Physicochemical Features to Predict Protein Secondary Structure

PubMed Central

Chen, Shu-Ying

2013-01-01

We propose a protein secondary structure prediction method based on position-specific scoring matrix (PSSM) profiles and four physicochemical features including conformation parameters, net charges, hydrophobic, and side chain mass. First, the SVM with the optimal window size and the optimal parameters of the kernel function is found. Then, we train the SVM using the PSSM profiles generated from PSI-BLAST and the physicochemical features extracted from the CB513 data set. Finally, we use the filter to refine the predicted results from the trained SVM. For all the performance measures of our method, Q 3 reaches 79.52, SOV94 reaches 86.10, and SOV99 reaches 74.60; all the measures are higher than those of the SVMpsi method and the SVMfreq method. This validates that considering these physicochemical features in predicting protein secondary structure would exhibit better performances. PMID:23766688

The Protein Structure Initiative Structural Biology Knowledgebase Technology Portal: a structural biology web resource.

PubMed

Gifford, Lida K; Carter, Lester G; Gabanyi, Margaret J; Berman, Helen M; Adams, Paul D

2012-06-01

The Technology Portal of the Protein Structure Initiative Structural Biology Knowledgebase (PSI SBKB; http://technology.sbkb.org/portal/ ) is a web resource providing information about methods and tools that can be used to relieve bottlenecks in many areas of protein production and structural biology research. Several useful features are available on the web site, including multiple ways to search the database of over 250 technological advances, a link to videos of methods on YouTube, and access to a technology forum where scientists can connect, ask questions, get news, and develop collaborations. The Technology Portal is a component of the PSI SBKB ( http://sbkb.org ), which presents integrated genomic, structural, and functional information for all protein sequence targets selected by the Protein Structure Initiative. Created in collaboration with the Nature Publishing Group, the SBKB offers an array of resources for structural biologists, such as a research library, editorials about new research advances, a featured biological system each month, and a functional sleuth for searching protein structures of unknown function. An overview of the various features and examples of user searches highlight the information, tools, and avenues for scientific interaction available through the Technology Portal.

Special Features of Structure Formation in Pipes from Medium-Carbon Low-Alloy Steel 32G2F Under Heat Treatment

NASA Astrophysics Data System (ADS)

Stepanov, A. I.; Belikov, S. V.; Musikhin, S. A.; Burmasov, S. P.; Popov, A. A.

2017-03-01

Special features of formation of structure and properties of seamless pipes from medium-carbon low-alloy steel for oil and gas applications are considered and associated with chemical inhomogeneity of the metal of the pipes.

Critical Song Features for Auditory Pattern Recognition in Crickets

PubMed Central

Meckenhäuser, Gundula; Hennig, R. Matthias; Nawrot, Martin P.

2013-01-01

Many different invertebrate and vertebrate species use acoustic communication for pair formation. In the cricket Gryllus bimaculatus, females recognize their species-specific calling song and localize singing males by positive phonotaxis. The song pattern of males has a clear structure consisting of brief and regular pulses that are grouped into repetitive chirps. Information is thus present on a short and a long time scale. Here, we ask which structural features of the song critically determine the phonotactic performance. To this end we employed artificial neural networks to analyze a large body of behavioral data that measured females’ phonotactic behavior under systematic variation of artificially generated song patterns. In a first step we used four non-redundant descriptive temporal features to predict the female response. The model prediction showed a high correlation with the experimental results. We used this behavioral model to explore the integration of the two different time scales. Our result suggested that only an attractive pulse structure in combination with an attractive chirp structure reliably induced phonotactic behavior to signals. In a further step we investigated all feature sets, each one consisting of a different combination of eight proposed temporal features. We identified feature sets of size two, three, and four that achieve highest prediction power by using the pulse period from the short time scale plus additional information from the long time scale. PMID:23437054

Vibrational tug-of-war: The pKA dependence of the broad vibrational features of strongly hydrogen-bonded carboxylic acids

NASA Astrophysics Data System (ADS)

Van Hoozen, Brian L.; Petersen, Poul B.

2018-04-01

Medium and strong hydrogen bonds give rise to broad vibrational features frequently spanning several hundred wavenumbers and oftentimes exhibiting unusual substructures. These broad vibrational features can be modeled from first principles, in a reduced dimensional calculation, that adiabatically separates low-frequency modes, which modulate the hydrogen bond length, from high-frequency OH stretch and bend modes that contribute to the vibrational structure. Previously this method was used to investigate the origin of an unusual vibrational feature frequently found in the spectra of dimers between carboxylic acids and nitrogen-containing aromatic bases that spans over 900 cm-1 and contains two broad peaks. It was found that the width of this feature largely originates from low-frequency modes modulating the hydrogen bond length and that the structure results from Fermi resonance interactions. In this report, we examine how these features change with the relative acid and base strength of the components as reflected by their aqueous pKA values. Dimers with large pKA differences are found to have features that can extend to frequencies below 1000 cm-1. The relationships between mean OH/NH frequency, aqueous pKA, and O-N distance are examined in order to obtain a more rigorous understanding of the origin and shape of the vibrational features. The mean OH/NH frequencies are found to correlate well with O-N distances. The lowest OH stretch frequencies are found in dimer geometries with O-N distances between 2.5 and 2.6 Å. At larger O-N distances, the hydrogen bonding interaction is not as strong, resulting in higher OH stretch frequencies. When the O-N distance is smaller than 2.5 Å, the limited space between the O and N determines the OH stretch frequency, which gives rise to frequencies that decrease with O-N distances. These two effects place a lower limit on the OH stretch frequency which is calculated to be near 700 cm-1. Understanding how the vibrational features of strongly hydrogen-bonded structures depend on the relative pKA and other structural parameters will guide studies of biological structures and analysis of proton transfer studies using photoacids.

Vehicle license plate recognition based on geometry restraints and multi-feature decision

NASA Astrophysics Data System (ADS)

Wu, Jianwei; Wang, Zongyue

2005-10-01

Vehicle license plate (VLP) recognition is of great importance to many traffic applications. Though researchers have paid much attention to VLP recognition there has not been a fully operational VLP recognition system yet for many reasons. This paper discusses a valid and practical method for vehicle license plate recognition based on geometry restraints and multi-feature decision including statistical and structural features. In general, the VLP recognition includes the following steps: the location of VLP, character segmentation, and character recognition. This paper discusses the three steps in detail. The characters of VLP are always declining caused by many factors, which makes it more difficult to recognize the characters of VLP, therefore geometry restraints such as the general ratio of length and width, the adjacent edges being perpendicular are used for incline correction. Image Moment has been proved to be invariant to translation, rotation and scaling therefore image moment is used as one feature for character recognition. Stroke is the basic element for writing and hence taking it as a feature is helpful to character recognition. Finally we take the image moment, the strokes and the numbers of each stroke for each character image and some other structural features and statistical features as the multi-feature to match each character image with sample character images so that each character image can be recognized by BP neural net. The proposed method combines statistical and structural features for VLP recognition, and the result shows its validity and efficiency.

Structural and phylogenetic analysis of Rhodobacter capsulatus NifF: uncovering general features of nitrogen-fixation (nif)-flavodoxins.

PubMed

Pérez-Dorado, Inmaculada; Bortolotti, Ana; Cortez, Néstor; Hermoso, Juan A

2013-01-09

Analysis of the crystal structure of NifF from Rhodobacter capsulatus and its homologues reported so far reflects the existence of unique structural features in nif flavodoxins: a leucine at the re face of the isoalloxazine, an eight-residue insertion at the C-terminus of the 50's loop and a remarkable difference in the electrostatic potential surface with respect to non-nif flavodoxins. A phylogenetic study on 64 sequences from 52 bacterial species revealed four clusters, including different functional prototypes, correlating the previously defined as "short-chain" with the firmicutes flavodoxins and the "long-chain" with gram-negative species. The comparison of Rhodobacter NifF structure with other bacterial flavodoxin prototypes discloses the concurrence of specific features of these functional electron donors to nitrogenase.

Features and characterization needs of rubber composite structures

NASA Technical Reports Server (NTRS)

Tabaddor, Farhad

1989-01-01

Some of the major unique features of rubber composite structures are outlined. The features covered are those related to the material properties, but the analytical features are also briefly discussed. It is essential to recognize these features at the planning stage of any long-range analytical, experimental, or application program. The development of a general and comprehensive program which fully accounts for all the important characteristics of tires, under all the relevant modes of operation, may present a prohibitively expensive and impractical task at the near future. There is therefore a need to develop application methodologies which can utilize the less general models, beyond their theoretical limitations and yet with reasonable reliability, by proper mix of analytical, experimental, and testing activities.

Predication of different stages of Alzheimer's disease using neighborhood component analysis and ensemble decision tree.

PubMed

Jin, Mingwu; Deng, Weishu

2018-05-15

There is a spectrum of the progression from healthy control (HC) to mild cognitive impairment (MCI) without conversion to Alzheimer's disease (AD), to MCI with conversion to AD (cMCI), and to AD. This study aims to predict the different disease stages using brain structural information provided by magnetic resonance imaging (MRI) data. The neighborhood component analysis (NCA) is applied to select most powerful features for prediction. The ensemble decision tree classifier is built to predict which group the subject belongs to. The best features and model parameters are determined by cross validation of the training data. Our results show that 16 out of a total of 429 features were selected by NCA using 240 training subjects, including MMSE score and structural measures in memory-related regions. The boosting tree model with NCA features can achieve prediction accuracy of 56.25% on 160 test subjects. Principal component analysis (PCA) and sequential feature selection (SFS) are used for feature selection, while support vector machine (SVM) is used for classification. The boosting tree model with NCA features outperforms all other combinations of feature selection and classification methods. The results suggest that NCA be a better feature selection strategy than PCA and SFS for the data used in this study. Ensemble tree classifier with boosting is more powerful than SVM to predict the subject group. However, more advanced feature selection and classification methods or additional measures besides structural MRI may be needed to improve the prediction performance. Copyright © 2018 Elsevier B.V. All rights reserved.

The future of primordial features with large-scale structure surveys

NASA Astrophysics Data System (ADS)

Chen, Xingang; Dvorkin, Cora; Huang, Zhiqi; Namjoo, Mohammad Hossein; Verde, Licia

2016-11-01

Primordial features are one of the most important extensions of the Standard Model of cosmology, providing a wealth of information on the primordial Universe, ranging from discrimination between inflation and alternative scenarios, new particle detection, to fine structures in the inflationary potential. We study the prospects of future large-scale structure (LSS) surveys on the detection and constraints of these features. We classify primordial feature models into several classes, and for each class we present a simple template of power spectrum that encodes the essential physics. We study how well the most ambitious LSS surveys proposed to date, including both spectroscopic and photometric surveys, will be able to improve the constraints with respect to the current Planck data. We find that these LSS surveys will significantly improve the experimental sensitivity on features signals that are oscillatory in scales, due to the 3D information. For a broad range of models, these surveys will be able to reduce the errors of the amplitudes of the features by a factor of 5 or more, including several interesting candidates identified in the recent Planck data. Therefore, LSS surveys offer an impressive opportunity for primordial feature discovery in the next decade or two. We also compare the advantages of both types of surveys.

The future of primordial features with large-scale structure surveys

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

Chen, Xingang; Namjoo, Mohammad Hossein; Dvorkin, Cora

2016-11-01

Primordial features are one of the most important extensions of the Standard Model of cosmology, providing a wealth of information on the primordial Universe, ranging from discrimination between inflation and alternative scenarios, new particle detection, to fine structures in the inflationary potential. We study the prospects of future large-scale structure (LSS) surveys on the detection and constraints of these features. We classify primordial feature models into several classes, and for each class we present a simple template of power spectrum that encodes the essential physics. We study how well the most ambitious LSS surveys proposed to date, including both spectroscopicmore » and photometric surveys, will be able to improve the constraints with respect to the current Planck data. We find that these LSS surveys will significantly improve the experimental sensitivity on features signals that are oscillatory in scales, due to the 3D information. For a broad range of models, these surveys will be able to reduce the errors of the amplitudes of the features by a factor of 5 or more, including several interesting candidates identified in the recent Planck data. Therefore, LSS surveys offer an impressive opportunity for primordial feature discovery in the next decade or two. We also compare the advantages of both types of surveys.« less

Crystallographic features of the approximant H (Mn7Si2V) phase in the Mn-Si-V alloy system

NASA Astrophysics Data System (ADS)

Nakayama, Kei; Komatsuzaki, Takumi; Koyama, Yasumasa

2018-07-01

The intermetallic compound H (Mn7Si2V) phase in the Mn-Si-V alloy system can be regarded as an approximant phase of the dodecagonal quasicrystal as one of the two-dimensional quasicrystals. To understand the features of the approximant H phase, in this study, the crystallographic features of both the H phase and the (σ → H) reaction in Mn-Si-V alloy samples were investigated, mainly by transmission electron microscopy. It was found that, in the H phase, there were characteristic structural disorders with respect to an array of a dodecagonal structural unit consisting of 19 dodecagonal atomic columns. Concretely, penetrated structural units consisting of two dodecagonal structural units were presumed to be typical of such disorders. An interesting feature of the (σ → H) reaction was that regions with a rectangular arrangement of penetrated structural units (RAPU) first appeared in the σ matrix as the initial state, and H regions were then nucleated in contact with RAPU regions. The subsequent conversion of RAPU regions into H regions eventually resulted in the formation of the approximant H state as the final state. Furthermore, atomic positions in both the H structure and the dodecagonal quasicrystal were examined using a simple plane-wave model with 12 plane waves.

Fungal prion HET-s as a model for structural complexity and self-propagation in prions.

PubMed

Wan, William; Stubbs, Gerald

2014-04-08

The highly ordered and reproducible structure of the fungal prion HET-s makes it an excellent model system for studying the inherent properties of prions, self-propagating infectious proteins that have been implicated in a number of fatal diseases. In particular, the HET-s prion-forming domain readily folds into a relatively complex two-rung β-solenoid amyloid. The faithful self-propagation of this fold involves a diverse array of inter- and intramolecular structural features. These features include a long flexible loop connecting the two rungs, buried polar residues, salt bridges, and asparagine ladders. We have used site-directed mutagenesis and X-ray fiber diffraction to probe the relative importance of these features for the formation of β-solenoid structure, as well as the cumulative effects of multiple mutations. Using fibrillization kinetics and chemical stability assays, we have determined the biophysical effects of our mutations on the assembly and stability of the prion-forming domain. We have found that a diversity of structural features provides a level of redundancy that allows robust folding and stability even in the face of significant sequence alterations and suboptimal environmental conditions. Our findings provide fundamental insights into the structural interactions necessary for self-propagation. Propagation of prion structure seems to require an obligatory level of complexity that may not be reproducible in short peptide models.

THE STRUCTURE OF RIFF.

ERIC Educational Resources Information Center

APPLEGATE, JOSEPH R.

THE PURPOSE OF THIS DESCRIPTIVE STUDY IS TO DEFINE THE MAJOR STRUCTURAL FEATURES OF RIFF, A BERBER LANGUAGE SPOKEN BY THE BERBER TRIBESMEN OF THE RIF IN NORTHERN MOROCCO. THE DESCRIPTION IS PRESENTED IN THREE PARTS--PHONOLOGY, MORPHOLOGY, AND SYNTAX. THE PHONEMES ARE DESCRIBED IN TERMS OF DISTINCTIVE FEATURES. PHARYNGEALIZATION AND TENSION ARE…

AN OUTLINE OF THE STRUCTURE OF KABYLE.

ERIC Educational Resources Information Center

APPLEGATE, JOSEPH R.

THE PURPOSE OF THIS DESCRIPTIVE STUDY IS TO DEFINE THE MAJOR STRUCTURAL FEATURES OF KABYLE, A GROUP OF BERBER DIALECTS SPOKEN CHIEFLY IN NORTHERN AND CENTRAL ALGERIA. THE DESCRIPTION IS PRESENTED IN THREE PARTS--PHONOLOGY, MORPHOLOGY, AND SYNTAX. THE PHONEMES ARE DESCRIBED IN TERMS OF DISTINCTIVE FEATURES. PHARYNGEALIZATION AND GEMINATION ARE…

20180311 - Use of ToxPrint chemotypes for exploring chemical feature enrichments across the ToxCast chemical-assay landscape (SOT)

EPA Science Inventory

EPA’s ToxCast chemical library spans diverse chemical use-types, functionalities, structures and features potentially relevant to toxicity and environmental exposure. However, this structural diversity, along with assay noise and low average hit rates across the varied Tox...

Collinearity Impairs Local Element Visual Search

ERIC Educational Resources Information Center

Jingling, Li; Tseng, Chia-Huei

2013-01-01

In visual searches, stimuli following the law of good continuity attract attention to the global structure and receive attentional priority. Also, targets that have unique features are of high feature contrast and capture attention in visual search. We report on a salient global structure combined with a high orientation contrast to the…

Use of ToxPrint chemotypes for exploring chemical feature enrichments across the ToxCast chemical-assay landscape

EPA Science Inventory

EPA’s ToxCast chemical library spans diverse chemical use-types, functionalities, structures and features potentially relevant to toxicity and environmental exposure. However, this structural diversity, along with assay noise and low average hit rates across the varied ToxCast h...

Emerging Issues in Genotoxicity and Carcinogenicity with Implications for Structure Activity Analyses

EPA Science Inventory

In silico systems for the prediction of the ability of chemicals to induce carcinogenicity in rodents have generally relied on knowledge of the structure and physical-chemical features of the compound, as well as the mutagenic and genotoxic features of the compound in various bio...

Activity Structures and the Unfolding of Problem-Solving Actions in High-School Chemistry Classrooms

NASA Astrophysics Data System (ADS)

Criswell, Brett A.; Rushton, Greg T.

2014-02-01

In this paper, we argue for a more systematic approach for studying the relationship between classroom practices and scientific practices—an approach that will likely better support the systemic reforms being promoted in the Next Generation Science Standards in the USA and similar efforts in other countries. One component of that approach is looking at how the nature of the activity structure may influence the relative alignment between classroom and scientific practices. To that end, we build on previously published research related to the practices utilized by five high-school chemistry teachers as they enacted problem-solving activities in which students were likely to generate proposals that were not aligned with normative scientific understandings. In that prior work, our analysis had emphasized micro-level features of the talk interactions and how they related to the way students' ideas were explored; in the current paper, the analysis zooms out to consider the macro-level nature of the enactments associated with the activity structure of each lesson examined. Our data show that there were two general patterns to the activity structure across the 14 lessons scrutinized, and that each pattern had associated with it a constellation of features that impinged on the way the problem space was navigated. A key finding is that both activity structures (the expansive and the open) had features that aligned with scientific practices espoused in the Next Generation Science Standards—and both had features that were not aligned with those practices. We discuss the nature of these two structures, evidence of the relationship of each structure to key features of how the lessons unfolded, and the implications of these findings for both future research and the training of teachers.

Protein structure based prediction of catalytic residues.

PubMed

Fajardo, J Eduardo; Fiser, Andras

2013-02-22

Worldwide structural genomics projects continue to release new protein structures at an unprecedented pace, so far nearly 6000, but only about 60% of these proteins have any sort of functional annotation. We explored a range of features that can be used for the prediction of functional residues given a known three-dimensional structure. These features include various centrality measures of nodes in graphs of interacting residues: closeness, betweenness and page-rank centrality. We also analyzed the distance of functional amino acids to the general center of mass (GCM) of the structure, relative solvent accessibility (RSA), and the use of relative entropy as a measure of sequence conservation. From the selected features, neural networks were trained to identify catalytic residues. We found that using distance to the GCM together with amino acid type provide a good discriminant function, when combined independently with sequence conservation. Using an independent test set of 29 annotated protein structures, the method returned 411 of the initial 9262 residues as the most likely to be involved in function. The output 411 residues contain 70 of the annotated 111 catalytic residues. This represents an approximately 14-fold enrichment of catalytic residues on the entire input set (corresponding to a sensitivity of 63% and a precision of 17%), a performance competitive with that of other state-of-the-art methods. We found that several of the graph based measures utilize the same underlying feature of protein structures, which can be simply and more effectively captured with the distance to GCM definition. This also has the added the advantage of simplicity and easy implementation. Meanwhile sequence conservation remains by far the most influential feature in identifying functional residues. We also found that due the rapid changes in size and composition of sequence databases, conservation calculations must be recalibrated for specific reference databases.

Optical coherence tomography angiography of normal skin and inflammatory dermatologic conditions.

PubMed

Deegan, Anthony J; Talebi-Liasi, Faezeh; Song, Shaozhen; Li, Yuandong; Xu, Jingjiang; Men, Shaojie; Shinohara, Michi M; Flowers, Mary E; Lee, Stephanie J; Wang, Ruikang K

2018-03-01

In clinical dermatology, the identification of subsurface vascular and structural features known to be associated with numerous cutaneous pathologies remains challenging without the use of invasive diagnostic tools. To present an advanced optical coherence tomography angiography (OCTA) method to directly visualize capillary-level vascular and structural features within skin in vivo. An advanced OCTA system with a 1310 nm wavelength was used to image the microvascular and structural features of various skin conditions. Subjects were enrolled and OCTA imaging was performed with a field of view of approximately 10 × 10 mm. Skin blood flow was identified using an optical microangiography (OMAG) algorithm. Depth-resolved microvascular networks and structural features were derived from segmented volume scans, representing tissue slabs of 0-132, 132-330, and 330-924 μm, measured from the surface of the skin. Subjects with both healthy and pathological conditions, such as benign skin lesions, psoriasis, chronic graft-versus-host-disease (cGvHD), and scleroderma, were OCTA scanned. Our OCTA results detailed variations in vascularization and local anatomical characteristics, for example, depth-dependent vascular, and structural alterations in psoriatic skin, alongside their resolve over time; vascular density changes and distribution irregularities, together with corresponding structural depositions in the skin of cGvHD patients; and vascular abnormalities in the nail folds of a patient with scleroderma. OCTA can image capillary blood flow and structural features within skin in vivo, which has the potential to provide new insights into the pathophysiology, as well as dynamic changes of skin diseases, valuable for diagnoses, and non-invasive monitoring of disease progression and treatment. Lasers Surg. Med. 50:183-193, 2018. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.

Spatio-temporal Event Classification using Time-series Kernel based Structured Sparsity

PubMed Central

Jeni, László A.; Lőrincz, András; Szabó, Zoltán; Cohn, Jeffrey F.; Kanade, Takeo

2016-01-01

In many behavioral domains, such as facial expression and gesture, sparse structure is prevalent. This sparsity would be well suited for event detection but for one problem. Features typically are confounded by alignment error in space and time. As a consequence, high-dimensional representations such as SIFT and Gabor features have been favored despite their much greater computational cost and potential loss of information. We propose a Kernel Structured Sparsity (KSS) method that can handle both the temporal alignment problem and the structured sparse reconstruction within a common framework, and it can rely on simple features. We characterize spatio-temporal events as time-series of motion patterns and by utilizing time-series kernels we apply standard structured-sparse coding techniques to tackle this important problem. We evaluated the KSS method using both gesture and facial expression datasets that include spontaneous behavior and differ in degree of difficulty and type of ground truth coding. KSS outperformed both sparse and non-sparse methods that utilize complex image features and their temporal extensions. In the case of early facial event classification KSS had 10% higher accuracy as measured by F1 score over kernel SVM methods1. PMID:27830214

The Rock Elm meteorite impact structure, Wisconsin: Geology and shock-metamorphic effects in quartz

USGS Publications Warehouse

French, B.M.; Cordua, W.S.; Plescia, J.B.

2004-01-01

The Rock Elm structure in southwest Wisconsin is an anomalous circular area of highly deformed rocks, ???6.5 km in diameter, located in a region of virtually horizontal undeformed sedimentary rocks. Shock-produced planar microstructures (PMs) have been identified in quartz grains in several lithologies associated with the structure: sandstones, quartzite pebbles, and breccia. Two distinct types of PMs are present: P1 features, which appear identical to planar fractures (PFs or cleavage), and P2 features, which are interpreted as possible incipient planar deformation features (PDFs). The latter are uniquely produced by the shock waves associated with meteorite impact events. Both types of PMs are oriented parallel to specific crystallographic planes in the quartz, most commonly to c(0001), ??112??2, and r/z101??1. The association of unusual, structurally deformed strata with distinct shock-produced microdeformation features in their quartz-bearing rocks establishes Rock Elm as a meteorite impact structure and supports the view that the presence of multiple parallel cleavages in quartz may be used independently as a criterion for meteorite impact. Preliminary paleontological studies indicate a minimum age of Middle Ordovician for the Rock Elm structure. A similar age estimate (450-400 Ma) is obtained independently by combining the results of studies of the general morphology of complex impact structures with estimated rates of sedimentation for the region. Such methods may be applicable to dating other old and deeply eroded impact structures formed in sedimentary target rocks.

A deep learning framework for modeling structural features of RNA-binding protein targets

PubMed Central

Zhang, Sai; Zhou, Jingtian; Hu, Hailin; Gong, Haipeng; Chen, Ligong; Cheng, Chao; Zeng, Jianyang

2016-01-01

RNA-binding proteins (RBPs) play important roles in the post-transcriptional control of RNAs. Identifying RBP binding sites and characterizing RBP binding preferences are key steps toward understanding the basic mechanisms of the post-transcriptional gene regulation. Though numerous computational methods have been developed for modeling RBP binding preferences, discovering a complete structural representation of the RBP targets by integrating their available structural features in all three dimensions is still a challenging task. In this paper, we develop a general and flexible deep learning framework for modeling structural binding preferences and predicting binding sites of RBPs, which takes (predicted) RNA tertiary structural information into account for the first time. Our framework constructs a unified representation that characterizes the structural specificities of RBP targets in all three dimensions, which can be further used to predict novel candidate binding sites and discover potential binding motifs. Through testing on the real CLIP-seq datasets, we have demonstrated that our deep learning framework can automatically extract effective hidden structural features from the encoded raw sequence and structural profiles, and predict accurate RBP binding sites. In addition, we have conducted the first study to show that integrating the additional RNA tertiary structural features can improve the model performance in predicting RBP binding sites, especially for the polypyrimidine tract-binding protein (PTB), which also provides a new evidence to support the view that RBPs may own specific tertiary structural binding preferences. In particular, the tests on the internal ribosome entry site (IRES) segments yield satisfiable results with experimental support from the literature and further demonstrate the necessity of incorporating RNA tertiary structural information into the prediction model. The source code of our approach can be found in https://github.com/thucombio/deepnet-rbp. PMID:26467480

Automatic Correction Algorithm of Hyfrology Feature Attribute in National Geographic Census

NASA Astrophysics Data System (ADS)

Li, C.; Guo, P.; Liu, X.

2017-09-01

A subset of the attributes of hydrologic features data in national geographic census are not clear, the current solution to this problem was through manual filling which is inefficient and liable to mistakes. So this paper proposes an automatic correction algorithm of hydrologic features attribute. Based on the analysis of the structure characteristics and topological relation, we put forward three basic principles of correction which include network proximity, structure robustness and topology ductility. Based on the WJ-III map workstation, we realize the automatic correction of hydrologic features. Finally, practical data is used to validate the method. The results show that our method is highly reasonable and efficient.

Robust Feature Matching in Terrestrial Image Sequences

NASA Astrophysics Data System (ADS)

Abbas, A.; Ghuffar, S.

2018-04-01

From the last decade, the feature detection, description and matching techniques are most commonly exploited in various photogrammetric and computer vision applications, which includes: 3D reconstruction of scenes, image stitching for panoramic creation, image classification, or object recognition etc. However, in terrestrial imagery of urban scenes contains various issues, which include duplicate and identical structures (i.e. repeated windows and doors) that cause the problem in feature matching phase and ultimately lead to failure of results specially in case of camera pose and scene structure estimation. In this paper, we will address the issue related to ambiguous feature matching in urban environment due to repeating patterns.

A stereo remote sensing feature selection method based on artificial bee colony algorithm

NASA Astrophysics Data System (ADS)

Yan, Yiming; Liu, Pigang; Zhang, Ye; Su, Nan; Tian, Shu; Gao, Fengjiao; Shen, Yi

2014-05-01

To improve the efficiency of stereo information for remote sensing classification, a stereo remote sensing feature selection method is proposed in this paper presents, which is based on artificial bee colony algorithm. Remote sensing stereo information could be described by digital surface model (DSM) and optical image, which contain information of the three-dimensional structure and optical characteristics, respectively. Firstly, three-dimensional structure characteristic could be analyzed by 3D-Zernike descriptors (3DZD). However, different parameters of 3DZD could descript different complexity of three-dimensional structure, and it needs to be better optimized selected for various objects on the ground. Secondly, features for representing optical characteristic also need to be optimized. If not properly handled, when a stereo feature vector composed of 3DZD and image features, that would be a lot of redundant information, and the redundant information may not improve the classification accuracy, even cause adverse effects. To reduce information redundancy while maintaining or improving the classification accuracy, an optimized frame for this stereo feature selection problem is created, and artificial bee colony algorithm is introduced for solving this optimization problem. Experimental results show that the proposed method can effectively improve the computational efficiency, improve the classification accuracy.

The structure of mushroom polysaccharides and their beneficial role in health.

PubMed

Huang, Xiaojun; Nie, Shaoping

2015-10-01

Mushroom is a kind of fungus that has been popular for its special flavour and renowned biological values. The polysaccharide contained in mushroom is regarded as one of the primary bioactive constituents and is beneficial for health. The structural features and bioactivities of mushroom polysaccharides have been studied extensively. It is believed that the diverse biological bioactivities of polysaccharides are closely related to their structure or conformation properties. In this review, the structural characteristics, conformational features and bioactivities of several mushroom polysaccharides are summarized, and their beneficial mechanisms and the relationships between their structure and bioactivities are also discussed.

Origin of the 900 cm{sup −1} broad double-hump OH vibrational feature of strongly hydrogen-bonded carboxylic acids

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

Van Hoozen, Brian L.; Petersen, Poul B.

2015-03-14

Medium and strong hydrogen bonds are common in biological systems. Here, they provide structural support and can act as proton transfer relays to drive electron and/or energy transfer. Infrared spectroscopy is a sensitive probe of molecular structure and hydrogen bond strength but strongly hydrogen-bonded structures often exhibit very broad and complex vibrational bands. As an example, strong hydrogen bonds between carboxylic acids and nitrogen-containing aromatic bases commonly display a 900 cm{sup −1} broad feature with a remarkable double-hump structure. Although previous studies have assigned this feature to the OH, the exact origin of the shape and width of this unusualmore » feature is not well understood. In this study, we present ab initio calculations of the contributions of the OH stretch and bend vibrational modes to the vibrational spectrum of strongly hydrogen-bonded heterodimers of carboxylic acids and nitrogen-containing aromatic bases, taking the 7-azaindole—acetic acid and pyridine—acetic acid dimers as examples. Our calculations take into account coupling between the OH stretch and bend modes as well as how both of these modes are affected by lower frequency dimer stretch modes, which modulate the distance between the monomers. Our calculations reproduce the broadness and the double-hump structure of the OH vibrational feature. Where the spectral broadness is primarily caused by the dimer stretch modes strongly modulating the frequency of the OH stretch mode, the double-hump structure results from a Fermi resonance between the out of the plane OH bend and the OH stretch modes.« less

A spectral-structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery

NASA Astrophysics Data System (ADS)

Zhao, Bei; Zhong, Yanfei; Zhang, Liangpei

2016-06-01

Land-use classification of very high spatial resolution remote sensing (VHSR) imagery is one of the most challenging tasks in the field of remote sensing image processing. However, the land-use classification is hard to be addressed by the land-cover classification techniques, due to the complexity of the land-use scenes. Scene classification is considered to be one of the expected ways to address the land-use classification issue. The commonly used scene classification methods of VHSR imagery are all derived from the computer vision community that mainly deal with terrestrial image recognition. Differing from terrestrial images, VHSR images are taken by looking down with airborne and spaceborne sensors, which leads to the distinct light conditions and spatial configuration of land cover in VHSR imagery. Considering the distinct characteristics, two questions should be answered: (1) Which type or combination of information is suitable for the VHSR imagery scene classification? (2) Which scene classification algorithm is best for VHSR imagery? In this paper, an efficient spectral-structural bag-of-features scene classifier (SSBFC) is proposed to combine the spectral and structural information of VHSR imagery. SSBFC utilizes the first- and second-order statistics (the mean and standard deviation values, MeanStd) as the statistical spectral descriptor for the spectral information of the VHSR imagery, and uses dense scale-invariant feature transform (SIFT) as the structural feature descriptor. From the experimental results, the spectral information works better than the structural information, while the combination of the spectral and structural information is better than any single type of information. Taking the characteristic of the spatial configuration into consideration, SSBFC uses the whole image scene as the scope of the pooling operator, instead of the scope generated by a spatial pyramid (SP) commonly used in terrestrial image classification. The experimental results show that the whole image as the scope of the pooling operator performs better than the scope generated by SP. In addition, SSBFC codes and pools the spectral and structural features separately to avoid mutual interruption between the spectral and structural features. The coding vectors of spectral and structural features are then concatenated into a final coding vector. Finally, SSBFC classifies the final coding vector by support vector machine (SVM) with a histogram intersection kernel (HIK). Compared with the latest scene classification methods, the experimental results with three VHSR datasets demonstrate that the proposed SSBFC performs better than the other classification methods for VHSR image scenes.

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

PubMed

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

2009-05-01

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

A general prediction model for the detection of ADHD and Autism using structural and functional MRI.

PubMed

Sen, Bhaskar; Borle, Neil C; Greiner, Russell; Brown, Matthew R G

2018-01-01

This work presents a novel method for learning a model that can diagnose Attention Deficit Hyperactivity Disorder (ADHD), as well as Autism, using structural texture and functional connectivity features obtained from 3-dimensional structural magnetic resonance imaging (MRI) and 4-dimensional resting-state functional magnetic resonance imaging (fMRI) scans of subjects. We explore a series of three learners: (1) The LeFMS learner first extracts features from the structural MRI images using the texture-based filters produced by a sparse autoencoder. These filters are then convolved with the original MRI image using an unsupervised convolutional network. The resulting features are used as input to a linear support vector machine (SVM) classifier. (2) The LeFMF learner produces a diagnostic model by first computing spatial non-stationary independent components of the fMRI scans, which it uses to decompose each subject's fMRI scan into the time courses of these common spatial components. These features can then be used with a learner by themselves or in combination with other features to produce the model. Regardless of which approach is used, the final set of features are input to a linear support vector machine (SVM) classifier. (3) Finally, the overall LeFMSF learner uses the combined features obtained from the two feature extraction processes in (1) and (2) above as input to an SVM classifier, achieving an accuracy of 0.673 on the ADHD-200 holdout data and 0.643 on the ABIDE holdout data. Both of these results, obtained with the same LeFMSF framework, are the best known, over all hold-out accuracies on these datasets when only using imaging data-exceeding previously-published results by 0.012 for ADHD and 0.042 for Autism. Our results show that combining multi-modal features can yield good classification accuracy for diagnosis of ADHD and Autism, which is an important step towards computer-aided diagnosis of these psychiatric diseases and perhaps others as well.

In vivo, label-free, three-dimensional quantitative imaging of liver surface using multi-photon microscopy

NASA Astrophysics Data System (ADS)

Zhuo, Shuangmu; Yan, Jie; Kang, Yuzhan; Xu, Shuoyu; Peng, Qiwen; So, Peter T. C.; Yu, Hanry

2014-07-01

Various structural features on the liver surface reflect functional changes in the liver. The visualization of these surface features with molecular specificity is of particular relevance to understanding the physiology and diseases of the liver. Using multi-photon microscopy (MPM), we have developed a label-free, three-dimensional quantitative and sensitive method to visualize various structural features of liver surface in living rat. MPM could quantitatively image the microstructural features of liver surface with respect to the sinuosity of collagen fiber, the elastic fiber structure, the ratio between elastin and collagen, collagen content, and the metabolic state of the hepatocytes that are correlative with the pathophysiologically induced changes in the regions of interest. This study highlights the potential of this technique as a useful tool for pathophysiological studies and possible diagnosis of the liver diseases with further development.

In vivo, label-free, three-dimensional quantitative imaging of liver surface using multi-photon microscopy

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

Zhuo, Shuangmu, E-mail: shuangmuzhuo@gmail.com, E-mail: hanry-yu@nuhs.edu.sg; Institute of Laser and Optoelectronics Technology, Fujian Normal University, Fuzhou 350007; Yan, Jie

2014-07-14

Various structural features on the liver surface reflect functional changes in the liver. The visualization of these surface features with molecular specificity is of particular relevance to understanding the physiology and diseases of the liver. Using multi-photon microscopy (MPM), we have developed a label-free, three-dimensional quantitative and sensitive method to visualize various structural features of liver surface in living rat. MPM could quantitatively image the microstructural features of liver surface with respect to the sinuosity of collagen fiber, the elastic fiber structure, the ratio between elastin and collagen, collagen content, and the metabolic state of the hepatocytes that are correlativemore » with the pathophysiologically induced changes in the regions of interest. This study highlights the potential of this technique as a useful tool for pathophysiological studies and possible diagnosis of the liver diseases with further development.« less

Automated discrimination of dementia spectrum disorders using extreme learning machine and structural T1 MRI features.

PubMed

Jongin Kim; Boreom Lee

2017-07-01

The classification of neuroimaging data for the diagnosis of Alzheimer's Disease (AD) is one of the main research goals of the neuroscience and clinical fields. In this study, we performed extreme learning machine (ELM) classifier to discriminate the AD, mild cognitive impairment (MCI) from normal control (NC). We compared the performance of ELM with that of a linear kernel support vector machine (SVM) for 718 structural MRI images from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The data consisted of normal control, MCI converter (MCI-C), MCI non-converter (MCI-NC), and AD. We employed SVM-based recursive feature elimination (RFE-SVM) algorithm to find the optimal subset of features. In this study, we found that the RFE-SVM feature selection approach in combination with ELM shows the superior classification accuracy to that of linear kernel SVM for structural T1 MRI data.

Conserved and variable domains of RNase MRP RNA.

PubMed

Dávila López, Marcela; Rosenblad, Magnus Alm; Samuelsson, Tore

2009-01-01

Ribonuclease MRP is a eukaryotic ribonucleoprotein complex consisting of one RNA molecule and 7-10 protein subunits. One important function of MRP is to catalyze an endonucleolytic cleavage during processing of rRNA precursors. RNase MRP is evolutionary related to RNase P which is critical for tRNA processing. A large number of MRP RNA sequences that now are available have been used to identify conserved primary and secondary structure features of the molecule. MRP RNA has structural features in common with P RNA such as a conserved catalytic core, but it also has unique features and is characterized by a domain highly variable between species. Information regarding primary and secondary structure features is of interest not only in basic studies of the function of MRP RNA, but also because mutations in the RNA give rise to human genetic diseases such as cartilage-hair hypoplasia.

Effects of Spatial and Feature Attention on Disparity-Rendered Structure-From-Motion Stimuli in the Human Visual Cortex

PubMed Central

Ip, Ifan Betina; Bridge, Holly; Parker, Andrew J.

2014-01-01

An important advance in the study of visual attention has been the identification of a non-spatial component of attention that enhances the response to similar features or objects across the visual field. Here we test whether this non-spatial component can co-select individual features that are perceptually bound into a coherent object. We combined human psychophysics and functional magnetic resonance imaging (fMRI) to demonstrate the ability to co-select individual features from perceptually coherent objects. Our study used binocular disparity and visual motion to define disparity structure-from-motion (dSFM) stimuli. Although the spatial attention system induced strong modulations of the fMRI response in visual regions, the non-spatial system’s ability to co-select features of the dSFM stimulus was less pronounced and variable across subjects. Our results demonstrate that feature and global feature attention effects are variable across participants, suggesting that the feature attention system may be limited in its ability to automatically select features within the attended object. Careful comparison of the task design suggests that even minor differences in the perceptual task may be critical in revealing the presence of global feature attention. PMID:24936974

Decomposition and extraction: a new framework for visual classification.

PubMed

Fang, Yuqiang; Chen, Qiang; Sun, Lin; Dai, Bin; Yan, Shuicheng

2014-08-01

In this paper, we present a novel framework for visual classification based on hierarchical image decomposition and hybrid midlevel feature extraction. Unlike most midlevel feature learning methods, which focus on the process of coding or pooling, we emphasize that the mechanism of image composition also strongly influences the feature extraction. To effectively explore the image content for the feature extraction, we model a multiplicity feature representation mechanism through meaningful hierarchical image decomposition followed by a fusion step. In particularly, we first propose a new hierarchical image decomposition approach in which each image is decomposed into a series of hierarchical semantical components, i.e, the structure and texture images. Then, different feature extraction schemes can be adopted to match the decomposed structure and texture processes in a dissociative manner. Here, two schemes are explored to produce property related feature representations. One is based on a single-stage network over hand-crafted features and the other is based on a multistage network, which can learn features from raw pixels automatically. Finally, those multiple midlevel features are incorporated by solving a multiple kernel learning task. Extensive experiments are conducted on several challenging data sets for visual classification, and experimental results demonstrate the effectiveness of the proposed method.

Features of Online Health Communities for Adolescents With Type 1 Diabetes

PubMed Central

Ho, Yun-Xian; O’Connor, Brendan H.; Mulvaney, Shelagh A.

2014-01-01

The aim of this exploratory study was to examine diabetes online health communities (OHCs) available to adolescents with type 1 diabetes (T1D). We sought to identify and classify site features and relate them to evidence-based processes for improving self-management. We reviewed 18 OHCs and identified the following five feature categories: social learning and networking, information, guidance, engagement, and personal health data sharing. While features that have been associated with improved self-management were present, such as social learning, results suggest that more guidance or structure would be helpful to ensure that those processes were focused on promoting positive beliefs and behaviors. Enhancing guidance-related features and structure to existing OHCs could provide greater opportunity for effective diabetes self-management support. To support clinical recommendations, more research is needed to quantitatively relate features and participation in OHCs to patient outcomes. PMID:24473058

Character feature integration of Chinese calligraphy and font

NASA Astrophysics Data System (ADS)

Shi, Cao; Xiao, Jianguo; Jia, Wenhua; Xu, Canhui

2013-01-01

A framework is proposed in this paper to effectively generate a new hybrid character type by means of integrating local contour feature of Chinese calligraphy with structural feature of font in computer system. To explore traditional art manifestation of calligraphy, multi-directional spatial filter is applied for local contour feature extraction. Then the contour of character image is divided into sub-images. The sub-images in the identical position from various characters are estimated by Gaussian distribution. According to its probability distribution, the dilation operator and erosion operator are designed to adjust the boundary of font image. And then new Chinese character images are generated which possess both contour feature of artistical calligraphy and elaborate structural feature of font. Experimental results demonstrate the new characters are visually acceptable, and the proposed framework is an effective and efficient strategy to automatically generate the new hybrid character of calligraphy and font.

Bubble structure evaluation method of sponge cake by using image morphology

NASA Astrophysics Data System (ADS)

Kato, Kunihito; Yamamoto, Kazuhiko; Nonaka, Masahiko; Katsuta, Yukiyo; Kasamatsu, Chinatsu

2007-01-01

Nowadays, many evaluation methods for food industry by using image processing are proposed. These methods are becoming new evaluation method besides the sensory test and the solid-state measurement that have been used for the quality evaluation recently. The goal of our research is structure evaluation of sponge cake by using the image processing. In this paper, we propose a feature extraction method of the bobble structure in the sponge cake. Analysis of the bubble structure is one of the important properties to understand characteristics of the cake from the image. In order to take the cake image, first we cut cakes and measured that's surface by using the CIS scanner, because the depth of field of this type scanner is very shallow. Therefore the bubble region of the surface has low gray scale value, and it has a feature that is blur. We extracted bubble regions from the surface images based on these features. The input image is binarized, and the feature of bubble is extracted by the morphology analysis. In order to evaluate the result of feature extraction, we compared correlation with "Size of the bubble" of the sensory test result. From a result, the bubble extraction by using morphology analysis gives good correlation. It is shown that our method is as well as the subjectivity evaluation.

Longitudinal Validation of General and Specific Structural Features of Personality Pathology

PubMed Central

Wright, Aidan G.C.; Hopwood, Christopher J.; Skodol, Andrew E.; Morey, Leslie C.

2016-01-01

Theorists have long argued that personality disorder (PD) is best understood in terms of general impairments shared across the disorders as well as more specific instantiations of pathology. A model based on this theoretical structure was proposed as part of the DSM-5 revision process. However, only recently has this structure been subjected to formal quantitative evaluation, with little in the way of validation efforts via external correlates or prospective longitudinal prediction. We used the Collaborative Longitudinal Study of Personality Disorders dataset to: (1) estimate structural models that parse general from specific variance in personality disorder features, (2) examine patterns of growth in general and specific features over the course of 10 years, and (3) establish concurrent and dynamic longitudinal associations in PD features and a host of external validators including basic personality traits and psychosocial functioning scales. We found that general PD exhibited much lower absolute stability and was most strongly related to broad markers of psychosocial functioning, concurrently and longitudinally, whereas specific features had much higher mean stability and exhibited more circumscribed associations with functioning. However, both general and specific factors showed recognizable associations with normative and pathological traits. These results can inform efforts to refine the conceptualization and diagnosis of personality pathology. PMID:27819472

Complex, multi-scale small intestinal topography replicated in cellular growth substrates fabricated via chemical vapor deposition of Parylene C.

PubMed

Koppes, Abigail N; Kamath, Megha; Pfluger, Courtney A; Burkey, Daniel D; Dokmeci, Mehmet; Wang, Lin; Carrier, Rebecca L

2016-08-22

Native small intestine possesses distinct multi-scale structures (e.g., crypts, villi) not included in traditional 2D intestinal culture models for drug delivery and regenerative medicine. The known impact of structure on cell function motivates exploration of the influence of intestinal topography on the phenotype of cultured epithelial cells, but the irregular, macro- to submicron-scale features of native intestine are challenging to precisely replicate in cellular growth substrates. Herein, we utilized chemical vapor deposition of Parylene C on decellularized porcine small intestine to create polymeric intestinal replicas containing biomimetic irregular, multi-scale structures. These replicas were used as molds for polydimethylsiloxane (PDMS) growth substrates with macro to submicron intestinal topographical features. Resultant PDMS replicas exhibit multiscale resolution including macro- to micro-scale folds, crypt and villus structures, and submicron-scale features of the underlying basement membrane. After 10 d of human epithelial colorectal cell culture on PDMS substrates, the inclusion of biomimetic topographical features enhanced alkaline phosphatase expression 2.3-fold compared to flat controls, suggesting biomimetic topography is important in induced epithelial differentiation. This work presents a facile, inexpensive method for precisely replicating complex hierarchal features of native tissue, towards a new model for regenerative medicine and drug delivery for intestinal disorders and diseases.

Coastal Geographic Structures in Coastal-Marine Environmental Management

NASA Astrophysics Data System (ADS)

Baklanov, P. Ya.; Ganzei, K. S.; Ermoshin, V. V.

2018-01-01

It has been proposed to distinguish the coastal geographic structures consisting of a spatial combination of three interconnected and mutually conditioned parts (coastal-territorial, coastal, coastal-marine), which are interlinked with each other by the cumulative effect of real-energy flows. Distinguishing specific resource features of the coastal structures, by which they play a connecting role in the complex coastalmarine management, has been considered. The main integral resource feature of the coastal structures is their connecting functions, which form transitional parts mutually connecting the coastal-territorial and coastalmarine environmental management.

Upper-Ocean Thermal Structure and the Western North Pacific Category 5 Typhoons. Part 1. Ocean Features and the Category 5 Typhoons’ Intensification

DTIC Science & Technology

2008-09-01

Structure and the Western North Pacific Category 5 Typhoons. Part 1: Ocean Features and the Category 5 Typhoons’ Intensification 5a. CONTRACT NUMBER...intensification of category 5 cyclones. Based on 13 yr of satellite altimetry data, in situ &climatological upper-ocean thermal structure data, best-track...Form 298 (Rev. 8/98) Prescribed by ANSI Std. Z39.18 3288 MONTHLY WEATHER REVIEW VOLUME 136 Upper-Ocean Thermal Structure and the Western North

Automated Geometry assisted PEC for electron beam direct write nanolithography

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

Ocola, Leonidas E.; Gosztola, David J.; Rosenmann, Daniel

Nanoscale geometry assisted proximity effect correction (NanoPEC) is demonstrated to improve PEC for nanoscale structures over standard PEC, in terms of feature sharpness for sub-100 nm structures. The method was implemented onto an existing commercially available PEC software. Plasmonic arrays of crosses were fabricated using regular PEC and NanoPEC, and optical absorbance was measured. Results confirm that the improved sharpness of the structures leads to increased sharpness in the optical absorbance spectrum features. We also demonstrated that this method of PEC is applicable to arbitrary shaped structures beyond crosses.

33 CFR 203.48 - Inspection guidelines for non-Federal flood control works.

Code of Federal Regulations, 2010 CFR

2010-07-01

... potential for catastrophic failure to cause significant loss of life, the economic benefits of the area... identify critical sections where levee stability appears weakest and will document the location, reach, and... stability of the structure. (4) Other structural features. Other features that may be present, such as pump...

The Semiotic Structure of Geometry Diagrams: How Textbook Diagrams Convey Meaning

ERIC Educational Resources Information Center

Dimmel, Justin K.; Herbst, Patricio G.

2015-01-01

Geometry diagrams use the visual features of specific drawn objects to convey meaning about generic mathematical entities. We examine the semiotic structure of these visual features in two parts. One, we conduct a semiotic inquiry to conceptualize geometry diagrams as mathematical texts that comprise choices from different semiotic systems. Two,…

Extending the Online Public Access Catalog into the Microcomputer Environment.

ERIC Educational Resources Information Center

Sutton, Brett

1990-01-01

Describes PCBIS, a database program for MS-DOS microcomputers that features a utility for automatically converting online public access catalog search results stored as text files into structured database files that can be searched, sorted, edited, and printed. Topics covered include the general features of the program, record structure, record…

A combination of feature extraction methods with an ensemble of different classifiers for protein structural class prediction problem.

PubMed

Dehzangi, Abdollah; Paliwal, Kuldip; Sharma, Alok; Dehzangi, Omid; Sattar, Abdul

2013-01-01

Better understanding of structural class of a given protein reveals important information about its overall folding type and its domain. It can also be directly used to provide critical information on general tertiary structure of a protein which has a profound impact on protein function determination and drug design. Despite tremendous enhancements made by pattern recognition-based approaches to solve this problem, it still remains as an unsolved issue for bioinformatics that demands more attention and exploration. In this study, we propose a novel feature extraction model that incorporates physicochemical and evolutionary-based information simultaneously. We also propose overlapped segmented distribution and autocorrelation-based feature extraction methods to provide more local and global discriminatory information. The proposed feature extraction methods are explored for 15 most promising attributes that are selected from a wide range of physicochemical-based attributes. Finally, by applying an ensemble of different classifiers namely, Adaboost.M1, LogitBoost, naive Bayes, multilayer perceptron (MLP), and support vector machine (SVM) we show enhancement of the protein structural class prediction accuracy for four popular benchmarks.

Preprocessing Structured Clinical Data for Predictive Modeling and Decision Support

PubMed Central

Oliveira, Mónica Duarte; Janela, Filipe; Martins, Henrique M. G.

2016-01-01

Summary Background EHR systems have high potential to improve healthcare delivery and management. Although structured EHR data generates information in machine-readable formats, their use for decision support still poses technical challenges for researchers due to the need to preprocess and convert data into a matrix format. During our research, we observed that clinical informatics literature does not provide guidance for researchers on how to build this matrix while avoiding potential pitfalls. Objectives This article aims to provide researchers a roadmap of the main technical challenges of preprocessing structured EHR data and possible strategies to overcome them. Methods Along standard data processing stages – extracting database entries, defining features, processing data, assessing feature values and integrating data elements, within an EDPAI framework –, we identified the main challenges faced by researchers and reflect on how to address those challenges based on lessons learned from our research experience and on best practices from related literature. We highlight the main potential sources of error, present strategies to approach those challenges and discuss implications of these strategies. Results Following the EDPAI framework, researchers face five key challenges: (1) gathering and integrating data, (2) identifying and handling different feature types, (3) combining features to handle redundancy and granularity, (4) addressing data missingness, and (5) handling multiple feature values. Strategies to address these challenges include: cross-checking identifiers for robust data retrieval and integration; applying clinical knowledge in identifying feature types, in addressing redundancy and granularity, and in accommodating multiple feature values; and investigating missing patterns adequately. Conclusions This article contributes to literature by providing a roadmap to inform structured EHR data preprocessing. It may advise researchers on potential pitfalls and implications of methodological decisions in handling structured data, so as to avoid biases and help realize the benefits of the secondary use of EHR data. PMID:27924347

An empirical assessment of which inland floods can be managed

USGS Publications Warehouse

Mogollón, Beatriz; Frimpong, Emmanuel A.; Hoegh, Andrew B.; Angermeier, Paul

2016-01-01

Riverine flooding is a significant global issue. Although it is well documented that the influence of landscape structure on floods decreases as flood size increases, studies that define a threshold flood-return period, above which landscape features such as topography, land cover and impoundments can curtail floods, are lacking. Further, the relative influences of natural versus built features on floods is poorly understood. Assumptions about the types of floods that can be managed have considerable implications for the cost-effectiveness of decisions to invest in transforming land cover (e.g., reforestation) and in constructing structures (e.g., storm-water ponds) to control floods. This study defines parameters of floods for which changes in landscape structure can have an impact. We compare nine flood-return periods across 31 watersheds with widely varying topography and land cover in the southeastern United States, using long-term hydrologic records (≥20 years). We also assess the effects of built flow-regulating features (best management practices and artificial water bodies) on selected flood metrics across urban watersheds. We show that landscape features affect magnitude and duration of only those floods with return periods ≤10 years, which suggests that larger floods cannot be managed effectively by manipulating landscape structure. Overall, urban watersheds exhibited larger (270 m3/s) but quicker (0.41 days) floods than non-urban watersheds (50 m3/s and 1.5 days). However, urban watersheds with more flow-regulating features had lower flood magnitudes (154 m3/s), but similar flood durations (0.55 days), compared to urban watersheds with fewer flow-regulating features (360 m3/s and 0.23 days). Our analysis provides insight into the magnitude, duration and count of floods that can be curtailed by landscape structure and its management. Our findings are relevant to other areas with similar climate, topography, and land use, and can help ensure that investments in flood management are made wisely after considering the limitations of landscape features to regulate floods.

Effects of lung disease on the three-dimensional structure and air flow pattern in the human airway tree

NASA Astrophysics Data System (ADS)

van de Moortele, Tristan; Nemes, Andras; Wendt, Christine; Coletti, Filippo

2016-11-01

The morphological features of the airway tree directly affect the air flow features during breathing, which determines the gas exchange and inhaled particle transport. Lung disease, Chronic Obstructive Pulmonary Disease (COPD) in this study, affects the structural features of the lungs, which in turn negatively affects the air flow through the airways. Here bronchial tree air volume geometries are segmented from Computed Tomography (CT) scans of healthy and diseased subjects. Geometrical analysis of the airway centerlines and corresponding cross-sectional areas provide insight into the specific effects of COPD on the airway structure. These geometries are also used to 3D print anatomically accurate, patient specific flow models. Three-component, three-dimensional velocity fields within these models are acquired using Magnetic Resonance Imaging (MRI). The three-dimensional flow fields provide insight into the change in flow patterns and features. Additionally, particle trajectories are determined using the velocity fields, to identify the fate of therapeutic and harmful inhaled aerosols. Correlation between disease-specific and patient-specific anatomical features with dysfunctional airflow patterns can be achieved by combining geometrical and flow analysis.

MODAL TRACKING of A Structural Device: A Subspace Identification Approach

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

Candy, J. V.; Franco, S. N.; Ruggiero, E. L.

Mechanical devices operating in an environment contaminated by noise, uncertainties, and extraneous disturbances lead to low signal-to-noise-ratios creating an extremely challenging processing problem. To detect/classify a device subsystem from noisy data, it is necessary to identify unique signatures or particular features. An obvious feature would be resonant (modal) frequencies emitted during its normal operation. In this report, we discuss a model-based approach to incorporate these physical features into a dynamic structure that can be used for such an identification. The approach we take after pre-processing the raw vibration data and removing any extraneous disturbances is to obtain a representation ofmore » the structurally unknown device along with its subsystems that capture these salient features. One approach is to recognize that unique modal frequencies (sinusoidal lines) appear in the estimated power spectrum that are solely characteristic of the device under investigation. Therefore, the objective of this effort is based on constructing a black box model of the device that captures these physical features that can be exploited to “diagnose” whether or not the particular device subsystem (track/detect/classify) is operating normally from noisy vibrational data. Here we discuss the application of a modern system identification approach based on stochastic subspace realization techniques capable of both (1) identifying the underlying black-box structure thereby enabling the extraction of structural modes that can be used for analysis and modal tracking as well as (2) indicators of condition and possible changes from normal operation.« less

Regular Topographic Patterning of Karst Depressions Suggests Landscape Self-Organization

NASA Astrophysics Data System (ADS)

Quintero, C.; Cohen, M. J.

2017-12-01

Thousands of wetland depressions that are commonly host to cypress domes dot the sub-tropical limestone landscape of South Florida. The origin of these depression features has been the topic of debate. Here we build upon the work of previous surveyors of this landscape to analyze the morphology and spatial distribution of depressions on the Big Cypress landscape. We took advantage of the emergence and availability of high resolution Light Direction and Ranging (LiDAR) technology and ArcMap GIS software to analyze the structure and regularity of landscape features with methods unavailable to past surveyors. Six 2.25 km2 LiDAR plots within the preserve were selected for remote analysis and one depression feature within each plot was selected for more intensive sediment and water depth surveying. Depression features on the Big Cypress landscape were found to show strong evidence of regular spatial patterning. Periodicity, a feature of regularly patterned landscapes, is apparent in both Variograms and Radial Spectrum Analyses. Size class distributions of the identified features indicate constrained feature sizes while Average Nearest Neighbor analyses support the inference of dispersed features with non-random spacing. The presence of regular patterning on this landscape strongly implies biotic reinforcement of spatial structure by way of the scale dependent feedback. In characterizing the structure of this wetland landscape we add to the growing body of work dedicated to documenting how water, life and geology may interact to shape the natural landscapes we see today.

Categorical Structure among Shared Features in Networks of Early-Learned Nouns

ERIC Educational Resources Information Center

Hills, Thomas T.; Maouene, Mounir; Maouene, Josita; Sheya, Adam; Smith, Linda

2009-01-01

The shared features that characterize the noun categories that young children learn first are a formative basis of the human category system. To investigate the potential categorical information contained in the features of early-learned nouns, we examine the graph-theoretic properties of noun-feature networks. The networks are built from the…

Crustal structure of the southeastern Brazilian margin, Campos Basin, from aeromagnetic data: New kinematic constraints

NASA Astrophysics Data System (ADS)

Stanton, N.; Schmitt, R.; Galdeano, A.; Maia, M.; Mane, M.

2010-07-01

The continental and adjacent marginal features along southeast Brazil were investigated, focusing on the basement structural relationships between onshore and offshore provinces. Lateral and vertical variations in the magnetic anomalies provided a good correlation with the regional tectonic features. The sin-rift dykes and faults are associated with the magnetic lineaments and lie sub parallel to the Precambrian N45E-S45W basement structure of the Ribeira Belt, but orthogonally to the Cabo Frio Tectonic Domain (CFTD) basement, implying that: (1) the upper portion of the continental crust was widely affected by Mesozoic extensional deformation; and (2) tectonic features related to the process of break up of the Gondwana at the CFTD were form regardless of the preexisting structural basement orientation being controlled by the stress orientation during the rift phase. The deep crustal structure (5 km depth) is characterized by NE-SW magnetic "provinces" related to the Ribeira Belt tectonic units, while deep suture zones are defined by magnetic lows. The offshore Campos structural framework is N30E-S30W oriented and resulted from a main WNW-ESE direction of extension in Early Cretaceous. Transfer zones are represented by NW-SE and E-W oriented discontinuities. A slight difference in orientation between onshore (N45E) and offshore (N30E) structural systems seems to reflect a re-orientation of stress during rifting. We proposed a kinematical model to explain the structural evolution of this portion of the margin, characterized by polyphase rifting, associated with the rotation of the South American plate. The Campos Magnetic High (CMH), an important tectonic feature of the Campos Basin corresponds to a wide area of high crustal magnetization. The CMH wass interpreted as a magmatic feature, mafic to ultramafic in composition that extends down to 14 km depth and constitutes an evidence of intense crustal extension at 60 km from the coast.

Quantifying the Hierarchical Order in Self-Aligned Carbon Nanotubes from Atomic to Micrometer Scale.

PubMed

Meshot, Eric R; Zwissler, Darwin W; Bui, Ngoc; Kuykendall, Tevye R; Wang, Cheng; Hexemer, Alexander; Wu, Kuang Jen J; Fornasiero, Francesco

2017-06-27

Fundamental understanding of structure-property relationships in hierarchically organized nanostructures is crucial for the development of new functionality, yet quantifying structure across multiple length scales is challenging. In this work, we used nondestructive X-ray scattering to quantitatively map the multiscale structure of hierarchically self-organized carbon nanotube (CNT) "forests" across 4 orders of magnitude in length scale, from 2.0 Å to 1.5 μm. Fully resolved structural features include the graphitic honeycomb lattice and interlayer walls (atomic), CNT diameter (nano), as well as the greater CNT ensemble (meso) and large corrugations (micro). Correlating orientational order across hierarchical levels revealed a cascading decrease as we probed finer structural feature sizes with enhanced sensitivity to small-scale disorder. Furthermore, we established qualitative relationships for single-, few-, and multiwall CNT forest characteristics, showing that multiscale orientational order is directly correlated with number density spanning 10 9 -10 12 cm -2 , yet order is inversely proportional to CNT diameter, number of walls, and atomic defects. Lastly, we captured and quantified ultralow-q meridional scattering features and built a phenomenological model of the large-scale CNT forest morphology, which predicted and confirmed that these features arise due to microscale corrugations along the vertical forest direction. Providing detailed structural information at multiple length scales is important for design and synthesis of CNT materials as well as other hierarchically organized nanostructures.

Juniper wood structure under the microscope.

PubMed

Bogolitsyn, Konstantin G; Zubov, Ivan N; Gusakova, Maria A; Chukhchin, Dmitry G; Krasikova, Anna A

2015-05-01

The investigations confirm the physicochemical nature of the structure and self-assembly of wood substance and endorse its application in plant species. The characteristic morphological features, ultra-microstructure, and submolecular structure of coniferous wood matrix using junipers as the representative tree were investigated by scanning electron (SEM) and atomic-force microscopy (AFM). Novel results on the specific composition and cell wall structure features of the common juniper (Juniperus Communis L.) were obtained. These data confirm the possibility of considering the wood substance as a nanobiocomposite. The cellulose nanofibrils (20-50 nm) and globular-shaped lignin-carbohydrate structures (diameter of 5-60 nm) form the base of such a nanobiocomposite.

Characterizing core-periphery structure of complex network by h-core and fingerprint curve

NASA Astrophysics Data System (ADS)

Li, Simon S.; Ye, Adam Y.; Qi, Eric P.; Stanley, H. Eugene; Ye, Fred Y.

2018-02-01

It is proposed that the core-periphery structure of complex networks can be simulated by h-cores and fingerprint curves. While the features of core structure are characterized by h-core, the features of periphery structure are visualized by rose or spiral curve as the fingerprint curve linking to entire-network parameters. It is suggested that a complex network can be approached by h-core and rose curves as the first-order Fourier-approach, where the core-periphery structure is characterized by five parameters: network h-index, network radius, degree power, network density and average clustering coefficient. The simulation looks Fourier-like analysis.

Some of the most interesting CASP11 targets through the eyes of their authors.

PubMed

Kryshtafovych, Andriy; Moult, John; Baslé, Arnaud; Burgin, Alex; Craig, Timothy K; Edwards, Robert A; Fass, Deborah; Hartmann, Marcus D; Korycinski, Mateusz; Lewis, Richard J; Lorimer, Donald; Lupas, Andrei N; Newman, Janet; Peat, Thomas S; Piepenbrink, Kurt H; Prahlad, Janani; van Raaij, Mark J; Rohwer, Forest; Segall, Anca M; Seguritan, Victor; Sundberg, Eric J; Singh, Abhimanyu K; Wilson, Mark A; Schwede, Torsten

2016-09-01

The Critical Assessment of protein Structure Prediction (CASP) experiment would not have been possible without the prediction targets provided by the experimental structural biology community. In this article, selected crystallographers providing targets for the CASP11 experiment discuss the functional and biological significance of the target proteins, highlight their most interesting structural features, and assess whether these features were correctly reproduced in the predictions submitted to CASP11. Proteins 2016; 84(Suppl 1):34-50. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.

Bioprospecting for Exopolysaccharides from Deep-Sea Hydrothermal Vent Bacteria: Relationship between Bacterial Diversity and Chemical Diversity

PubMed Central

Delbarre-Ladrat, Christine; Leyva Salas, Marcia; Zykwinska, Agata; Colliec-Jouault, Sylvia

2017-01-01

Many bacteria biosynthesize structurally diverse exopolysaccharides (EPS) and excrete them into their surrounding environment. The EPS functional features have found many applications in industries such as cosmetics and pharmaceutics. In particular, some EPS produced by marine bacteria are composed of uronic acids, neutral sugars, and N-acetylhexosamines, and may also bear some functional sulfate groups. This suggests that they can share common structural features with glycosaminoglycans (GAG) like the two EPS (HE800 and GY785) originating from the deep sea. In an attempt to discover new EPS that may be promising candidates as GAG-mimetics, fifty-one marine bacterial strains originating from deep-sea hydrothermal vents were screened. The analysis of the EPS chemical structure in relation to bacterial species showed that Vibrio, Alteromonas, and Pseudoalteromonas strains were the main producers. Moreover, they produced EPS with distinct structural features, which might be useful for targeting marine bacteria that could possibly produce structurally GAG-mimetic EPS. PMID:28930185

10 CFR 830.3 - Definitions.

Code of Federal Regulations, 2012 CFR

2012-01-01

.... Critical assembly means special nuclear devices designed and used to sustain nuclear reactions, which may... reaction becomes self-sustaining. Design features means the design features of a nuclear facility specified..., or the environment, including (1) Physical, design, structural, and engineering features; (2) Safety...

10 CFR 830.3 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-01-01

.... Critical assembly means special nuclear devices designed and used to sustain nuclear reactions, which may... reaction becomes self-sustaining. Design features means the design features of a nuclear facility specified..., or the environment, including (1) Physical, design, structural, and engineering features; (2) Safety...

Discrete cloud structure on Neptune

NASA Technical Reports Server (NTRS)

Hammel, H. B.

1989-01-01

Recent CCD imaging data for the discrete cloud structure of Neptune shows that while cloud features at CH4-band wavelengths are manifest in the southern hemisphere, they have not been encountered in the northern hemisphere since 1986. A literature search has shown the reflected CH4-band light from the planet to have come from a single discrete feature at least twice in the last 10 years. Disk-integrated photometry derived from the imaging has demonstrated that a bright cloud feature was responsible for the observed 8900 A diurnal variation in 1986 and 1987.

Vibrational tug-of-war: The pKA dependence of the broad vibrational features of strongly hydrogen-bonded carboxylic acids.

PubMed

Van Hoozen, Brian L; Petersen, Poul B

2018-04-07

Medium and strong hydrogen bonds give rise to broad vibrational features frequently spanning several hundred wavenumbers and oftentimes exhibiting unusual substructures. These broad vibrational features can be modeled from first principles, in a reduced dimensional calculation, that adiabatically separates low-frequency modes, which modulate the hydrogen bond length, from high-frequency OH stretch and bend modes that contribute to the vibrational structure. Previously this method was used to investigate the origin of an unusual vibrational feature frequently found in the spectra of dimers between carboxylic acids and nitrogen-containing aromatic bases that spans over 900 cm -1 and contains two broad peaks. It was found that the width of this feature largely originates from low-frequency modes modulating the hydrogen bond length and that the structure results from Fermi resonance interactions. In this report, we examine how these features change with the relative acid and base strength of the components as reflected by their aqueous pK A values. Dimers with large pK A differences are found to have features that can extend to frequencies below 1000 cm -1 . The relationships between mean OH/NH frequency, aqueous pK A , and O-N distance are examined in order to obtain a more rigorous understanding of the origin and shape of the vibrational features. The mean OH/NH frequencies are found to correlate well with O-N distances. The lowest OH stretch frequencies are found in dimer geometries with O-N distances between 2.5 and 2.6 Å. At larger O-N distances, the hydrogen bonding interaction is not as strong, resulting in higher OH stretch frequencies. When the O-N distance is smaller than 2.5 Å, the limited space between the O and N determines the OH stretch frequency, which gives rise to frequencies that decrease with O-N distances. These two effects place a lower limit on the OH stretch frequency which is calculated to be near 700 cm -1 . Understanding how the vibrational features of strongly hydrogen-bonded structures depend on the relative pK A and other structural parameters will guide studies of biological structures and analysis of proton transfer studies using photoacids.

A field investigation of the basaltic ring structures of the Channeled Scabland and the relevance to Mars

USGS Publications Warehouse

Kestay, Laszlo P.; Jaeger, Windy L.

2015-01-01

The basaltic ring structure (BRS) is a class of peculiar features only reported in the Channeled Scabland of eastern Washington State. They have been suggested to be good analogs, however, for some circular features on Mars. BRSs are found where Pleistocene floods scoured the Columbia River Basin, stripping off the uppermost part of the Miocene Columbia River Basalt Group and exposing structures that were previously embedded in the lava. The “Odessa Craters,” near Odessa, WA, are 50–500-m-wide BRSs that are comprised of discontinuous, concentric outcrops of subvertically-jointed basalt and autointrusive dikes. Detailed field investigation of the Odessa Craters in planform and a cross-sectional exposure of a similar structure above Banks Lake, WA, lead us to propose that BRSs formed by concurrent phreatovolcanism and lava flow inflation. In this model, phreatovolcanic (a.k.a., “rootless”) cones formed on a relatively thin, active lava flow; the lava flow inflated around the cones, locally inverting topography; tensile stresses caused concentric fracturing of the lava crust; lava from within the molten interior of the flow exploited the fractures and buried the phreatovolcanic cones; and subsequent erosive floods excavated the structures. Another population of BRSs near Tokio Station, WA, consists of single-ringed, raised-rimmed structures that are smaller and more randomly distributed than the Odessa Craters. We find evidence for a phreatovolcanic component to the origin as well, and hypothesize that they are either flood-eroded phreatovolcanic cones or Odessa Crater-like BRSs. This work indicates that BRSs are not good analogs to the features on Mars because the martian features are found on the uneroded surfaces. Despite this, the now superseded concepts for BRS formation are useful for understanding the formation of the martian features.

A novel Multi-Agent Ada-Boost algorithm for predicting protein structural class with the information of protein secondary structure.

PubMed

Fan, Ming; Zheng, Bin; Li, Lihua

2015-10-01

Knowledge of the structural class of a given protein is important for understanding its folding patterns. Although a lot of efforts have been made, it still remains a challenging problem for prediction of protein structural class solely from protein sequences. The feature extraction and classification of proteins are the main problems in prediction. In this research, we extended our earlier work regarding these two aspects. In protein feature extraction, we proposed a scheme by calculating the word frequency and word position from sequences of amino acid, reduced amino acid, and secondary structure. For an accurate classification of the structural class of protein, we developed a novel Multi-Agent Ada-Boost (MA-Ada) method by integrating the features of Multi-Agent system into Ada-Boost algorithm. Extensive experiments were taken to test and compare the proposed method using four benchmark datasets in low homology. The results showed classification accuracies of 88.5%, 96.0%, 88.4%, and 85.5%, respectively, which are much better compared with the existing methods. The source code and dataset are available on request.

Local structure-based image decomposition for feature extraction with applications to face recognition.

PubMed

Qian, Jianjun; Yang, Jian; Xu, Yong

2013-09-01

This paper presents a robust but simple image feature extraction method, called image decomposition based on local structure (IDLS). It is assumed that in the local window of an image, the macro-pixel (patch) of the central pixel, and those of its neighbors, are locally linear. IDLS captures the local structural information by describing the relationship between the central macro-pixel and its neighbors. This relationship is represented with the linear representation coefficients determined using ridge regression. One image is actually decomposed into a series of sub-images (also called structure images) according to a local structure feature vector. All the structure images, after being down-sampled for dimensionality reduction, are concatenated into one super-vector. Fisher linear discriminant analysis is then used to provide a low-dimensional, compact, and discriminative representation for each super-vector. The proposed method is applied to face recognition and examined using our real-world face image database, NUST-RWFR, and five popular, publicly available, benchmark face image databases (AR, Extended Yale B, PIE, FERET, and LFW). Experimental results show the performance advantages of IDLS over state-of-the-art algorithms.

CMOS Active-Pixel Image Sensor With Simple Floating Gates

NASA Technical Reports Server (NTRS)

Fossum, Eric R.; Nakamura, Junichi; Kemeny, Sabrina E.

1996-01-01

Experimental complementary metal-oxide/semiconductor (CMOS) active-pixel image sensor integrated circuit features simple floating-gate structure, with metal-oxide/semiconductor field-effect transistor (MOSFET) as active circuit element in each pixel. Provides flexibility of readout modes, no kTC noise, and relatively simple structure suitable for high-density arrays. Features desirable for "smart sensor" applications.

Students' Demand for Smartphones: Structural Relationships of Product Features, Brand Name, Product Price and Social Infuence

ERIC Educational Resources Information Center

Suki, Norazah Mohd

2013-01-01

Purpose: The study aims to examine structural relationships of product features, brand name, product price and social influence with demand for Smartphones among Malaysian students'. Design/methodology/approach: Data collected from 320 valid pre-screened university students studying at the pubic higher learning institution in Federal Territory of…

Learning in Structured Connectionist Networks

DTIC Science & Technology

1988-04-01

the structure is too rigid and learning too difficult for cognitive modeling. Two algorithms for learning simple, feature-based concept descriptions...and learning too difficult for cognitive model- ing. Two algorithms for learning simple, feature-based concept descriptions were also implemented. The...Term Goals Recent progress in connectionist research has been encouraging; networks have success- fully modeled human performance for various cognitive

Mapping accuracy via spectrally and structurally based filtering techniques: comparisons through visual observations

NASA Astrophysics Data System (ADS)

Chockalingam, Letchumanan

2005-01-01

The data of Gunung Ledang region of Malaysia acquired through LANDSAT are considered to map certain hydrogeolocial features. To map these significant features, image-processing tools such as contrast enhancement, edge detection techniques are employed. The advantages of these techniques over the other methods are evaluated from the point of their validity in properly isolating features of hydrogeolocial interest are discussed. As these techniques take the advantage of spectral aspects of the images, these techniques have several limitations to meet the objectives. To discuss these limitations, a morphological transformation, which generally considers the structural aspects rather than spectral aspects from the image, are applied to provide comparisons between the results derived from spectral based and the structural based filtering techniques.

Experimental Evaluation of a Structure-Based Connectionist Network for Fault Diagnosis of Helicopter Gearboxes

NASA Technical Reports Server (NTRS)

Jammu, V. B.; Danai, K.; Lewicki, D. G.

1998-01-01

This paper presents the experimental evaluation of the Structure-Based Connectionist Network (SBCN) fault diagnostic system introduced in the preceding article. For this vibration data from two different helicopter gearboxes: OH-58A and S-61, are used. A salient feature of SBCN is its reliance on the knowledge of the gearbox structure and the type of features obtained from processed vibration signals as a substitute to training. To formulate this knowledge, approximate vibration transfer models are developed for the two gearboxes and utilized to derive the connection weights representing the influence of component faults on vibration features. The validity of the structural influences is evaluated by comparing them with those obtained from experimental RMS values. These influences are also evaluated ba comparing them with the weights of a connectionist network trained though supervised learning. The results indicate general agreement between the modeled and experimentally obtained influences. The vibration data from the two gearboxes are also used to evaluate the performance of SBCN in fault diagnosis. The diagnostic results indicate that the SBCN is effective in directing the presence of faults and isolating them within gearbox subsystems based on structural influences, but its performance is not as good in isolating faulty components, mainly due to lack of appropriate vibration features.

Structural and lithologic study of northern coast ranges and Sacramento Valley, California

NASA Technical Reports Server (NTRS)

Rich, E. I. (Principal Investigator)

1973-01-01

The author has identified the following significant results. The pattern of linear systems within the project area has been extended into the western foothill belt of the Sierra Nevada. The chief pattern of linear features in the western Sierran foothill belt trends about N. 10 - 15 deg W., but in the vicinity of the Feather River the trend of the features abruptly changes to about N. 50-60 deg W and appears to be contiguous across the Sacramento Valley with a similar system of linear features in the Coast Ranges. The linear features in the Modoc Plateau and Klamath Mt. areas appear unrelated to the systems detected in the Coast Ranges of Sierran foothill belt. Although the change in trend of the Sierran structural features has been previously suggested and the interrelationship of the Klamath Mt. region with the northern Sierra Nevadas has been postulated, the data obtained from the ERTS-1 imagery strengthens these notions and provides for the first time evidence of a direct connection of the structural trends within the alluviated part of the Sacramento Valley. In addition rocks of Pleistocene and Holocene age are offset by some of the linear features seen on ERTS-1 imagery and hence may record the latest episode of geologic deformation in north-central California.

Cointegration as a data normalization tool for structural health monitoring applications

NASA Astrophysics Data System (ADS)

Harvey, Dustin Y.; Todd, Michael D.

2012-04-01

The structural health monitoring literature has shown an abundance of features sensitive to various types of damage in laboratory tests. However, robust feature extraction in the presence of varying operational and environmental conditions has proven to be one of the largest obstacles in the development of practical structural health monitoring systems. Cointegration, a technique adapted from the field of econometrics, has recently been introduced to the SHM field as one solution to the data normalization problem. Response measurements and feature histories often show long-run nonstationarity due to fluctuating temperature, load conditions, or other factors that leads to the occurrence of false positives. Cointegration theory allows nonstationary trends common to two or more time series to be modeled and subsequently removed. Thus, the residual retains sensitivity to damage with dependence on operational and environmental variability removed. This study further explores the use of cointegration as a data normalization tool for structural health monitoring applications.

Robust Learning of High-dimensional Biological Networks with Bayesian Networks

NASA Astrophysics Data System (ADS)

Nägele, Andreas; Dejori, Mathäus; Stetter, Martin

Structure learning of Bayesian networks applied to gene expression data has become a potentially useful method to estimate interactions between genes. However, the NP-hardness of Bayesian network structure learning renders the reconstruction of the full genetic network with thousands of genes unfeasible. Consequently, the maximal network size is usually restricted dramatically to a small set of genes (corresponding with variables in the Bayesian network). Although this feature reduction step makes structure learning computationally tractable, on the downside, the learned structure might be adversely affected due to the introduction of missing genes. Additionally, gene expression data are usually very sparse with respect to the number of samples, i.e., the number of genes is much greater than the number of different observations. Given these problems, learning robust network features from microarray data is a challenging task. This chapter presents several approaches tackling the robustness issue in order to obtain a more reliable estimation of learned network features.

Hidden electronic rule in the “cluster-plus-glue-atom” model

PubMed Central

Du, Jinglian; Dong, Chuang; Melnik, Roderick; Kawazoe, Yoshiyuki; Wen, Bin

2016-01-01

Electrons and their interactions are intrinsic factors to affect the structure and properties of materials. Based on the “cluster-cluster-plus-glue-atom” model, an electron counting rule for complex metallic alloys (CMAs) has been revealed in this work (i. e. the CPGAMEC rule). Our results on the cluster structure and electron concentration of CMAs with apparent cluster features, indicate that the valence electrons’ number per unit cluster formula for these CMAs are specific constants of eight-multiples and twelve-multiples. It is thus termed as specific electrons cluster formula. This CPGAMEC rule has been demonstrated as a useful guidance to direct the design of CMAs with desired properties, while its practical applications and underlying mechanism have been illustrated on the basis of CMAs’ cluster structural features. Our investigation provides an aggregate picture with intriguing electronic rule and atomic structural features of CMAs. PMID:27642002

Examining Brain Morphometry Associated with Self-Esteem in Young Adults Using Multilevel-ROI-Features-Based Classification Method

PubMed Central

Peng, Bo; Lu, Jieru; Saxena, Aditya; Zhou, Zhiyong; Zhang, Tao; Wang, Suhong; Dai, Yakang

2017-01-01

Purpose: This study is to exam self-esteem related brain morphometry on brain magnetic resonance (MR) images using multilevel-features-based classification method. Method: The multilevel region of interest (ROI) features consist of two types of features: (i) ROI features, which include gray matter volume, white matter volume, cerebrospinal fluid volume, cortical thickness, and cortical surface area, and (ii) similarity features, which are based on similarity calculation of cortical thickness between ROIs. For each feature type, a hybrid feature selection method, comprising of filter-based and wrapper-based algorithms, is used to select the most discriminating features. ROI features and similarity features are integrated by using multi-kernel support vector machines (SVMs) with appropriate weighting factor. Results: The classification performance is improved by using multilevel ROI features with an accuracy of 96.66%, a specificity of 96.62%, and a sensitivity of 95.67%. The most discriminating ROI features that are related to self-esteem spread over occipital lobe, frontal lobe, parietal lobe, limbic lobe, temporal lobe, and central region, mainly involving white matter and cortical thickness. The most discriminating similarity features are distributed in both the right and left hemisphere, including frontal lobe, occipital lobe, limbic lobe, parietal lobe, and central region, which conveys information of structural connections between different brain regions. Conclusion: By using ROI features and similarity features to exam self-esteem related brain morphometry, this paper provides a pilot evidence that self-esteem is linked to specific ROIs and structural connections between different brain regions. PMID:28588470

Examining Brain Morphometry Associated with Self-Esteem in Young Adults Using Multilevel-ROI-Features-Based Classification Method.

PubMed

Peng, Bo; Lu, Jieru; Saxena, Aditya; Zhou, Zhiyong; Zhang, Tao; Wang, Suhong; Dai, Yakang

2017-01-01

Purpose: This study is to exam self-esteem related brain morphometry on brain magnetic resonance (MR) images using multilevel-features-based classification method. Method: The multilevel region of interest (ROI) features consist of two types of features: (i) ROI features, which include gray matter volume, white matter volume, cerebrospinal fluid volume, cortical thickness, and cortical surface area, and (ii) similarity features, which are based on similarity calculation of cortical thickness between ROIs. For each feature type, a hybrid feature selection method, comprising of filter-based and wrapper-based algorithms, is used to select the most discriminating features. ROI features and similarity features are integrated by using multi-kernel support vector machines (SVMs) with appropriate weighting factor. Results: The classification performance is improved by using multilevel ROI features with an accuracy of 96.66%, a specificity of 96.62%, and a sensitivity of 95.67%. The most discriminating ROI features that are related to self-esteem spread over occipital lobe, frontal lobe, parietal lobe, limbic lobe, temporal lobe, and central region, mainly involving white matter and cortical thickness. The most discriminating similarity features are distributed in both the right and left hemisphere, including frontal lobe, occipital lobe, limbic lobe, parietal lobe, and central region, which conveys information of structural connections between different brain regions. Conclusion: By using ROI features and similarity features to exam self-esteem related brain morphometry, this paper provides a pilot evidence that self-esteem is linked to specific ROIs and structural connections between different brain regions.

Guiding Students through Expository Text with Text Feature Walks

ERIC Educational Resources Information Center

Kelley, Michelle J.; Clausen-Grace, Nicki

2010-01-01

The Text Feature Walk is a structure created and employed by the authors that guides students in the reading of text features in order to access prior knowledge, make connections, and set a purpose for reading expository text. Results from a pilot study are described in order to illustrate the benefits of using the Text Feature Walk over…

The Structured Intuitive Model for Product Line Economics (SIMPLE)

DTIC Science & Technology

2005-02-01

units are features and use cases. A feature is just as nebulous as a requirement, but techniques such as feature-oriented domain analysis ( FODA ) [Kang 90...cost avoidance DM design modified DOCU degree of documentation GQM Goal Question Metric FODA feature-oriented domain analysis IM integration effort...Hess, J.; Novak, W.; & Peterson, A. Feature- Oriented Domain Analysis ( FODA ) Feasibility Study (CMU/SEI- 90-TR-02 1, ADA235785). Pittsburgh, PA

Multiscale Feature Analysis of Salivary Gland Branching Morphogenesis

PubMed Central

Baydil, Banu; Daley, William P.; Larsen, Melinda; Yener, Bülent

2012-01-01

Pattern formation in developing tissues involves dynamic spatio-temporal changes in cellular organization and subsequent evolution of functional adult structures. Branching morphogenesis is a developmental mechanism by which patterns are generated in many developing organs, which is controlled by underlying molecular pathways. Understanding the relationship between molecular signaling, cellular behavior and resulting morphological change requires quantification and categorization of the cellular behavior. In this study, tissue-level and cellular changes in developing salivary gland in response to disruption of ROCK-mediated signaling by are modeled by building cell-graphs to compute mathematical features capturing structural properties at multiple scales. These features were used to generate multiscale cell-graph signatures of untreated and ROCK signaling disrupted salivary gland organ explants. From confocal images of mouse submandibular salivary gland organ explants in which epithelial and mesenchymal nuclei were marked, a multiscale feature set capturing global structural properties, local structural properties, spectral, and morphological properties of the tissues was derived. Six feature selection algorithms and multiway modeling of the data was performed to identify distinct subsets of cell graph features that can uniquely classify and differentiate between different cell populations. Multiscale cell-graph analysis was most effective in classification of the tissue state. Cellular and tissue organization, as defined by a multiscale subset of cell-graph features, are both quantitatively distinct in epithelial and mesenchymal cell types both in the presence and absence of ROCK inhibitors. Whereas tensor analysis demonstrate that epithelial tissue was affected the most by inhibition of ROCK signaling, significant multiscale changes in mesenchymal tissue organization were identified with this analysis that were not identified in previous biological studies. We here show how to define and calculate a multiscale feature set as an effective computational approach to identify and quantify changes at multiple biological scales and to distinguish between different states in developing tissues. PMID:22403724

The Words of Children's Television.

ERIC Educational Resources Information Center

Rice, Mabel L.

1984-01-01

Dialog features--communication flow, language structures, and meaning/content--and nonverbal formal features of six children's television programs are examined to determine if there is dialog simplification, if certain dialog characteristics differentiate among shows sampled, and if there are different combinations of linguistic features and…

System Complexity Reduction via Feature Selection

ERIC Educational Resources Information Center

Deng, Houtao

2011-01-01

This dissertation transforms a set of system complexity reduction problems to feature selection problems. Three systems are considered: classification based on association rules, network structure learning, and time series classification. Furthermore, two variable importance measures are proposed to reduce the feature selection bias in tree…

Some of the most interesting CASP11 targets through the eyes of their authors

PubMed Central

Kryshtafovych, Andriy; Moult, John; Baslé, Arnaud; Burgin, Alex; Craig, Timothy K.; Edwards, Robert A.; Fass, Deborah; Hartmann, Marcus D.; Korycinski, Mateusz; Lewis, Richard J.; Lorimer, Donald; Lupas, Andrei N.; Newman, Janet; Peat, Thomas S.; Piepenbrink, Kurt H.; Prahlad, Janani; van Raaij, Mark J.; Rohwer, Forest; Segall, Anca M.; Seguritan, Victor; Sundberg, Eric J.; Singh, Abhimanyu K.; Wilson, Mark A.

2015-01-01

ABSTRACT The Critical Assessment of protein Structure Prediction (CASP) experiment would not have been possible without the prediction targets provided by the experimental structural biology community. In this article, selected crystallographers providing targets for the CASP11 experiment discuss the functional and biological significance of the target proteins, highlight their most interesting structural features, and assess whether these features were correctly reproduced in the predictions submitted to CASP11. Proteins 2016; 84(Suppl 1):34–50. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc. PMID:26473983

Site-specific electronic structure analysis by channeling EELS and first-principles calculations.

PubMed

Tatsumi, Kazuyoshi; Muto, Shunsuke; Yamamoto, Yu; Ikeno, Hirokazu; Yoshioka, Satoru; Tanaka, Isao

2006-01-01

Site-specific electronic structures were investigated by electron energy loss spectroscopy (EELS) under electron channeling conditions. The Al-K and Mn-L(2,3) electron energy loss near-edge structure (ELNES) of, respectively, NiAl2O4 and Mn3O4 were measured. Deconvolution of the raw spectra with the instrumental resolution function restored the blunt and hidden fine features, which allowed us to interpret the experimental spectral features by comparing with theoretical spectra obtained by first-principles calculations. The present method successfully revealed the electronic structures specific to the differently coordinated cationic sites.

Predicting film genres with implicit ideals.

PubMed

Olney, Andrew McGregor

2012-01-01

We present a new approach to defining film genre based on implicit ideals. When viewers rate the likability of a film, they indirectly express their ideal of what a film should be. Across six studies we investigate the category structure that emerges from likability ratings and the category structure that emerges from the features of film. We further compare these data-driven category structures with human annotated film genres. We conclude that film genres are structured more around ideals than around features of film. This finding lends experimental support to the notion that film genres are set of shifting, fuzzy, and highly contextualized psychological categories.

Quantitative evaluation method of the bubble structure of sponge cake by using morphology image processing

NASA Astrophysics Data System (ADS)

Tatebe, Hironobu; Kato, Kunihito; Yamamoto, Kazuhiko; Katsuta, Yukio; Nonaka, Masahiko

2005-12-01

Now a day, many evaluation methods for the food industry by using image processing are proposed. These methods are becoming new evaluation method besides the sensory test and the solid-state measurement that are using for the quality evaluation. An advantage of the image processing is to be able to evaluate objectively. The goal of our research is structure evaluation of sponge cake by using image processing. In this paper, we propose a feature extraction method of the bobble structure in the sponge cake. Analysis of the bubble structure is one of the important properties to understand characteristics of the cake from the image. In order to take the cake image, first we cut cakes and measured that's surface by using the CIS scanner. Because the depth of field of this type scanner is very shallow, the bubble region of the surface has low gray scale values, and it has a feature that is blur. We extracted bubble regions from the surface images based on these features. First, input image is binarized, and the feature of bubble is extracted by the morphology analysis. In order to evaluate the result of feature extraction, we compared correlation with "Size of the bubble" of the sensory test result. From a result, the bubble extraction by using morphology analysis gives good correlation. It is shown that our method is as well as the subjectivity evaluation.

UV absorption investigation of ferromagnetically filled ultra-thick carbon onions, carriers of the 217.5 nm Interstellar Absorption Feature

NASA Astrophysics Data System (ADS)

Boi, Filippo S.; Zhang, Xiaotian; Ivaturi, Sameera; Liu, Qianyang; Wen, Jiqiu; Wang, Shanling

2017-12-01

Carbon nano-onions (CNOs) are fullerene-like structures which consist of quasi-spherical closed carbon shells. These structures have become a subject of great interest thanks to their characteristic absorption feature of interstellar origin (at 217.5 nm, 4.6 μm-1). An additional extinction peak at 3.8 μm-1 has also been reported and attributed to absorption by graphitic residues between the as-grown CNOs. Here, we report the ultraviolet absorption properties of ultra-thick CNOs filled with FePt3 crystals, which also exhibit two main absorption peaks—features located at 4.58 μm-1 and 3.44 μm-1. The presence of this additional feature is surprising and is attributed to nonmagnetic graphite flakes produced as a by-product in the pyrolysis experiment (as confirmed by magnetic separation methods). Instead, the feature at 4.58 μm-1 is associated with the π-plasmonic resonance of the CNOs structures. The FePt3 filled CNOs were fabricated in situ by an advanced one-step fast process consisting in the direct sublimation and pyrolysis of two molecular precursors, namely, ferrocene and dichloro-cyclooctadiene-platinum in a chemical vapour deposition system. The morphological, structural, and magnetic properties of the as-grown filled CNOs were characterized by a means of scanning and transmission electron microscopy, X-ray diffraction, and magnetometry.

Characterizing spatial structure of sediment E. coli populations to inform sampling design.

PubMed

Piorkowski, Gregory S; Jamieson, Rob C; Hansen, Lisbeth Truelstrup; Bezanson, Greg S; Yost, Chris K

2014-01-01

Escherichia coli can persist in streambed sediments and influence water quality monitoring programs through their resuspension into overlying waters. This study examined the spatial patterns in E. coli concentration and population structure within streambed morphological features during baseflow and following stormflow to inform sampling strategies for representative characterization of E. coli populations within a stream reach. E. coli concentrations in bed sediments were significantly different (p = 0.002) among monitoring sites during baseflow, and significant interactive effects (p = 0.002) occurred among monitoring sites and morphological features following stormflow. Least absolute shrinkage and selection operator (LASSO) regression revealed that water velocity and effective particle size (D 10) explained E. coli concentration during baseflow, whereas sediment organic carbon, water velocity and median particle diameter (D 50) were important explanatory variables following stormflow. Principle Coordinate Analysis illustrated the site-scale differences in sediment E. coli populations between disconnected stream segments. Also, E. coli populations were similar among depositional features within a reach, but differed in relation to high velocity features (e.g., riffles). Canonical correspondence analysis resolved that E. coli population structure was primarily explained by spatial (26.9–31.7 %) over environmental variables (9.2–13.1 %). Spatial autocorrelation existed among monitoring sites and morphological features for both sampling events, and gradients in mean particle diameter and water velocity influenced E. coli population structure for the baseflow and stormflow sampling events, respectively. Representative characterization of streambed E. coli requires sampling of depositional and high velocity environments to accommodate strain selectivity among these features owing to sediment and water velocity heterogeneity.

Predictive brain networks for major depression in a semi-multimodal fusion hierarchical feature reduction framework.

PubMed

Yang, Jie; Yin, Yingying; Zhang, Zuping; Long, Jun; Dong, Jian; Zhang, Yuqun; Xu, Zhi; Li, Lei; Liu, Jie; Yuan, Yonggui

2018-02-05

Major depressive disorder (MDD) is characterized by dysregulation of distributed structural and functional networks. It is now recognized that structural and functional networks are related at multiple temporal scales. The recent emergence of multimodal fusion methods has made it possible to comprehensively and systematically investigate brain networks and thereby provide essential information for influencing disease diagnosis and prognosis. However, such investigations are hampered by the inconsistent dimensionality features between structural and functional networks. Thus, a semi-multimodal fusion hierarchical feature reduction framework is proposed. Feature reduction is a vital procedure in classification that can be used to eliminate irrelevant and redundant information and thereby improve the accuracy of disease diagnosis. Our proposed framework primarily consists of two steps. The first step considers the connection distances in both structural and functional networks between MDD and healthy control (HC) groups. By adding a constraint based on sparsity regularization, the second step fully utilizes the inter-relationship between the two modalities. However, in contrast to conventional multi-modality multi-task methods, the structural networks were considered to play only a subsidiary role in feature reduction and were not included in the following classification. The proposed method achieved a classification accuracy, specificity, sensitivity, and area under the curve of 84.91%, 88.6%, 81.29%, and 0.91, respectively. Moreover, the frontal-limbic system contributed the most to disease diagnosis. Importantly, by taking full advantage of the complementary information from multimodal neuroimaging data, the selected consensus connections may be highly reliable biomarkers of MDD. Copyright © 2017 Elsevier B.V. All rights reserved.

Prediction of phenotypes of missense mutations in human proteins from biological assemblies.

PubMed

Wei, Qiong; Xu, Qifang; Dunbrack, Roland L

2013-02-01

Single nucleotide polymorphisms (SNPs) are the most frequent variation in the human genome. Nonsynonymous SNPs that lead to missense mutations can be neutral or deleterious, and several computational methods have been presented that predict the phenotype of human missense mutations. These methods use sequence-based and structure-based features in various combinations, relying on different statistical distributions of these features for deleterious and neutral mutations. One structure-based feature that has not been studied significantly is the accessible surface area within biologically relevant oligomeric assemblies. These assemblies are different from the crystallographic asymmetric unit for more than half of X-ray crystal structures. We find that mutations in the core of proteins or in the interfaces in biological assemblies are significantly more likely to be disease-associated than those on the surface of the biological assemblies. For structures with more than one protein in the biological assembly (whether the same sequence or different), we find the accessible surface area from biological assemblies provides a statistically significant improvement in prediction over the accessible surface area of monomers from protein crystal structures (P = 6e-5). When adding this information to sequence-based features such as the difference between wildtype and mutant position-specific profile scores, the improvement from biological assemblies is statistically significant but much smaller (P = 0.018). Combining this information with sequence-based features in a support vector machine leads to 82% accuracy on a balanced dataset of 50% disease-associated mutations from SwissVar and 50% neutral mutations from human/primate sequence differences in orthologous proteins. Copyright © 2012 Wiley Periodicals, Inc.

Segmentation of retinal blood vessels using artificial neural networks for early detection of diabetic retinopathy

NASA Astrophysics Data System (ADS)

Mann, Kulwinder S.; Kaur, Sukhpreet

2017-06-01

There are various eye diseases in the patients suffering from the diabetes which includes Diabetic Retinopathy, Glaucoma, Hypertension etc. These all are the most common sight threatening eye diseases due to the changes in the blood vessel structure. The proposed method using supervised methods concluded that the segmentation of the retinal blood vessels can be performed accurately using neural networks training. It uses features which include Gray level features; Moment Invariant based features, Gabor filtering, Intensity feature, Vesselness feature for feature vector computation. Then the feature vector is calculated using only the prominent features.

Structure and weights optimisation of a modified Elman network emotion classifier using hybrid computational intelligence algorithms: a comparative study

NASA Astrophysics Data System (ADS)

Sheikhan, Mansour; Abbasnezhad Arabi, Mahdi; Gharavian, Davood

2015-10-01

Artificial neural networks are efficient models in pattern recognition applications, but their performance is dependent on employing suitable structure and connection weights. This study used a hybrid method for obtaining the optimal weight set and architecture of a recurrent neural emotion classifier based on gravitational search algorithm (GSA) and its binary version (BGSA), respectively. By considering the features of speech signal that were related to prosody, voice quality, and spectrum, a rich feature set was constructed. To select more efficient features, a fast feature selection method was employed. The performance of the proposed hybrid GSA-BGSA method was compared with similar hybrid methods based on particle swarm optimisation (PSO) algorithm and its binary version, PSO and discrete firefly algorithm, and hybrid of error back-propagation and genetic algorithm that were used for optimisation. Experimental tests on Berlin emotional database demonstrated the superior performance of the proposed method using a lighter network structure.

Supportability of a High-Yield-Stress Slurry in a New Stereolithography-Based Ceramic Fabrication Process

NASA Astrophysics Data System (ADS)

He, Li; Song, Xuan

2018-03-01

In recent years, ceramic fabrication using stereolithography (SLA) has gained in popularity because of its high accuracy and density that can be achieved in the final part of production. One of the key challenges in ceramic SLA is that support structures are required for building overhanging features, whereas removing these support structures without damaging the components is difficult. In this research, a suspension-enclosing projection-stereolithography process is developed to overcome this challenge. This process uses a high-yield-stress ceramic slurry as the feedstock material and exploits the elastic force of the material to support overhanging features without the need for building additional support structures. Ceramic slurries with different solid loadings are studied to identify the rheological properties most suitable for supporting overhanging features. An analytical model of a double doctor-blade module is established to obtain uniform and thin recoating layers from a high-yield-stress slurry. Several test cases highlight the feasibility of using a high-yield-stress slurry to support overhanging features in SLA.

A novel method for the fabrication of microfluidic devices by photopolymerization of polymethylmethacrylate

NASA Astrophysics Data System (ADS)

Forstater, Jacob; Augustine, Brian; Hughes, Chris

2006-11-01

We have developed a new technique for the rapid fabrication of structures useful for microfluidic devices called micromolding by photopolymerization in capillaries (μ-PIC). The technique involves the replication of features from a silicon master in which features on the order of tens to hundreds of microns have been formed by crystallographic etching. The negative of the features is then transferred to a sheet of polymethylmethacrylate (PMMA) by placing the PMMA sheet over the silicon master and injecting a solution of methylmethacrylate monomer with a benzoin methyl ether photoinitiator. This solution is drawn between the PMMA and the silicon by capillary action forming a liquid layer that is no more than a few hundred microns thick. This liquid is then polymerized by exposure to ultraviolet light for less than a half hour. The features transferred in this manner have nearly identical surface structure and roughness. Analysis of these surfaces and structures by atomic force microscopy and scanning electron microscopy will be presented.

Structural analysis of oligomeric and protofibrillar Aβ amyloid pair structures considering F20L mutation effects using molecular dynamics simulations.

PubMed

Lee, Myeongsang; Chang, Hyun Joon; Baek, Inchul; Na, Sungsoo

2017-04-01

Aβ amyloid proteins are involved in neuro-degenerative diseases such as Alzheimer's, Parkinson's, and so forth. Because of its structurally stable feature under physiological conditions, Aβ amyloid protein disrupts the normal cell function. Because of these concerns, understanding the structural feature of Aβ amyloid protein in detail is crucial. There have been some efforts on lowering the structural stabilities of Aβ amyloid fibrils by decreasing the aromatic residues characteristic and hydrophobic effect. Yet, there is a lack of understanding of Aβ amyloid pair structures considering those effects. In this study, we provide the structural characteristics of wildtype (WT) and phenylalanine residue mutation to leucine (F20L) Aβ amyloid pair structures using molecular dynamics simulation in detail. We also considered the polymorphic feature of F20L and WT Aβ pair amyloids based on the facing β-strand directions between the amyloid pairs. As a result, we were able to observe the varying effects of mutation, polymorphism, and protofibril lengths on the structural stability of pair amyloids. Furthermore, we have also found that opposite structural stability exists on a certain polymorphic Aβ pair amyloids depending on its oligomeric or protofibrillar state, which can be helpful for understanding the amyloid growth mechanism via repetitive fragmentation and elongation mechanism. Proteins 2017; 85:580-592. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Realistic Free-Spins Features Increase Preference for Slot Machines.

PubMed

Taylor, Lorance F; Macaskill, Anne C; Hunt, Maree J

2017-06-01

Despite increasing research into how the structural characteristics of slot machines influence gambling behaviour there have been no experimental investigations into the effect of free-spins bonus features-a structural characteristic that is commonly central to the design of slot machines. This series of three experiments investigated the free-spins feature using slot machine simulations to determine whether participants allocate more wagers to a machine with free spins, and, which components of free-spins features drive this preference. In each experiment, participants were exposed to two computer-simulated slot machines-one with a free-spins feature or similar bonus feature and one without. Participants then completed a testing phase where they could freely switch between the two machines. In Experiment 1, participants did not prefer the machine with a simple free-spins feature. In Experiment 2 the free-spins feature incorporated additional elements such as sounds, animations, and an increased win frequency; participants preferred to gamble on this machine. The Experiment 3 "bonus feature" machine resembled the free spins machine in Experiment 2 except spins were not free; participants showed a clear preference for this machine also. These findings indicate that (1) free-spins features have a major influence over machine choice and (2) the "freeness" of the free-spins bonus features is not an important driver of preference, contrary to self-report and interview research with gamblers.

The circular Uneged Uul structure (East Gobi Basin, Mongolia) - Geomorphic and structural evidence for meteorite impact into an unconsolidated coarse-clastic target?

NASA Astrophysics Data System (ADS)

Schmieder, Martin; Seyfried, Hartmut; Gerel, Ochir

2013-03-01

The Uneged Uul structure is a ˜10 km circular, complex, multi-ridged domal feature in the Unegt subbasin of the East Gobi Basin, southeastern Mongolia. As revealed by remote sensing and recent field reconnaissance, the central part of the Uneged Uul structure comprises a complex central peak of outward-radiating curved ridges, composed of stratigraphically uplifted greenschist-facies basement schists, surrounded by an annular moat. The most prominent feature of the structure is a central annular ridge ˜3 km in diameter composed of pebble-boulder conglomerates and gravels of the Upper Jurassic Sharilyn Formation, surrounded by three outer domal ridges composed of Lower Cretaceous conglomeratic sandstones and gypsum clays. Jurassic conglomerates forming the main part of the central annular ridge show effects of severe internal deformation. The original population of pebbles, cobbles and boulders appears moderately displaced and mostly broken but nowhere aligned along shear planes or foliated. Primary sedimentary features, such as cross-lamination or imbrication, have been obliterated. We explain this penetrative brecciation as a result of dissipative shearing caused by a strong and rapid singular event that in magnitude was beyond the range of the common crustal tectonics recorded elsewhere in this region. Disrupted and chaotically distributed conglomeratic sandstone beds in the central annular ridge dip in highly variable directions on a local scale but show an apparent SE-NW trend of bedding plane alignment. Further outside, the tilted and uplifted Upper Jurassic to Lower Cretaceous strata of the domal area are overlain by the flat-lying Upper Cretaceous, which stratigraphically constrains the timing of deformation at the Uneged Uul structure to most likely the Early Cretaceous. Endogenic formation models, such as magmatism and salt, gypsum, or mud diapirism, fail to explain the nature of the Uneged Uul structure. The Uneged Uul structure bears a set of geomorphic and structural features resembling those at some eroded complex impact structures on Earth. Morphologically similar central peaks are observed at the Spider and Matt Wilson impact structures in Australia; the central annular ridge reminds of that at Gosses Bluff in Australia; the outer domal ridges might correspond to ring-like features as known from Tin Bider in Algeria. We, therefore, cautiously propose that an impact may have produced the Uneged Uul feature causing structural uplift (˜1000 m) of basement rocks at its center. So far, no convincing evidence for shock metamorphism could be proven by field work and petrographic analyses. However, it is likely that at the time of the deformation event the unconsolidated conglomerates were highly porous and possibly immersed in groundwater buffering the propagation of sudden stress-reducing deformation. Further studies will be in order to unravel the nature of the Uneged Uul structure, which should be considered a promising possible impact structure.

Protein structure based prediction of catalytic residues

PubMed Central

2013-01-01

Background Worldwide structural genomics projects continue to release new protein structures at an unprecedented pace, so far nearly 6000, but only about 60% of these proteins have any sort of functional annotation. Results We explored a range of features that can be used for the prediction of functional residues given a known three-dimensional structure. These features include various centrality measures of nodes in graphs of interacting residues: closeness, betweenness and page-rank centrality. We also analyzed the distance of functional amino acids to the general center of mass (GCM) of the structure, relative solvent accessibility (RSA), and the use of relative entropy as a measure of sequence conservation. From the selected features, neural networks were trained to identify catalytic residues. We found that using distance to the GCM together with amino acid type provide a good discriminant function, when combined independently with sequence conservation. Using an independent test set of 29 annotated protein structures, the method returned 411 of the initial 9262 residues as the most likely to be involved in function. The output 411 residues contain 70 of the annotated 111 catalytic residues. This represents an approximately 14-fold enrichment of catalytic residues on the entire input set (corresponding to a sensitivity of 63% and a precision of 17%), a performance competitive with that of other state-of-the-art methods. Conclusions We found that several of the graph based measures utilize the same underlying feature of protein structures, which can be simply and more effectively captured with the distance to GCM definition. This also has the added the advantage of simplicity and easy implementation. Meanwhile sequence conservation remains by far the most influential feature in identifying functional residues. We also found that due the rapid changes in size and composition of sequence databases, conservation calculations must be recalibrated for specific reference databases. PMID:23433045

Features of Inner Structure of Placer Gold of the North-Eastern Part Siberian Platform

NASA Astrophysics Data System (ADS)

Gerasimov, Boris; Zhuravlev, Anatolii; Ivanov, Alexey

2017-12-01

Mineral and raw material base of placer and ore gold is based on prognosis evaluation, which allows to define promising areas regarding gold-bearing deposit prospecting. But there are some difficulties in gold primary source predicting and prospecting at the North-east Siberian platform, because the studied area is overlapped by thick cover of the Cenozoic deposits, where traditional methods of gold deposit prospecting are ineffective. In this connection, detailed study of typomorphic features of placer gold is important, because it contains key genetic information, necessary for development of mineralogical criteria of prognosis evaluation of ore gold content. Authors studied mineralogical-geochemical features of placer gold of the Anabar placer area for 15 years, with a view to identify indicators of gold, typical for different formation types of primary sources. This article presents results of these works. In placer regions, where primary sources of gold are not identified, there is need to study typomorphic features of placer gold, because it contains important genetic information, necessary for the development of mineralogical criteria of prognosis evaluation of ore gold content. Inner structures of gold from the Anabar placer region are studied, as one of the diagnostic typomorphic criteria as described in prominent method, developed by N.V. Petrovskaya [1980]. Etching of gold was carried out using reagent: HCl + HNO3 + FeCl3 × 6H2O + CrO3 +thioureat + water. Identified inner structures wer studied in details by means of scanning electron microscope JEOL JSM-6480LV. Two types of gold are identified according to the features of inner structure of placer gold of the Anabar region. First type - medium-high karat fine, well processed gold with significantly changed inner structure. This gold is allochthonous, which was redeposited many times from ancient intermediate reservoirs to younger deposits. Second type - low-medium karat, poorly rounded gold with unchanged inner structure. Poor roundness of gold particles and preservation of their primary inner structures indicate close proximity of primary source.

The discovery of a new infrared emission feature at 1905 wavenumbers (5.25 microns) in the spectrum of BD +30 degrees 3639 and its relation to the polycyclic aromatic hydrocarbon model

NASA Technical Reports Server (NTRS)

Allamandola, L. J.; Bregman, J. D.; Sandford, S. A.; Tielens, A. G.; Witteborn, F. C.; Wooden, D. H.; Rank, D.

1989-01-01

We have discovered a new IR emission feature at 1905 cm-1 (5.25 microns) in the spectrum of BD +30 degrees 3639. This feature joins the family of well-known IR emission features at 3040, 2940, 1750, 1610, "1310," 1160, and 890 cm-1 (3.3, 3.4, 5.7, 6.2, "7.7," 8.6, and 11.2 microns). The origin of this new feature is discussed and it is assigned to an overtone or combination band involving C-H bending modes of polycyclic aromatic hydrocarbons (PAHs). Laboratory work suggests that spectral studies of the 2000-1650 cm-1 (5.0-6.1 microns) region may be very useful in elucidating the molecular structure of interstellar PAHs. The new feature, in conjunction with other recently discovered spectral structure, suggests that the narrow IR emission features originate in PAH molecules rather than large carbon grains. Larger species are likely to be the source of the broad underlying "plateaus" seen in many of the spectra.

Geological map of parts of the state of Sao Paulo based on LANDSAT images. [Brazil

NASA Technical Reports Server (NTRS)

Dejususparada, N. (Principal Investigator); Amaral, G.; Liu, C. C.; Filho, R. A.

1979-01-01

The author has identified the following significant results. Interpretation of LANDSAT images revealed the subdivision of the Bauru formation into three distinct lithofacies. Delineation of structural features yielded new information on paleoenvironmental reconstitution and hydrogeology. Structural features and photogeological units were revealed in the precambrian basement at the eastern portion of the state.

Linguistic Features and Schematic Textual Structure in Look-Good Advertisements in the Indian Print Media in English

ERIC Educational Resources Information Center

Singh, Sukhdev; Bedi, Navkiran Kaur

2013-01-01

Every text has a communicative purpose that it performs by dividing itself into generic stages. These stages are assigned specific goals and have differing linguistic structures. This paper makes an attempt to investigate whether there is a definable co-relation between linguistic features and stages in the genre of look-good advertisements. It…

Money-center structures in dynamic banking systems

NASA Astrophysics Data System (ADS)

Li, Shouwei; Zhang, Minghui

2016-10-01

In this paper, we propose a dynamic model for banking systems based on the description of balance sheets. It generates some features identified through empirical analysis. Through simulation analysis of the model, we find that banking systems have the feature of money-center structures, that bank asset distributions are power-law distributions, and that contract size distributions are log-normal distributions.

Variable density thinning promotes variable structural responses 14 years after treatment in the Pacific Northwest

Treesearch

John L. Willis; Scott D. Roberts; Constance A. Harrington

2018-01-01

Young stands are commonly assumed to require centuries to develop into late-successional forest habitat. This viewpoint reflects the fact that young stands often lack many of the structural features that define late-successional habitat, and that these features derive from complex stand dynamics that are difficult to mimic with forest management. Variable density...

Features of electrophoretic deposition process of nanostructured electrode materials for planar Li-ion batteries

NASA Astrophysics Data System (ADS)

Melkozyorova, N. A.; Zinkevich, K. G.; Lebedev, E. A.; Alekseyev, A. V.; Gromov, D. G.; Kitsyuk, E. P.; Ryazanov, R. M.; Sysa, A. V.

2017-11-01

The features of electrophoretic deposition process of composite LiCoO2-based cathode and Si-based anode materials were researched. The influence of the deposition process parameters on the structure and composition of the deposit was revealed. The possibility of a local deposition of composites on a planar lithium-ion battery structure was demonstrated.

Automatic quantification of morphological features for hepatic trabeculae analysis in stained liver specimens

PubMed Central

Ishikawa, Masahiro; Murakami, Yuri; Ahi, Sercan Taha; Yamaguchi, Masahiro; Kobayashi, Naoki; Kiyuna, Tomoharu; Yamashita, Yoshiko; Saito, Akira; Abe, Tokiya; Hashiguchi, Akinori; Sakamoto, Michiie

2016-01-01

Abstract. This paper proposes a digital image analysis method to support quantitative pathology by automatically segmenting the hepatocyte structure and quantifying its morphological features. To structurally analyze histopathological hepatic images, we isolate the trabeculae by extracting the sinusoids, fat droplets, and stromata. We then measure the morphological features of the extracted trabeculae, divide the image into cords, and calculate the feature values of the local cords. We propose a method of calculating the nuclear–cytoplasmic ratio, nuclear density, and number of layers using the local cords. Furthermore, we evaluate the effectiveness of the proposed method using surgical specimens. The proposed method was found to be an effective method for the quantification of the Edmondson grade. PMID:27335894

Characterizing microstructural features of biomedical samples by statistical analysis of Mueller matrix images

NASA Astrophysics Data System (ADS)

He, Honghui; Dong, Yang; Zhou, Jialing; Ma, Hui

2017-03-01

As one of the salient features of light, polarization contains abundant structural and optical information of media. Recently, as a comprehensive description of polarization property, the Mueller matrix polarimetry has been applied to various biomedical studies such as cancerous tissues detections. In previous works, it has been found that the structural information encoded in the 2D Mueller matrix images can be presented by other transformed parameters with more explicit relationship to certain microstructural features. In this paper, we present a statistical analyzing method to transform the 2D Mueller matrix images into frequency distribution histograms (FDHs) and their central moments to reveal the dominant structural features of samples quantitatively. The experimental results of porcine heart, intestine, stomach, and liver tissues demonstrate that the transformation parameters and central moments based on the statistical analysis of Mueller matrix elements have simple relationships to the dominant microstructural properties of biomedical samples, including the density and orientation of fibrous structures, the depolarization power, diattenuation and absorption abilities. It is shown in this paper that the statistical analysis of 2D images of Mueller matrix elements may provide quantitative or semi-quantitative criteria for biomedical diagnosis.

Human action recognition with group lasso regularized-support vector machine

NASA Astrophysics Data System (ADS)

Luo, Huiwu; Lu, Huanzhang; Wu, Yabei; Zhao, Fei

2016-05-01

The bag-of-visual-words (BOVW) and Fisher kernel are two popular models in human action recognition, and support vector machine (SVM) is the most commonly used classifier for the two models. We show two kinds of group structures in the feature representation constructed by BOVW and Fisher kernel, respectively, since the structural information of feature representation can be seen as a prior for the classifier and can improve the performance of the classifier, which has been verified in several areas. However, the standard SVM employs L2-norm regularization in its learning procedure, which penalizes each variable individually and cannot express the structural information of feature representation. We replace the L2-norm regularization with group lasso regularization in standard SVM, and a group lasso regularized-support vector machine (GLRSVM) is proposed. Then, we embed the group structural information of feature representation into GLRSVM. Finally, we introduce an algorithm to solve the optimization problem of GLRSVM by alternating directions method of multipliers. The experiments evaluated on KTH, YouTube, and Hollywood2 datasets show that our method achieves promising results and improves the state-of-the-art methods on KTH and YouTube datasets.

Structured Kernel Dictionary Learning with Correlation Constraint for Object Recognition.

PubMed

Wang, Zhengjue; Wang, Yinghua; Liu, Hongwei; Zhang, Hao

2017-06-21

In this paper, we propose a new discriminative non-linear dictionary learning approach, called correlation constrained structured kernel KSVD, for object recognition. The objective function for dictionary learning contains a reconstructive term and a discriminative term. In the reconstructive term, signals are implicitly non-linearly mapped into a space, where a structured kernel dictionary, each sub-dictionary of which lies in the span of the mapped signals from the corresponding class, is established. In the discriminative term, by analyzing the classification mechanism, the correlation constraint is proposed in kernel form, constraining the correlations between different discriminative codes, and restricting the coefficient vectors to be transformed into a feature space, where the features are highly correlated inner-class and nearly independent between-classes. The objective function is optimized by the proposed structured kernel KSVD. During the classification stage, the specific form of the discriminative feature is needless to be known, while the inner product of the discriminative feature with kernel matrix embedded is available, and is suitable for a linear SVM classifier. Experimental results demonstrate that the proposed approach outperforms many state-of-the-art dictionary learning approaches for face, scene and synthetic aperture radar (SAR) vehicle target recognition.

Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences.

PubMed

Mizianty, Marcin J; Kurgan, Lukasz

2009-12-13

Knowledge of structural class is used by numerous methods for identification of structural/functional characteristics of proteins and could be used for the detection of remote homologues, particularly for chains that share twilight-zone similarity. In contrast to existing sequence-based structural class predictors, which target four major classes and which are designed for high identity sequences, we predict seven classes from sequences that share twilight-zone identity with the training sequences. The proposed MODular Approach to Structural class prediction (MODAS) method is unique as it allows for selection of any subset of the classes. MODAS is also the first to utilize a novel, custom-built feature-based sequence representation that combines evolutionary profiles and predicted secondary structure. The features quantify information relevant to the definition of the classes including conservation of residues and arrangement and number of helix/strand segments. Our comprehensive design considers 8 feature selection methods and 4 classifiers to develop Support Vector Machine-based classifiers that are tailored for each of the seven classes. Tests on 5 twilight-zone and 1 high-similarity benchmark datasets and comparison with over two dozens of modern competing predictors show that MODAS provides the best overall accuracy that ranges between 80% and 96.7% (83.5% for the twilight-zone datasets), depending on the dataset. This translates into 19% and 8% error rate reduction when compared against the best performing competing method on two largest datasets. The proposed predictor provides accurate predictions at 58% accuracy for membrane proteins class, which is not considered by majority of existing methods, in spite that this class accounts for only 2% of the data. Our predictive model is analyzed to demonstrate how and why the input features are associated with the corresponding classes. The improved predictions stem from the novel features that express collocation of the secondary structure segments in the protein sequence and that combine evolutionary and secondary structure information. Our work demonstrates that conservation and arrangement of the secondary structure segments predicted along the protein chain can successfully predict structural classes which are defined based on the spatial arrangement of the secondary structures. A web server is available at http://biomine.ece.ualberta.ca/MODAS/.

Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences

PubMed Central

2009-01-01

Background Knowledge of structural class is used by numerous methods for identification of structural/functional characteristics of proteins and could be used for the detection of remote homologues, particularly for chains that share twilight-zone similarity. In contrast to existing sequence-based structural class predictors, which target four major classes and which are designed for high identity sequences, we predict seven classes from sequences that share twilight-zone identity with the training sequences. Results The proposed MODular Approach to Structural class prediction (MODAS) method is unique as it allows for selection of any subset of the classes. MODAS is also the first to utilize a novel, custom-built feature-based sequence representation that combines evolutionary profiles and predicted secondary structure. The features quantify information relevant to the definition of the classes including conservation of residues and arrangement and number of helix/strand segments. Our comprehensive design considers 8 feature selection methods and 4 classifiers to develop Support Vector Machine-based classifiers that are tailored for each of the seven classes. Tests on 5 twilight-zone and 1 high-similarity benchmark datasets and comparison with over two dozens of modern competing predictors show that MODAS provides the best overall accuracy that ranges between 80% and 96.7% (83.5% for the twilight-zone datasets), depending on the dataset. This translates into 19% and 8% error rate reduction when compared against the best performing competing method on two largest datasets. The proposed predictor provides accurate predictions at 58% accuracy for membrane proteins class, which is not considered by majority of existing methods, in spite that this class accounts for only 2% of the data. Our predictive model is analyzed to demonstrate how and why the input features are associated with the corresponding classes. Conclusions The improved predictions stem from the novel features that express collocation of the secondary structure segments in the protein sequence and that combine evolutionary and secondary structure information. Our work demonstrates that conservation and arrangement of the secondary structure segments predicted along the protein chain can successfully predict structural classes which are defined based on the spatial arrangement of the secondary structures. A web server is available at http://biomine.ece.ualberta.ca/MODAS/. PMID:20003388

Imaging the Buried Chicxulub Crater with Gravity Gradients and Cenotes

NASA Astrophysics Data System (ADS)

Hildebrand, A. R.; Pilkington, M.; Halpenny, J. F.; Ortiz-Aleman, C.; Chavez, R. E.; Urrutia-Fucugauchi, J.; Connors, M.; Graniel-Castro, E.; Camara-Zi, A.; Vasquez, J.

1995-09-01

Differing interpretations of the Bouguer gravity anomaly over the Chicxulub crater, Yucatan Peninsula, Mexico, have yielded diameter estimates of 170 to 320 km. Knowing the crater's size is necessary to quantify the lethal perturbations to the Cretaceous environment associated with its formation. The crater's size (and internal structure) is revealed by the horizontal gradient of the Bouguer gravity anomaly over the structure, and by mapping the karst features of the Yucatan region. To improve our resolution of the crater's gravity signature we collected additional gravity measurements primarily along radial profiles, but also to fill in previously unsurveyed areas. Horizontal gradient analysis of Bouguer gravity data objectively highlights the lateral density contrasts of the impact lithologies and suppresses regional anomalies which may obscure the gravity signature of the Chicxulub crater lithologies. This gradient technique yields a striking circular structure with at least 6 concentric gradient features between 25 and 85 km radius. These features are most distinct in the southwest probably because of denser sampling of the gravity field. Our detailed profiles detected an additional feature and steeper gradients (up to 5 mGal/km) than the original survey. We interpret the outer four gradient maxima to represent concentric faults in the crater's zone of slumping as is also revealed by seismic reflection data. The inner two probably represent the margin of the central uplift and the peak ring and or collapsed transient cavity. Radial gradients in the SW quadrant over the inferred ~40 km-diameter central uplift (4) may represent structural "puckering" as revealed at eroded terrestrial craters. Gradient features related to regional gravity highs and lows are visible outside the crater, but no concentric gradient features are apparent at distances > 90 km radius. The marginal gradient features may be modelled by slump faults as observed in large complex craters on the other terrestrial planets. A modeled fault of 1.5 km displacement (slightly slumped block exterior and impact breccia interior) reproduces the steepest gradient feature. This model is incompatible with models that place these gradient features inside the collapsed transient cavity. Locations of the karst features of the northern Yucatan region were digitized from 1:50,000 topographic maps, which show most but not all the water-filled sinkholes (locally known as cenotes). A prominent ring of cenotes is visible over the crater that is spatially correlated to the outer steep gravity gradient feature. The mapped cenotes constitute an unbiased sampling of the region's karst surface features of >50 m diameter. The gradient maximum and the cenote ring both meander with amplitudes of up to 2 km. The wiggles in the gradient feature and the cenote distribution probably correspond to the "scalloping" observed at the headwall of terraces in large complex craters. A second partial cenote ring exterior to the southwest side of the main ring corresponds to a less-prominent gravity gradient feature. No concentric structure is observable in the distribution of karst features at radii >90 km. The cenote ring is bounded by the outer peripheral steep gradient feature and must be related to it; the slump faults must have been reactivated sufficiently to create fracturing in the overlying and much younger sediment. Long term subsidence, as found at other terrestrial craters is a possible mechanism for the reactivation. Such long term subsidence may be caused by differential compaction or thermal relaxation. Elevations acquired during gravity surveys show that the cenote ring also corresponds to a topographic low along some of its length that probably reflects preferential erosion.

Asymmetric bagging and feature selection for activities prediction of drug molecules.

PubMed

Li, Guo-Zheng; Meng, Hao-Hua; Lu, Wen-Cong; Yang, Jack Y; Yang, Mary Qu

2008-05-28

Activities of drug molecules can be predicted by QSAR (quantitative structure activity relationship) models, which overcomes the disadvantages of high cost and long cycle by employing the traditional experimental method. With the fact that the number of drug molecules with positive activity is rather fewer than that of negatives, it is important to predict molecular activities considering such an unbalanced situation. Here, asymmetric bagging and feature selection are introduced into the problem and asymmetric bagging of support vector machines (asBagging) is proposed on predicting drug activities to treat the unbalanced problem. At the same time, the features extracted from the structures of drug molecules affect prediction accuracy of QSAR models. Therefore, a novel algorithm named PRIFEAB is proposed, which applies an embedded feature selection method to remove redundant and irrelevant features for asBagging. Numerical experimental results on a data set of molecular activities show that asBagging improve the AUC and sensitivity values of molecular activities and PRIFEAB with feature selection further helps to improve the prediction ability. Asymmetric bagging can help to improve prediction accuracy of activities of drug molecules, which can be furthermore improved by performing feature selection to select relevant features from the drug molecules data sets.

Multi-Contrast Multi-Atlas Parcellation of Diffusion Tensor Imaging of the Human Brain

PubMed Central

Tang, Xiaoying; Yoshida, Shoko; Hsu, John; Huisman, Thierry A. G. M.; Faria, Andreia V.; Oishi, Kenichi; Kutten, Kwame; Poretti, Andrea; Li, Yue; Miller, Michael I.; Mori, Susumu

2014-01-01

In this paper, we propose a novel method for parcellating the human brain into 193 anatomical structures based on diffusion tensor images (DTIs). This was accomplished in the setting of multi-contrast diffeomorphic likelihood fusion using multiple DTI atlases. DTI images are modeled as high dimensional fields, with each voxel exhibiting a vector valued feature comprising of mean diffusivity (MD), fractional anisotropy (FA), and fiber angle. For each structure, the probability distribution of each element in the feature vector is modeled as a mixture of Gaussians, the parameters of which are estimated from the labeled atlases. The structure-specific feature vector is then used to parcellate the test image. For each atlas, a likelihood is iteratively computed based on the structure-specific vector feature. The likelihoods from multiple atlases are then fused. The updating and fusing of the likelihoods is achieved based on the expectation-maximization (EM) algorithm for maximum a posteriori (MAP) estimation problems. We first demonstrate the performance of the algorithm by examining the parcellation accuracy of 18 structures from 25 subjects with a varying degree of structural abnormality. Dice values ranging 0.8–0.9 were obtained. In addition, strong correlation was found between the volume size of the automated and the manual parcellation. Then, we present scan-rescan reproducibility based on another dataset of 16 DTI images – an average of 3.73%, 1.91%, and 1.79% for volume, mean FA, and mean MD respectively. Finally, the range of anatomical variability in the normal population was quantified for each structure. PMID:24809486

A statistical learning approach to the modeling of chromatographic retention of oligonucleotides incorporating sequence and secondary structure data

PubMed Central

Sturm, Marc; Quinten, Sascha; Huber, Christian G.; Kohlbacher, Oliver

2007-01-01

We propose a new model for predicting the retention time of oligonucleotides. The model is based on ν support vector regression using features derived from base sequence and predicted secondary structure of oligonucleotides. Because of the secondary structure information, the model is applicable even at relatively low temperatures where the secondary structure is not suppressed by thermal denaturing. This makes the prediction of oligonucleotide retention time for arbitrary temperatures possible, provided that the target temperature lies within the temperature range of the training data. We describe different possibilities of feature calculation from base sequence and secondary structure, present the results and compare our model to existing models. PMID:17567619

The role of emotion in musical improvisation: an analysis of structural features.

PubMed

McPherson, Malinda J; Lopez-Gonzalez, Monica; Rankin, Summer K; Limb, Charles J

2014-01-01

One of the primary functions of music is to convey emotion, yet how music accomplishes this task remains unclear. For example, simple correlations between mode (major vs. minor) and emotion (happy vs. sad) do not adequately explain the enormous range, subtlety or complexity of musically induced emotions. In this study, we examined the structural features of unconstrained musical improvisations generated by jazz pianists in response to emotional cues. We hypothesized that musicians would not utilize any universal rules to convey emotions, but would instead combine heterogeneous musical elements together in order to depict positive and negative emotions. Our findings demonstrate a lack of simple correspondence between emotions and musical features of spontaneous musical improvisation. While improvisations in response to positive emotional cues were more likely to be in major keys, have faster tempos, faster key press velocities and more staccato notes when compared to negative improvisations, there was a wide distribution for each emotion with components that directly violated these primary associations. The finding that musicians often combine disparate features together in order to convey emotion during improvisation suggests that structural diversity may be an essential feature of the ability of music to express a wide range of emotion.

The Role of Emotion in Musical Improvisation: An Analysis of Structural Features

PubMed Central

McPherson, Malinda J.; Lopez-Gonzalez, Monica; Rankin, Summer K.; Limb, Charles J.

2014-01-01

One of the primary functions of music is to convey emotion, yet how music accomplishes this task remains unclear. For example, simple correlations between mode (major vs. minor) and emotion (happy vs. sad) do not adequately explain the enormous range, subtlety or complexity of musically induced emotions. In this study, we examined the structural features of unconstrained musical improvisations generated by jazz pianists in response to emotional cues. We hypothesized that musicians would not utilize any universal rules to convey emotions, but would instead combine heterogeneous musical elements together in order to depict positive and negative emotions. Our findings demonstrate a lack of simple correspondence between emotions and musical features of spontaneous musical improvisation. While improvisations in response to positive emotional cues were more likely to be in major keys, have faster tempos, faster key press velocities and more staccato notes when compared to negative improvisations, there was a wide distribution for each emotion with components that directly violated these primary associations. The finding that musicians often combine disparate features together in order to convey emotion during improvisation suggests that structural diversity may be an essential feature of the ability of music to express a wide range of emotion. PMID:25144200

Iris-based medical analysis by geometric deformation features.

PubMed

Ma, Lin; Zhang, D; Li, Naimin; Cai, Yan; Zuo, Wangmeng; Wang, Kuanguan

2013-01-01

Iris analysis studies the relationship between human health and changes in the anatomy of the iris. Apart from the fact that iris recognition focuses on modeling the overall structure of the iris, iris diagnosis emphasizes the detecting and analyzing of local variations in the characteristics of irises. This paper focuses on studying the geometrical structure changes in irises that are caused by gastrointestinal diseases, and on measuring the observable deformations in the geometrical structures of irises that are related to roundness, diameter and other geometric forms of the pupil and the collarette. Pupil and collarette based features are defined and extracted. A series of experiments are implemented on our experimental pathological iris database, including manual clustering of both normal and pathological iris images, manual classification by non-specialists, manual classification by individuals with a medical background, classification ability verification for the proposed features, and disease recognition by applying the proposed features. The results prove the effectiveness and clinical diagnostic significance of the proposed features and a reliable recognition performance for automatic disease diagnosis. Our research results offer a novel systematic perspective for iridology studies and promote the progress of both theoretical and practical work in iris diagnosis.

The evaluation of multi-structure, multi-atlas pelvic anatomy features in a prostate MR lymphography CAD system

NASA Astrophysics Data System (ADS)

Meijs, M.; Debats, O.; Huisman, H.

2015-03-01

In prostate cancer, the detection of metastatic lymph nodes indicates progression from localized disease to metastasized cancer. The detection of positive lymph nodes is, however, a complex and time consuming task for experienced radiologists. Assistance of a two-stage Computer-Aided Detection (CAD) system in MR Lymphography (MRL) is not yet feasible due to the large number of false positives in the first stage of the system. By introducing a multi-structure, multi-atlas segmentation, using an affine transformation followed by a B-spline transformation for registration, the organ location is given by a mean density probability map. The atlas segmentation is semi-automatically drawn with ITK-SNAP, using Active Contour Segmentation. Each anatomic structure is identified by a label number. Registration is performed using Elastix, using Mutual Information and an Adaptive Stochastic Gradient optimization. The dataset consists of the MRL scans of ten patients, with lymph nodes manually annotated in consensus by two expert readers. The feature map of the CAD system consists of the Multi-Atlas and various other features (e.g. Normalized Intensity and multi-scale Blobness). The voxel-based Gentleboost classifier is evaluated using ROC analysis with cross validation. We show in a set of 10 studies that adding multi-structure, multi-atlas anatomical structure likelihood features improves the quality of the lymph node voxel likelihood map. Multiple structure anatomy maps may thus make MRL CAD more feasible.

Structural damage detection based on stochastic subspace identification and statistical pattern recognition: I. Theory

NASA Astrophysics Data System (ADS)

Ren, W. X.; Lin, Y. Q.; Fang, S. E.

2011-11-01

One of the key issues in vibration-based structural health monitoring is to extract the damage-sensitive but environment-insensitive features from sampled dynamic response measurements and to carry out the statistical analysis of these features for structural damage detection. A new damage feature is proposed in this paper by using the system matrices of the forward innovation model based on the covariance-driven stochastic subspace identification of a vibrating system. To overcome the variations of the system matrices, a non-singularity transposition matrix is introduced so that the system matrices are normalized to their standard forms. For reducing the effects of modeling errors, noise and environmental variations on measured structural responses, a statistical pattern recognition paradigm is incorporated into the proposed method. The Mahalanobis and Euclidean distance decision functions of the damage feature vector are adopted by defining a statistics-based damage index. The proposed structural damage detection method is verified against one numerical signal and two numerical beams. It is demonstrated that the proposed statistics-based damage index is sensitive to damage and shows some robustness to the noise and false estimation of the system ranks. The method is capable of locating damage of the beam structures under different types of excitations. The robustness of the proposed damage detection method to the variations in environmental temperature is further validated in a companion paper by a reinforced concrete beam tested in the laboratory and a full-scale arch bridge tested in the field.

Discriminative analysis of schizophrenia using support vector machine and recursive feature elimination on structural MRI images.

PubMed

Lu, Xiaobing; Yang, Yongzhe; Wu, Fengchun; Gao, Minjian; Xu, Yong; Zhang, Yue; Yao, Yongcheng; Du, Xin; Li, Chengwei; Wu, Lei; Zhong, Xiaomei; Zhou, Yanling; Fan, Ni; Zheng, Yingjun; Xiong, Dongsheng; Peng, Hongjun; Escudero, Javier; Huang, Biao; Li, Xiaobo; Ning, Yuping; Wu, Kai

2016-07-01

Structural abnormalities in schizophrenia (SZ) patients have been well documented with structural magnetic resonance imaging (MRI) data using voxel-based morphometry (VBM) and region of interest (ROI) analyses. However, these analyses can only detect group-wise differences and thus, have a poor predictive value for individuals. In the present study, we applied a machine learning method that combined support vector machine (SVM) with recursive feature elimination (RFE) to discriminate SZ patients from normal controls (NCs) using their structural MRI data. We first employed both VBM and ROI analyses to compare gray matter volume (GMV) and white matter volume (WMV) between 41 SZ patients and 42 age- and sex-matched NCs. The method of SVM combined with RFE was used to discriminate SZ patients from NCs using significant between-group differences in both GMV and WMV as input features. We found that SZ patients showed GM and WM abnormalities in several brain structures primarily involved in the emotion, memory, and visual systems. An SVM with a RFE classifier using the significant structural abnormalities identified by the VBM analysis as input features achieved the best performance (an accuracy of 88.4%, a sensitivity of 91.9%, and a specificity of 84.4%) in the discriminative analyses of SZ patients. These results suggested that distinct neuroanatomical profiles associated with SZ patients might provide a potential biomarker for disease diagnosis, and machine-learning methods can reveal neurobiological mechanisms in psychiatric diseases.

36 CFR 67.2 - Definitions.

Code of Federal Regulations, 2011 CFR

2011-07-01

... structure encompasses the historic building and its site, landscape features, and environment, generally... means a building and its site and landscape features. Registered Historic District means any district...

36 CFR 67.2 - Definitions.

Code of Federal Regulations, 2013 CFR

2013-07-01

... structure encompasses the historic building and its site, landscape features, and environment, generally... means a building and its site and landscape features. Registered Historic District means any district...

36 CFR 67.2 - Definitions.

Code of Federal Regulations, 2012 CFR

2012-07-01

... structure encompasses the historic building and its site, landscape features, and environment, generally... means a building and its site and landscape features. Registered Historic District means any district...

36 CFR 67.2 - Definitions.

Code of Federal Regulations, 2014 CFR

2014-07-01

... structure encompasses the historic building and its site, landscape features, and environment, generally... means a building and its site and landscape features. Registered Historic District means any district...

A method for real-time implementation of HOG feature extraction

NASA Astrophysics Data System (ADS)

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

2011-08-01

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

Structural lineament and pattern analysis of Missouri, using LANDSAT imagery

NASA Technical Reports Server (NTRS)

Martin, J. A.; Kisvarsanyi, G. (Principal Investigator)

1977-01-01

The author has identified the following significant results. Major linear, circular, and arcuate traces were observed on LANDSAT imagery of Missouri. Lineaments plotted within the state boundaries range from 20 to nearly 500 km in length. Several extend into adjoining states. Lineaments plots indicate a distinct pattern and in general reflect structural features of the Precambrian basement of the platform. Coincidence of lineaments traced from the imagery and known structural features in Missouri is high, thus supporting a causative relation between them. The lineament pattern apparently reveals a fundamental style of the deformation of the intracontinental craton. Dozens of heretofore unknown linear features related to epirogenic movements and deformation of this segment of the continental crust were delineated. Lineaments and mineralization are interrelated in a geometrically classifiable pattern.

Structures composing protein domains.

PubMed

Kubrycht, Jaroslav; Sigler, Karel; Souček, Pavel; Hudeček, Jiří

2013-08-01

This review summarizes available data concerning intradomain structures (IS) such as functionally important amino acid residues, short linear motifs, conserved or disordered regions, peptide repeats, broadly occurring secondary structures or folds, etc. IS form structural features (units or elements) necessary for interactions with proteins or non-peptidic ligands, enzyme reactions and some structural properties of proteins. These features have often been related to a single structural level (e.g. primary structure) mostly requiring certain structural context of other levels (e.g. secondary structures or supersecondary folds) as follows also from some examples reported or demonstrated here. In addition, we deal with some functionally important dynamic properties of IS (e.g. flexibility and different forms of accessibility), and more special dynamic changes of IS during enzyme reactions and allosteric regulation. Selected notes concern also some experimental methods, still more necessary tools of bioinformatic processing and clinically interesting relationships. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

On the importance of cotranscriptional RNA structure formation

PubMed Central

Lai, Daniel; Proctor, Jeff R.; Meyer, Irmtraud M.

2013-01-01

The expression of genes, both coding and noncoding, can be significantly influenced by RNA structural features of their corresponding transcripts. There is by now mounting experimental and some theoretical evidence that structure formation in vivo starts during transcription and that this cotranscriptional folding determines the functional RNA structural features that are being formed. Several decades of research in bioinformatics have resulted in a wide range of computational methods for predicting RNA secondary structures. Almost all state-of-the-art methods in terms of prediction accuracy, however, completely ignore the process of structure formation and focus exclusively on the final RNA structure. This review hopes to bridge this gap. We summarize the existing evidence for cotranscriptional folding and then review the different, currently used strategies for RNA secondary-structure prediction. Finally, we propose a range of ideas on how state-of-the-art methods could be potentially improved by explicitly capturing the process of cotranscriptional structure formation. PMID:24131802

An Eye-Tracking Study of Multiple Feature Value Category Structure Learning: The Role of Unique Features

PubMed Central

Liu, Zhiya; Song, Xiaohong; Seger, Carol A.

2015-01-01

We examined whether the degree to which a feature is uniquely characteristic of a category can affect categorization above and beyond the typicality of the feature. We developed a multiple feature value category structure with different dimensions within which feature uniqueness and typicality could be manipulated independently. Using eye tracking, we found that the highest attentional weighting (operationalized as number of fixations, mean fixation time, and the first fixation of the trial) was given to a dimension that included a feature that was both unique and highly typical of the category. Dimensions that included features that were highly typical but not unique, or were unique but not highly typical, received less attention. A dimension with neither a unique nor a highly typical feature received least attention. On the basis of these results we hypothesized that subjects categorized via a rule learning procedure in which they performed an ordered evaluation of dimensions, beginning with unique and strongly typical dimensions, and in which earlier dimensions received higher weighting in the decision. This hypothesis accounted for performance on transfer stimuli better than simple implementations of two other common theories of category learning, exemplar models and prototype models, in which all dimensions were evaluated in parallel and received equal weighting. PMID:26274332

An Eye-Tracking Study of Multiple Feature Value Category Structure Learning: The Role of Unique Features.

PubMed

Liu, Zhiya; Song, Xiaohong; Seger, Carol A

2015-01-01

We examined whether the degree to which a feature is uniquely characteristic of a category can affect categorization above and beyond the typicality of the feature. We developed a multiple feature value category structure with different dimensions within which feature uniqueness and typicality could be manipulated independently. Using eye tracking, we found that the highest attentional weighting (operationalized as number of fixations, mean fixation time, and the first fixation of the trial) was given to a dimension that included a feature that was both unique and highly typical of the category. Dimensions that included features that were highly typical but not unique, or were unique but not highly typical, received less attention. A dimension with neither a unique nor a highly typical feature received least attention. On the basis of these results we hypothesized that subjects categorized via a rule learning procedure in which they performed an ordered evaluation of dimensions, beginning with unique and strongly typical dimensions, and in which earlier dimensions received higher weighting in the decision. This hypothesis accounted for performance on transfer stimuli better than simple implementations of two other common theories of category learning, exemplar models and prototype models, in which all dimensions were evaluated in parallel and received equal weighting.

Design and performance of optimal detectors for guided wave structural health monitoring

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

Dib, G.; Udpa, L.

2016-01-01

Ultrasonic guided wave measurements in a long term structural health monitoring system are affected by measurement noise, environmental conditions, transducer aging and malfunction. This results in measurement variability which affects detection performance, especially in complex structures where baseline data comparison is required. This paper derives the optimal detector structure, within the framework of detection theory, where a guided wave signal at the sensor is represented by a single feature value that can be used for comparison with a threshold. Three different types of detectors are derived depending on the underlying structure’s complexity: (i) Simple structures where defect reflections can bemore » identified without the need for baseline data; (ii) Simple structures that require baseline data due to overlap of defect scatter with scatter from structural features; (iii) Complex structure with dense structural features that require baseline data. The detectors are derived by modeling the effects of variabilities and uncertainties as random processes. Analytical solutions for the performance of detectors in terms of the probability of detection and false alarm are derived. A finite element model is used to generate guided wave signals and the performance results of a Monte-Carlo simulation are compared with the theoretical performance. initial results demonstrate that the problems of signal complexity and environmental variability can in fact be exploited to improve detection performance.« less

New Era of Studying RNA Secondary Structure and Its Influence on Gene Regulation in Plants.

PubMed

Yang, Xiaofei; Yang, Minglei; Deng, Hongjing; Ding, Yiliang

2018-01-01

The dynamic structure of RNA plays a central role in post-transcriptional regulation of gene expression such as RNA maturation, degradation, and translation. With the rise of next-generation sequencing, the study of RNA structure has been transformed from in vitro low-throughput RNA structure probing methods to in vivo high-throughput RNA structure profiling. The development of these methods enables incremental studies on the function of RNA structure to be performed, revealing new insights of novel regulatory mechanisms of RNA structure in plants. Genome-wide scale RNA structure profiling allows us to investigate general RNA structural features over 10s of 1000s of mRNAs and to compare RNA structuromes between plant species. Here, we provide a comprehensive and up-to-date overview of: (i) RNA structure probing methods; (ii) the biological functions of RNA structure; (iii) genome-wide RNA structural features corresponding to their regulatory mechanisms; and (iv) RNA structurome evolution in plants.

An empirical assessment of which inland floods can be managed.

PubMed

Mogollón, Beatriz; Frimpong, Emmanuel A; Hoegh, Andrew B; Angermeier, Paul L

2016-02-01

Riverine flooding is a significant global issue. Although it is well documented that the influence of landscape structure on floods decreases as flood size increases, studies that define a threshold flood-return period, above which landscape features such as topography, land cover and impoundments can curtail floods, are lacking. Further, the relative influences of natural versus built features on floods is poorly understood. Assumptions about the types of floods that can be managed have considerable implications for the cost-effectiveness of decisions to invest in transforming land cover (e.g., reforestation) and in constructing structures (e.g., storm-water ponds) to control floods. This study defines parameters of floods for which changes in landscape structure can have an impact. We compare nine flood-return periods across 31 watersheds with widely varying topography and land cover in the southeastern United States, using long-term hydrologic records (≥20 years). We also assess the effects of built flow-regulating features (best management practices and artificial water bodies) on selected flood metrics across urban watersheds. We show that landscape features affect magnitude and duration of only those floods with return periods ≤10 years, which suggests that larger floods cannot be managed effectively by manipulating landscape structure. Overall, urban watersheds exhibited larger (270 m(3)/s) but quicker (0.41 days) floods than non-urban watersheds (50 m(3)/s and 1.5 days). However, urban watersheds with more flow-regulating features had lower flood magnitudes (154 m(3)/s), but similar flood durations (0.55 days), compared to urban watersheds with fewer flow-regulating features (360 m(3)/s and 0.23 days). Our analysis provides insight into the magnitude, duration and count of floods that can be curtailed by landscape structure and its management. Our findings are relevant to other areas with similar climate, topography, and land use, and can help ensure that investments in flood management are made wisely after considering the limitations of landscape features to regulate floods. Copyright © 2015 Elsevier Ltd. All rights reserved.

Phase space interrogation of the empirical response modes for seismically excited structures

NASA Astrophysics Data System (ADS)

Paul, Bibhas; George, Riya C.; Mishra, Sudib K.

2017-07-01

Conventional Phase Space Interrogation (PSI) for structural damage assessment relies on exciting the structure with low dimensional chaotic waveform, thereby, significantly limiting their applicability to large structures. The PSI technique is presently extended for structure subjected to seismic excitations. The high dimensionality of the phase space for seismic response(s) are overcome by the Empirical Mode Decomposition (EMD), decomposing the responses to a number of intrinsic low dimensional oscillatory modes, referred as Intrinsic Mode Functions (IMFs). Along with their low dimensionality, a few IMFs, retain sufficient information of the system dynamics to reflect the damage induced changes. The mutually conflicting nature of low-dimensionality and the sufficiency of dynamic information are taken care by the optimal choice of the IMF(s), which is shown to be the third/fourth IMFs. The optimal IMF(s) are employed for the reconstruction of the Phase space attractor following Taken's embedding theorem. The widely referred Changes in Phase Space Topology (CPST) feature is then employed on these Phase portrait(s) to derive the damage sensitive feature, referred as the CPST of the IMFs (CPST-IMF). The legitimacy of the CPST-IMF is established as a damage sensitive feature by assessing its variation with a number of damage scenarios benchmarked in the IASC-ASCE building. The damage localization capability, remarkable tolerance to noise contamination and the robustness under different seismic excitations of the feature are demonstrated.

System and technique for retrieving depth information about a surface by projecting a composite image of modulated light patterns

NASA Technical Reports Server (NTRS)

Hassebrook, Laurence G. (Inventor); Lau, Daniel L. (Inventor); Guan, Chun (Inventor)

2010-01-01

A technique, associated system and program code, for retrieving depth information about at least one surface of an object, such as an anatomical feature. Core features include: projecting a composite image comprising a plurality of modulated structured light patterns, at the anatomical feature; capturing an image reflected from the surface; and recovering pattern information from the reflected image, for each of the modulated structured light patterns. Pattern information is preferably recovered for each modulated structured light pattern used to create the composite, by performing a demodulation of the reflected image. Reconstruction of the surface can be accomplished by using depth information from the recovered patterns to produce a depth map/mapping thereof. Each signal waveform used for the modulation of a respective structured light pattern, is distinct from each of the other signal waveforms used for the modulation of other structured light patterns of a composite image; these signal waveforms may be selected from suitable types in any combination of distinct signal waveforms, provided the waveforms used are uncorrelated with respect to each other. The depth map/mapping to be utilized in a host of applications, for example: displaying a 3-D view of the object; virtual reality user-interaction interface with a computerized device; face--or other animal feature or inanimate object--recognition and comparison techniques for security or identification purposes; and 3-D video teleconferencing/telecollaboration.

Grid point extraction and coding for structured light system

NASA Astrophysics Data System (ADS)

Song, Zhan; Chung, Ronald

2011-09-01

A structured light system simplifies three-dimensional reconstruction by illuminating a specially designed pattern to the target object, thereby generating a distinct texture on it for imaging and further processing. Success of the system hinges upon what features are to be coded in the projected pattern, extracted in the captured image, and matched between the projector's display panel and the camera's image plane. The codes have to be such that they are largely preserved in the image data upon illumination from the projector, reflection from the target object, and projective distortion in the imaging process. The features also need to be reliably extracted in the image domain. In this article, a two-dimensional pseudorandom pattern consisting of rhombic color elements is proposed, and the grid points between the pattern elements are chosen as the feature points. We describe how a type classification of the grid points plus the pseudorandomness of the projected pattern can equip each grid point with a unique label that is preserved in the captured image. We also present a grid point detector that extracts the grid points without the need of segmenting the pattern elements, and that localizes the grid points in subpixel accuracy. Extensive experiments are presented to illustrate that, with the proposed pattern feature definition and feature detector, more features points in higher accuracy can be reconstructed in comparison with the existing pseudorandomly encoded structured light systems.

Clavulanic acid production estimation based on color and structural features of Streptomyces clavuligerus bacteria using self-organizing map and genetic algorithm.

PubMed

Nurmohamadi, Maryam; Pourghassem, Hossein

2014-05-01

The utilization of antibiotics produced by Clavulanic acid (CA) is an increasing need in medicine and industry. Usually, the CA is created from the fermentation of Streptomycen Clavuligerus (SC) bacteria. Analysis of visual and morphological features of SC bacteria is an appropriate measure to estimate the growth of CA. In this paper, an automatic and fast CA production level estimation algorithm based on visual and structural features of SC bacteria instead of statistical methods and experimental evaluation by microbiologist is proposed. In this algorithm, structural features such as the number of newborn branches, thickness of hyphal and bacterial density and also color features such as acceptance color levels are extracted from the SC bacteria. Moreover, PH and biomass of the medium provided by microbiologists are considered as specified features. The level of CA production is estimated by using a new application of Self-Organizing Map (SOM), and a hybrid model of genetic algorithm with back propagation network (GA-BPN). The proposed algorithm is evaluated on four carbonic resources including malt, starch, wheat flour and glycerol that had used as different mediums of bacterial growth. Then, the obtained results are compared and evaluated with observation of specialist. Finally, the Relative Error (RE) for the SOM and GA-BPN are achieved 14.97% and 16.63%, respectively. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Impact of roadway geometric features on crash severity on rural two-lane highways.

PubMed

Haghighi, Nima; Liu, Xiaoyue Cathy; Zhang, Guohui; Porter, Richard J

2018-02-01

This study examines the impact of a wide range of roadway geometric features on the severity outcomes of crashes occurred on rural two-lane highways. We argue that crash data have a hierarchical structure which needs to be addressed in modeling procedure. Moreover, most of previous studies ignored the impact of geometric features on crash types when developing crash severity models. We hypothesis that geometric features are more likely to determine crash type, and crash type together with other occupant, environmental and vehicle characteristics determine crash severity outcome. This paper presents an application of multilevel models to successfully capture both hierarchical structure of crash data and indirect impact of geometric features on crash severity. Using data collected in Illinois from 2007 to 2009, multilevel ordered logit model is developed to quantify the impact of geometric features and environmental conditions on crash severity outcome. Analysis results revealed that there is a significant variation in severity outcomes of crashes occurred across segments which verifies the presence of hierarchical structure. Lower risk of severe crashes is found to be associated with the presence of 10-ft lane and/or narrow shoulders, lower roadside hazard rate, higher driveway density, longer barrier length, and shorter barrier offset. The developed multilevel model offers greater consistency with data generating mechanism and can be utilized to evaluate safety effects of geometric design improvement projects. Published by Elsevier Ltd.

Accurate predictions of population-level changes in sequence and structural properties of HIV-1 Env using a volatility-controlled diffusion model

PubMed Central

DeLeon, Orlando; Hodis, Hagit; O’Malley, Yunxia; Johnson, Jacklyn; Salimi, Hamid; Zhai, Yinjie; Winter, Elizabeth; Remec, Claire; Eichelberger, Noah; Van Cleave, Brandon; Puliadi, Ramya; Harrington, Robert D.; Stapleton, Jack T.; Haim, Hillel

2017-01-01

The envelope glycoproteins (Envs) of HIV-1 continuously evolve in the host by random mutations and recombination events. The resulting diversity of Env variants circulating in the population and their continuing diversification process limit the efficacy of AIDS vaccines. We examined the historic changes in Env sequence and structural features (measured by integrity of epitopes on the Env trimer) in a geographically defined population in the United States. As expected, many Env features were relatively conserved during the 1980s. From this state, some features diversified whereas others remained conserved across the years. We sought to identify “clues” to predict the observed historic diversification patterns. Comparison of viruses that cocirculate in patients at any given time revealed that each feature of Env (sequence or structural) exists at a defined level of variance. The in-host variance of each feature is highly conserved among individuals but can vary between different HIV-1 clades. We designate this property “volatility” and apply it to model evolution of features as a linear diffusion process that progresses with increasing genetic distance. Volatilities of different features are highly correlated with their divergence in longitudinally monitored patients. Volatilities of features also correlate highly with their population-level diversification. Using volatility indices measured from a small number of patient samples, we accurately predict the population diversity that developed for each feature over the course of 30 years. Amino acid variants that evolved at key antigenic sites are also predicted well. Therefore, small “fluctuations” in feature values measured in isolated patient samples accurately describe their potential for population-level diversification. These tools will likely contribute to the design of population-targeted AIDS vaccines by effectively capturing the diversity of currently circulating strains and addressing properties of variants expected to appear in the future. PMID:28384158

Features of the Correlation Structure of Price Indices

PubMed Central

Gao, Xiangyun; An, Haizhong; Zhong, Weiqiong

2013-01-01

What are the features of the correlation structure of price indices? To answer this question, 5 types of price indices, including 195 specific price indices from 2003 to 2011, were selected as sample data. To build a weighted network of price indices each price index is represented by a vertex, and a positive correlation between two price indices is represented by an edge. We studied the features of the weighted network structure by applying economic theory to the analysis of complex network parameters. We found that the frequency of the price indices follows a normal distribution by counting the weighted degrees of the nodes, and we identified the price indices which have an important impact on the network's structure. We found out small groups in the weighted network by the methods of k-core and k-plex. We discovered structure holes in the network by calculating the hierarchy of the nodes. Finally, we found that the price indices weighted network has a small-world effect by calculating the shortest path. These results provide a scientific basis for macroeconomic control policies. PMID:23593399

Numerical Simulation of Flow Features and Energy Exchange Physics in Near-Wall Region with Fluid-Structure Interaction

NASA Astrophysics Data System (ADS)

Zhang, Lixiang; Wang, Wenquan; Guo, Yakun

Large eddy simulation is used to explore flow features and energy exchange physics between turbulent flow and structure vibration in the near-wall region with fluid-structure interaction (FSI). The statistical turbulence characteristics in the near-wall region of a vibrating wall, such as the skin frictional coefficient, velocity, pressure, vortices, and the coherent structures have been studied for an aerofoil blade passage of a true three-dimensional hydroturbine. The results show that (i) FSI greatly strengthens the turbulence in the inner region of y+ < 25; and (ii) the energy exchange mechanism between the flow and the vibration depends strongly on the vibration-induced vorticity in the inner region. The structural vibration provokes a frequent action between the low- and high-speed streaks to balance the energy deficit caused by the vibration. The velocity profile in the inner layer near the vibrating wall has a significant distinctness, and the viscosity effect of the fluid in the inner region decreases due to the vibration. The flow features in the inner layer are altered by a suitable wall vibration.

Star formation and ISM morphology in tidally induced spiral structures

NASA Astrophysics Data System (ADS)

Pettitt, Alex R.; Tasker, Elizabeth J.; Wadsley, James W.; Keller, Ben W.; Benincasa, Samantha M.

2017-07-01

Tidal encounters are believed to be one of the key drivers of galactic spiral structure in the Universe. Such spirals are expected to produce different morphological and kinematic features compared to density wave and dynamic spiral arms. In this work, we present high-resolution simulations of a tidal encounter of a small mass companion with a disc galaxy. Included are the effects of gas cooling and heating, star formation and stellar feedback. The structure of the perturbed disc differs greatly from the isolated galaxy, showing clear spiral features that act as sites of new star formation, and displaying interarm spurs. The two arms of the galaxy, the bridge and tail, appear to behave differently; with different star formation histories and structure. Specific attention is focused on offsets between gas and stellar spiral features which can be directly compared to observations. We find that some offsets do exist between different media, with gaseous arms appearing mostly on the convex side of the stellar arms, though the exact locations appear highly time dependent. These results further highlight the differences between tidal spirals and other theories of arm structure.

Mimas: Tectonic structure and geologic history

NASA Technical Reports Server (NTRS)

Croft, Steven K.

1991-01-01

Mimas, the innermost of the major saturnian satellites, occupies an important place in comparative studies of icy satellites. It is the smallest icy satellite known to have a mostly spherical shape. Smaller icy objects like Hyperion and Puck are generally irregular in shape, while larger ones like Miranda and Enceladus are spherical. Thus Mimas is near the diameter where the combination of increasing surface gravity and internal heating begin to have a significant effect on global structure. The nature and extent of endogenic surface features provide important constraints on the interior structure and history of this transitional body. The major landforms on Mimas are impact craters. Mimas has one of the most heavily cratered surfaces in the solar system. The most prominent single feature on Mimas is Herschel, an unrelaxed complex crater 130 km in diameter. The only other recognized landforms on Mimas are tectonic grooves and lineaments. Groove locations were mapped by Schenk, but without analysis of groove structures or superposition relationships. Mimas' tectonic structures are remapped here in more detail than previously has been done, as part of a general study of tectonic features on icy satellites.

Influence of Embedded Fibers and an Epithelium Layer on the Glottal Closure Pattern in a Physical Vocal Fold Model

ERIC Educational Resources Information Center

Xuan, Yue; Zhang, Zhaoyan

2014-01-01

Purpose: The purpose of this study was to explore the possible structural and material property features that may facilitate complete glottal closure in an otherwise isotropic physical vocal fold model. Method: Seven vocal fold models with different structural features were used in this study. An isotropic model was used as the baseline model, and…

Investigation of LANDSAT imagery on correlations between ore deposits and major shield structures in Finland. [Baltic Shield

NASA Technical Reports Server (NTRS)

Tuominen, H. V. (Principal Investigator); Kuosmanen, V.

1975-01-01

The author has identified the following significant results. On the central Baltic Shield, the concept of drainage patterns can be extended to smaller scales in which case many cultural features become involved to the spatial patterns influenced by bedrock structure. Features resulting from agriculture activity and timbering often exaggerate the influence of the bedrock on the image texture.

Predicting Film Genres with Implicit Ideals

PubMed Central

Olney, Andrew McGregor

2013-01-01

We present a new approach to defining film genre based on implicit ideals. When viewers rate the likability of a film, they indirectly express their ideal of what a film should be. Across six studies we investigate the category structure that emerges from likability ratings and the category structure that emerges from the features of film. We further compare these data-driven category structures with human annotated film genres. We conclude that film genres are structured more around ideals than around features of film. This finding lends experimental support to the notion that film genres are set of shifting, fuzzy, and highly contextualized psychological categories. PMID:23423823

Effects of geometry on blast-induced loadings

NASA Astrophysics Data System (ADS)

Moore, Christopher Dyer

Simulations of blasts in an urban environment were performed using Loci/BLAST, a full-featured fluid dynamics simulation code, and analyzed. A two-structure urban environment blast case was used to perform a mesh refinement study. Results show that mesh spacing on and around the structure must be 12.5 cm or less to resolve fluid dynamic features sufficiently to yield accurate results. The effects of confinement were illustrated by analyzing a blast initiated from the same location with and without the presence of a neighboring structure. Analysis of extreme pressures and impulses on structures showed that confinement can increase blast loading by more than 200 percent.

A Content-Addressable Memory structure using quantum cells in nanotechnology with energy dissipation analysis

NASA Astrophysics Data System (ADS)

Sadoghifar, Ali; Heikalabad, Saeed Rasouli

2018-05-01

Quantum-dot cellular automata is one of the recent new technologies at the nanoscale that can be a suitable replacement for CMOS technology. The circuits constructed in QCA technology have desirable features such as low power consumption, high speed and small size. These features can be more distinct in memory structures. In this paper, we design a new structure for content addressable memory cell in QCA. For this purpose, first, a unique gate is introduced for mask operation in QCA and then this gate is used to improve the performance of CAM. These structures are evaluated with QCADesigner simulator.

Immediate effects of EVA midsole resilience and upper shoe structure on running biomechanics: a machine learning approach

PubMed Central

Gavião Neto, Wilson P.; Roveri, Maria Isabel; Oliveira, Wagner R.

2017-01-01

Background Resilience of midsole material and the upper structure of the shoe are conceptual characteristics that can interfere in running biomechanics patterns. Artificial intelligence techniques can capture features from the entire waveform, adding new perspective for biomechanical analysis. This study tested the influence of shoe midsole resilience and upper structure on running kinematics and kinetics of non-professional runners by using feature selection, information gain, and artificial neural network analysis. Methods Twenty-seven experienced male runners (63 ± 44 km/week run) ran in four-shoe design that combined two resilience-cushioning materials (low and high) and two uppers (minimalist and structured). Kinematic data was acquired by six infrared cameras at 300 Hz, and ground reaction forces were acquired by two force plates at 1,200 Hz. We conducted a Machine Learning analysis to identify features from the complete kinematic and kinetic time series and from 42 discrete variables that had better discriminate the four shoes studied. For that analysis, we built an input data matrix of dimensions 1,080 (10 trials × 4 shoes × 27 subjects) × 1,254 (3 joints × 3 planes of movement × 101 data points + 3 vectors forces × 101 data points + 42 discrete calculated kinetic and kinematic features). Results The applied feature selection by information gain and artificial neural networks successfully differentiated the two resilience materials using 200(16%) biomechanical variables with an accuracy of 84.8% by detecting alterations of running biomechanics, and the two upper structures with an accuracy of 93.9%. Discussion The discrimination of midsole resilience resulted in lower accuracy levels than did the discrimination of the shoe uppers. In both cases, the ground reaction forces were among the 25 most relevant features. The resilience of the cushioning material caused significant effects on initial heel impact, while the effects of different uppers were distributed along the stance phase of running. Biomechanical changes due to shoe midsole resilience seemed to be subject-dependent, while those due to upper structure seemed to be subject-independent. PMID:28265506

Immediate effects of EVA midsole resilience and upper shoe structure on running biomechanics: a machine learning approach.

PubMed

Onodera, Andrea N; Gavião Neto, Wilson P; Roveri, Maria Isabel; Oliveira, Wagner R; Sacco, Isabel Cn

2017-01-01

Resilience of midsole material and the upper structure of the shoe are conceptual characteristics that can interfere in running biomechanics patterns. Artificial intelligence techniques can capture features from the entire waveform, adding new perspective for biomechanical analysis. This study tested the influence of shoe midsole resilience and upper structure on running kinematics and kinetics of non-professional runners by using feature selection, information gain, and artificial neural network analysis. Twenty-seven experienced male runners (63 ± 44 km/week run) ran in four-shoe design that combined two resilience-cushioning materials (low and high) and two uppers (minimalist and structured). Kinematic data was acquired by six infrared cameras at 300 Hz, and ground reaction forces were acquired by two force plates at 1,200 Hz. We conducted a Machine Learning analysis to identify features from the complete kinematic and kinetic time series and from 42 discrete variables that had better discriminate the four shoes studied. For that analysis, we built an input data matrix of dimensions 1,080 (10 trials × 4 shoes × 27 subjects) × 1,254 (3 joints × 3 planes of movement × 101 data points + 3 vectors forces × 101 data points + 42 discrete calculated kinetic and kinematic features). The applied feature selection by information gain and artificial neural networks successfully differentiated the two resilience materials using 200(16%) biomechanical variables with an accuracy of 84.8% by detecting alterations of running biomechanics, and the two upper structures with an accuracy of 93.9%. The discrimination of midsole resilience resulted in lower accuracy levels than did the discrimination of the shoe uppers. In both cases, the ground reaction forces were among the 25 most relevant features. The resilience of the cushioning material caused significant effects on initial heel impact, while the effects of different uppers were distributed along the stance phase of running. Biomechanical changes due to shoe midsole resilience seemed to be subject-dependent, while those due to upper structure seemed to be subject-independent.

Discourse-Pragmatic Features in English and Spanish among Bilinguals

ERIC Educational Resources Information Center

Kern, Joseph

2017-01-01

A great amount of sociolinguistic research in contact situations has centered on phonological and morphosyntactic variables, but studies of discourse-pragmatic features in contact situations are scarce and incipient. Discourse-pragmatic features are syntactically optional elements that are used to guide, structure, or express a stance towards…

Feature Inference and the Causal Structure of Categories

ERIC Educational Resources Information Center

Rehder, B.; Burnett, R.C.

2005-01-01

The purpose of this article was to establish how theoretical category knowledge-specifically, knowledge of the causal relations that link the features of categories-supports the ability to infer the presence of unobserved features. Our experiments were designed to test proposals that causal knowledge is represented psychologically as Bayesian…

Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments.

PubMed

Zheng, Ce; Kurgan, Lukasz

2008-10-10

beta-turn is a secondary protein structure type that plays significant role in protein folding, stability, and molecular recognition. To date, several methods for prediction of beta-turns from protein sequences were developed, but they are characterized by relatively poor prediction quality. The novelty of the proposed sequence-based beta-turn predictor stems from the usage of a window based information extracted from four predicted three-state secondary structures, which together with a selected set of position specific scoring matrix (PSSM) values serve as an input to the support vector machine (SVM) predictor. We show that (1) all four predicted secondary structures are useful; (2) the most useful information extracted from the predicted secondary structure includes the structure of the predicted residue, secondary structure content in a window around the predicted residue, and features that indicate whether the predicted residue is inside a secondary structure segment; (3) the PSSM values of Asn, Asp, Gly, Ile, Leu, Met, Pro, and Val were among the top ranked features, which corroborates with recent studies. The Asn, Asp, Gly, and Pro indicate potential beta-turns, while the remaining four amino acids are useful to predict non-beta-turns. Empirical evaluation using three nonredundant datasets shows favorable Q total, Q predicted and MCC values when compared with over a dozen of modern competing methods. Our method is the first to break the 80% Q total barrier and achieves Q total = 80.9%, MCC = 0.47, and Q predicted higher by over 6% when compared with the second best method. We use feature selection to reduce the dimensionality of the feature vector used as the input for the proposed prediction method. The applied feature set is smaller by 86, 62 and 37% when compared with the second and two third-best (with respect to MCC) competing methods, respectively. Experiments show that the proposed method constitutes an improvement over the competing prediction methods. The proposed prediction model can better discriminate between beta-turns and non-beta-turns due to obtaining lower numbers of false positive predictions. The prediction model and datasets are freely available at http://biomine.ece.ualberta.ca/BTNpred/BTNpred.html.

Prediction of beta-turns at over 80% accuracy based on an ensemble of predicted secondary structures and multiple alignments

PubMed Central

Zheng, Ce; Kurgan, Lukasz

2008-01-01

Background β-turn is a secondary protein structure type that plays significant role in protein folding, stability, and molecular recognition. To date, several methods for prediction of β-turns from protein sequences were developed, but they are characterized by relatively poor prediction quality. The novelty of the proposed sequence-based β-turn predictor stems from the usage of a window based information extracted from four predicted three-state secondary structures, which together with a selected set of position specific scoring matrix (PSSM) values serve as an input to the support vector machine (SVM) predictor. Results We show that (1) all four predicted secondary structures are useful; (2) the most useful information extracted from the predicted secondary structure includes the structure of the predicted residue, secondary structure content in a window around the predicted residue, and features that indicate whether the predicted residue is inside a secondary structure segment; (3) the PSSM values of Asn, Asp, Gly, Ile, Leu, Met, Pro, and Val were among the top ranked features, which corroborates with recent studies. The Asn, Asp, Gly, and Pro indicate potential β-turns, while the remaining four amino acids are useful to predict non-β-turns. Empirical evaluation using three nonredundant datasets shows favorable Qtotal, Qpredicted and MCC values when compared with over a dozen of modern competing methods. Our method is the first to break the 80% Qtotal barrier and achieves Qtotal = 80.9%, MCC = 0.47, and Qpredicted higher by over 6% when compared with the second best method. We use feature selection to reduce the dimensionality of the feature vector used as the input for the proposed prediction method. The applied feature set is smaller by 86, 62 and 37% when compared with the second and two third-best (with respect to MCC) competing methods, respectively. Conclusion Experiments show that the proposed method constitutes an improvement over the competing prediction methods. The proposed prediction model can better discriminate between β-turns and non-β-turns due to obtaining lower numbers of false positive predictions. The prediction model and datasets are freely available at . PMID:18847492

Fast and Efficient Feature Engineering for Multi-Cohort Analysis of EHR Data.

PubMed

Ozery-Flato, Michal; Yanover, Chen; Gottlieb, Assaf; Weissbrod, Omer; Parush Shear-Yashuv, Naama; Goldschmidt, Yaara

2017-01-01

We present a framework for feature engineering, tailored for longitudinal structured data, such as electronic health records (EHRs). To fast-track feature engineering and extraction, the framework combines general-use plug-in extractors, a multi-cohort management mechanism, and modular memoization. Using this framework, we rapidly extracted thousands of features from diverse and large healthcare data sources in multiple projects.

Features of structure-phase transformations and segregation processes under irradiation of austenitic and ferritic-martensitic steels

NASA Astrophysics Data System (ADS)

Neklyudov, I. M.; Voyevodin, V. N.

1994-09-01

The difference between crystal lattices of austenitic and ferritic steels leads to distinctive features in mechanisms of physical-mechanical change. This paper presents the results of investigations of dislocation structure and phase evolution, and segregation phenomena in austenitic and ferritic-martensitic steels and alloys during irradiation with heavy ions in the ESUVI and UTI accelerators and by neutrons in fast reactors BOR-60 and BN-600. The influence of different factors (including different alloying elements) on processes of structure-phase transformation was studied.

Voroprot: an interactive tool for the analysis and visualization of complex geometric features of protein structure.

PubMed

Olechnovic, Kliment; Margelevicius, Mindaugas; Venclovas, Ceslovas

2011-03-01

We present Voroprot, an interactive cross-platform software tool that provides a unique set of capabilities for exploring geometric features of protein structure. Voroprot allows the construction and visualization of the Apollonius diagram (also known as the additively weighted Voronoi diagram), the Apollonius graph, protein alpha shapes, interatomic contact surfaces, solvent accessible surfaces, pockets and cavities inside protein structure. Voroprot is available for Windows, Linux and Mac OS X operating systems and can be downloaded from http://www.ibt.lt/bioinformatics/voroprot/.

Aeroelastic characteristics of composite bearingless rotor blades

NASA Technical Reports Server (NTRS)

Bielawa, R. L.

1976-01-01

Owing to the inherent unique structural features of composite bearingless rotors, various assumptions upon which conventional rotor aeroelastic analyses are formulated, are violated. Three such features identified are highly nonlinear and time-varying structural twist, structural redundancy in bending and torsion, and for certain configurations a strongly coupled low frequency bending-torsion mode. An examination of these aeroelastic considerations and appropriate formulations required for accurate analyses of such rotor systems is presented. Also presented are test results from a dynamically scaled model rotor and complementary analytic results obtained with the appropriately reformulated aeroelastic analysis.

Mechanical features of the shuttle rotating service structure

NASA Technical Reports Server (NTRS)

Crump, J. M.

1985-01-01

With the development of the space shuttle launching facilities, it became mandatory to develop a shuttle rotating service structure to provide for the insertion and/or removal of payloads at the launch pads. The rotating service structure is a welded tubular steel space frame 189 feet high, 65 feet wide, and weighing 2100 tons. At the pivot column the structure is supported on a 30 inch diameter hemispherical bearing. At the opposite terminus the structure is supported on two truck assemblies each having eight 36 inch diameter double flanged wheels. The following features of the rotating service structure are discussed: (1) thermal expansion and contraction; (2) hurricane tie downs; (3) payload changeout room; (4) payload ground handling mechanism; (5) payload and orbiter access platforms; and (6) orbiter cargo bay access.

Effect of structural distortion on the electronic band structure of NaOsO3 studied within density functional theory and a three-orbital model

NASA Astrophysics Data System (ADS)

Mohapatra, Shubhajyoti; Bhandari, Churna; Satpathy, Sashi; Singh, Avinash

2018-04-01

Effects of the structural distortion associated with the OsO6 octahedral rotation and tilting on the electronic band structure and magnetic anisotropy energy for the 5 d3 compound NaOsO3 are investigated using the density functional theory (DFT) and within a three-orbital model. Comparison of the essential features of the DFT band structures with the three-orbital model for both the undistorted and distorted structures provides insight into the orbital and directional asymmetry in the electron hopping terms resulting from the structural distortion. The orbital mixing terms obtained in the transformed hopping Hamiltonian resulting from the octahedral rotations are shown to account for the fine features in the DFT band structure. Staggered magnetization and the magnetic character of states near the Fermi energy indicate weak coupling behavior.

Ion spectral structures observed by the Van Allen Probes

NASA Astrophysics Data System (ADS)

Ferradas, C.; Zhang, J.; Spence, H. E.; Kistler, L. M.; Larsen, B.; Reeves, G. D.; Skoug, R. M.; Funsten, H. O.

2015-12-01

During the last decades several missions have recorded the presence of dynamic spectral features of energetic ions in the inner magnetosphere. Previous studies have reported single "nose-like" structures occurring alone and simultaneous nose-like structures (up to three). These ion structures are named after the characteristic shapes of energy bands or gaps in the energy-time spectrograms of in situ measured ion fluxes. They constitute the observational signatures of ion acceleration, transport, and loss in the global magnetosphere. The HOPE mass spectrometer onboard the Van Allen Probes measures energetic hydrogen, helium, and oxygen ions near the inner edge of the plasma sheet, where these ion structures are observed. We present a statistical study of nose-like structures, using 2-years measurements from the HOPE instrument. The results provide important details about the spatial distribution (dependence on geocentric distance), spectral features of the structures (differences among species), and geomagnetic conditions under which these structures occur.

Object similarity affects the perceptual strategy underlying invariant visual object recognition in rats

PubMed Central

Rosselli, Federica B.; Alemi, Alireza; Ansuini, Alessio; Zoccolan, Davide

2015-01-01

In recent years, a number of studies have explored the possible use of rats as models of high-level visual functions. One central question at the root of such an investigation is to understand whether rat object vision relies on the processing of visual shape features or, rather, on lower-order image properties (e.g., overall brightness). In a recent study, we have shown that rats are capable of extracting multiple features of an object that are diagnostic of its identity, at least when those features are, structure-wise, distinct enough to be parsed by the rat visual system. In the present study, we have assessed the impact of object structure on rat perceptual strategy. We trained rats to discriminate between two structurally similar objects, and compared their recognition strategies with those reported in our previous study. We found that, under conditions of lower stimulus discriminability, rat visual discrimination strategy becomes more view-dependent and subject-dependent. Rats were still able to recognize the target objects, in a way that was largely tolerant (i.e., invariant) to object transformation; however, the larger structural and pixel-wise similarity affected the way objects were processed. Compared to the findings of our previous study, the patterns of diagnostic features were: (i) smaller and more scattered; (ii) only partially preserved across object views; and (iii) only partially reproducible across rats. On the other hand, rats were still found to adopt a multi-featural processing strategy and to make use of part of the optimal discriminatory information afforded by the two objects. Our findings suggest that, as in humans, rat invariant recognition can flexibly rely on either view-invariant representations of distinctive object features or view-specific object representations, acquired through learning. PMID:25814936

Built-up Areas Extraction in High Resolution SAR Imagery based on the method of Multiple Feature Weighted Fusion

NASA Astrophysics Data System (ADS)

Liu, X.; Zhang, J. X.; Zhao, Z.; Ma, A. D.

2015-06-01

Synthetic aperture radar in the application of remote sensing technology is becoming more and more widely because of its all-time and all-weather operation, feature extraction research in high resolution SAR image has become a hot topic of concern. In particular, with the continuous improvement of airborne SAR image resolution, image texture information become more abundant. It's of great significance to classification and extraction. In this paper, a novel method for built-up areas extraction using both statistical and structural features is proposed according to the built-up texture features. First of all, statistical texture features and structural features are respectively extracted by classical method of gray level co-occurrence matrix and method of variogram function, and the direction information is considered in this process. Next, feature weights are calculated innovatively according to the Bhattacharyya distance. Then, all features are weighted fusion. At last, the fused image is classified with K-means classification method and the built-up areas are extracted after post classification process. The proposed method has been tested by domestic airborne P band polarization SAR images, at the same time, two groups of experiments based on the method of statistical texture and the method of structural texture were carried out respectively. On the basis of qualitative analysis, quantitative analysis based on the built-up area selected artificially is enforced, in the relatively simple experimentation area, detection rate is more than 90%, in the relatively complex experimentation area, detection rate is also higher than the other two methods. In the study-area, the results show that this method can effectively and accurately extract built-up areas in high resolution airborne SAR imagery.

Insights into multimodal imaging classification of ADHD

PubMed Central

Colby, John B.; Rudie, Jeffrey D.; Brown, Jesse A.; Douglas, Pamela K.; Cohen, Mark S.; Shehzad, Zarrar

2012-01-01

Attention deficit hyperactivity disorder (ADHD) currently is diagnosed in children by clinicians via subjective ADHD-specific behavioral instruments and by reports from the parents and teachers. Considering its high prevalence and large economic and societal costs, a quantitative tool that aids in diagnosis by characterizing underlying neurobiology would be extremely valuable. This provided motivation for the ADHD-200 machine learning (ML) competition, a multisite collaborative effort to investigate imaging classifiers for ADHD. Here we present our ML approach, which used structural and functional magnetic resonance imaging data, combined with demographic information, to predict diagnostic status of individuals with ADHD from typically developing (TD) children across eight different research sites. Structural features included quantitative metrics from 113 cortical and non-cortical regions. Functional features included Pearson correlation functional connectivity matrices, nodal and global graph theoretical measures, nodal power spectra, voxelwise global connectivity, and voxelwise regional homogeneity. We performed feature ranking for each site and modality using the multiple support vector machine recursive feature elimination (SVM-RFE) algorithm, and feature subset selection by optimizing the expected generalization performance of a radial basis function kernel SVM (RBF-SVM) trained across a range of the top features. Site-specific RBF-SVMs using these optimal feature sets from each imaging modality were used to predict the class labels of an independent hold-out test set. A voting approach was used to combine these multiple predictions and assign final class labels. With this methodology we were able to predict diagnosis of ADHD with 55% accuracy (versus a 39% chance level in this sample), 33% sensitivity, and 80% specificity. This approach also allowed us to evaluate predictive structural and functional features giving insight into abnormal brain circuitry in ADHD. PMID:22912605

Purely Structural Protein Scoring Functions Using Support Vector Machine and Ensemble Learning.

PubMed

Mirzaei, Shokoufeh; Sidi, Tomer; Keasar, Chen; Crivelli, Silvia

2016-08-24

The function of a protein is determined by its structure, which creates a need for efficient methods of protein structure determination to advance scientific and medical research. Because current experimental structure determination methods carry a high price tag, computational predictions are highly desirable. Given a protein sequence, computational methods produce numerous 3D structures known as decoys. However, selection of the best quality decoys is challenging as the end users can handle only a few ones. Therefore, scoring functions are central to decoy selection. They combine measurable features into a single number indicator of decoy quality. Unfortunately, current scoring functions do not consistently select the best decoys. Machine learning techniques offer great potential to improve decoy scoring. This paper presents two machine-learning based scoring functions to predict the quality of proteins structures, i.e., the similarity between the predicted structure and the experimental one without knowing the latter. We use different metrics to compare these scoring functions against three state-of-the-art scores. This is a first attempt at comparing different scoring functions using the same non-redundant dataset for training and testing and the same features. The results show that adding informative features may be more significant than the method used.

A common feature pharmacophore for FDA-approved drugs inhibiting the Ebola virus.

PubMed

Ekins, Sean; Freundlich, Joel S; Coffee, Megan

2014-01-01

We are currently faced with a global infectious disease crisis which has been anticipated for decades. While many promising biotherapeutics are being tested, the search for a small molecule has yet to deliver an approved drug or therapeutic for the Ebola or similar filoviruses that cause haemorrhagic fever. Two recent high throughput screens published in 2013 did however identify several hits that progressed to animal studies that are FDA approved drugs used for other indications. The current computational analysis uses these molecules from two different structural classes to construct a common features pharmacophore. This ligand-based pharmacophore implicates a possible common target or mechanism that could be further explored. A recent structure based design project yielded nine co-crystal structures of pyrrolidinone inhibitors bound to the viral protein 35 (VP35). When receptor-ligand pharmacophores based on the analogs of these molecules and the protein structures were constructed, the molecular features partially overlapped with the common features of solely ligand-based pharmacophore models based on FDA approved drugs. These previously identified FDA approved drugs with activity against Ebola were therefore docked into this protein. The antimalarials chloroquine and amodiaquine docked favorably in VP35. We propose that these drugs identified to date as inhibitors of the Ebola virus may be targeting VP35. These computational models may provide preliminary insights into the molecular features that are responsible for their activity against Ebola virus in vitro and in vivo and we propose that this hypothesis could be readily tested.

Structural geologic interpretations from radar imagery

USGS Publications Warehouse

Reeves, Robert G.

1969-01-01

Certain structural geologic features may be more readily recognized on sidelooking airborne radar (SLAR) images than on conventional aerial photographs, other remote sensor imagery, or by ground observations. SLAR systems look obliquely to one or both sides and their images resemble aerial photographs taken at low sun angle with the sun directly behind the camera. They differ from air photos in geometry, resolution, and information content. Radar operates at much lower frequencies than the human eye, camera, or infrared sensors, and thus "sees" differently. The lower frequency enables it to penetrate most clouds and some precipitation, haze, dust, and some vegetation. Radar provides its own illumination, which can be closely controlled in intensity and frequency. It is narrow band, or essentially monochromatic. Low relief and subdued features are accentuated when viewed from the proper direction. Runs over the same area in significantly different directions (more than 45° from each other), show that images taken in one direction may emphasize features that are not emphasized on those taken in the other direction; optimum direction is determined by those features which need to be emphasized for study purposes. Lineaments interpreted as faults stand out on radar imagery of central and western Nevada; folded sedimentary rocks cut by faults can be clearly seen on radar imagery of northern Alabama. In these areas, certain structural and stratigraphic features are more pronounced on radar images than on conventional photographs; thus radar imagery materially aids structural interpretation.

A common feature pharmacophore for FDA-approved drugs inhibiting the Ebola virus

PubMed Central

Ekins, Sean; Freundlich, Joel S.; Coffee, Megan

2014-01-01

We are currently faced with a global infectious disease crisis which has been anticipated for decades. While many promising biotherapeutics are being tested, the search for a small molecule has yet to deliver an approved drug or therapeutic for the Ebola or similar filoviruses that cause haemorrhagic fever. Two recent high throughput screens published in 2013 did however identify several hits that progressed to animal studies that are FDA approved drugs used for other indications. The current computational analysis uses these molecules from two different structural classes to construct a common features pharmacophore. This ligand-based pharmacophore implicates a possible common target or mechanism that could be further explored. A recent structure based design project yielded nine co-crystal structures of pyrrolidinone inhibitors bound to the viral protein 35 (VP35). When receptor-ligand pharmacophores based on the analogs of these molecules and the protein structures were constructed, the molecular features partially overlapped with the common features of solely ligand-based pharmacophore models based on FDA approved drugs. These previously identified FDA approved drugs with activity against Ebola were therefore docked into this protein. The antimalarials chloroquine and amodiaquine docked favorably in VP35. We propose that these drugs identified to date as inhibitors of the Ebola virus may be targeting VP35. These computational models may provide preliminary insights into the molecular features that are responsible for their activity against Ebola virus in vitro and in vivo and we propose that this hypothesis could be readily tested. PMID:25653841

Understanding Genetic Toxicity Through Data Mining: The ...

EPA Pesticide Factsheets

This paper demonstrates the usefulness of representing a chemical by its structural features and the use of these features to profile a battery of tests rather than relying on a single toxicity test of a given chemical. This paper presents data mining/profiling methods applied in a weight-of-evidence approach to assess potential for genetic toxicity, and to guide the development of intelligent testing strategies. This paper demonstrates the usefulness of representing a chemical by its structural features and the use of these features to profile a battery of tests rather than relying on a single toxicity test of a given chemical. This paper presents data mining/profiling methods applied in a weight-of-evidence approach to assess potential for genetic toxicity, and to guide the development of intelligent testing strategies.

Improving protein fold recognition by extracting fold-specific features from predicted residue-residue contacts.

PubMed

Zhu, Jianwei; Zhang, Haicang; Li, Shuai Cheng; Wang, Chao; Kong, Lupeng; Sun, Shiwei; Zheng, Wei-Mou; Bu, Dongbo

2017-12-01

Accurate recognition of protein fold types is a key step for template-based prediction of protein structures. The existing approaches to fold recognition mainly exploit the features derived from alignments of query protein against templates. These approaches have been shown to be successful for fold recognition at family level, but usually failed at superfamily/fold levels. To overcome this limitation, one of the key points is to explore more structurally informative features of proteins. Although residue-residue contacts carry abundant structural information, how to thoroughly exploit these information for fold recognition still remains a challenge. In this study, we present an approach (called DeepFR) to improve fold recognition at superfamily/fold levels. The basic idea of our approach is to extract fold-specific features from predicted residue-residue contacts of proteins using deep convolutional neural network (DCNN) technique. Based on these fold-specific features, we calculated similarity between query protein and templates, and then assigned query protein with fold type of the most similar template. DCNN has showed excellent performance in image feature extraction and image recognition; the rational underlying the application of DCNN for fold recognition is that contact likelihood maps are essentially analogy to images, as they both display compositional hierarchy. Experimental results on the LINDAHL dataset suggest that even using the extracted fold-specific features alone, our approach achieved success rate comparable to the state-of-the-art approaches. When further combining these features with traditional alignment-related features, the success rate of our approach increased to 92.3%, 82.5% and 78.8% at family, superfamily and fold levels, respectively, which is about 18% higher than the state-of-the-art approach at fold level, 6% higher at superfamily level and 1% higher at family level. An independent assessment on SCOP_TEST dataset showed consistent performance improvement, indicating robustness of our approach. Furthermore, bi-clustering results of the extracted features are compatible with fold hierarchy of proteins, implying that these features are fold-specific. Together, these results suggest that the features extracted from predicted contacts are orthogonal to alignment-related features, and the combination of them could greatly facilitate fold recognition at superfamily/fold levels and template-based prediction of protein structures. Source code of DeepFR is freely available through https://github.com/zhujianwei31415/deepfr, and a web server is available through http://protein.ict.ac.cn/deepfr. zheng@itp.ac.cn or dbu@ict.ac.cn. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

a Robust Descriptor Based on Spatial and Frequency Structural Information for Visible and Thermal Infrared Image Matching

NASA Astrophysics Data System (ADS)

Fu, Z.; Qin, Q.; Wu, C.; Chang, Y.; Luo, B.

2017-09-01

Due to the differences of imaging principles, image matching between visible and thermal infrared images still exist new challenges and difficulties. Inspired by the complementary spatial and frequency information of geometric structural features, a robust descriptor is proposed for visible and thermal infrared images matching. We first divide two different spatial regions to the region around point of interest, using the histogram of oriented magnitudes, which corresponds to the 2-D structural shape information to describe the larger region and the edge oriented histogram to describe the spatial distribution for the smaller region. Then the two vectors are normalized and combined to a higher feature vector. Finally, our proposed descriptor is obtained by applying principal component analysis (PCA) to reduce the dimension of the combined high feature vector to make our descriptor more robust. Experimental results showed that our proposed method was provided with significant improvements in correct matching numbers and obvious advantages by complementing information within spatial and frequency structural information.

An evaluation of the suitability of ERTS data for the purposes of petroleum exploration. [lithology and geological structure of Anadarko Basin of Oklahoma and Texas

NASA Technical Reports Server (NTRS)

Collins, R. J. (Principal Investigator); Mccown, F. P.; Stonis, L. P.; Petzel, G. J.; Everett, J. R.

1974-01-01

The author has identified the following significant results. ERTS-1 data give exploration geologists a new perspective for looking at the earth. The data are excellent for interpreting regional lithologic and structural relationships and quickly directing attention to areas of greatest exploration interest. Information derived from ERTS data useful for petroleum exploration include: linear features, general lithologic distribution, identification of various anomalous features, some details of structures controlling hydrocarbon accumulation, overall structural relationships, and the regional context of the exploration province. Many anomalies (particularly geomorphic anomalies) correlate with known features of petroleum exploration interest. Linears interpreted from the imagery that were checked in the field correlate with fractures. Bands 5 and 7 and color composite imagery acquired during the periods of maximum and minimum vegetation vigor are best for geologic interpretation. Preliminary analysis indicates that use of ERTS imagery can substantially reduce the cost of petroleum exploration in relatively unexplored areas.

Remote sensing image segmentation using local sparse structure constrained latent low rank representation

NASA Astrophysics Data System (ADS)

Tian, Shu; Zhang, Ye; Yan, Yimin; Su, Nan; Zhang, Junping

2016-09-01

Latent low-rank representation (LatLRR) has been attached considerable attention in the field of remote sensing image segmentation, due to its effectiveness in exploring the multiple subspace structures of data. However, the increasingly heterogeneous texture information in the high spatial resolution remote sensing images, leads to more severe interference of pixels in local neighborhood, and the LatLRR fails to capture the local complex structure information. Therefore, we present a local sparse structure constrainted latent low-rank representation (LSSLatLRR) segmentation method, which explicitly imposes the local sparse structure constraint on LatLRR to capture the intrinsic local structure in manifold structure feature subspaces. The whole segmentation framework can be viewed as two stages in cascade. In the first stage, we use the local histogram transform to extract the texture local histogram features (LHOG) at each pixel, which can efficiently capture the complex and micro-texture pattern. In the second stage, a local sparse structure (LSS) formulation is established on LHOG, which aims to preserve the local intrinsic structure and enhance the relationship between pixels having similar local characteristics. Meanwhile, by integrating the LSS and the LatLRR, we can efficiently capture the local sparse and low-rank structure in the mixture of feature subspace, and we adopt the subspace segmentation method to improve the segmentation accuracy. Experimental results on the remote sensing images with different spatial resolution show that, compared with three state-of-the-art image segmentation methods, the proposed method achieves more accurate segmentation results.

The Atmosphere of Uranus as Imaged with Keck Adaptive Optics

NASA Astrophysics Data System (ADS)

Hammel, H. B.; de Pater, I.; Gibbard, S. G.; Lockwood, G. W.; Rages, K.

2004-12-01

Adaptive optics imaging of Uranus was obtained with NIRC2 on the Keck II 10-meter telescope in October 2003 and July 2004 through J, H, and K' filters. Dozens of discrete features were detected in the atmosphere of Uranus. We report the first measurements of winds northward of +43 deg, the first direct measurement of equatorial winds, and the highest wind velocity seen yet on Uranus. At northern mid-latitudes, the winds may have accelerated when compared to earlier HST and Keck observations; southern wind speeds have not changed since Voyager measurements in 1986. The equator of Uranus exhibits a subtle wave structure, with diffuse patches roughly every 30 degs in longitude. There is no sign of a northern "polar collar" as is seen in the south, but a number of discrete features seen at the "expected" latitudes may signal its early stages of development. The largest cloud features on Uranus show complex structure extending over tens of degrees. On 4 July 2004, we detected a southern hemispheric cloud feature on Uranus at K', the first detection of a southern feature at or longward of 2 microns. H images showed an extended structure whose condensed core was co-located with the K'-bright feature. The core exhibited marked brightness variation, fading within just a few days. The initial brightness at K' indicates that the core's scattering particles reached altitudes above the 1-bar level, with the extended H feature residing below 1.1 bars. The core's rapid disappearance at K' indicates dynamical processes in the local vertical aerosol structure. HBH acknowledges support from NASA grants NAG5-11961 and NAG5-10451. IdP acknowledges support from NSF and the Technology Center for Adaptive Optics, managed by UCSC under cooperative agreement No. AST-9876783. SGG's work was performed under the auspices of the U.S. DoE National Nuclear Security Administration by the UC, LLNL under contract No. W-7405-Eng-48.

Defining and predicting structurally conserved regions in protein superfamilies

PubMed Central

Huang, Ivan K.; Grishin, Nick V.

2013-01-01

Motivation: The structures of homologous proteins are generally better conserved than their sequences. This phenomenon is demonstrated by the prevalence of structurally conserved regions (SCRs) even in highly divergent protein families. Defining SCRs requires the comparison of two or more homologous structures and is affected by their availability and divergence, and our ability to deduce structurally equivalent positions among them. In the absence of multiple homologous structures, it is necessary to predict SCRs of a protein using information from only a set of homologous sequences and (if available) a single structure. Accurate SCR predictions can benefit homology modelling and sequence alignment. Results: Using pairwise DaliLite alignments among a set of homologous structures, we devised a simple measure of structural conservation, termed structural conservation index (SCI). SCI was used to distinguish SCRs from non-SCRs. A database of SCRs was compiled from 386 SCOP superfamilies containing 6489 protein domains. Artificial neural networks were then trained to predict SCRs with various features deduced from a single structure and homologous sequences. Assessment of the predictions via a 5-fold cross-validation method revealed that predictions based on features derived from a single structure perform similarly to ones based on homologous sequences, while combining sequence and structural features was optimal in terms of accuracy (0.755) and Matthews correlation coefficient (0.476). These results suggest that even without information from multiple structures, it is still possible to effectively predict SCRs for a protein. Finally, inspection of the structures with the worst predictions pinpoints difficulties in SCR definitions. Availability: The SCR database and the prediction server can be found at http://prodata.swmed.edu/SCR. Contact: 91huangi@gmail.com or grishin@chop.swmed.edu Supplementary information: Supplementary data are available at Bioinformatics Online PMID:23193223

G Protein-Coupled Receptor Rhodopsin: A Prospectus

PubMed Central

Filipek, Sławomir; Stenkamp, Ronald E.; Teller, David C.; Palczewski, Krzysztof

2006-01-01

Rhodopsin is a retinal photoreceptor protein of bipartite structure consisting of the transmembrane protein opsin and a light-sensitive chromophore 11-cis-retinal, linked to opsin via a protonated Schiff base. Studies on rhodopsin have unveiled many structural and functional features that are common to a large and pharmacologically important group of proteins from the G protein-coupled receptor (GPCR) superfamily, of which rhodopsin is the best-studied member. In this work, we focus on structural features of rhodopsin as revealed by many biochemical and structural investigations. In particular, the high-resolution structure of bovine rhodopsin provides a template for understanding how GPCRs work. We describe the sensitivity and complexity of rhodopsin that lead to its important role in vision. PMID:12471166

Determining crystal structures through crowdsourcing and coursework

NASA Astrophysics Data System (ADS)

Horowitz, Scott; Koepnick, Brian; Martin, Raoul; Tymieniecki, Agnes; Winburn, Amanda A.; Cooper, Seth; Flatten, Jeff; Rogawski, David S.; Koropatkin, Nicole M.; Hailu, Tsinatkeab T.; Jain, Neha; Koldewey, Philipp; Ahlstrom, Logan S.; Chapman, Matthew R.; Sikkema, Andrew P.; Skiba, Meredith A.; Maloney, Finn P.; Beinlich, Felix R. M.; Caglar, Ahmet; Coral, Alan; Jensen, Alice Elizabeth; Lubow, Allen; Boitano, Amanda; Lisle, Amy Elizabeth; Maxwell, Andrew T.; Failer, Barb; Kaszubowski, Bartosz; Hrytsiv, Bohdan; Vincenzo, Brancaccio; de Melo Cruz, Breno Renan; McManus, Brian Joseph; Kestemont, Bruno; Vardeman, Carl; Comisky, Casey; Neilson, Catherine; Landers, Catherine R.; Ince, Christopher; Buske, Daniel Jon; Totonjian, Daniel; Copeland, David Marshall; Murray, David; Jagieła, Dawid; Janz, Dietmar; Wheeler, Douglas C.; Cali, Elie; Croze, Emmanuel; Rezae, Farah; Martin, Floyd Orville; Beecher, Gil; de Jong, Guido Alexander; Ykman, Guy; Feldmann, Harald; Chan, Hugo Paul Perez; Kovanecz, Istvan; Vasilchenko, Ivan; Connellan, James C.; Borman, Jami Lynne; Norrgard, Jane; Kanfer, Jebbie; Canfield, Jeffrey M.; Slone, Jesse David; Oh, Jimmy; Mitchell, Joanne; Bishop, John; Kroeger, John Douglas; Schinkler, Jonas; McLaughlin, Joseph; Brownlee, June M.; Bell, Justin; Fellbaum, Karl Willem; Harper, Kathleen; Abbey, Kirk J.; Isaksson, Lennart E.; Wei, Linda; Cummins, Lisa N.; Miller, Lori Anne; Bain, Lyn; Carpenter, Lynn; Desnouck, Maarten; Sharma, Manasa G.; Belcastro, Marcus; Szew, Martin; Szew, Martin; Britton, Matthew; Gaebel, Matthias; Power, Max; Cassidy, Michael; Pfützenreuter, Michael; Minett, Michele; Wesselingh, Michiel; Yi, Minjune; Cameron, Neil Haydn Tormey; Bolibruch, Nicholas I.; Benevides, Noah; Kathleen Kerr, Norah; Barlow, Nova; Crevits, Nykole Krystyne; Dunn, Paul; Silveira Belo Nascimento Roque, Paulo Sergio; Riber, Peter; Pikkanen, Petri; Shehzad, Raafay; Viosca, Randy; James Fraser, Robert; Leduc, Robert; Madala, Roman; Shnider, Scott; de Boisblanc, Sharon; Butkovich, Slava; Bliven, Spencer; Hettler, Stephen; Telehany, Stephen; Schwegmann, Steven A.; Parkes, Steven; Kleinfelter, Susan C.; Michael Holst, Sven; van der Laan, T. J. A.; Bausewein, Thomas; Simon, Vera; Pulley, Warwick; Hull, William; Kim, Annes Yukyung; Lawton, Alexis; Ruesch, Amanda; Sundar, Anjali; Lawrence, Anna-Lisa; Afrin, Antara; Maheshwer, Bhargavi; Turfe, Bilal; Huebner, Christian; Killeen, Courtney Elizabeth; Antebi-Lerrman, Dalia; Luan, Danny; Wolfe, Derek; Pham, Duc; Michewicz, Elaina; Hull, Elizabeth; Pardington, Emily; Galal, Galal Osama; Sun, Grace; Chen, Grace; Anderson, Halie E.; Chang, Jane; Hewlett, Jeffrey Thomas; Sterbenz, Jennifer; Lim, Jiho; Morof, Joshua; Lee, Junho; Inn, Juyoung Samuel; Hahm, Kaitlin; Roth, Kaitlin; Nair, Karun; Markin, Katherine; Schramm, Katie; Toni Eid, Kevin; Gam, Kristina; Murphy, Lisha; Yuan, Lucy; Kana, Lulia; Daboul, Lynn; Shammas, Mario Karam; Chason, Max; Sinan, Moaz; Andrew Tooley, Nicholas; Korakavi, Nisha; Comer, Patrick; Magur, Pragya; Savliwala, Quresh; Davison, Reid Michael; Sankaran, Roshun Rajiv; Lewe, Sam; Tamkus, Saule; Chen, Shirley; Harvey, Sho; Hwang, Sin Ye; Vatsia, Sohrab; Withrow, Stefan; Luther, Tahra K.; Manett, Taylor; Johnson, Thomas James; Ryan Brash, Timothy; Kuhlman, Wyatt; Park, Yeonjung; Popović, Zoran; Baker, David; Khatib, Firas; Bardwell, James C. A.

2016-09-01

We show here that computer game players can build high-quality crystal structures. Introduction of a new feature into the computer game Foldit allows players to build and real-space refine structures into electron density maps. To assess the usefulness of this feature, we held a crystallographic model-building competition between trained crystallographers, undergraduate students, Foldit players and automatic model-building algorithms. After removal of disordered residues, a team of Foldit players achieved the most accurate structure. Analysing the target protein of the competition, YPL067C, uncovered a new family of histidine triad proteins apparently involved in the prevention of amyloid toxicity. From this study, we conclude that crystallographers can utilize crowdsourcing to interpret electron density information and to produce structure solutions of the highest quality.

Determining crystal structures through crowdsourcing and coursework.

PubMed

Horowitz, Scott; Koepnick, Brian; Martin, Raoul; Tymieniecki, Agnes; Winburn, Amanda A; Cooper, Seth; Flatten, Jeff; Rogawski, David S; Koropatkin, Nicole M; Hailu, Tsinatkeab T; Jain, Neha; Koldewey, Philipp; Ahlstrom, Logan S; Chapman, Matthew R; Sikkema, Andrew P; Skiba, Meredith A; Maloney, Finn P; Beinlich, Felix R M; Popović, Zoran; Baker, David; Khatib, Firas; Bardwell, James C A

2016-09-16

We show here that computer game players can build high-quality crystal structures. Introduction of a new feature into the computer game Foldit allows players to build and real-space refine structures into electron density maps. To assess the usefulness of this feature, we held a crystallographic model-building competition between trained crystallographers, undergraduate students, Foldit players and automatic model-building algorithms. After removal of disordered residues, a team of Foldit players achieved the most accurate structure. Analysing the target protein of the competition, YPL067C, uncovered a new family of histidine triad proteins apparently involved in the prevention of amyloid toxicity. From this study, we conclude that crystallographers can utilize crowdsourcing to interpret electron density information and to produce structure solutions of the highest quality.

Identifying Planar Deformation Features Using EBSD and FIB

NASA Astrophysics Data System (ADS)

Pickersgill, A. E.; Lee, M. R.

2015-09-01

Planar deformation features in quartz grains from the Gow Lake impact structure have been successfully identified and indexed using electron backscatter diffraction in combination with focused ion beam milling.

Seismogeodynamics of lineament structures in the mountainous regions bordering the Scythian-Turan plate

NASA Astrophysics Data System (ADS)

Ulomov, V. I.; Danilova, T. I.; Medvedeva, N. S.; Polyakova, T. P.

2006-07-01

The Scythian-Turan platform, together with the Alpine Iran-Caucasus-Anatolia and Hercynian Central Tien Shan orogenic structures adjacent to it, represents a coherent seismogeodynamic system responsible for regional seismicity features in the territory under consideration. Investigations of the spatiotemporal and energy evolution of seismogeodynamic processes along the main lineament structures of the orogen reveal characteristic features directly related to the prediction of seismic hazard in this region, as well as in southern European Russia. These characteristics primarily include kinematic features in the sequences of seismic events of various magnitudes and an ordered migration of seismic activation, enabling the more or less reliable determination of the occurrence time intervals (years) and areas of forthcoming large earthquakes (magnitudes of 7.0 ± 0.2, 7.5 ± 0.2, and 8.0 ± 0.2).

Article and process for producing an article

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

Lacy, Benjamin Paul; Jacala, Ariel Caesar Prepena; Kottilingam, Srikanth Chandrudu

An article and a process of producing an article are provided. The article includes a base material, a cooling feature arrangement positioned on the base material, the cooling feature arrangement including an additive-structured material, and a cover material. The cooling feature arrangement is between the base material and the cover material. The process of producing the article includes manufacturing a cooling feature arrangement by an additive manufacturing technique, and then positioning the cooling feature arrangement between a base material and a cover material.

Using Multispectral False Color Imaging to Characterize Tropical Cyclone Structure and Environment

NASA Astrophysics Data System (ADS)

Cossuth, J.; Bankert, R.; Richardson, K.; Surratt, M. L.

2016-12-01

The Naval Research Laboratory's (NRL) tropical cyclone (TC) web page (http://www.nrlmry.navy.mil/TC.html) has provided nearly two decades of near real-time access to TC-centric images and products by TC forecasters and enthusiasts around the world. Particularly, microwave imager and sounder information that is featured on this site provides crucial internal storm structure information by allowing users to perceive hydrometeor structure, providing key details beyond cloud top information provided by visible and infrared channels. Towards improving TC analysis techniques and helping advance the utility of the NRL TC webpage resource, new research efforts are presented. This work demonstrates results as well as the methodology used to develop new automated, objective satellite-based TC structure and intensity guidance and enhanced data fusion imagery products that aim to bolster and streamline TC forecast operations. This presentation focuses on the creation and interpretation of false color RGB composite imagery that leverages the different emissive and scattering properties of atmospheric ice, liquid, and vapor water as well as ocean surface roughness as seen by microwave radiometers. Specifically, a combination of near-realtime data and a standardized digital database of global TCs in microwave imagery from 1987-2012 is employed as a climatology of TC structures. The broad range of TC structures, from pinhole eyes through multiple eyewall configurations, is characterized as resolved by passive microwave sensors. The extraction of these characteristic features from historical data also lends itself to statistical analysis. For example, histograms of brightness temperature distributions allows a rigorous examination of how structural features are conveyed in image products, allowing a better representation of colors and breakpoints as they relate to physical features. Such climatological work also suggests steps to better inform the near-real time application of upcoming satellite datasets to TC analyses.

A Comparison of Supervised Machine Learning Algorithms and Feature Vectors for MS Lesion Segmentation Using Multimodal Structural MRI

PubMed Central

Sweeney, Elizabeth M.; Vogelstein, Joshua T.; Cuzzocreo, Jennifer L.; Calabresi, Peter A.; Reich, Daniel S.; Crainiceanu, Ciprian M.; Shinohara, Russell T.

2014-01-01

Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance. PMID:24781953

A comparison of supervised machine learning algorithms and feature vectors for MS lesion segmentation using multimodal structural MRI.

PubMed

Sweeney, Elizabeth M; Vogelstein, Joshua T; Cuzzocreo, Jennifer L; Calabresi, Peter A; Reich, Daniel S; Crainiceanu, Ciprian M; Shinohara, Russell T

2014-01-01

Machine learning is a popular method for mining and analyzing large collections of medical data. We focus on a particular problem from medical research, supervised multiple sclerosis (MS) lesion segmentation in structural magnetic resonance imaging (MRI). We examine the extent to which the choice of machine learning or classification algorithm and feature extraction function impacts the performance of lesion segmentation methods. As quantitative measures derived from structural MRI are important clinical tools for research into the pathophysiology and natural history of MS, the development of automated lesion segmentation methods is an active research field. Yet, little is known about what drives performance of these methods. We evaluate the performance of automated MS lesion segmentation methods, which consist of a supervised classification algorithm composed with a feature extraction function. These feature extraction functions act on the observed T1-weighted (T1-w), T2-weighted (T2-w) and fluid-attenuated inversion recovery (FLAIR) MRI voxel intensities. Each MRI study has a manual lesion segmentation that we use to train and validate the supervised classification algorithms. Our main finding is that the differences in predictive performance are due more to differences in the feature vectors, rather than the machine learning or classification algorithms. Features that incorporate information from neighboring voxels in the brain were found to increase performance substantially. For lesion segmentation, we conclude that it is better to use simple, interpretable, and fast algorithms, such as logistic regression, linear discriminant analysis, and quadratic discriminant analysis, and to develop the features to improve performance.

Enhanced Imaging of Corrosion in Aircraft Structures with Reverse Geometry X-ray(registered tm)

NASA Technical Reports Server (NTRS)

Winfree, William P.; Cmar-Mascis, Noreen A.; Parker, F. Raymond

2000-01-01

The application of Reverse Geometry X-ray to the detection and characterization of corrosion in aircraft structures is presented. Reverse Geometry X-ray is a unique system that utilizes an electronically scanned x-ray source and a discrete detector for real time radiographic imaging of a structure. The scanned source system has several advantages when compared to conventional radiography. First, the discrete x-ray detector can be miniaturized and easily positioned inside a complex structure (such as an aircraft wing) enabling images of each surface of the structure to be obtained separately. Second, using a measurement configuration with multiple detectors enables the simultaneous acquisition of data from several different perspectives without moving the structure or the measurement system. This provides a means for locating the position of flaws and enhances separation of features at the surface from features inside the structure. Data is presented on aircraft specimens with corrosion in the lap joint. Advanced laminographic imaging techniques utilizing data from multiple detectors are demonstrated to be capable of separating surface features from corrosion in the lap joint and locating the corrosion in multilayer structures. Results of this technique are compared to computed tomography cross sections obtained from a microfocus x-ray tomography system. A method is presented for calibration of the detectors of the Reverse Geometry X-ray system to enable quantification of the corrosion to within 2%.

a Probabilistic Embedding Clustering Method for Urban Structure Detection

NASA Astrophysics Data System (ADS)

Lin, X.; Li, H.; Zhang, Y.; Gao, L.; Zhao, L.; Deng, M.

2017-09-01

Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM) to find latent features from high dimensional urban sensing data by "learning" via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China) proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.

Control-group feature normalization for multivariate pattern analysis of structural MRI data using the support vector machine.

PubMed

Linn, Kristin A; Gaonkar, Bilwaj; Satterthwaite, Theodore D; Doshi, Jimit; Davatzikos, Christos; Shinohara, Russell T

2016-05-15

Normalization of feature vector values is a common practice in machine learning. Generally, each feature value is standardized to the unit hypercube or by normalizing to zero mean and unit variance. Classification decisions based on support vector machines (SVMs) or by other methods are sensitive to the specific normalization used on the features. In the context of multivariate pattern analysis using neuroimaging data, standardization effectively up- and down-weights features based on their individual variability. Since the standard approach uses the entire data set to guide the normalization, it utilizes the total variability of these features. This total variation is inevitably dependent on the amount of marginal separation between groups. Thus, such a normalization may attenuate the separability of the data in high dimensional space. In this work we propose an alternate approach that uses an estimate of the control-group standard deviation to normalize features before training. We study our proposed approach in the context of group classification using structural MRI data. We show that control-based normalization leads to better reproducibility of estimated multivariate disease patterns and improves the classifier performance in many cases. Copyright © 2016 Elsevier Inc. All rights reserved.

Structural optimization of large ocean-going structures

NASA Technical Reports Server (NTRS)

Hughes, O. F.

1984-01-01

Ocean-going vehicles and platforms are among the largest structures in the world and are subjected to relatively harsh conditions of motions and loads. Some of them, such as semi-submersible platforms, are a relatively new type of structure and hence there is no formal, well evolved and established structural design code as there is for more traditional structures. More recently, efforts have also been made to develop a design method of this type for ships and other ocean structures. One of the many advantages of a rationally based design method is versatility; it can be used for structures that have widely differing purposes, measures of merit, shapes and sizes. The purpose is to describe a rationally based design method that has been developed within the field of ocean structures, in order that persons dealing with other types of structure can judge whether and to what extent its various features may be useful for those other types. Also, even though some features may not be applicable they might stimulate some useful ideas.

A symmetry measure for damage detection with mode shapes

NASA Astrophysics Data System (ADS)

Chen, Justin G.; Büyüköztürk, Oral

2017-11-01

This paper introduces a feature for detecting damage or changes in structures, the continuous symmetry measure, which can quantify the amount of a particular rotational, mirror, or translational symmetry in a mode shape of a structure. Many structures in the built environment have geometries that are either symmetric or almost symmetric, however damage typically occurs in a local manner causing asymmetric changes in the structure's geometry or material properties, and alters its mode shapes. The continuous symmetry measure can quantify these changes in symmetry as a novel indicator of damage for data-based structural health monitoring approaches. This paper describes the concept as a basis for detecting changes in mode shapes and detecting structural damage. Application of the method is demonstrated in various structures with different symmetrical properties: a pipe cross-section with a finite element model and experimental study, the NASA 8-bay truss model, and the simulated IASC-ASCE structural health monitoring benchmark structure. The applicability and limitations of the feature in applying it to structures of varying geometries is discussed.

Investigating Molecular Structures of Bio-Fuel and Bio-Oil Seeds as Predictors To Estimate Protein Bioavailability for Ruminants by Advanced Nondestructive Vibrational Molecular Spectroscopy.

PubMed

Ban, Yajing; L Prates, Luciana; Yu, Peiqiang

2017-10-18

This study was conducted to (1) determine protein and carbohydrate molecular structure profiles and (2) quantify the relationship between structural features and protein bioavailability of newly developed carinata and canola seeds for dairy cows by using Fourier transform infrared molecular spectroscopy. Results showed similarity in protein structural makeup within the entire protein structural region between carinata and canola seeds. The highest area ratios related to structural CHO, total CHO, and cellulosic compounds were obtained for carinata seeds. Carinata and canola seeds showed similar carbohydrate and protein molecular structures by multivariate analyses. Carbohydrate molecular structure profiles were highly correlated to protein rumen degradation and intestinal digestion characteristics. In conclusion, the molecular spectroscopy can detect inherent structural characteristics in carinata and canola seeds in which carbohydrate-relative structural features are related to protein metabolism and utilization. Protein and carbohydrate spectral profiles could be used as predictors of rumen protein bioavailability in cows.

Prediction of redox-sensitive cysteines using sequential distance and other sequence-based features.

PubMed

Sun, Ming-An; Zhang, Qing; Wang, Yejun; Ge, Wei; Guo, Dianjing

2016-08-24

Reactive oxygen species can modify the structure and function of proteins and may also act as important signaling molecules in various cellular processes. Cysteine thiol groups of proteins are particularly susceptible to oxidation. Meanwhile, their reversible oxidation is of critical roles for redox regulation and signaling. Recently, several computational tools have been developed for predicting redox-sensitive cysteines; however, those methods either only focus on catalytic redox-sensitive cysteines in thiol oxidoreductases, or heavily depend on protein structural data, thus cannot be widely used. In this study, we analyzed various sequence-based features potentially related to cysteine redox-sensitivity, and identified three types of features for efficient computational prediction of redox-sensitive cysteines. These features are: sequential distance to the nearby cysteines, PSSM profile and predicted secondary structure of flanking residues. After further feature selection using SVM-RFE, we developed Redox-Sensitive Cysteine Predictor (RSCP), a SVM based classifier for redox-sensitive cysteine prediction using primary sequence only. Using 10-fold cross-validation on RSC758 dataset, the accuracy, sensitivity, specificity, MCC and AUC were estimated as 0.679, 0.602, 0.756, 0.362 and 0.727, respectively. When evaluated using 10-fold cross-validation with BALOSCTdb dataset which has structure information, the model achieved performance comparable to current structure-based method. Further validation using an independent dataset indicates it is robust and of relatively better accuracy for predicting redox-sensitive cysteines from non-enzyme proteins. In this study, we developed a sequence-based classifier for predicting redox-sensitive cysteines. The major advantage of this method is that it does not rely on protein structure data, which ensures more extensive application compared to other current implementations. Accurate prediction of redox-sensitive cysteines not only enhances our understanding about the redox sensitivity of cysteine, it may also complement the proteomics approach and facilitate further experimental investigation of important redox-sensitive cysteines.

An enhanced structure tensor method for sea ice ridge detection from GF-3 SAR imagery

NASA Astrophysics Data System (ADS)

Zhu, T.; Li, F.; Zhang, Y.; Zhang, S.; Spreen, G.; Dierking, W.; Heygster, G.

2017-12-01

In SAR imagery, ridges or leads are shown as the curvilinear features. The proposed ridge detection method is facilitated by their curvilinear shapes. The bright curvilinear features are recognized as the ridges while the dark curvilinear features are classified as the leads. In dual-polarization HH or HV channel of C-band SAR imagery, the bright curvilinear feature may be false alarm because the frost flowers of young leads may show as bright pixels associated with changes in the surface salinity under calm surface conditions. Wind roughened leads also trigger the backscatter increasing that can be misclassified as ridges [1]. Thus the width limitation is considered in this proposed structure tensor method [2], since only shape feature based method is not enough for detecting ridges. The ridge detection algorithm is based on the hypothesis that the bright pixels are ridges with curvilinear shapes and the ridge width is less 30 meters. Benefited from GF-3 with high spatial resolution of 3 meters, we provide an enhanced structure tensor method for detecting the significant ridge. The preprocessing procedures including the calibration and incidence angle normalization are also investigated. The bright pixels will have strong response to the bandpass filtering. The ridge training samples are delineated from the SAR imagery in the Log-Gabor filters to construct structure tensor. From the tensor, the dominant orientation of the pixel representing the ridge is determined by the dominant eigenvector. For the post-processing of structure tensor, the elongated kernel is desired to enhance the ridge curvilinear shape. Since ridge presents along a certain direction, the ratio of the dominant eigenvector will be used to measure the intensity of local anisotropy. The convolution filter has been utilized in the constructed structure tensor is used to model spatial contextual information. Ridge detection results from GF-3 show the proposed method performs better compared to the direct threshold method.

FEX: A Knowledge-Based System For Planimetric Feature Extraction

NASA Astrophysics Data System (ADS)

Zelek, John S.

1988-10-01

Topographical planimetric features include natural surfaces (rivers, lakes) and man-made surfaces (roads, railways, bridges). In conventional planimetric feature extraction, a photointerpreter manually interprets and extracts features from imagery on a stereoplotter. Visual planimetric feature extraction is a very labour intensive operation. The advantages of automating feature extraction include: time and labour savings; accuracy improvements; and planimetric data consistency. FEX (Feature EXtraction) combines techniques from image processing, remote sensing and artificial intelligence for automatic feature extraction. The feature extraction process co-ordinates the information and knowledge in a hierarchical data structure. The system simulates the reasoning of a photointerpreter in determining the planimetric features. Present efforts have concentrated on the extraction of road-like features in SPOT imagery. Keywords: Remote Sensing, Artificial Intelligence (AI), SPOT, image understanding, knowledge base, apars.

Parenchymal texture analysis in digital mammography: robust texture feature identification and equivalence across devices.

PubMed

Keller, Brad M; Oustimov, Andrew; Wang, Yan; Chen, Jinbo; Acciavatti, Raymond J; Zheng, Yuanjie; Ray, Shonket; Gee, James C; Maidment, Andrew D A; Kontos, Despina

2015-04-01

An analytical framework is presented for evaluating the equivalence of parenchymal texture features across different full-field digital mammography (FFDM) systems using a physical breast phantom. Phantom images (FOR PROCESSING) are acquired from three FFDM systems using their automated exposure control setting. A panel of texture features, including gray-level histogram, co-occurrence, run length, and structural descriptors, are extracted. To identify features that are robust across imaging systems, a series of equivalence tests are performed on the feature distributions, in which the extent of their intersystem variation is compared to their intrasystem variation via the Hodges-Lehmann test statistic. Overall, histogram and structural features tend to be most robust across all systems, and certain features, such as edge enhancement, tend to be more robust to intergenerational differences between detectors of a single vendor than to intervendor differences. Texture features extracted from larger regions of interest (i.e., [Formula: see text]) and with a larger offset length (i.e., [Formula: see text]), when applicable, also appear to be more robust across imaging systems. This framework and observations from our experiments may benefit applications utilizing mammographic texture analysis on images acquired in multivendor settings, such as in multicenter studies of computer-aided detection and breast cancer risk assessment.

Movement of feeder-using songbirds: the influence of urban features.

PubMed

Cox, Daniel T C; Inger, Richard; Hancock, Steven; Anderson, Karen; Gaston, Kevin J

2016-11-23

Private gardens provide vital opportunities for people to interact with nature. The most popular form of interaction is through garden bird feeding. Understanding how landscape features and seasons determine patterns of movement of feeder-using songbirds is key to maximising the well-being benefits they provide. To determine these patterns we established three networks of automated data loggers along a gradient of greenspace fragmentation. Over a 12-month period we tracked 452 tagged blue tits Cyantistes caeruleus and great tits Parus major moving between feeder pairs 9,848 times, to address two questions: (i) Do urban features within different forms, and season, influence structural (presence-absence of connections between feeders by birds) and functional (frequency of these connections) connectivity? (ii) Are there general patterns of structural and functional connectivity across forms? Vegetation cover increased connectivity in all three networks, whereas the presence of road gaps negatively affected functional but not structural connectivity. Across networks structural connectivity was lowest in the summer when birds maintain breeding territories, however patterns of functional connectivity appeared to vary with habitat fragmentation. Using empirical data this study shows how key urban features and season influence movement of feeder-using songbirds, and we provide evidence that this is related to greenspace fragmentation.

Efficient enumeration of monocyclic chemical graphs with given path frequencies

PubMed Central

2014-01-01

Background The enumeration of chemical graphs (molecular graphs) satisfying given constraints is one of the fundamental problems in chemoinformatics and bioinformatics because it leads to a variety of useful applications including structure determination and development of novel chemical compounds. Results We consider the problem of enumerating chemical graphs with monocyclic structure (a graph structure that contains exactly one cycle) from a given set of feature vectors, where a feature vector represents the frequency of the prescribed paths in a chemical compound to be constructed and the set is specified by a pair of upper and lower feature vectors. To enumerate all tree-like (acyclic) chemical graphs from a given set of feature vectors, Shimizu et al. and Suzuki et al. proposed efficient branch-and-bound algorithms based on a fast tree enumeration algorithm. In this study, we devise a novel method for extending these algorithms to enumeration of chemical graphs with monocyclic structure by designing a fast algorithm for testing uniqueness. The results of computational experiments reveal that the computational efficiency of the new algorithm is as good as those for enumeration of tree-like chemical compounds. Conclusions We succeed in expanding the class of chemical graphs that are able to be enumerated efficiently. PMID:24955135

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 .

Remote monitoring of bond line defects between a composite panel and a stiffener using distributed piezoelectric sensors

NASA Astrophysics Data System (ADS)

Yu, Xudong; Fan, Zheng; Puliyakote, Sreedhar; Castaings, Michel

2018-03-01

Structural health monitoring (SHM) using ultrasonic guided waves has proven to be attractive for the identification of damage in composite plate-like structures, due to its realization of both significant propagation distances and reasonable sensitivity to defects. However, topographical features such as bends, lap joints, and bonded stiffeners are often encountered in these structures, and they are susceptible to various types of defects as a consequence of stress concentration and cyclic loading during the service life. Therefore, the health condition of such features has to be assessed effectively to ensure the safe operation of the entire structure. This paper proposes a novel feature guided wave (FGW) based SHM strategy, in which proper FGWs are exploited as a screening tool to rapidly interrogate the representative stiffener-adhesive bond-composite skin assembly. An array of sensors permanently attached to the vicinity of the feature is used to capture scattered waves from the localized damage occurring in the bond line. This technique is combined with an imaging approach, and the damage reconstruction is achieved by the synthetic focusing algorithm using these scattered signals. The proposed SHM scheme is implemented in both the 3D finite element simulation and the experiment, and the results are in good agreement, demonstrating the feasibility of such SHM strategy.

Ligand-based and structure-based approaches in identifying ideal pharmacophore against c-Jun N-terminal kinase-3.

PubMed

Kumar, B V S Suneel; Kotla, Rohith; Buddiga, Revanth; Roy, Jyoti; Singh, Sardar Shamshair; Gundla, Rambabu; Ravikumar, Muttineni; Sarma, Jagarlapudi A R P

2011-01-01

Structure and ligand based pharmacophore modeling and docking studies carried out using diversified set of c-Jun N-terminal kinase-3 (JNK3) inhibitors are presented in this paper. Ligand based pharmacophore model (LBPM) was developed for 106 inhibitors of JNK3 using a training set of 21 compounds to reveal structural and chemical features necessary for these molecules to inhibit JNK3. Hypo1 consisted of two hydrogen bond acceptors (HBA), one hydrogen bond donor (HBD), and a hydrophobic (HY) feature with a correlation coefficient (r²) of 0.950. This pharmacophore model was validated using test set containing 85 inhibitors and had a good r² of 0.846. All the molecules were docked using Glide software and interestingly, all the docked conformations showed hydrogen bond interactions with important hinge region amino acids (Gln155 and Met149)and these interactions were compared with Hypo1 features. The results of ligand based pharmacophore model (LBPM)and docking studies are validated each other. The structure based pharmacophore model (SBPM) studies have identified additional features, two hydrogen bond donors and one hydrogen bond acceptor. The combination of these methodologies is useful in designing ideal pharmacophore which provides a powerful tool for the discovery of novel and selective JNK3 inhibitors.

A geographic comparison of selected large-scale planetary surface features

NASA Technical Reports Server (NTRS)

Meszaros, S. P.

1984-01-01

Photographic and cartographic comparisons of geographic features on Mercury, the Moon, Earth, Mars, Ganymede, Callisto, Mimas, and Tethys are presented. Planetary structures caused by impacts, volcanism, tectonics, and other natural forces are included. Each feature is discussed individually and then those of similar origin are compared at the same scale.

Deriving Case, Agreement and Voice Phenomena in Syntax

ERIC Educational Resources Information Center

Sigurdsson, Einar Freyr

2017-01-01

This dissertation places case, agreement and Voice phenomena in syntax. It argues that the derivation is driven by so-called derivational features, that is, structure-building features (Merge) and probe features (Agree) (Heck and Muller 2007 and Muller 2010; see also Chomsky 2000, 2001). Both types are essential in deriving case and agreement in…

The Features of Female Managers' Personality Traits in Organization

ERIC Educational Resources Information Center

Gabdreeva, Guzel Sh.; Khalfieva, Alisa R.

2016-01-01

The relevance of the "female" management features study is driven by the active penetration of women to management in various fields and the emergence of a new social category "Business-women". The article contains the results of a study aimed to identify the features of personal properties and structure of low-level,…

Investigation of Time Series Representations and Similarity Measures for Structural Damage Pattern Recognition

PubMed Central

Swartz, R. Andrew

2013-01-01

This paper investigates the time series representation methods and similarity measures for sensor data feature extraction and structural damage pattern recognition. Both model-based time series representation and dimensionality reduction methods are studied to compare the effectiveness of feature extraction for damage pattern recognition. The evaluation of feature extraction methods is performed by examining the separation of feature vectors among different damage patterns and the pattern recognition success rate. In addition, the impact of similarity measures on the pattern recognition success rate and the metrics for damage localization are also investigated. The test data used in this study are from the System Identification to Monitor Civil Engineering Structures (SIMCES) Z24 Bridge damage detection tests, a rigorous instrumentation campaign that recorded the dynamic performance of a concrete box-girder bridge under progressively increasing damage scenarios. A number of progressive damage test case datasets and damage test data with different damage modalities are used. The simulation results show that both time series representation methods and similarity measures have significant impact on the pattern recognition success rate. PMID:24191136

Predictive analysis of the influence of the chemical composition and pre-processing regimen on structural properties of steel alloys using machine learning techniques

NASA Astrophysics Data System (ADS)

Krishnamurthy, Narayanan; Maddali, Siddharth; Romanov, Vyacheslav; Hawk, Jeffrey

We present some structural properties of multi-component steel alloys as predicted by a random forest machine-learning model. These non-parametric models are trained on high-dimensional data sets defined by features such as chemical composition, pre-processing temperatures and environmental influences, the latter of which are based upon standardized testing procedures for tensile, creep and rupture properties as defined by the American Society of Testing and Materials (ASTM). We quantify the goodness of fit of these models as well as the inferred relative importance of each of these features, all with a conveniently defined metric and scale. The models are tested with synthetic data points, generated subject to the appropriate mathematical constraints for the various features. By this we highlight possible trends in the increase or degradation of the structural properties with perturbations in the features of importance. This work is presented as part of the Data Science Initiative at the National Energy Technology Laboratory, directed specifically towards the computational design of steel alloys.

Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study

PubMed Central

Qureshi, Muhammad Naveed Iqbal; Min, Beomjun; Jo, Hang Joon; Lee, Boreom

2016-01-01

The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM) classifier. We compared the performance of this classifier with that of a support vector machine (SVM) and basic extreme learning machine (ELM) for cortical MRI data from attention deficit/hyperactivity disorder (ADHD) patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC), ADHD-inattentive (ADHD-I), and ADHD-combined (ADHD-C). We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM) that enabled us to achieve good classification accuracy (60.78%). In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex. PMID:27500640

Object segmentation controls image reconstruction from natural scenes

PubMed Central

2017-01-01

The structure of the physical world projects images onto our eyes. However, those images are often poorly representative of environmental structure: well-defined boundaries within the eye may correspond to irrelevant features of the physical world, while critical features of the physical world may be nearly invisible at the retinal projection. The challenge for the visual cortex is to sort these two types of features according to their utility in ultimately reconstructing percepts and interpreting the constituents of the scene. We describe a novel paradigm that enabled us to selectively evaluate the relative role played by these two feature classes in signal reconstruction from corrupted images. Our measurements demonstrate that this process is quickly dominated by the inferred structure of the environment, and only minimally controlled by variations of raw image content. The inferential mechanism is spatially global and its impact on early visual cortex is fast. Furthermore, it retunes local visual processing for more efficient feature extraction without altering the intrinsic transduction noise. The basic properties of this process can be partially captured by a combination of small-scale circuit models and large-scale network architectures. Taken together, our results challenge compartmentalized notions of bottom-up/top-down perception and suggest instead that these two modes are best viewed as an integrated perceptual mechanism. PMID:28827801

Multiclass Classification for the Differential Diagnosis on the ADHD Subtypes Using Recursive Feature Elimination and Hierarchical Extreme Learning Machine: Structural MRI Study.

PubMed

Qureshi, Muhammad Naveed Iqbal; Min, Beomjun; Jo, Hang Joon; Lee, Boreom

2016-01-01

The classification of neuroimaging data for the diagnosis of certain brain diseases is one of the main research goals of the neuroscience and clinical communities. In this study, we performed multiclass classification using a hierarchical extreme learning machine (H-ELM) classifier. We compared the performance of this classifier with that of a support vector machine (SVM) and basic extreme learning machine (ELM) for cortical MRI data from attention deficit/hyperactivity disorder (ADHD) patients. We used 159 structural MRI images of children from the publicly available ADHD-200 MRI dataset. The data consisted of three types, namely, typically developing (TDC), ADHD-inattentive (ADHD-I), and ADHD-combined (ADHD-C). We carried out feature selection by using standard SVM-based recursive feature elimination (RFE-SVM) that enabled us to achieve good classification accuracy (60.78%). In this study, we found the RFE-SVM feature selection approach in combination with H-ELM to effectively enable the acquisition of high multiclass classification accuracy rates for structural neuroimaging data. In addition, we found that the most important features for classification were the surface area of the superior frontal lobe, and the cortical thickness, volume, and mean surface area of the whole cortex.

Features extraction in anterior and posterior cruciate ligaments analysis.

PubMed

Zarychta, P

2015-12-01

The main aim of this research is finding the feature vectors of the anterior and posterior cruciate ligaments (ACL and PCL). These feature vectors have to clearly define the ligaments structure and make it easier to diagnose them. Extraction of feature vectors is obtained by analysis of both anterior and posterior cruciate ligaments. This procedure is performed after the extraction process of both ligaments. In the first stage in order to reduce the area of analysis a region of interest including cruciate ligaments (CL) is outlined in order to reduce the area of analysis. In this case, the fuzzy C-means algorithm with median modification helping to reduce blurred edges has been implemented. After finding the region of interest (ROI), the fuzzy connectedness procedure is performed. This procedure permits to extract the anterior and posterior cruciate ligament structures. In the last stage, on the basis of the extracted anterior and posterior cruciate ligament structures, 3-dimensional models of the anterior and posterior cruciate ligament are built and the feature vectors created. This methodology has been implemented in MATLAB and tested on clinical T1-weighted magnetic resonance imaging (MRI) slices of the knee joint. The 3D display is based on the Visualization Toolkit (VTK). Copyright © 2015 Elsevier Ltd. All rights reserved.

Tensor-driven extraction of developmental features from varying paediatric EEG datasets.

PubMed

Kinney-Lang, Eli; Spyrou, Loukianos; Ebied, Ahmed; Chin, Richard Fm; Escudero, Javier

2018-05-21

Constant changes in developing children's brains can pose a challenge in EEG dependant technologies. Advancing signal processing methods to identify developmental differences in paediatric populations could help improve function and usability of such technologies. Taking advantage of the multi-dimensional structure of EEG data through tensor analysis may offer a framework for extracting relevant developmental features of paediatric datasets. A proof of concept is demonstrated through identifying latent developmental features in resting-state EEG. Approach. Three paediatric datasets (n = 50, 17, 44) were analyzed using a two-step constrained parallel factor (PARAFAC) tensor decomposition. Subject age was used as a proxy measure of development. Classification used support vector machines (SVM) to test if PARAFAC identified features could predict subject age. The results were cross-validated within each dataset. Classification analysis was complemented by visualization of the high-dimensional feature structures using t-distributed Stochastic Neighbour Embedding (t-SNE) maps. Main Results. Development-related features were successfully identified for the developmental conditions of each dataset. SVM classification showed the identified features could accurately predict subject at a significant level above chance for both healthy and impaired populations. t-SNE maps revealed suitable tensor factorization was key in extracting the developmental features. Significance. The described methods are a promising tool for identifying latent developmental features occurring throughout childhood EEG. © 2018 IOP Publishing Ltd.

Bio-functions and molecular carbohydrate structure association study in forage with different source origins revealed using non-destructive vibrational molecular spectroscopy techniques

NASA Astrophysics Data System (ADS)

Ji, Cuiying; Zhang, Xuewei; Yan, Xiaogang; Mostafizar Rahman, M.; Prates, Luciana L.; Yu, Peiqiang

2017-08-01

The objectives of this study were to: 1) investigate forage carbohydrate molecular structure profiles; 2) bio-functions in terms of CHO rumen degradation characteristics and hourly effective degradation ratio of N to OM (HEDN/OM), and 3) quantify interactive association between molecular structures, bio-functions and nutrient availability. The vibrational molecular spectroscopy was applied to investigate the structure feature on a molecular basis. Two sourced-origin alfalfa forages were used as modeled forages. The results showed that the carbohydrate molecular structure profiles were highly linked to the bio-functions in terms of rumen degradation characteristics and hourly effective degradation ratio. The molecular spectroscopic technique can be used to detect forage carbohydrate structure features on a molecular basis and can be used to study interactive association between forage molecular structure and bio-functions.

Structural health monitoring of plates with surface features using guided ultrasonic waves

NASA Astrophysics Data System (ADS)

Fromme, P.

2009-03-01

Distributed array systems for guided ultrasonic waves offer an efficient way for the long-term monitoring of the structural integrity of large plate-like structures. The measurement concept involving baseline subtraction has been demonstrated under laboratory conditions. For the application to real technical structures it needs to be shown that the methodology works equally well in the presence of structural and surface features. Problems employing this structural health monitoring concept can occur due to the presence of additional changes in the signal reflected at undamaged parts of the structure. The influence of the signal processing parameters and transducer placement on the damage detection and localization accuracy is discussed. The use of permanently attached, distributed sensors for the A0 Lamb wave mode has been investigated. Results are presented using experimental data obtained from laboratory measurements and Finite Element simulated signals for a large steel plate with a welded stiffener.

Controlling the intermediate structure of an ionic liquid for f-block element separations

DOE PAGES

Abney, Carter W.; Do, Changwoo; Luo, Huimin; ...

2017-04-19

Recent research has revealed molecular structure beyond the inner coordination sphere is essential in defining the performance of separations processes, but nevertheless remains largely unexplored. Here we apply small angle neutron scattering (SANS) and x-ray absorption fine structure (XAFS) spectroscopy to investigate the structure of an ionic liquid system studied for f-block element separations. SANS data reveal dramatic changes in the ionic liquid microstructure (~150 Å) which we demonstrate can be controlled by judicious selection of counter ion. Mesoscale structural features (> 500 Å) are also observed as a function of metal concentration. XAFS analysis supports formation of extended aggregatemore » structures, similar to those observed in traditional solvent extraction processes, and suggest additional parallels may be drawn from further study. As a result, achieving precise tunability over the intermediate features is an important development in controlling mesoscale structure and realizing advanced new forms of soft matter.« less

Sentiment analysis of feature ranking methods for classification accuracy

NASA Astrophysics Data System (ADS)

Joseph, Shashank; Mugauri, Calvin; Sumathy, S.

2017-11-01

Text pre-processing and feature selection are important and critical steps in text mining. Text pre-processing of large volumes of datasets is a difficult task as unstructured raw data is converted into structured format. Traditional methods of processing and weighing took much time and were less accurate. To overcome this challenge, feature ranking techniques have been devised. A feature set from text preprocessing is fed as input for feature selection. Feature selection helps improve text classification accuracy. Of the three feature selection categories available, the filter category will be the focus. Five feature ranking methods namely: document frequency, standard deviation information gain, CHI-SQUARE, and weighted-log likelihood -ratio is analyzed.

Damage classification and estimation in experimental structures using time series analysis and pattern recognition

NASA Astrophysics Data System (ADS)

de Lautour, Oliver R.; Omenzetter, Piotr

2010-07-01

Developed for studying long sequences of regularly sampled data, time series analysis methods are being increasingly investigated for the use of Structural Health Monitoring (SHM). In this research, Autoregressive (AR) models were used to fit the acceleration time histories obtained from two experimental structures: a 3-storey bookshelf structure and the ASCE Phase II Experimental SHM Benchmark Structure, in undamaged and limited number of damaged states. The coefficients of the AR models were considered to be damage-sensitive features and used as input into an Artificial Neural Network (ANN). The ANN was trained to classify damage cases or estimate remaining structural stiffness. The results showed that the combination of AR models and ANNs are efficient tools for damage classification and estimation, and perform well using small number of damage-sensitive features and limited sensors.

Effects of system size and cooling rate on the structure and properties of sodium borosilicate glasses from molecular dynamics simulations.

PubMed

Deng, Lu; Du, Jincheng

2018-01-14

Borosilicate glasses form an important glass forming system in both glass science and technologies. The structure and property changes of borosilicate glasses as a function of thermal history in terms of cooling rate during glass formation and simulation system sizes used in classical molecular dynamics (MD) simulation were investigated with recently developed composition dependent partial charge potentials. Short and medium range structural features such as boron coordination, Si and B Q n distributions, and ring size distributions were analyzed to elucidate the effects of cooling rate and simulation system size on these structure features and selected glass properties such as glass transition temperature, vibration density of states, and mechanical properties. Neutron structure factors, neutron broadened pair distribution functions, and vibrational density of states were calculated and compared with results from experiments as well as ab initio calculations to validate the structure models. The results clearly indicate that both cooling rate and system size play an important role on the structures of these glasses, mainly by affecting the 3 B and 4 B distributions and consequently properties of the glasses. It was also found that different structure features and properties converge at different sizes or cooling rates; thus convergence tests are needed in simulations of the borosilicate glasses depending on the targeted properties. The results also shed light on the complex thermal history dependence on structure and properties in borosilicate glasses and the protocols in MD simulations of these and other glass materials.

Indel PDB: a database of structural insertions and deletions derived from sequence alignments of closely related proteins.

PubMed

Hsing, Michael; Cherkasov, Artem

2008-06-25

Insertions and deletions (indels) represent a common type of sequence variations, which are less studied and pose many important biological questions. Recent research has shown that the presence of sizable indels in protein sequences may be indicative of protein essentiality and their role in protein interaction networks. Examples of utilization of indels for structure-based drug design have also been recently demonstrated. Nonetheless many structural and functional characteristics of indels remain less researched or unknown. We have created a web-based resource, Indel PDB, representing a structural database of insertions/deletions identified from the sequence alignments of highly similar proteins found in the Protein Data Bank (PDB). Indel PDB utilized large amounts of available structural information to characterize 1-, 2- and 3-dimensional features of indel sites. Indel PDB contains 117,266 non-redundant indel sites extracted from 11,294 indel-containing proteins. Unlike loop databases, Indel PDB features more indel sequences with secondary structures including alpha-helices and beta-sheets in addition to loops. The insertion fragments have been characterized by their sequences, lengths, locations, secondary structure composition, solvent accessibility, protein domain association and three dimensional structures. By utilizing the data available in Indel PDB, we have studied and presented here several sequence and structural features of indels. We anticipate that Indel PDB will not only enable future functional studies of indels, but will also assist protein modeling efforts and identification of indel-directed drug binding sites.

Effects of system size and cooling rate on the structure and properties of sodium borosilicate glasses from molecular dynamics simulations

NASA Astrophysics Data System (ADS)

Deng, Lu; Du, Jincheng

2018-01-01

Borosilicate glasses form an important glass forming system in both glass science and technologies. The structure and property changes of borosilicate glasses as a function of thermal history in terms of cooling rate during glass formation and simulation system sizes used in classical molecular dynamics (MD) simulation were investigated with recently developed composition dependent partial charge potentials. Short and medium range structural features such as boron coordination, Si and B Qn distributions, and ring size distributions were analyzed to elucidate the effects of cooling rate and simulation system size on these structure features and selected glass properties such as glass transition temperature, vibration density of states, and mechanical properties. Neutron structure factors, neutron broadened pair distribution functions, and vibrational density of states were calculated and compared with results from experiments as well as ab initio calculations to validate the structure models. The results clearly indicate that both cooling rate and system size play an important role on the structures of these glasses, mainly by affecting the 3B and 4B distributions and consequently properties of the glasses. It was also found that different structure features and properties converge at different sizes or cooling rates; thus convergence tests are needed in simulations of the borosilicate glasses depending on the targeted properties. The results also shed light on the complex thermal history dependence on structure and properties in borosilicate glasses and the protocols in MD simulations of these and other glass materials.

Structure and engineering of celluloses.

PubMed

Pérez, Serge; Samain, Daniel

2010-01-01

This chapter collates the developments and conclusions of many of the extensive studies that have been conducted on cellulose, with particular emphasis on the structural and morphological features while not ignoring the most recent results derived from the elucidation of unique biosynthetic pathways. The presentation of structural and morphological data gathered together in this chapter follows the historical development of our knowledge of the different structural levels of cellulose and its various organizational levels. These levels concern features such as chain conformation, chain polarity, chain association, crystal polarity, and microfibril structure and organization. This chapter provides some historical landmarks related to the evolution of concepts in the field of biopolymer science, which parallel the developments of novel methods for characterization of complex macromolecular structures. The elucidation of the different structural levels of organization opens the way to relating structure to function and properties. The chemical and biochemical methods that have been developed to dissolve and further modify cellulose chains are briefly covered. Particular emphasis is given to the facets of topochemistry and topoenzymology where the morphological features play a key role in determining unique physicochemical properties. A final chapter addresses what might be considered tomorrow's goal in amplifying the economic importance of cellulose in the context of sustainable development. Selected examples illustrate the types of result that can be obtained when cellulose fibers are no longer viewed as inert substrates, and when the polyhydroxyl nature of their surfaces, as well as their entire structural complexity, are taken into account. Copyright © 2010 Elsevier Inc. All rights reserved.

Preliminary investigation of structural controls of ground-water movement in Pipe Spring National Monument, Arizona

USGS Publications Warehouse

Truini, Margot; Fleming, John B.; Pierce, Herb A.

2004-01-01

Pipe Spring National Monument, near the border of Arizona and Utah, includes several low-discharge springs that are the primary natural features of the monument. The National Park Service is concerned about the declines in spring discharge. Seismic-refraction and frequency-domain electromagnetic-induction methods were employed in an attempt to better understand the relation between spring discharge and geologic structure. The particular method used for the seismic-refraction surveys was unable to resolve structural features in the monument. Electromagnetic surveys delineated differences in apparent conductivity of the shallow subsurface deposits. The differences are attributable to differences in saturation, lithology, and structure of these deposits.

On the structure of Bayesian network for Indonesian text document paraphrase identification

NASA Astrophysics Data System (ADS)

Prayogo, Ario Harry; Syahrul Mubarok, Mohamad; Adiwijaya

2018-03-01

Paraphrase identification is an important process within natural language processing. The idea is to automatically recognize phrases that have different forms but contain same meanings. For examples if we input query “causing fire hazard”, then the computer has to recognize this query that this query has same meaning as “the cause of fire hazard. Paraphrasing is an activity that reveals the meaning of an expression, writing, or speech using different words or forms, especially to achieve greater clarity. In this research we will focus on classifying two Indonesian sentences whether it is a paraphrase to each other or not. There are four steps in this research, first is preprocessing, second is feature extraction, third is classifier building, and the last is performance evaluation. Preprocessing consists of tokenization, non-alphanumerical removal, and stemming. After preprocessing we will conduct feature extraction in order to build new features from given dataset. There are two kinds of features in the research, syntactic features and semantic features. Syntactic features consist of normalized levenshtein distance feature, term-frequency based cosine similarity feature, and LCS (Longest Common Subsequence) feature. Semantic features consist of Wu and Palmer feature and Shortest Path Feature. We use Bayesian Networks as the method of training the classifier. Parameter estimation that we use is called MAP (Maximum A Posteriori). For structure learning of Bayesian Networks DAG (Directed Acyclic Graph), we use BDeu (Bayesian Dirichlet equivalent uniform) scoring function and for finding DAG with the best BDeu score, we use K2 algorithm. In evaluation step we perform cross-validation. The average result that we get from testing the classifier as follows: Precision 75.2%, Recall 76.5%, F1-Measure 75.8% and Accuracy 75.6%.

Teaching structure: student use of software tools for understanding macromolecular structure in an undergraduate biochemistry course.

PubMed

Jaswal, Sheila S; O'Hara, Patricia B; Williamson, Patrick L; Springer, Amy L

2013-01-01

Because understanding the structure of biological macromolecules is critical to understanding their function, students of biochemistry should become familiar not only with viewing, but also with generating and manipulating structural representations. We report a strategy from a one-semester undergraduate biochemistry course to integrate use of structural representation tools into both laboratory and homework activities. First, early in the course we introduce the use of readily available open-source software for visualizing protein structure, coincident with modules on amino acid and peptide bond properties. Second, we use these same software tools in lectures and incorporate images and other structure representations in homework tasks. Third, we require a capstone project in which teams of students examine a protein-nucleic acid complex and then use the software tools to illustrate for their classmates the salient features of the structure, relating how the structure helps explain biological function. To ensure engagement with a range of software and database features, we generated a detailed template file that can be used to explore any structure, and that guides students through specific applications of many of the software tools. In presentations, students demonstrate that they are successfully interpreting structural information, and using representations to illustrate particular points relevant to function. Thus, over the semester students integrate information about structural features of biological macromolecules into the larger discussion of the chemical basis of function. Together these assignments provide an accessible introduction to structural representation tools, allowing students to add these methods to their biochemical toolboxes early in their scientific development. © 2013 by The International Union of Biochemistry and Molecular Biology.

Effects of contact network structure on epidemic transmission trees: implications for data required to estimate network structure.

PubMed

Carnegie, Nicole Bohme

2018-01-30

Understanding the dynamics of disease spread is key to developing effective interventions to control or prevent an epidemic. The structure of the network of contacts over which the disease spreads has been shown to have a strong influence on the outcome of the epidemic, but an open question remains as to whether it is possible to estimate contact network features from data collected in an epidemic. The approach taken in this paper is to examine the distributions of epidemic outcomes arising from epidemics on networks with particular structural features to assess whether that structure could be measured from epidemic data and what other constraints might be needed to make the problem identifiable. To this end, we vary the network size, mean degree, and transmissibility of the pathogen, as well as the network feature of interest: clustering, degree assortativity, or attribute-based preferential mixing. We record several standard measures of the size and spread of the epidemic, as well as measures that describe the shape of the transmission tree in order to ascertain whether there are detectable signals in the final data from the outbreak. The results suggest that there is potential to estimate contact network features from transmission trees or pure epidemic data, particularly for diseases with high transmissibility or for which the relevant contact network is of low mean degree. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Automatic segmentation of solitary pulmonary nodules based on local intensity structure analysis and 3D neighborhood features in 3D chest CT images

NASA Astrophysics Data System (ADS)

Chen, Bin; Kitasaka, Takayuki; Honma, Hirotoshi; Takabatake, Hirotsugu; Mori, Masaki; Natori, Hiroshi; Mori, Kensaku

2012-03-01

This paper presents a solitary pulmonary nodule (SPN) segmentation method based on local intensity structure analysis and neighborhood feature analysis in chest CT images. Automated segmentation of SPNs is desirable for a chest computer-aided detection/diagnosis (CAS) system since a SPN may indicate early stage of lung cancer. Due to the similar intensities of SPNs and other chest structures such as blood vessels, many false positives (FPs) are generated by nodule detection methods. To reduce such FPs, we introduce two features that analyze the relation between each segmented nodule candidate and it neighborhood region. The proposed method utilizes a blob-like structure enhancement (BSE) filter based on Hessian analysis to augment the blob-like structures as initial nodule candidates. Then a fine segmentation is performed to segment much more accurate region of each nodule candidate. FP reduction is mainly addressed by investigating two neighborhood features based on volume ratio and eigenvector of Hessian that are calculates from the neighborhood region of each nodule candidate. We evaluated the proposed method by using 40 chest CT images, include 20 standard-dose CT images that we randomly chosen from a local database and 20 low-dose CT images that were randomly chosen from a public database: LIDC. The experimental results revealed that the average TP rate of proposed method was 93.6% with 12.3 FPs/case.

Progressive Fracture of Composite Structures

NASA Technical Reports Server (NTRS)

Minnetyan, Levon

2001-01-01

This report includes the results of a research in which the COmposite Durability STRuctural ANalysis (CODSTRAN) computational simulation capabilities were augmented and applied to various structures for demonstration of the new features and verification. The first chapter of this report provides an introduction to the computational simulation or virtual laboratory approach for the assessment of damage and fracture progression characteristics in composite structures. The second chapter outlines the details of the overall methodology used, including the failure criteria and the incremental/iterative loading procedure with the definitions of damage, fracture, and equilibrium states. The subsequent chapters each contain an augmented feature of the code and/or demonstration examples. All but one of the presented examples contains laminated composite structures with various fiber/matrix constituents. For each structure simulated, damage initiation and progression mechanisms are identified and the structural damage tolerance is quantified at various degradation stages. Many chapters contain the simulation of defective and defect free structures to evaluate the effects of existing defects on structural durability.

MAWRID: A Model of Arabic Word Reading in Development.

PubMed

Saiegh-Haddad, Elinor

2017-07-01

This article offers a model of Arabic word reading according to which three conspicuous features of the Arabic language and orthography shape the development of word reading in this language: (a) vowelization/vocalization, or the use of diacritical marks to represent short vowels and other features of articulation; (b) morphological structure, namely, the predominance and transparency of derivational morphological structure in the linguistic and orthographic representation of the Arabic word; and (c) diglossia, specifically, the lexical and lexico-phonological distance between the spoken and the standard forms of Arabic words. It is argued that the triangulation of these features governs the acquisition and deployment of reading mechanisms across development. Moreover, the difficulties that readers encounter in their journey from beginning to skilled reading may be better understood if evaluated within these language-specific features of Arabic language and orthography.

Research on recognition of the geologic framework of porphyry copper deposits on ERTS-1 imagery. [New Guinea, Alaska, and Colorado

NASA Technical Reports Server (NTRS)

Wilson, J. C. (Principal Investigator)

1975-01-01

The author has identified the following significant results. Many new linear and circular features were found. These features prompted novel tectonic classification and analysis especially in the Ray and Ely areas. Tectonic analyses of the Ok Tedi, Tanacross, and Silvertone areas follow conventional interpretations. Circular features are mapped in many cases and are interpreted as exposed or covered intrusive centers. The small circular features reported in the Ok Tedi test area are valid and useful correlations with tertiary intrusion and volcanism in this remote part of New Guinea. Several major faults of regional dimensions, such as the Denali fault in Alaska and the Colorado mineral belt structures in Colorado are detected in the imagery. Many more faults and regional structures are found in the imagery than exist on present maps.

Supplementary Microstructural Features Induced During Laser Surface Melting of Thermally Sprayed Inconel 625 Coatings

NASA Astrophysics Data System (ADS)

Ahmed, Nauman; Voisey, K. T.; McCartney, D. G.

2014-02-01

Laser surface melting of thermally sprayed coatings has the potential to enhance their corrosion properties by incorporating favorable microstructural changes. Besides homogenizing the as-sprayed structure, laser melting may induce certain microstructural modifications (i.e., supplementary features) in addition to those that directly improve the corrosion performance. Such features, being a direct result of the laser treatment process, are described in this paper which is part of a broader study in which high velocity oxy-fuel sprayed Inconel 625 coatings on mild-steel substrates were treated with a diode laser and the modified microstructure characterized using optical and scanning electron microscopy and x-ray diffraction. The laser treated coating features several different zones, including a region with a microstructure in which there is a continuous columnar dendritic structure through a network of retained oxide stringers.

Geology and impact features of Vargeão Dome, southern Brazil

NASA Astrophysics Data System (ADS)

Crósta, Alvaro P.; Kazzuo-Vieira, César; Pitarello, Lidia; Koeberl, Christian; Kenkmann, Thomas

2012-01-01

Vargeão Dome (southern Brazil) is a circular feature formed in lava flows of the Lower Cretaceous Serra Geral Formation and in sandstones of the Paraná Basin. Even though its impact origin was already proposed in the 1980s, little information about its geological and impact features is available in the literature. The structure has a rim-rim diameter of approximately 12 km and comprises several ring-like concentric features with multiple concentric lineaments. The presence of a central uplift is suggested by the occurrence of deformed sandstone strata of the Botucatu and Pirambóia formations. We present the morphological/structural characteristics of Vargeão Dome, characterize the different rock types that occur in its interior, mainly brecciated volcanic rocks (BVR) of the Serra Geral Formation, and discuss the deformation and shock features in the volcanic rocks and in sandstones. These features comprise shatter cones in sandstone and basalt, as well as planar microstructures in quartz. A geochemical comparison of the target rock equivalents from outside the structure with the shocked rocks from its interior shows that both the BVRs and the brecciated sandstone have a composition largely similar to that of the corresponding unshocked lithologies. No traces of meteoritic material have been found so far. The results confirm the impact origin of Vargeão Dome, making it one of the largest among the rare impact craters in basaltic targets known on Earth.

Enhancement of the Feature Extraction Capability in Global Damage Detection Using Wavelet Theory

NASA Technical Reports Server (NTRS)

Saleeb, Atef F.; Ponnaluru, Gopi Krishna

2006-01-01

The main objective of this study is to assess the specific capabilities of the defect energy parameter technique for global damage detection developed by Saleeb and coworkers. The feature extraction is the most important capability in any damage-detection technique. Features are any parameters extracted from the processed measurement data in order to enhance damage detection. The damage feature extraction capability was studied extensively by analyzing various simulation results. The practical significance in structural health monitoring is that the detection at early stages of small-size defects is always desirable. The amount of changes in the structure's response due to these small defects was determined to show the needed level of accuracy in the experimental methods. The arrangement of fine/extensive sensor network to measure required data for the detection is an "unlimited" ability, but there is a difficulty to place extensive number of sensors on a structure. Therefore, an investigation was conducted using the measurements of coarse sensor network. The white and the pink noises, which cover most of the frequency ranges that are typically encountered in the many measuring devices used (e.g., accelerometers, strain gauges, etc.) are added to the displacements to investigate the effect of noisy measurements in the detection technique. The noisy displacements and the noisy damage parameter values are used to study the signal feature reconstruction using wavelets. The enhancement of the feature extraction capability was successfully achieved by the wavelet theory.

Nonlocal sparse model with adaptive structural clustering for feature extraction of aero-engine bearings

NASA Astrophysics Data System (ADS)

Zhang, Han; Chen, Xuefeng; Du, Zhaohui; Li, Xiang; Yan, Ruqiang

2016-04-01

Fault information of aero-engine bearings presents two particular phenomena, i.e., waveform distortion and impulsive feature frequency band dispersion, which leads to a challenging problem for current techniques of bearing fault diagnosis. Moreover, although many progresses of sparse representation theory have been made in feature extraction of fault information, the theory also confronts inevitable performance degradation due to the fact that relatively weak fault information has not sufficiently prominent and sparse representations. Therefore, a novel nonlocal sparse model (coined NLSM) and its algorithm framework has been proposed in this paper, which goes beyond simple sparsity by introducing more intrinsic structures of feature information. This work adequately exploits the underlying prior information that feature information exhibits nonlocal self-similarity through clustering similar signal fragments and stacking them together into groups. Within this framework, the prior information is transformed into a regularization term and a sparse optimization problem, which could be solved through block coordinate descent method (BCD), is formulated. Additionally, the adaptive structural clustering sparse dictionary learning technique, which utilizes k-Nearest-Neighbor (kNN) clustering and principal component analysis (PCA) learning, is adopted to further enable sufficient sparsity of feature information. Moreover, the selection rule of regularization parameter and computational complexity are described in detail. The performance of the proposed framework is evaluated through numerical experiment and its superiority with respect to the state-of-the-art method in the field is demonstrated through the vibration signals of experimental rig of aircraft engine bearings.

EEG Sleep Stages Classification Based on Time Domain Features and Structural Graph Similarity.

PubMed

Diykh, Mohammed; Li, Yan; Wen, Peng

2016-11-01

The electroencephalogram (EEG) signals are commonly used in diagnosing and treating sleep disorders. Many existing methods for sleep stages classification mainly depend on the analysis of EEG signals in time or frequency domain to obtain a high classification accuracy. In this paper, the statistical features in time domain, the structural graph similarity and the K-means (SGSKM) are combined to identify six sleep stages using single channel EEG signals. Firstly, each EEG segment is partitioned into sub-segments. The size of a sub-segment is determined empirically. Secondly, statistical features are extracted, sorted into different sets of features and forwarded to the SGSKM to classify EEG sleep stages. We have also investigated the relationships between sleep stages and the time domain features of the EEG data used in this paper. The experimental results show that the proposed method yields better classification results than other four existing methods and the support vector machine (SVM) classifier. A 95.93% average classification accuracy is achieved by using the proposed method.

Feature extraction based on semi-supervised kernel Marginal Fisher analysis and its application in bearing fault diagnosis

NASA Astrophysics Data System (ADS)

Jiang, Li; Xuan, Jianping; Shi, Tielin

2013-12-01

Generally, the vibration signals of faulty machinery are non-stationary and nonlinear under complicated operating conditions. Therefore, it is a big challenge for machinery fault diagnosis to extract optimal features for improving classification accuracy. This paper proposes semi-supervised kernel Marginal Fisher analysis (SSKMFA) for feature extraction, which can discover the intrinsic manifold structure of dataset, and simultaneously consider the intra-class compactness and the inter-class separability. Based on SSKMFA, a novel approach to fault diagnosis is put forward and applied to fault recognition of rolling bearings. SSKMFA directly extracts the low-dimensional characteristics from the raw high-dimensional vibration signals, by exploiting the inherent manifold structure of both labeled and unlabeled samples. Subsequently, the optimal low-dimensional features are fed into the simplest K-nearest neighbor (KNN) classifier to recognize different fault categories and severities of bearings. The experimental results demonstrate that the proposed approach improves the fault recognition performance and outperforms the other four feature extraction methods.

Phenomenological features of dreams: Results from dream log studies using the Subjective Experiences Rating Scale (SERS).

PubMed

Kahan, Tracey L; Claudatos, Stephanie

2016-04-01

Self-ratings of dream experiences were obtained from 144 college women for 788 dreams, using the Subjective Experiences Rating Scale (SERS). Consistent with past studies, dreams were characterized by a greater prevalence of vision, audition, and movement than smell, touch, or taste, by both positive and negative emotion, and by a range of cognitive processes. A Principal Components Analysis of SERS ratings revealed ten subscales: four sensory, three affective, one cognitive, and two structural (events/actions, locations). Correlations (Pearson r) among subscale means showed a stronger relationship among the process-oriented features (sensory, cognitive, affective) than between the process-oriented and content-centered (structural) features--a pattern predicted from past research (e.g., Bulkeley & Kahan, 2008). Notably, cognition and positive emotion were associated with a greater number of other phenomenal features than was negative emotion; these findings are consistent with studies of the qualitative features of waking autobiographical memory (e.g., Fredrickson, 2001). Copyright © 2016 Elsevier Inc. All rights reserved.

PL-VIO: Tightly-Coupled Monocular Visual–Inertial Odometry Using Point and Line Features

PubMed Central

Zhao, Ji; Guo, Yue; He, Wenhao; Yuan, Kui

2018-01-01

To address the problem of estimating camera trajectory and to build a structural three-dimensional (3D) map based on inertial measurements and visual observations, this paper proposes point–line visual–inertial odometry (PL-VIO), a tightly-coupled monocular visual–inertial odometry system exploiting both point and line features. Compared with point features, lines provide significantly more geometrical structure information on the environment. To obtain both computation simplicity and representational compactness of a 3D spatial line, Plücker coordinates and orthonormal representation for the line are employed. To tightly and efficiently fuse the information from inertial measurement units (IMUs) and visual sensors, we optimize the states by minimizing a cost function which combines the pre-integrated IMU error term together with the point and line re-projection error terms in a sliding window optimization framework. The experiments evaluated on public datasets demonstrate that the PL-VIO method that combines point and line features outperforms several state-of-the-art VIO systems which use point features only. PMID:29642648

Flight State Identification of a Self-Sensing Wing via an Improved Feature Selection Method and Machine Learning Approaches.

PubMed

Chen, Xi; Kopsaftopoulos, Fotis; Wu, Qi; Ren, He; Chang, Fu-Kuo

2018-04-29

In this work, a data-driven approach for identifying the flight state of a self-sensing wing structure with an embedded multi-functional sensing network is proposed. The flight state is characterized by the structural vibration signals recorded from a series of wind tunnel experiments under varying angles of attack and airspeeds. A large feature pool is created by extracting potential features from the signals covering the time domain, the frequency domain as well as the information domain. Special emphasis is given to feature selection in which a novel filter method is developed based on the combination of a modified distance evaluation algorithm and a variance inflation factor. Machine learning algorithms are then employed to establish the mapping relationship from the feature space to the practical state space. Results from two case studies demonstrate the high identification accuracy and the effectiveness of the model complexity reduction via the proposed method, thus providing new perspectives of self-awareness towards the next generation of intelligent air vehicles.

Computer ranking of the sequence of appearance of 73 features of the brain and related structures in staged human embryos during the sixth week of development.

PubMed

O'Rahilly, R; Müller, F; Hutchins, G M; Moore, G W

1987-09-01

The sequence of events in the development of the brain in human embryos, already published for stages 8-15, is here continued for stages 16 and 17. With the aid of a computerized bubble-sort algorithm, 71 individual embryos were ranked in ascending order of the features present. Whereas these numbered 100 in the previous study, the increasing structural complexity gave 27 new features in the two stages now under investigation. The chief characteristics of stage 16 (approximately 37 postovulatory days) are protruding basal nuclei, the caudal olfactory elevation (olfactory tubercle), the tectobulbar tracts, and ascending fibers to the cerebellum. The main features of stage 17 (approximately 41 postovulatory days) are the cortical nucleus of the amygdaloid body, an intermediate layer in the tectum mesencephali, the posterior commissure, and the habenulo-interpeduncular tract. In addition, a typical feature at stage 17 is the crescentic shape of the lens cavity.

Patterning of nanocrystalline diamond films for diamond microstructures useful in MEMS and other devices

DOEpatents

Gruen, Dieter M [Downers Grove, IL; Busmann, Hans-Gerd [Bremen, DE; Meyer, Eva-Maria [Bremen, DE; Auciello, Orlando [Bolingbrook, IL; Krauss, Alan R [late of Naperville, IL; Krauss, Julie R [Naperville, IL

2004-11-02

MEMS structure and a method of fabricating them from ultrananocrystalline diamond films having average grain sizes of less than about 10 nm and feature resolution of less than about one micron . The MEMS structures are made by contacting carbon dimer species with an oxide substrate forming a carbide layer on the surface onto which ultrananocrystalline diamond having average grain sizes of less than about 10 nm is deposited. Thereafter, microfabrication process are used to form a structure of predetermined shape having a feature resolution of less than about one micron.

Diagnosis of helicopter gearboxes using structure-based networks

NASA Technical Reports Server (NTRS)

Jammu, Vinay B.; Danai, Kourosh; Lewicki, David G.

1995-01-01

A connectionist network is introduced for fault diagnosis of helicopter gearboxes that incorporates knowledge of the gearbox structure and characteristics of the vibration features as its fuzzy weights. Diagnosis is performed by propagating the abnormal features of vibration measurements through this Structure-Based Connectionist Network (SBCN), the outputs of which represent the fault possibility values for individual components of the gearbox. The performance of this network is evaluated by applying it to experimental vibration data from an OH-58A helicopter gearbox. The diagnostic results indicate that the network performance is comparable to those obtained from supervised pattern classification.

Checkpoint and restart procedures for single and multi-stage structural model analysis in NASTRAN/COSMIC on a CDC 176

NASA Technical Reports Server (NTRS)

Camp, George H.; Fallon, Dennis J.

1987-01-01

The Underwater Explosions Research Division (UERD) of the David Taylor Naval Ship Research and Development Center makes extensive use of NASTRAN/COSMIC on a CDC 176 to evaluate the structural response of ship structures subjected to underwater explosion shock loadings in the time domain. As relatively new users, UERD engineers have experienced difficulties with the checkpoint/restart feature because of the vague instructions in the user manual. Working procedures for the application of the checkpoint/restart feature to the transient analysis using NASTRAN/COSMIC are illustrated.

Fabrication method for small-scale structures with non-planar features

DOEpatents

Burckel, David Bruce; Ten Eyck, Gregory A.

2016-09-20

The fabrication of small-scale structures is disclosed. A unit-cell of a small-scale structure with non-planar features is fabricated by forming a membrane on a suitable material. A pattern is formed in the membrane and a portion of the substrate underneath the membrane is removed to form a cavity. Resonators are then directionally deposited on the wall or sides of the cavity. The cavity may be rotated during deposition to form closed-loop resonators. The resonators may be non-planar. The unit-cells can be formed in a layer that includes an array of unit-cells.

Fabrication of small-scale structures with non-planar features

DOEpatents

Burckel, David B.; Ten Eyck, Gregory A.

2015-11-19

The fabrication of small-scale structures is disclosed. A unit-cell of a small-scale structure with non-planar features is fabricated by forming a membrane on a suitable material. A pattern is formed in the membrane and a portion of the substrate underneath the membrane is removed to form a cavity. Resonators are then directionally deposited on the wall or sides of the cavity. The cavity may be rotated during deposition to form closed-loop resonators. The resonators may be non-planar. The unit-cells can be formed in a layer that includes an array of unit-cells.

Determining crystal structures through crowdsourcing and coursework

PubMed Central

Horowitz, Scott; Koepnick, Brian; Martin, Raoul; Tymieniecki, Agnes; Winburn, Amanda A.; Cooper, Seth; Flatten, Jeff; Rogawski, David S.; Koropatkin, Nicole M.; Hailu, Tsinatkeab T.; Jain, Neha; Koldewey, Philipp; Ahlstrom, Logan S.; Chapman, Matthew R.; Sikkema, Andrew P.; Skiba, Meredith A.; Maloney, Finn P.; Beinlich, Felix R. M.; Caglar, Ahmet; Coral, Alan; Jensen, Alice Elizabeth; Lubow, Allen; Boitano, Amanda; Lisle, Amy Elizabeth; Maxwell, Andrew T.; Failer, Barb; Kaszubowski, Bartosz; Hrytsiv, Bohdan; Vincenzo, Brancaccio; de Melo Cruz, Breno Renan; McManus, Brian Joseph; Kestemont, Bruno; Vardeman, Carl; Comisky, Casey; Neilson, Catherine; Landers, Catherine R.; Ince, Christopher; Buske, Daniel Jon; Totonjian, Daniel; Copeland, David Marshall; Murray, David; Jagieła, Dawid; Janz, Dietmar; Wheeler, Douglas C.; Cali, Elie; Croze, Emmanuel; Rezae, Farah; Martin, Floyd Orville; Beecher, Gil; de Jong, Guido Alexander; Ykman, Guy; Feldmann, Harald; Chan, Hugo Paul Perez; Kovanecz, Istvan; Vasilchenko, Ivan; Connellan, James C.; Borman, Jami Lynne; Norrgard, Jane; Kanfer, Jebbie; Canfield, Jeffrey M.; Slone, Jesse David; Oh, Jimmy; Mitchell, Joanne; Bishop, John; Kroeger, John Douglas; Schinkler, Jonas; McLaughlin, Joseph; Brownlee, June M.; Bell, Justin; Fellbaum, Karl Willem; Harper, Kathleen; Abbey, Kirk J.; Isaksson, Lennart E.; Wei, Linda; Cummins, Lisa N.; Miller, Lori Anne; Bain, Lyn; Carpenter, Lynn; Desnouck, Maarten; Sharma, Manasa G.; Belcastro, Marcus; Szew, Martin; Szew, Martin; Britton, Matthew; Gaebel, Matthias; Power, Max; Cassidy, Michael; Pfützenreuter, Michael; Minett, Michele; Wesselingh, Michiel; Yi, Minjune; Cameron, Neil Haydn Tormey; Bolibruch, Nicholas I.; Benevides, Noah; Kathleen Kerr, Norah; Barlow, Nova; Crevits, Nykole Krystyne; Dunn, Paul; Roque, Paulo Sergio Silveira Belo Nascimento; Riber, Peter; Pikkanen, Petri; Shehzad, Raafay; Viosca, Randy; James Fraser, Robert; Leduc, Robert; Madala, Roman; Shnider, Scott; de Boisblanc, Sharon; Butkovich, Slava; Bliven, Spencer; Hettler, Stephen; Telehany, Stephen; Schwegmann, Steven A.; Parkes, Steven; Kleinfelter, Susan C.; Michael Holst, Sven; van der Laan, T. J. A.; Bausewein, Thomas; Simon, Vera; Pulley, Warwick; Hull, William; Kim, Annes Yukyung; Lawton, Alexis; Ruesch, Amanda; Sundar, Anjali; Lawrence, Anna-Lisa; Afrin, Antara; Maheshwer, Bhargavi; Turfe, Bilal; Huebner, Christian; Killeen, Courtney Elizabeth; Antebi-Lerrman, Dalia; Luan, Danny; Wolfe, Derek; Pham, Duc; Michewicz, Elaina; Hull, Elizabeth; Pardington, Emily; Galal, Galal Osama; Sun, Grace; Chen, Grace; Anderson, Halie E.; Chang, Jane; Hewlett, Jeffrey Thomas; Sterbenz, Jennifer; Lim, Jiho; Morof, Joshua; Lee, Junho; Inn, Juyoung Samuel; Hahm, Kaitlin; Roth, Kaitlin; Nair, Karun; Markin, Katherine; Schramm, Katie; Toni Eid, Kevin; Gam, Kristina; Murphy, Lisha; Yuan, Lucy; Kana, Lulia; Daboul, Lynn; Shammas, Mario Karam; Chason, Max; Sinan, Moaz; Andrew Tooley, Nicholas; Korakavi, Nisha; Comer, Patrick; Magur, Pragya; Savliwala, Quresh; Davison, Reid Michael; Sankaran, Roshun Rajiv; Lewe, Sam; Tamkus, Saule; Chen, Shirley; Harvey, Sho; Hwang, Sin Ye; Vatsia, Sohrab; Withrow, Stefan; Luther, Tahra K; Manett, Taylor; Johnson, Thomas James; Ryan Brash, Timothy; Kuhlman, Wyatt; Park, Yeonjung; Popović, Zoran; Baker, David; Khatib, Firas; Bardwell, James C. A.

2016-01-01

We show here that computer game players can build high-quality crystal structures. Introduction of a new feature into the computer game Foldit allows players to build and real-space refine structures into electron density maps. To assess the usefulness of this feature, we held a crystallographic model-building competition between trained crystallographers, undergraduate students, Foldit players and automatic model-building algorithms. After removal of disordered residues, a team of Foldit players achieved the most accurate structure. Analysing the target protein of the competition, YPL067C, uncovered a new family of histidine triad proteins apparently involved in the prevention of amyloid toxicity. From this study, we conclude that crystallographers can utilize crowdsourcing to interpret electron density information and to produce structure solutions of the highest quality. PMID:27633552

Solving the mystery of the internal structure of casein micelles.

PubMed

Ingham, B; Erlangga, G D; Smialowska, A; Kirby, N M; Wang, C; Matia-Merino, L; Haverkamp, R G; Carr, A J

2015-04-14

The interpretation of milk X-ray and neutron scattering data in relation to the internal structure of the casein micelle is an ongoing debate. We performed resonant X-ray scattering measurements on liquid milk and conclusively identified key scattering features, namely those corresponding to the size of and the distance between colloidal calcium phosphate particles. An X-ray scattering feature commonly assigned to the particle size is instead due to protein inhomogeneities.

Analysis and application of ERTS-1 data for regional geological mapping

NASA Technical Reports Server (NTRS)

Gold, D. P.; Parizek, R. R.; Alexander, S. A.

1973-01-01

Combined visual and digital techniques of analysing ERTS-1 data for geologic information have been tried on selected areas in Pennsylvania. The major physiolographic and structural provinces show up well. Supervised mapping, following the imaged expression of known geologic features on ERTS band 5 enlargements (1:250,000) of parts of eastern Pennsylvania, delimited the Diabase Sills and the Precambrian rocks of the Reading Prong with remarkable accuracy. From unsupervised mapping, transgressive linear features are apparent in unexpected density, and exhibit strong control over river valley and stream channel directions. They are unaffected by bedrock type, age, or primary structural boundaries, which suggests they are either rejuvenated basement joint directions on different scales, or they are a recently impressed structure possibly associated with a drifting North American plate. With ground mapping and underflight data, 6 scales of linear features have been recognized.

SEGMENTATION OF MITOCHONDRIA IN ELECTRON MICROSCOPY IMAGES USING ALGEBRAIC CURVES.

PubMed

Seyedhosseini, Mojtaba; Ellisman, Mark H; Tasdizen, Tolga

2013-01-01

High-resolution microscopy techniques have been used to generate large volumes of data with enough details for understanding the complex structure of the nervous system. However, automatic techniques are required to segment cells and intracellular structures in these multi-terabyte datasets and make anatomical analysis possible on a large scale. We propose a fully automated method that exploits both shape information and regional statistics to segment irregularly shaped intracellular structures such as mitochondria in electron microscopy (EM) images. The main idea is to use algebraic curves to extract shape features together with texture features from image patches. Then, these powerful features are used to learn a random forest classifier, which can predict mitochondria locations precisely. Finally, the algebraic curves together with regional information are used to segment the mitochondria at the predicted locations. We demonstrate that our method outperforms the state-of-the-art algorithms in segmentation of mitochondria in EM images.

Conceptual Hierarchies in a Flat Attractor Network

PubMed Central

O’Connor, Christopher M.; Cree, George S.; McRae, Ken

2009-01-01

The structure of people’s conceptual knowledge of concrete nouns has traditionally been viewed as hierarchical (Collins & Quillian, 1969). For example, superordinate concepts (vegetable) are assumed to reside at a higher level than basic-level concepts (carrot). A feature-based attractor network with a single layer of semantic features developed representations of both basic-level and superordinate concepts. No hierarchical structure was built into the network. In Experiment and Simulation 1, the graded structure of categories (typicality ratings) is accounted for by the flat attractor-network. Experiment and Simulation 2 show that, as with basic-level concepts, such a network predicts feature verification latencies for superordinate concepts (vegetable ). In Experiment and Simulation 3, counterintuitive results regarding the temporal dynamics of similarity in semantic priming are explained by the model. By treating both types of concepts the same in terms of representation, learning, and computations, the model provides new insights into semantic memory. PMID:19543434

Initial evaluation of the geologic applications of ERTS-1 imagery for New Mexico

NASA Technical Reports Server (NTRS)

Vonderlinden, K.; Kottlowski, F. E.

1973-01-01

Coverage of approximately one-third of the test site, the state of New Mexico, had been received by January 31, 1973 and all of the images received were MSS products. Features noted during visual inspection of 91/2 x 91/2 prints include major structural forms, vegetation patterns, drainage patterns and outcrops of geologic formations having marked color contrasts. The Border Hills Structural Zone and the Y-O Structural Zone are prominently reflected in coverage of the Pecos Valley. A study of available maps and remote sensing material covering the Deming-Columbus area indicated that the limit of detection and the resolution of MSS products are not as good as those of aerial photographs, geologic maps, and manned-satellite photographs. The limit of detection of high contrast features on MSS prints in approximately 1000 feet or 300 meters for linear features and about 18 acres for roughly circular areas.

Compressive strain in Lunae Planum-shortening across wrinkle ridges

NASA Technical Reports Server (NTRS)

Plescia, J. B.

1991-01-01

Wrinkle ridges have long been considered to be structural or structurally controlled features. Most, but not all, recent studies have converged on a model in which wrinkle ridges are structural features formed under compressive stress; the deformation being accommodated by faulting and folding. Given that wrinkle ridges are compressive tectonic features, an analysis of the associated shortening and strain provides important quantitative information about local and regional deformation. Lunae Planum is dominated by north-south trending ridges extending from Kasei Valles in the north to Valles Marineris in the south. To quantify the morphometric character, a photoclinometric study was undertaken for ridges on Lunae Planum using the Davis and Soderblom. More than 25 ridges were examined between long. 57 and 80 deg, lat. 5 to 25 deg N. For each ridge, several profiles were obtained along its length. Ridge width, total relief, and elevation offset were measured for each ridge. Analyses are given.

Volcanology and morphology

NASA Technical Reports Server (NTRS)

Bryan, W. B.

1976-01-01

Apollo 15 photographs of the southern parts of Serenitatis and Imbrium were used for a study of the morphology and distribution of wrinkle ridges. Volcanic and structural features along the south margin of Serenitatis were also studied, including the Dawes basalt cinder cones. Volcanic and structural features in crater Aitken were investigated as well. Study of crater Goclenius showed a close relationship between morphology of the impact crater and grabens which tend to parallel directions of the lunar grid. Similar trends were observed in the walls of crater Tsiolkovsky and other linear structures. Small craters of possible volcanic origin were also studied. Possible cinder cones were found associated with the Dawes basalt and in the floor of craters Aitken and Goclenius. Small pit craters were observed in the floors of these craters. Attempts were made to obtain contour maps of specific small features and to compare Orbiter and Apollo photographs to determine short term changes associated with other processes.

Separation of alkylphenols by normal-phase and reversed-phase high-performance liquid chromatography

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

Schabron, J.F.; Hurtubise, R.J.; Silver, H.F.

1978-11-01

Empirical correlation factors were developed which relate log k' values for alkylphenols, the naphthols, and two phenylphenols to structural features. Both normal-phase and reversed-phase chromatographic systems were studied. The stationary phases employed in the normal-phase work were ..mu..-Bondapak CN, ..mu..-Bondapak NH/sub 2/, and ..mu..-Porasil. The structural features which affect retention in the normal-phase chromatographic systems are the number of ortho substituents, the number of aliphatic carbons, and the number of aromatic rings. The stationary phases employed in the reversed-phase work were ..mu..-Bondapak C/sub 18/ and ..mu..-Bondapak CN. The structural features which affect retention in the reversed-phase chromatographic systems are themore » number of aliphatic carbons and the number of aromatic double bonds. On ..mu..-Bondapak C/sub 18/, the presence or absence of a nonaromatic ring is of added importance.« less

Large-scale oscillation of structure-related DNA sequence features in human chromosome 21

NASA Astrophysics Data System (ADS)

Li, Wentian; Miramontes, Pedro

2006-08-01

Human chromosome 21 is the only chromosome in the human genome that exhibits oscillation of the (G+C) content of a cycle length of hundreds kilobases (kb) ( 500kb near the right telomere). We aim at establishing the existence of a similar periodicity in structure-related sequence features in order to relate this (G+C)% oscillation to other biological phenomena. The following quantities are shown to oscillate with the same 500kb periodicity in human chromosome 21: binding energy calculated by two sets of dinucleotide-based thermodynamic parameters, AA/TT and AAA/TTT bi- and tri-nucleotide density, 5'-TA-3' dinucleotide density, and signal for 10- or 11-base periodicity of AA/TT or AAA/TTT. These intrinsic quantities are related to structural features of the double helix of DNA molecules, such as base-pair binding, untwisting or unwinding, stiffness, and a putative tendency for nucleosome formation.

A Systematic Review of Behavioral Interventions to Reduce Condomless Sex and Increase HIV Testing for Latino MSM.

PubMed

Pérez, Ashley; Santamaria, E Karina; Operario, Don

2017-12-15

Latino men who have sex with men (MSM) in the United States are disproportionately affected by HIV, and there have been calls to improve availability of culturally sensitive HIV prevention programs for this population. This article provides a systematic review of intervention programs to reduce condomless sex and/or increase HIV testing among Latino MSM. We searched four electronic databases using a systematic review protocol, screened 1777 unique records, and identified ten interventions analyzing data from 2871 Latino MSM. Four studies reported reductions in condomless anal intercourse, and one reported reductions in number of sexual partners. All studies incorporated surface structure cultural features such as bilingual study recruitment, but the incorporation of deep structure cultural features, such as machismo and sexual silence, was lacking. There is a need for rigorously designed interventions that incorporate deep structure cultural features in order to reduce HIV among Latino MSM.

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

NASA Astrophysics Data System (ADS)

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

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

Four structural risk factors identify most fibril-forming kappa light chains.

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

Stevens, F. J.; Biosciences Division

2000-09-01

Antibody light chains (LCs) comprise the most structurally diverse family of proteins involved in amyloidosis. Many antibody LCs incorporate structural features that impair their stability and solubility, leading to their assembly into fibrils and to their subsequent pathological deposition when produced in excess during multiple myeloma and primary amyloidosis. The particular amino acid variations in antibody LCs that account for fibril formation and amyloidogenesis have not been identified. This study focuses on amyloidogenesis within the Kl family of human LCs. Reanalysis of the current database of primary structures of proteins from more than 100 patients who produced Kl LCS, 37more » of which were amyloidogenic, reveals apparent structural features that may contribute to amyloidosis. These features include loss of conserved residues or the gain of particular residues through mutation at sites involving a repertoire of approximately 20% of the amino acid positions in the light chain variable domain (V{sub L}). Moreover, 80% of all K1 amyloidogenic V{sub L}s are identifiable by the presence of at least one of three single-site substitutions or the acquisition of an N-linked glycosylation site through mutations. These findings suggest that it is feasible to predict fibril propensity by analysis of primary structure.« less

DNAproDB: an interactive tool for structural analysis of DNA–protein complexes

PubMed Central

Sagendorf, Jared M.

2017-01-01

Abstract Many biological processes are mediated by complex interactions between DNA and proteins. Transcription factors, various polymerases, nucleases and histones recognize and bind DNA with different levels of binding specificity. To understand the physical mechanisms that allow proteins to recognize DNA and achieve their biological functions, it is important to analyze structures of DNA–protein complexes in detail. DNAproDB is a web-based interactive tool designed to help researchers study these complexes. DNAproDB provides an automated structure-processing pipeline that extracts structural features from DNA–protein complexes. The extracted features are organized in structured data files, which are easily parsed with any programming language or viewed in a browser. We processed a large number of DNA–protein complexes retrieved from the Protein Data Bank and created the DNAproDB database to store this data. Users can search the database by combining features of the DNA, protein or DNA–protein interactions at the interface. Additionally, users can upload their own structures for processing privately and securely. DNAproDB provides several interactive and customizable tools for creating visualizations of the DNA–protein interface at different levels of abstraction that can be exported as high quality figures. All functionality is documented and freely accessible at http://dnaprodb.usc.edu. PMID:28431131

Method to assess the temporal persistence of potential biometric features: Application to oculomotor, gait, face and brain structure databases

PubMed Central

Nixon, Mark S.; Komogortsev, Oleg V.

2017-01-01

We introduce the intraclass correlation coefficient (ICC) to the biometric community as an index of the temporal persistence, or stability, of a single biometric feature. It requires, as input, a feature on an interval or ratio scale, and which is reasonably normally distributed, and it can only be calculated if each subject is tested on 2 or more occasions. For a biometric system, with multiple features available for selection, the ICC can be used to measure the relative stability of each feature. We show, for 14 distinct data sets (1 synthetic, 8 eye-movement-related, 2 gait-related, and 2 face-recognition-related, and one brain-structure-related), that selecting the most stable features, based on the ICC, resulted in the best biometric performance generally. Analyses based on using only the most stable features produced superior Rank-1-Identification Rate (Rank-1-IR) performance in 12 of 14 databases (p = 0.0065, one-tailed), when compared to other sets of features, including the set of all features. For Equal Error Rate (EER), using a subset of only high-ICC features also produced superior performance in 12 of 14 databases (p = 0. 0065, one-tailed). In general, then, for our databases, prescreening potential biometric features, and choosing only highly reliable features yields better performance than choosing lower ICC features or than choosing all features combined. We also determined that, as the ICC of a group of features increases, the median of the genuine similarity score distribution increases and the spread of this distribution decreases. There was no statistically significant similar relationships for the impostor distributions. We believe that the ICC will find many uses in biometric research. In case of the eye movement-driven biometrics, the use of reliable features, as measured by ICC, allowed to us achieve the authentication performance with EER = 2.01%, which was not possible before. PMID:28575030

Method to assess the temporal persistence of potential biometric features: Application to oculomotor, gait, face and brain structure databases.

PubMed

Friedman, Lee; Nixon, Mark S; Komogortsev, Oleg V

2017-01-01

We introduce the intraclass correlation coefficient (ICC) to the biometric community as an index of the temporal persistence, or stability, of a single biometric feature. It requires, as input, a feature on an interval or ratio scale, and which is reasonably normally distributed, and it can only be calculated if each subject is tested on 2 or more occasions. For a biometric system, with multiple features available for selection, the ICC can be used to measure the relative stability of each feature. We show, for 14 distinct data sets (1 synthetic, 8 eye-movement-related, 2 gait-related, and 2 face-recognition-related, and one brain-structure-related), that selecting the most stable features, based on the ICC, resulted in the best biometric performance generally. Analyses based on using only the most stable features produced superior Rank-1-Identification Rate (Rank-1-IR) performance in 12 of 14 databases (p = 0.0065, one-tailed), when compared to other sets of features, including the set of all features. For Equal Error Rate (EER), using a subset of only high-ICC features also produced superior performance in 12 of 14 databases (p = 0. 0065, one-tailed). In general, then, for our databases, prescreening potential biometric features, and choosing only highly reliable features yields better performance than choosing lower ICC features or than choosing all features combined. We also determined that, as the ICC of a group of features increases, the median of the genuine similarity score distribution increases and the spread of this distribution decreases. There was no statistically significant similar relationships for the impostor distributions. We believe that the ICC will find many uses in biometric research. In case of the eye movement-driven biometrics, the use of reliable features, as measured by ICC, allowed to us achieve the authentication performance with EER = 2.01%, which was not possible before.

Phosphatization Associated Features of Ferromanganese Crusts at Lemkein Seamount, Marshall Islands

NASA Astrophysics Data System (ADS)

Choi, J.; Lee, I.; Park, B. K.; Kim, J.

2014-12-01

Old layers of ferromanganese crusts, especially in the Pacific Ocean, have been affected by phosphatization. Ferromanganese crusts on Lemkein seamount in Marshall Islands also are phosphatized (3.3 to 4.2 wt % of P concentration). Furthermore, they have characteristic features that are different from other ferromanganese crusts. These features occur near the phosphorite, which were thought to fill the pore spaces of ferromanganese crusts. Inside the features, ferromanganese crusts are botryoidally precipitated from the round-boundary. The features of the phosphatized lower crusts of Lemkein seamount are observed using microscope and SEM. Elemental compositions of the selected samples were analyzed by SEM-EDS. Based on the observation and analysis of samples, three characteristic structures are identified: (1) phosphate-filled circles, (2) tongue-shaped framboidal crust, and (3) massive framboidal crust. The phosphate-filled circles are mostly composed of phosphorite, and they include trace fossils such as foraminifera. Phosphatized ferromanganese crusts exist at the boundary of this structure. The tongue-shaped crust is connected with the lips downward, and ferromanganese crusts inside the tongue show distinct growth rim. The massive framboidal crust is located below the tongue. Ferromanganese crusts in the massive framboidal crust are enveloped by phosphate, and some of the crusts are phosphatized. Around the structures, Mn oxide phase is concentrated as a shape of corona on BSE image. All of the structures are in the phosphatized crusts that show columnar growth of ferromanganese crusts and have sub-parallel lamination. These observation and chemical analysis of the ferromanganese crusts can provide a clue of diagenetic processes during the formation of ferromanganese crusts.

Impact of Online Instructional Game Features on College Students' Perceived Motivational Support and Cognitive Investment: A Structural Equation Modeling Study

ERIC Educational Resources Information Center

Huang, Wenhao David; Johnson, Tristan E.; Han, Seung-Hyun Caleb

2013-01-01

Colleges and universities have begun to understand the instructional potential of digital game-based learning (DGBL) due to digital games' immersive features. These features, however, might overload learners as excessive motivational and cognitive stimuli thus impeding intended learning. Current research, however, lacks empirical evidences to…

Comparing Curriculum Types: 'Powerful Knowledge' and '21st Century Learning'

ERIC Educational Resources Information Center

McPhail, Graham; Rata, Elizabeth

2016-01-01

This paper theorises a curriculum model containing four features. We use these features as criteria to analyse and evaluate two distinctive curriculum design types: '21st Century Learning' and 'Powerful Knowledge'. The four features are: (i) the underpinning theory of knowledge in each curriculum design type; (ii) the knowledge structures used to…

78 FR 76980 - Special Conditions: Airbus, A350-900 Series Airplane; Interaction of Systems and Structures

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-20

... series airplanes. These airplanes will have novel or unusual design features when compared to the state...-900 series because of a novel or unusual design feature, special conditions are prescribed under Sec...)(2). Novel or Unusual Design Features The Airbus Model A350-900 series will incorporate the following...

Cathodoluminescence studies of chevron features in semi-polar (11 2 ¯ 2 ) InGaN/GaN multiple quantum well structures

NASA Astrophysics Data System (ADS)

Brasser, C.; Bruckbauer, J.; Gong, Y.; Jiu, L.; Bai, J.; Warzecha, M.; Edwards, P. R.; Wang, T.; Martin, R. W.

2018-05-01

Epitaxial overgrowth of semi-polar III-nitride layers and devices often leads to arrowhead-shaped surface features, referred to as chevrons. We report on a study into the optical, structural, and electrical properties of these features occurring in two very different semi-polar structures, a blue-emitting multiple quantum well structure, and an amber-emitting light-emitting diode. Cathodoluminescence (CL) hyperspectral imaging has highlighted shifts in their emission energy, occurring in the region of the chevron. These variations are due to different semi-polar planes introduced in the chevron arms resulting in a lack of uniformity in the InN incorporation across samples, and the disruption of the structure which could cause a narrowing of the quantum wells (QWs) in this region. Atomic force microscopy has revealed that chevrons can penetrate over 150 nm into the sample and quench light emission from the active layers. The dominance of non-radiative recombination in the chevron region was exposed by simultaneous measurement of CL and the electron beam-induced current. Overall, these results provide an overview of the nature and impact of chevrons on the luminescence of semi-polar devices.

Atmospheric pressure chemical ionization studies of non-polar isomeric hydrocarbons using ion mobility spectrometry and mass spectrometry with different ionization techniques

NASA Technical Reports Server (NTRS)

Borsdorf, H.; Nazarov, E. G.; Eiceman, G. A.

2002-01-01

The ionization pathways were determined for sets of isomeric non-polar hydrocarbons (structural isomers, cis/trans isomers) using ion mobility spectrometry and mass spectrometry with different techniques of atmospheric pressure chemical ionization to assess the influence of structural features on ion formation. Depending on the structural features, different ions were observed using mass spectrometry. Unsaturated hydrocarbons formed mostly [M - 1]+ and [(M - 1)2H]+ ions while mainly [M - 3]+ and [(M - 3)H2O]+ ions were found for saturated cis/trans isomers using photoionization and 63Ni ionization. These ionization methods and corona discharge ionization were used for ion mobility measurements of these compounds. Different ions were detected for compounds with different structural features. 63Ni ionization and photoionization provide comparable ions for every set of isomers. The product ions formed can be clearly attributed to the structures identified. However, differences in relative abundance of product ions were found. Although corona discharge ionization permits the most sensitive detection of non-polar hydrocarbons, the spectra detected are complex and differ from those obtained with 63Ni ionization and photoionization. c. 2002 American Society for Mass Spectrometry.

Novel drift structures for silicon and compound semiconductor X-ray and gamma-ray detectors

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

Patt, B.E.; Iwanczyk, J.S.

Recently developed silicon- and compound-semiconductor-based drift detector structures have produced excellent performance for charged particles, X-rays, and gamma rays and for low-signal visible light detection. The silicon drift detector (SDD) structures that the authors discuss relate to direct X-ray detectors and scintillation photon detectors coupled with scintillators for gamma rays. Recent designs include several novel features that ensure very low dark current and hence low noise. In addition, application of thin window technology ensures a very high quantum efficiency entrance window on the drift photodetector. The main features of the silicon drift structures for X rays and light detection aremore » very small anode capacitance independent of the overall detector size, low noise, and high throughput. To take advantage of the small detector capacitance, the first stage of the electronics needs to be integrated into the detector anode. In the gamma-ray application, factors other than electronic noise dominate, and there is no need to integrate the electronics into the anode. Thus, a different drift structure is needed in conjunction with a high-Z material. The main features in this case are large active detector volume and electron-only induced signal.« less

Structural classification of proteins using texture descriptors extracted from the cellular automata image.

PubMed

Kavianpour, Hamidreza; Vasighi, Mahdi

2017-02-01

Nowadays, having knowledge about cellular attributes of proteins has an important role in pharmacy, medical science and molecular biology. These attributes are closely correlated with the function and three-dimensional structure of proteins. Knowledge of protein structural class is used by various methods for better understanding the protein functionality and folding patterns. Computational methods and intelligence systems can have an important role in performing structural classification of proteins. Most of protein sequences are saved in databanks as characters and strings and a numerical representation is essential for applying machine learning methods. In this work, a binary representation of protein sequences is introduced based on reduced amino acids alphabets according to surrounding hydrophobicity index. Many important features which are hidden in these long binary sequences can be clearly displayed through their cellular automata images. The extracted features from these images are used to build a classification model by support vector machine. Comparing to previous studies on the several benchmark datasets, the promising classification rates obtained by tenfold cross-validation imply that the current approach can help in revealing some inherent features deeply hidden in protein sequences and improve the quality of predicting protein structural class.

Unique Structural Features and Sequence Motifs of Proline Utilization A (PutA)

PubMed Central

Singh, Ranjan K.; Tanner, John J.

2013-01-01

Proline utilization A proteins (PutAs) are bifunctional enzymes that catalyze the oxidation of proline to glutamate using spatially separated proline dehydrogenase and pyrroline-5-carboxylate dehydrogenase active sites. Here we use the crystal structure of the minimalist PutA from Bradyrhizobium japonicum (BjPutA) along with sequence analysis to identify unique structural features of PutAs. This analysis shows that PutAs have secondary structural elements and domains not found in the related monofunctional enzymes. Some of these extra features are predicted to be important for substrate channeling in BjPutA. Multiple sequence alignment analysis shows that some PutAs have a 17-residue conserved motif in the C-terminal 20–30 residues of the polypeptide chain. The BjPutA structure shows that this motif helps seal the internal substrate-channeling cavity from the bulk medium. Finally, it is shown that some PutAs have a 100–200 residue domain of unknown function in the C-terminus that is not found in minimalist PutAs. Remote homology detection suggests that this domain is homologous to the oligomerization beta-hairpin and Rossmann fold domain of BjPutA. PMID:22201760

Crystal structures of the structure-selective nuclease Mus81-Eme1 bound to flap DNA substrates

PubMed Central

Gwon, Gwang Hyeon; Jo, Aera; Baek, Kyuwon; Jin, Kyeong Sik; Fu, Yaoyao; Lee, Jong-Bong; Kim, YoungChang; Cho, Yunje

2014-01-01

The Mus81-Eme1 complex is a structure-selective endonuclease with a critical role in the resolution of recombination intermediates during DNA repair after interstrand cross-links, replication fork collapse, or double-strand breaks. To explain the molecular basis of 3′ flap substrate recognition and cleavage mechanism by Mus81-Eme1, we determined crystal structures of human Mus81-Eme1 bound to various flap DNA substrates. Mus81-Eme1 undergoes gross substrate-induced conformational changes that reveal two key features: (i) a hydrophobic wedge of Mus81 that separates pre- and post-nick duplex DNA and (ii) a “5′ end binding pocket” that hosts the 5′ nicked end of post-nick DNA. These features are crucial for comprehensive protein-DNA interaction, sharp bending of the 3′ flap DNA substrate, and incision strand placement at the active site. While Mus81-Eme1 unexpectedly shares several common features with members of the 5′ flap nuclease family, the combined structural, biochemical, and biophysical analyses explain why Mus81-Eme1 preferentially cleaves 3′ flap DNA substrates with 5′ nicked ends. PMID:24733841

Medical Image Fusion Based on Feature Extraction and Sparse Representation

PubMed Central

Wei, Gao; Zongxi, Song

2017-01-01

As a novel multiscale geometric analysis tool, sparse representation has shown many advantages over the conventional image representation methods. However, the standard sparse representation does not take intrinsic structure and its time complexity into consideration. In this paper, a new fusion mechanism for multimodal medical images based on sparse representation and decision map is proposed to deal with these problems simultaneously. Three decision maps are designed including structure information map (SM) and energy information map (EM) as well as structure and energy map (SEM) to make the results reserve more energy and edge information. SM contains the local structure feature captured by the Laplacian of a Gaussian (LOG) and EM contains the energy and energy distribution feature detected by the mean square deviation. The decision map is added to the normal sparse representation based method to improve the speed of the algorithm. Proposed approach also improves the quality of the fused results by enhancing the contrast and reserving more structure and energy information from the source images. The experiment results of 36 groups of CT/MR, MR-T1/MR-T2, and CT/PET images demonstrate that the method based on SR and SEM outperforms five state-of-the-art methods. PMID:28321246

In silico methods for co-transcriptional RNA secondary structure prediction and for investigating alternative RNA structure expression.

PubMed

Meyer, Irmtraud M

2017-05-01

RNA transcripts are the primary products of active genes in any living organism, including many viruses. Their cellular destiny not only depends on primary sequence signals, but can also be determined by RNA structure. Recent experimental evidence shows that many transcripts can be assigned more than a single functional RNA structure throughout their cellular life and that structure formation happens co-transcriptionally, i.e. as the transcript is synthesised in the cell. Moreover, functional RNA structures are not limited to non-coding transcripts, but can also feature in coding transcripts. The picture that now emerges is that RNA structures constitute an additional layer of information that can be encoded in any RNA transcript (and on top of other layers of information such as protein-context) in order to exert a wide range of functional roles. Moreover, different encoded RNA structures can be expressed at different stages of a transcript's life in order to alter the transcript's behaviour depending on its actual cellular context. Similar to the concept of alternative splicing for protein-coding genes, where a single transcript can yield different proteins depending on cellular context, it is thus appropriate to propose the notion of alternative RNA structure expression for any given transcript. This review introduces several computational strategies that my group developed to detect different aspects of RNA structure expression in vivo. Two aspects are of particular interest to us: (1) RNA secondary structure features that emerge during co-transcriptional folding and (2) functional RNA structure features that are expressed at different times of a transcript's life and potentially mutually exclusive. Copyright © 2017. Published by Elsevier Inc.

Precursor state of oxygen molecules on the Si(001) surface during the initial room-temperature adsorption

NASA Astrophysics Data System (ADS)

Hwang, Eunkyung; Chang, Yun Hee; Kim, Yong-Sung; Koo, Ja-Yong; Kim, Hanchul

2012-10-01

The initial adsorption of oxygen molecules on Si(001) is investigated at room temperature. The scanning tunneling microscopy images reveal a unique bright O2-induced feature. The very initial sticking coefficient of O2 below 0.04 Langmuir is measured to be ˜0.16. Upon thermal annealing at 250-600 °C, the bright O2-induced feature is destroyed, and the Si(001) surface is covered with dark depressions that seem to be oxidized structures with -Si-O-Si- bonds. This suggests that the observed bright O2-induced feature is an intermediate precursor state that may be either a silanone species or a molecular adsorption structure.

Ab initio calculation of the ion feature in x-ray Thomson scattering.

PubMed

Plagemann, Kai-Uwe; Rüter, Hannes R; Bornath, Thomas; Shihab, Mohammed; Desjarlais, Michael P; Fortmann, Carsten; Glenzer, Siegfried H; Redmer, Ronald

2015-07-01

The spectrum of x-ray Thomson scattering is proportional to the dynamic structure factor. An important contribution is the ion feature which describes elastic scattering of x rays off electrons. We apply an ab initio method for the calculation of the form factor of bound electrons, the slope of the screening cloud of free electrons, and the ion-ion structure factor in warm dense beryllium. With the presented method we can calculate the ion feature from first principles. These results will facilitate a better understanding of x-ray scattering in warm dense matter and an accurate measurement of ion temperatures which would allow determining nonequilibrium conditions, e.g., along shock propagation.

Development of a methodology for structured reporting of information in echocardiography.

PubMed

Homorodean, Călin; Olinic, Maria; Olinic, Dan

2012-03-01

In order to conduct research relying on ultrasound images, it is necessary to access a large number of relevant cases represented by images and their interpretation. DICOM standard defines the structured reporting information object. Templates are tree-like structures which offer structural guidance in report construction. Laying the foundations of a structured reporting methodology in echocardiography, through the generation of a consistent set of DICOM templates. We developed an information system with the ability of managing echocardiographic images and structured reports. In order to perform a complete description of the cardiac structures, we used 1900 coded concepts organized into 344 contexts by their semantic meaning in a variety of cardiac diseases. We developed 30 templates, with up to 10 nesting levels. The list of templates has a pyramid-like architecture. Two templates are used for reporting every measurement and description: "EchoMeasurement" and "EchoDescription". Intermediate level templates specify how to report the features of echoDoppler findings: "Spectral Curve", "Color Jet", "Intracardiac mass". Templates for every cardiovascular structure include the previous ones. "Echocardiography Procedure Report" includes all other templates. The templates were tested in reporting echo features of 100 patients by analyzing 500 DICOM images. The benefits of these templates has been proven during the testing process, through the quality of the echocardiography report, the ability to argue and to link every diagnostic feature to a defining image and by opening up opportunities for education, research. In the future, our template-based reporting methodology might be extended to other imaging modalities.

Nakhla: a Martian Meteorite with Indigenous Organic Carbonaceous Features

NASA Technical Reports Server (NTRS)

McKay, D. S.; Gibson, E. K.; Thomas-Keprta, K. L.; Clemett, S. J.; Le, L.; Rahman, Z.; Wentworth, S. J.

2011-01-01

The Nakhla meteorite possesses discrete, well defined, structurally coherent morphologies of carbonaceous phases present within iddingsite alteration zones. Based upon both isotopic measurements and analysis of organic phases the presence of pre-terrestrial organics is now recognized. Within the microcrystalline layers of Nakhla s iddingsite, discrete clusters of salt crystals are present. These salts are predominantly halite (NaCl) with minor MgCl2 crystals. Some CaSO4, likely gypsum, appears to be partially intergrown with some of the halite. EDX mapping shows discrete C-rich features are interspersed among these crystals. A hollow semi-spherical bowl structure ( 3 m ) has been identified and analyzed after using a focused ion beam (FIB) to cut a transverse TEM thin section of the feature and the underlying iddingsite. TEM/EDX analysis reveals that the feature is primarily carbonaceous containing C with lesser amounts of Si, S, Ca, Cl, F, Na, and minor Mn and Fe; additionally a small peak consistent with N, which has been previously seen in Nakhla carbonaceous matter, is also present. Selected area electron diffraction (SAED) shows that this C-rich material is amorphous (lacking any long-range crystallographic order) and is not graphite or carbonate. Micro-Raman spectra acquired from the same surface from which the FIB section was extracted demonstrate a typical kerogen-like D and G band structure with a weak absorption peak at 1350 and a stronger peak at 1600/cm. The C-rich feature is intimately associated with both the surrounding halite and underlying iddingsite matrix. Both iddingsite and salts are interpreted as having formed as evaporate assemblages from progressive evaporation of water bodies on Mars. This assemblage, sans the carbonaceous moieties, closely resembles iddingsite alteration features previously described which were interpreted as indigenous Martian assemblages. These distinctive macromolecular carbonaceous structures in Nakhla may represent one of the sources of the high molecular weight organic material previously identified in Nakhla. While we do not speculate on the origin of these unique carbonaceous structures, we note that the significance of such observations is that it may allow us to construct a C-cycle for Mars based on the C chemistry of the Martian meteorites with obvious implications for astrobiology and the prebiotic evolution of Mars. In any case, our observations strongly suggest that organic C exists as micrometersize, discrete structures on Mars.

Guided wave crack detection and size estimation in stiffened structures

NASA Astrophysics Data System (ADS)

Bhuiyan, Md Yeasin; Faisal Haider, Mohammad; Poddar, Banibrata; Giurgiutiu, Victor

2018-03-01

Structural health monitoring (SHM) and nondestructive evaluation (NDE) deals with the nondestructive inspection of defects, corrosion, leaks in engineering structures by using ultrasonic guided waves. In the past, simplistic structures were often considered for analyzing the guided wave interaction with the defects. In this study, we focused on more realistic and relatively complicated structure for detecting any defect by using a non-contact sensing approach. A plate with a stiffener was considered for analyzing the guided wave interactions. Piezoelectric wafer active transducers were used to produce excitation in the structures. The excitation generated the multimodal guided waves (aka Lamb waves) that propagate in the plate with stiffener. The presence of stiffener in the plate generated scattered waves. The direct wave and the additional scattered waves from the stiffener were experimentally recorded and studied. These waves were considered as a pristine case in this research. A fine horizontal semi-circular crack was manufactured by using electric discharge machining in the same stiffener. The presence of crack in the stiffener produces additional scattered waves as well as trapped waves. These scattered waves and trapped wave modes from the cracked stiffener were experimentally measured by using a scanning laser Doppler vibrometer (SLDV). These waves were analyzed and compared with that from the pristine case. The analyses suggested that both size and shape of the horizontal crack may be predicted from the pattern of the scattered waves. Different features (reflection, transmission, and mode-conversion) of the scattered wave signals are analyzed. We found direct transmission feature for incident A0 wave mode and modeconversion feature for incident S0 mode are most suitable for detecting the crack in the stiffener. The reflection feature may give a better idea of sizing the crack.

A Comparison of Hyporheic Transport at a Cross-Vane Structure and Natural Riffle.

PubMed

Smidt, Samuel J; Cullin, Joseph A; Ward, Adam S; Robinson, Jesse; Zimmer, Margaret A; Lautz, Laura K; Endreny, Theodore A

2015-01-01

While restoring hyporheic flowpaths has been cited as a benefit to stream restoration structures, little documentation exists confirming that constructed restoration structures induce comparable hyporheic exchange to natural stream features. This study compares a stream restoration structure (cross-vane) to a natural feature (riffle) concurrently in the same stream reach using time-lapsed electrical resistivity (ER) tomography. Using this hydrogeophysical approach, we were able to quantify hyporheic extent and transport beneath the cross-vane structure and the riffle. We interpret from the geophysical data that the cross-vane and the natural riffle induced spatially and temporally unique hyporheic extent and transport, and the cross-vane created both spatially larger and temporally longer hyporheic flowpaths than the natural riffle. Tracer from the 4.67-h injection was detected along flowpaths for 4.6 h at the cross-vane and 4.2 h at the riffle. The spatial extent of the hyporheic zone at the cross-vane was 12% larger than that at the riffle. We compare ER results of this study to vertical fluxes calculated from temperature profiles and conclude significant differences in the interpretation of hyporheic transport from these different field techniques. Results of this study demonstrate a high degree of heterogeneity in transport metrics at both the cross-vane and the riffle and differences between the hyporheic flowpath networks at the two different features. Our results suggest that restoration structures may be capable of creating sufficient exchange flux and timescales of transport to achieve the same ecological functions as natural features, but engineering of the physical and biogeochemical environment may be necessary to realize these benefits. © 2014, National Ground Water Association.

Differential role of molten globule and protein folding in distinguishing unique features of botulinum neurotoxin.

PubMed

Kumar, Raj; Kukreja, Roshan V; Cai, Shuowei; Singh, Bal R

2014-06-01

Botulinum neurotoxins (BoNTs) are proteins of great interest not only because of their extreme toxicity but also paradoxically for their therapeutic applications. All the known serotypes (A-G) have varying degrees of longevity and potency inside the neuronal cell. Differential chemical modifications such as phosphorylation and ubiquitination have been suggested as possible mechanisms for their longevity, but the molecular basis of the longevity remains unclear. Since the endopeptidase domain (light chain; LC) of toxin apparently survives inside the neuronal cells for months, it is important to examine the structural features of this domain to understand its resistance to intracellular degradation. Published crystal structures (both botulinum neurotoxins and endopeptidase domain) have not provided adequate explanation for the intracellular longevity of the domain. Structural features obtained from spectroscopic analysis of LCA and LCB were similar, and a PRIME (PReImminent Molten Globule Enzyme) conformation appears to be responsible for their optimal enzymatic activity at 37°C. LCE, on the other hand, was although optimally active at 37°C, but its active conformation differed from the PRIME conformation of LCA and LCB. This study establishes and confirms our earlier finding that an optimally active conformation of these proteins in the form of PRIME exists for the most poisonous poison, botulinum neurotoxin. There are substantial variations in the structural and functional characteristics of these active molten globule related structures among the three BoNT endopeptidases examined. These differential conformations of LCs are important in understanding the fundamental structural features of proteins, and their possible connection to intracellular longevity could provide significant clues for devising new countermeasures and effective therapeutics. Copyright © 2014 Elsevier B.V. All rights reserved.

Neurocomputational Consequences of Evolutionary Connectivity Changes in Perisylvian Language Cortex.

PubMed

Schomers, Malte R; Garagnani, Max; Pulvermüller, Friedemann

2017-03-15

The human brain sets itself apart from that of its primate relatives by specific neuroanatomical features, especially the strong linkage of left perisylvian language areas (frontal and temporal cortex) by way of the arcuate fasciculus (AF). AF connectivity has been shown to correlate with verbal working memory-a specifically human trait providing the foundation for language abilities-but a mechanistic explanation of any related causal link between anatomical structure and cognitive function is still missing. Here, we provide a possible explanation and link, by using neurocomputational simulations in neuroanatomically structured models of the perisylvian language cortex. We compare networks mimicking key features of cortical connectivity in monkeys and humans, specifically the presence of relatively stronger higher-order "jumping links" between nonadjacent perisylvian cortical areas in the latter, and demonstrate that the emergence of working memory for syllables and word forms is a functional consequence of this structural evolutionary change. We also show that a mere increase of learning time is not sufficient, but that this specific structural feature, which entails higher connectivity degree of relevant areas and shorter sensorimotor path length, is crucial. These results offer a better understanding of specifically human anatomical features underlying the language faculty and their evolutionary selection advantage. SIGNIFICANCE STATEMENT Why do humans have superior language abilities compared to primates? Recently, a uniquely human neuroanatomical feature has been demonstrated in the strength of the arcuate fasciculus (AF), a fiber pathway interlinking the left-hemispheric language areas. Although AF anatomy has been related to linguistic skills, an explanation of how this fiber bundle may support language abilities is still missing. We use neuroanatomically structured computational models to investigate the consequences of evolutionary changes in language area connectivity and demonstrate that the human-specific higher connectivity degree and comparatively shorter sensorimotor path length implicated by the AF entail emergence of verbal working memory, a prerequisite for language learning. These results offer a better understanding of specifically human anatomical features for language and their evolutionary selection advantage. Copyright © 2017 Schomers et al.

Structural vibration-based damage classification of delaminated smart composite laminates

NASA Astrophysics Data System (ADS)

Khan, Asif; Kim, Heung Soo; Sohn, Jung Woo

2018-03-01

Separation along the interfaces of layers (delamination) is a principal mode of failure in laminated composites and its detection is of prime importance for structural integrity of composite materials. In this work, structural vibration response is employed to detect and classify delaminations in piezo-bonded laminated composites. Improved layerwise theory and finite element method are adopted to develop the electromechanically coupled governing equation of a smart composite laminate with and without delaminations. Transient responses of the healthy and damaged structures are obtained through a surface bonded piezoelectric sensor by solving the governing equation in the time domain. Wavelet packet transform (WPT) and linear discriminant analysis (LDA) are employed to extract discriminative features from the structural vibration response of the healthy and delaminated structures. Dendrogram-based support vector machine (DSVM) is used to classify the discriminative features. The confusion matrix of the classification algorithm provided physically consistent results.

New breast cancer prognostic factors identified by computer-aided image analysis of HE stained histopathology images

PubMed Central

Chen, Jia-Mei; Qu, Ai-Ping; Wang, Lin-Wei; Yuan, Jing-Ping; Yang, Fang; Xiang, Qing-Ming; Maskey, Ninu; Yang, Gui-Fang; Liu, Juan; Li, Yan

2015-01-01

Computer-aided image analysis (CAI) can help objectively quantify morphologic features of hematoxylin-eosin (HE) histopathology images and provide potentially useful prognostic information on breast cancer. We performed a CAI workflow on 1,150 HE images from 230 patients with invasive ductal carcinoma (IDC) of the breast. We used a pixel-wise support vector machine classifier for tumor nests (TNs)-stroma segmentation, and a marker-controlled watershed algorithm for nuclei segmentation. 730 morphologic parameters were extracted after segmentation, and 12 parameters identified by Kaplan-Meier analysis were significantly associated with 8-year disease free survival (P < 0.05 for all). Moreover, four image features including TNs feature (HR 1.327, 95%CI [1.001 - 1.759], P = 0.049), TNs cell nuclei feature (HR 0.729, 95%CI [0.537 - 0.989], P = 0.042), TNs cell density (HR 1.625, 95%CI [1.177 - 2.244], P = 0.003), and stromal cell structure feature (HR 1.596, 95%CI [1.142 - 2.229], P = 0.006) were identified by multivariate Cox proportional hazards model to be new independent prognostic factors. The results indicated that CAI can assist the pathologist in extracting prognostic information from HE histopathology images for IDC. The TNs feature, TNs cell nuclei feature, TNs cell density, and stromal cell structure feature could be new prognostic factors. PMID:26022540

Classification of AB O 3 perovskite solids: a machine learning study

DOE PAGES

Pilania, G.; Balachandran, P. V.; Gubernatis, J. E.; ...

2015-07-23

Here we explored the use of machine learning methods for classifying whether a particularABO 3chemistry forms a perovskite or non-perovskite structured solid. Starting with three sets of feature pairs (the tolerance and octahedral factors, theAandBionic radii relative to the radius of O, and the bond valence distances between theAandBions from the O atoms), we used machine learning to create a hyper-dimensional partial dependency structure plot using all three feature pairs or any two of them. Doing so increased the accuracy of our predictions by 2–3 percentage points over using any one pair. We also included the Mendeleev numbers of theAandBatomsmore » to this set of feature pairs. Moreover, doing this and using the capabilities of our machine learning algorithm, the gradient tree boosting classifier, enabled us to generate a new type of structure plot that has the simplicity of one based on using just the Mendeleev numbers, but with the added advantages of having a higher accuracy and providing a measure of likelihood of the predicted structure.« less

Detection and analysis of diamond fingerprinting feature and its application

NASA Astrophysics Data System (ADS)

Li, Xin; Huang, Guoliang; Li, Qiang; Chen, Shengyi

2011-01-01

Before becoming a jewelry diamonds need to be carved artistically with some special geometric features as the structure of the polyhedron. There are subtle differences in the structure of this polyhedron in each diamond. With the spatial frequency spectrum analysis of diamond surface structure, we can obtain the diamond fingerprint information which represents the "Diamond ID" and has good specificity. Based on the optical Fourier Transform spatial spectrum analysis, the fingerprinting identification of surface structure of diamond in spatial frequency domain was studied in this paper. We constructed both the completely coherent diamond fingerprinting detection system illuminated by laser and the partially coherent diamond fingerprinting detection system illuminated by led, and analyzed the effect of the coherence of light source to the diamond fingerprinting feature. We studied rotation invariance and translation invariance of the diamond fingerprinting and verified the feasibility of real-time and accurate identification of diamond fingerprint. With the profit of this work, we can provide customs, jewelers and consumers with a real-time and reliable diamonds identification instrument, which will curb diamond smuggling, theft and other crimes, and ensure the healthy development of the diamond industry.

Dark mammoth trunks in the merging galaxy NGC 1316 and a mechanism of cosmic double helices

NASA Astrophysics Data System (ADS)

Carlqvist, Per

2010-06-01

NGC 1316 is a giant, elliptical galaxy containing a complex network of dark, dust features. The morphology of these features has been examined in some detail using a Hubble Space Telescope, Advanced Camera for Surveys image. It is found that most of the features are constituted of long filaments. There also exist a great number of dark structures protruding inwards from the filaments. Many of these structures are strikingly similar to elephant trunks in H ii regions in the Milky Way Galaxy, although much larger. The structures, termed mammoth trunks, generally are filamentary and often have shapes resembling the letters V or Y. In some of the mammoth trunks the stem of the Y can be resolved into two or more filaments, many of which showing signs of being intertwined. A model of the mammoth trunks, related to a recent theory of elephant trunks, is proposed. Based on magnetized filaments, the model is capable of giving an account of the various shapes of the mammoth trunks observed, including the twined structures.

Implicit Learning of Recursive Context-Free Grammars

PubMed Central

Rohrmeier, Martin; Fu, Qiufang; Dienes, Zoltan

2012-01-01

Context-free grammars are fundamental for the description of linguistic syntax. However, most artificial grammar learning experiments have explored learning of simpler finite-state grammars, while studies exploring context-free grammars have not assessed awareness and implicitness. This paper explores the implicit learning of context-free grammars employing features of hierarchical organization, recursive embedding and long-distance dependencies. The grammars also featured the distinction between left- and right-branching structures, as well as between centre- and tail-embedding, both distinctions found in natural languages. People acquired unconscious knowledge of relations between grammatical classes even for dependencies over long distances, in ways that went beyond learning simpler relations (e.g. n-grams) between individual words. The structural distinctions drawn from linguistics also proved important as performance was greater for tail-embedding than centre-embedding structures. The results suggest the plausibility of implicit learning of complex context-free structures, which model some features of natural languages. They support the relevance of artificial grammar learning for probing mechanisms of language learning and challenge existing theories and computational models of implicit learning. PMID:23094021

Some design constraints required for the assembly of software components: The incorporation of atomic abstract types into generically structured abstract types

NASA Technical Reports Server (NTRS)

Johnson, Charles S.

1986-01-01

It is nearly axiomatic, that to take the greatest advantage of the useful features available in a development system, and to avoid the negative interactions of those features, requires the exercise of a design methodology which constrains their use. A major design support feature of the Ada language is abstraction: for data, functions processes, resources, and system elements in general. Atomic abstract types can be created in packages defining those private types and all of the overloaded operators, functions, and hidden data required for their use in an application. Generically structured abstract types can be created in generic packages defining those structured private types, as buildups from the user-defined data types which are input as parameters. A study is made of the design constraints required for software incorporating either atomic or generically structured abstract types, if the integration of software components based on them is to be subsequently performed. The impact of these techniques on the reusability of software and the creation of project-specific software support environments is also discussed.

Consumers' Preferences for Electronic Nicotine Delivery System Product Features: A Structured Content Analysis.

PubMed

Kistler, Christine E; Crutchfield, Trisha M; Sutfin, Erin L; Ranney, Leah M; Berman, Micah L; Zarkin, Gary A; Goldstein, Adam O

2017-06-07

To inform potential governmental regulations, we aimed to develop a list of electronic nicotine delivery system (ENDS) product features important to U.S. consumers by age and gender. We employed qualitative data methods. Participants were eligible if they had used an ENDS at least once. Groups were selected by age and gender (young adult group aged 18-25, n = 11; middle-age group aged 26-64, n = 9; and women's group aged 26-64, n = 9). We conducted five individual older adult interviews (aged 68-80). Participants discussed important ENDS features. We conducted a structured content analysis of the group and interview responses. Of 34 participants, 68% were white and 56% were female. Participants mentioned 12 important ENDS features, including: (1) user experience; (2) social acceptability; (3) cost; (4) health risks/benefits; (5) ease of use; (6) flavors; (7) smoking cessation aid; (8) nicotine content; (9) modifiability; (10) ENDS regulation; (11) bridge between tobacco cigarettes; (12) collectability. The most frequently mentioned ENDS feature was modifiability for young adults, user experience for middle-age and older adults, and flavor for the women's group. This study identified multiple features important to ENDS consumers. Groups differed in how they viewed various features by age and gender. These results can inform ongoing regulatory efforts.

Consumers’ Preferences for Electronic Nicotine Delivery System Product Features: A Structured Content Analysis

PubMed Central

Kistler, Christine E.; Crutchfield, Trisha M.; Sutfin, Erin L.; Ranney, Leah M.; Berman, Micah L.; Zarkin, Gary A.; Goldstein, Adam O.

2017-01-01

To inform potential governmental regulations, we aimed to develop a list of electronic nicotine delivery system (ENDS) product features important to U.S. consumers by age and gender. We employed qualitative data methods. Participants were eligible if they had used an ENDS at least once. Groups were selected by age and gender (young adult group aged 18–25, n = 11; middle-age group aged 26–64, n = 9; and women’s group aged 26–64, n = 9). We conducted five individual older adult interviews (aged 68–80). Participants discussed important ENDS features. We conducted a structured content analysis of the group and interview responses. Of 34 participants, 68% were white and 56% were female. Participants mentioned 12 important ENDS features, including: (1) user experience; (2) social acceptability; (3) cost; (4) health risks/benefits; (5) ease of use; (6) flavors; (7) smoking cessation aid; (8) nicotine content; (9) modifiability; (10) ENDS regulation; (11) bridge between tobacco cigarettes; (12) collectability. The most frequently mentioned ENDS feature was modifiability for young adults, user experience for middle-age and older adults, and flavor for the women’s group. This study identified multiple features important to ENDS consumers. Groups differed in how they viewed various features by age and gender. These results can inform ongoing regulatory efforts. PMID:28590444

Method and apparatus for fringe-scanning chromosome analysis

DOEpatents

Norgren, R.M.; Gray, J.W.; Hirschfeld, T.B.

1983-08-31

Apparatus and method are provided for analyzing sub-micron-sized features of microscopic particles. Two central features of the invention are (1) constraining microscopic particles to flow with substantially constant orientation through a predetermined interference fringe pattern, and (2) estimating particle structure by analyzing its fringe profile. The invention allows nearly an order of magnitude higher resolution of chromosome structure than possible with currently available flow system techniques. The invention allows rapid and accurate flow karyotyping of chromosomes.

Molecular Beam Epitaxial Growth of GaAs on (631) Oriented Substrates

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

Cruz Hernandez, Esteban; Rojas Ramirez, Juan-Salvador; Contreras Hernandez, Rocio

2007-02-09

In this work, we report the study of the homoepitaxial growth of GaAs on (631) oriented substrates by molecular beam epitaxy (MBE). We observed the spontaneous formation of a high density of large scale features on the surface. The hilly like features are elongated towards the [-5, 9, 3] direction. We show the dependence of these structures with the growth conditions and we present the possibility of to create quantum wires structures on this surface.

A quasi-Hertzian stress field from an internal source: A possible working model for the Vredefort structure

NASA Technical Reports Server (NTRS)

Antoine, L. A. G.; Reimold, W. U.; Colliston, W. P.

1992-01-01

The Vredefort structure is a large domal feature about 110 km southeast of Johannesburg, South Africa, situated within and almost central to the large intracratonic Witwatersrand Basin. This structure consists of an Archean core of ca. 45 km in diameter, consisting largely of granitic gneiss, surrounded by a collar of metasedimentary and metavolcanic supracrustal rocks of the Dominian Group, Witwatersrand and Ventersdorp Supergroups, and Transvaal Sequence. The interpretation of images of the gravity and magnetic fields over Vredefort has permitted the delineation of several important features of the structure and of its environment. The outline of the collar strata is a prominent feature of both the gravity and the magnetic fields. The Vredefort structure shares this distinctive geometry with other structures (e.g., Manicouagan, Decaturville, Sierra Madera) of debated impact origin. In all these, successively older strata with steep outward dips are encountered while traversing inward to the center of the structure. A further attribute of these structures is the shortening of the outcrop of a particular stratigraphic unit compared to the original perimeter of that unit. To account for the geometric attributes of the Vredefort structure a mechanical scheme is required where there is radial movement of horizontal strata toward, with uplift in, the center of the Vredefort structure. Two models can be proposed: (1) one in which there is a rapid rise and violent disruption of cover rocks in response to expansion of a fluid accumulation; and (2) one in which there is, in contrast, a nonexplosive, quasi-Hertzian stress field resulting from a diapiric process. Both models can accommodate the geometry and structural components of Vredefort.

Knowledge-based fragment binding prediction.

PubMed

Tang, Grace W; Altman, Russ B

2014-04-01

Target-based drug discovery must assess many drug-like compounds for potential activity. Focusing on low-molecular-weight compounds (fragments) can dramatically reduce the chemical search space. However, approaches for determining protein-fragment interactions have limitations. Experimental assays are time-consuming, expensive, and not always applicable. At the same time, computational approaches using physics-based methods have limited accuracy. With increasing high-resolution structural data for protein-ligand complexes, there is now an opportunity for data-driven approaches to fragment binding prediction. We present FragFEATURE, a machine learning approach to predict small molecule fragments preferred by a target protein structure. We first create a knowledge base of protein structural environments annotated with the small molecule substructures they bind. These substructures have low-molecular weight and serve as a proxy for fragments. FragFEATURE then compares the structural environments within a target protein to those in the knowledge base to retrieve statistically preferred fragments. It merges information across diverse ligands with shared substructures to generate predictions. Our results demonstrate FragFEATURE's ability to rediscover fragments corresponding to the ligand bound with 74% precision and 82% recall on average. For many protein targets, it identifies high scoring fragments that are substructures of known inhibitors. FragFEATURE thus predicts fragments that can serve as inputs to fragment-based drug design or serve as refinement criteria for creating target-specific compound libraries for experimental or computational screening.

Knowledge-based Fragment Binding Prediction

PubMed Central

Tang, Grace W.; Altman, Russ B.

2014-01-01

Target-based drug discovery must assess many drug-like compounds for potential activity. Focusing on low-molecular-weight compounds (fragments) can dramatically reduce the chemical search space. However, approaches for determining protein-fragment interactions have limitations. Experimental assays are time-consuming, expensive, and not always applicable. At the same time, computational approaches using physics-based methods have limited accuracy. With increasing high-resolution structural data for protein-ligand complexes, there is now an opportunity for data-driven approaches to fragment binding prediction. We present FragFEATURE, a machine learning approach to predict small molecule fragments preferred by a target protein structure. We first create a knowledge base of protein structural environments annotated with the small molecule substructures they bind. These substructures have low-molecular weight and serve as a proxy for fragments. FragFEATURE then compares the structural environments within a target protein to those in the knowledge base to retrieve statistically preferred fragments. It merges information across diverse ligands with shared substructures to generate predictions. Our results demonstrate FragFEATURE's ability to rediscover fragments corresponding to the ligand bound with 74% precision and 82% recall on average. For many protein targets, it identifies high scoring fragments that are substructures of known inhibitors. FragFEATURE thus predicts fragments that can serve as inputs to fragment-based drug design or serve as refinement criteria for creating target-specific compound libraries for experimental or computational screening. PMID:24762971

Morphometry Based on Effective and Accurate Correspondences of Localized Patterns (MEACOLP)

PubMed Central

Wang, Hu; Ren, Yanshuang; Bai, Lijun; Zhang, Wensheng; Tian, Jie

2012-01-01

Local features in volumetric images have been used to identify correspondences of localized anatomical structures for brain morphometry. However, the correspondences are often sparse thus ineffective in reflecting the underlying structures, making it unreliable to evaluate specific morphological differences. This paper presents a morphometry method (MEACOLP) based on correspondences with improved effectiveness and accuracy. A novel two-level scale-invariant feature transform is used to enhance the detection repeatability of local features and to recall the correspondences that might be missed in previous studies. Template patterns whose correspondences could be commonly identified in each group are constructed to serve as the basis for morphometric analysis. A matching algorithm is developed to reduce the identification errors by comparing neighboring local features and rejecting unreliable matches. The two-sample t-test is finally adopted to analyze specific properties of the template patterns. Experiments are performed on the public OASIS database to clinically analyze brain images of Alzheimer's disease (AD) and normal controls (NC). MEACOLP automatically identifies known morphological differences between AD and NC brains, and characterizes the differences well as the scaling and translation of underlying structures. Most of the significant differences are identified in only a single hemisphere, indicating that AD-related structures are characterized by strong anatomical asymmetry. In addition, classification trials to differentiate AD subjects from NC confirm that the morphological differences are reliably related to the groups of interest. PMID:22540000

A diagnosis model for early Tourette syndrome children based on brain structural network characteristics

NASA Astrophysics Data System (ADS)

Wen, Hongwei; Liu, Yue; Wang, Jieqiong; Zhang, Jishui; Peng, Yun; He, Huiguang

2016-03-01

Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder characterized by the presence of multiple motor and vocal tics. Tic generation has been linked to disturbed networks of brain areas involved in planning, controlling and execution of action. The aim of our work is to select topological characteristics of structural network which were most efficient for estimating the classification models to identify early TS children. Here we employed the diffusion tensor imaging (DTI) and deterministic tractography to construct the structural networks of 44 TS children and 48 age and gender matched healthy children. We calculated four different connection matrices (fiber number, mean FA, averaged fiber length weighted and binary matrices) and then applied graph theoretical methods to extract the regional nodal characteristics of structural network. For each weighted or binary network, nodal degree, nodal efficiency and nodal betweenness were selected as features. Support Vector Machine Recursive Feature Extraction (SVM-RFE) algorithm was used to estimate the best feature subset for classification. The accuracy of 88.26% evaluated by a nested cross validation was achieved on combing best feature subset of each network characteristic. The identified discriminative brain nodes mostly located in the basal ganglia and frontal cortico-cortical networks involved in TS children which was associated with tic severity. Our study holds promise for early identification and predicting prognosis of TS children.

Application of PALSAR-2 Remote Sensing Data for Landslide Hazard Mapping in Kelantan River Basin, Peninsular Malaysia

NASA Astrophysics Data System (ADS)

Beiranvand Pour, Amin; Hashim, Mazlan

2016-06-01

Yearly, several landslides ensued during heavy monsoons rainfall in Kelantan river basin, peninsular Malaysia, which are obviously connected to geological structures and topographical features of the region. In this study, the recently launched Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) onboard the Advanced Land Observing Satellite-2 (ALOS-2), remote sensing data were used to map geological structural and topographical features in the Kelantan river basin for identification of high potential risk and susceptible zones for landslides. Adaptive Local Sigma filter was selected and applied to accomplish speckle reduction and preserving both edges and features in PALSAR-2 fine mode observation images. Different polarization images were integrated to enhance geological structures. Additionally, directional filters were applied to the PALSAR-2 Local Sigma resultant image for edge enhancement and detailed identification of linear features. Several faults, drainage patterns and lithological contact layers were identified at regional scale. In order to assess the results, fieldwork and GPS survey were conducted in the landslide affected zones in the Kelantan river basin. Results demonstrate the most of the landslides were associated with N-S, NNW-SSE and NE-SW trending faults, angulated drainage pattern and metamorphic and Quaternary units. Consequently, structural and topographical geology maps were produced for Kelantan river basin using PALSAR-2 data, which could be broadly applicable for landslide hazard mapping.

Experimental study on pore structure and performance of sintered porous wick

NASA Astrophysics Data System (ADS)

He, Da; Wang, Shufan; Liu, Rutie; Wang, Zhubo; Xiong, Xiang; Zou, Jianpeng

2018-02-01

Porous wicks were prepared via powder metallurgy using NH4HCO3 powders as pore-forming agent. The pore-forming agent particle size was varied to control the pore structure and equivalent pore size distribution feature of porous wick. The effect of pore-forming agent particle size on the porosity, pore structures, equivalent pore size distribution and capillary pumping performance were investigated. Results show that with the particle size of pore-forming agent decrease, the green density and the volume shrinkage of the porous wicks gradually increase and the porosity reduces slightly. There are two types of pores inside the porous wick, large-sized prefabricated pores and small-sized gap pores. With the particle size of pore-forming agent decrease, the size of the prefabricated pores becomes smaller and the distribution tends to be uniform. Gap pores and prefabricated pores inside the wick can make up different types of pore channels. The equivalent pore size of wick is closely related to the structure of pore channels. Furthermore, the equivalent pore size distribution of wick shows an obvious double-peak feature when the pore-forming agent particle size is large. With the particle size of pore-forming agent decrease, the two peaks of equivalent pore size distribution approach gradually to each other, resulting in a single-peak feature. Porous wick with single-peak feature equivalent pore size distribution possesses the better capillary pumping performances.

Fan in the F Ring

NASA Image and Video Library

2010-07-20

This mosaic of images from NASA Cassini spacecraft depicts fan-like structures in Saturn tenuous F ring. Bright features are also visible near the core of the ring. Such features suggest the existence of additional objects in the F ring.

Comparison of two matrix data structures for advanced CSM testbed applications

NASA Technical Reports Server (NTRS)

Regelbrugge, M. E.; Brogan, F. A.; Nour-Omid, B.; Rankin, C. C.; Wright, M. A.

1989-01-01

The first section describes data storage schemes presently used by the Computational Structural Mechanics (CSM) testbed sparse matrix facilities and similar skyline (profile) matrix facilities. The second section contains a discussion of certain features required for the implementation of particular advanced CSM algorithms, and how these features might be incorporated into the data storage schemes described previously. The third section presents recommendations, based on the discussions of the prior sections, for directing future CSM testbed development to provide necessary matrix facilities for advanced algorithm implementation and use. The objective is to lend insight into the matrix structures discussed and to help explain the process of evaluating alternative matrix data structures and utilities for subsequent use in the CSM testbed.

The track structure in condensed matter

NASA Astrophysics Data System (ADS)

Kaplan, I. G.

1995-11-01

The physical stage of track formation in a condensed phase is discussed. For interaction of charged particles with condensed molecular media its most important specific features are: (a) the continuous oscillator strength distribution with the broak peak in the energy range 21-22 eV attributed to the collective plasmon-type state; (b) the lowering of ionization potential compared to a gas phase. These specific features must be taken into account for simulation of track structures. The great difference in mass and charge for a electron and heavy ions cause a qualitative difference in their track structures. We analyse the structure of heavy ion tracks and prove the impossibility to use the LET as a universal characteristic for the radiation action of different ions.

Syntheses, structures and photoluminescence properties of three M(II)-coordination polymers (M dbnd Zn(II), Mn(II)) based on a pyridine N-oxide bridging ligand

NASA Astrophysics Data System (ADS)

Ren, Xiu-Hui; Wang, Peng; Cheng, Jun-Yan; Dong, Yu-Bin

2018-06-01

Three M(II)-coordination polymers (M dbnd Zn(II), Mn(II)) were synthesized based on a pyridine N-oxide bridging ligand 3,5-bis(4-carboxylphenyl)-pyridine N-oxide (L1). Compounds 1-3 all have novel complicated structures. Compound 1 (Zn(L1)2(H2O)2) and 2 (Zn2(L1)2(H2O)2) are two single crystals obtained in "one pot" and 1 features 1D double chains motif and 2 features 3D network structure. Compound 3 shows 3D network structure with triangular tunnels. The thermogravimetric analyses and photoluminescence properties were also used to investigate the title compounds.

Multiscale vector fields for image pattern recognition

NASA Technical Reports Server (NTRS)

Low, Kah-Chan; Coggins, James M.

1990-01-01

A uniform processing framework for low-level vision computing in which a bank of spatial filters maps the image intensity structure at each pixel into an abstract feature space is proposed. Some properties of the filters and the feature space are described. Local orientation is measured by a vector sum in the feature space as follows: each filter's preferred orientation along with the strength of the filter's output determine the orientation and the length of a vector in the feature space; the vectors for all filters are summed to yield a resultant vector for a particular pixel and scale. The orientation of the resultant vector indicates the local orientation, and the magnitude of the vector indicates the strength of the local orientation preference. Limitations of the vector sum method are discussed. Investigations show that the processing framework provides a useful, redundant representation of image structure across orientation and scale.

Effects of achievement contexts on the meaning structure of emotion words.

PubMed

Gentsch, Kornelia; Loderer, Kristina; Soriano, Cristina; Fontaine, Johnny R J; Eid, Michael; Pekrun, Reinhard; Scherer, Klaus R

2018-03-01

Little is known about the impact of context on the meaning of emotion words. In the present study, we used a semantic profiling instrument (GRID) to investigate features representing five emotion components (appraisal, bodily reaction, expression, action tendencies, and feeling) of 11 emotion words in situational contexts involving success or failure. We compared these to the data from an earlier study in which participants evaluated the typicality of features out of context. Profile analyses identified features for which typicality changed as a function of context for all emotion words, except contentment, with appraisal features being most frequently affected. Those context effects occurred for both hypothesised basic and non-basic emotion words. Moreover, both data sets revealed a four-dimensional structure. The four dimensions were largely similar (valence, power, arousal, and novelty). The results suggest that context may not change the underlying dimensionality but affects facets of the meaning of emotion words.

Hierarchical Diagnosis of Vocal Fold Disorders

NASA Astrophysics Data System (ADS)

Nikkhah-Bahrami, Mansour; Ahmadi-Noubari, Hossein; Seyed Aghazadeh, Babak; Khadivi Heris, Hossein

This paper explores the use of hierarchical structure for diagnosis of vocal fold disorders. The hierarchical structure is initially used to train different second-level classifiers. At the first level normal and pathological signals have been distinguished. Next, pathological signals have been classified into neurogenic and organic vocal fold disorders. At the final level, vocal fold nodules have been distinguished from polyps in organic disorders category. For feature selection at each level of hierarchy, the reconstructed signal at each wavelet packet decomposition sub-band in 5 levels of decomposition with mother wavelet of (db10) is used to extract the nonlinear features of self-similarity and approximate entropy. Also, wavelet packet coefficients are used to measure energy and Shannon entropy features at different spectral sub-bands. Davies-Bouldin criterion has been employed to find the most discriminant features. Finally, support vector machines have been adopted as classifiers at each level of hierarchy resulting in the diagnosis accuracy of 92%.

Security system

DOEpatents

Baumann, Mark J.; Kuca, Michal; Aragon, Mona L.

2016-02-02

A security system includes a structure having a structural surface. The structure is sized to contain an asset therein and configured to provide a forceful breaching delay. The structure has an opening formed therein to permit predetermined access to the asset contained within the structure. The structure includes intrusion detection features within or associated with the structure that are activated in response to at least a partial breach of the structure.

Robust evaluation of time series classification algorithms for structural health monitoring

NASA Astrophysics Data System (ADS)

Harvey, Dustin Y.; Worden, Keith; Todd, Michael D.

2014-03-01

Structural health monitoring (SHM) systems provide real-time damage and performance information for civil, aerospace, and mechanical infrastructure through analysis of structural response measurements. The supervised learning methodology for data-driven SHM involves computation of low-dimensional, damage-sensitive features from raw measurement data that are then used in conjunction with machine learning algorithms to detect, classify, and quantify damage states. However, these systems often suffer from performance degradation in real-world applications due to varying operational and environmental conditions. Probabilistic approaches to robust SHM system design suffer from incomplete knowledge of all conditions a system will experience over its lifetime. Info-gap decision theory enables nonprobabilistic evaluation of the robustness of competing models and systems in a variety of decision making applications. Previous work employed info-gap models to handle feature uncertainty when selecting various components of a supervised learning system, namely features from a pre-selected family and classifiers. In this work, the info-gap framework is extended to robust feature design and classifier selection for general time series classification through an efficient, interval arithmetic implementation of an info-gap data model. Experimental results are presented for a damage type classification problem on a ball bearing in a rotating machine. The info-gap framework in conjunction with an evolutionary feature design system allows for fully automated design of a time series classifier to meet performance requirements under maximum allowable uncertainty.

Generalized Multilevel Structural Equation Modeling

ERIC Educational Resources Information Center

Rabe-Hesketh, Sophia; Skrondal, Anders; Pickles, Andrew

2004-01-01

A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent…

The Structures of Life

ERIC Educational Resources Information Center

National Institute of General Medical Sciences (NIGMS), 2007

2007-01-01

This booklet reveals how structural biology provides insight into health and disease and is useful in developing new medications. It contains a general introduction to proteins, coverage of the techniques used to determine protein structures, and a chapter on structure-based drug design. The booklet features "Student Snapshots," designed to…

Remarks on forensically interesting Sony Playstation 3 console features

NASA Astrophysics Data System (ADS)

Daugs, Gunnar; Kröger, Knut; Creutzburg, Reiner

2012-02-01

This paper deals with forensically interesting features of the Sony Playstation 3 game console. The construction and the internal structure are analyzed more precisely. Interesting forensic features of the operating system and the file system are presented. Differences between a PS3 with and without jailbreak are introduced and possible forensic attempts when using an installed Linux are discussed.

Conceptual Structure within and between Modalities

PubMed Central

Dilkina, Katia; Lambon Ralph, Matthew A.

2012-01-01

Current views of semantic memory share the assumption that conceptual representations are based on multimodal experience, which activates distinct modality-specific brain regions. This proposition is widely accepted, yet little is known about how each modality contributes to conceptual knowledge and how the structure of this contribution varies across these multiple information sources. We used verbal feature lists, features from drawings, and verbal co-occurrence statistics from latent semantic analysis to examine the informational structure in four domains of knowledge: perceptual, functional, encyclopedic, and verbal. The goals of the analysis were three-fold: (1) to assess the structure within individual modalities; (2) to compare structures between modalities; and (3) to assess the degree to which concepts organize categorically or randomly. Our results indicated significant and unique structure in all four modalities: perceptually, concepts organize based on prominent features such as shape, size, color, and parts; functionally, they group based on use and interaction; encyclopedically, they arrange based on commonality in location or behavior; and verbally, they group associatively or relationally. Visual/perceptual knowledge gives rise to the strongest hierarchical organization and is closest to classic taxonomic structure. Information is organized somewhat similarly in the perceptual and encyclopedic domains, which differs significantly from the structure in the functional and verbal domains. Notably, the verbal modality has the most unique organization, which is not at all categorical but also not random. The idiosyncrasy and complexity of conceptual structure across modalities raise the question of how all of these modality-specific experiences are fused together into coherent, multifaceted yet unified concepts. Accordingly, both methodological and theoretical implications of the present findings are discussed. PMID:23293593

Hydrologic connectivity: Quantitative assessments of hydrologic-enforced drainage structures in an elevation model

USGS Publications Warehouse

Poppenga, Sandra K.; Worstell, Bruce B.

2016-01-01

Elevation data derived from light detection and ranging present challenges for hydrologic modeling as the elevation surface includes bridge decks and elevated road features overlaying culvert drainage structures. In reality, water is carried through these structures; however, in the elevation surface these features impede modeled overland surface flow. Thus, a hydrologically-enforced elevation surface is needed for hydrodynamic modeling. In the Delaware River Basin, hydrologic-enforcement techniques were used to modify elevations to simulate how constructed drainage structures allow overland surface flow. By calculating residuals between unfilled and filled elevation surfaces, artificially pooled depressions that formed upstream of constructed drainage structure features were defined, and elevation values were adjusted by generating transects at the location of the drainage structures. An assessment of each hydrologically-enforced drainage structure was conducted using field-surveyed culvert and bridge coordinates obtained from numerous public agencies, but it was discovered the disparate drainage structure datasets were not comprehensive enough to assess all remotely located depressions in need of hydrologic-enforcement. Alternatively, orthoimagery was interpreted to define drainage structures near each depression, and these locations were used as reference points for a quantitative hydrologic-enforcement assessment. The orthoimagery-interpreted reference points resulted in a larger corresponding sample size than the assessment between hydrologic-enforced transects and field-surveyed data. This assessment demonstrates the viability of rules-based hydrologic-enforcement that is needed to achieve hydrologic connectivity, which is valuable for hydrodynamic models in sensitive coastal regions. Hydrologic-enforced elevation data are also essential for merging with topographic/bathymetric elevation data that extend over vulnerable urbanized areas and dynamic coastal regions.

Feature-based Morphometry

PubMed Central

Toews, Matthew; Wells, William M.; Collins, Louis; Arbel, Tal

2013-01-01

This paper presents feature-based morphometry (FBM), a new, fully data-driven technique for identifying group-related differences in volumetric imagery. In contrast to most morphometry methods which assume one-to-one correspondence between all subjects, FBM models images as a collage of distinct, localized image features which may not be present in all subjects. FBM thus explicitly accounts for the case where the same anatomical tissue cannot be reliably identified in all subjects due to disease or anatomical variability. A probabilistic model describes features in terms of their appearance, geometry, and relationship to sub-groups of a population, and is automatically learned from a set of subject images and group labels. Features identified indicate group-related anatomical structure that can potentially be used as disease biomarkers or as a basis for computer-aided diagnosis. Scale-invariant image features are used, which reflect generic, salient patterns in the image. Experiments validate FBM clinically in the analysis of normal (NC) and Alzheimer’s (AD) brain images using the freely available OASIS database. FBM automatically identifies known structural differences between NC and AD subjects in a fully data-driven fashion, and obtains an equal error classification rate of 0.78 on new subjects. PMID:20426102

A data driven approach for condition monitoring of wind turbine blade using vibration signals through best-first tree algorithm and functional trees algorithm: A comparative study.

PubMed

Joshuva, A; Sugumaran, V

2017-03-01

Wind energy is one of the important renewable energy resources available in nature. It is one of the major resources for production of energy because of its dependability due to the development of the technology and relatively low cost. Wind energy is converted into electrical energy using rotating blades. Due to environmental conditions and large structure, the blades are subjected to various vibration forces that may cause damage to the blades. This leads to a liability in energy production and turbine shutdown. The downtime can be reduced when the blades are diagnosed continuously using structural health condition monitoring. These are considered as a pattern recognition problem which consists of three phases namely, feature extraction, feature selection, and feature classification. In this study, statistical features were extracted from vibration signals, feature selection was carried out using a J48 decision tree algorithm and feature classification was performed using best-first tree algorithm and functional trees algorithm. The better algorithm is suggested for fault diagnosis of wind turbine blade. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Neighborhood Structural Similarity Mapping for the Classification of Masses in Mammograms.

PubMed

Rabidas, Rinku; Midya, Abhishek; Chakraborty, Jayasree

2018-05-01

In this paper, two novel feature extraction methods, using neighborhood structural similarity (NSS), are proposed for the characterization of mammographic masses as benign or malignant. Since gray-level distribution of pixels is different in benign and malignant masses, more regular and homogeneous patterns are visible in benign masses compared to malignant masses; the proposed method exploits the similarity between neighboring regions of masses by designing two new features, namely, NSS-I and NSS-II, which capture global similarity at different scales. Complementary to these global features, uniform local binary patterns are computed to enhance the classification efficiency by combining with the proposed features. The performance of the features are evaluated using the images from the mini-mammographic image analysis society (mini-MIAS) and digital database for screening mammography (DDSM) databases, where a tenfold cross-validation technique is incorporated with Fisher linear discriminant analysis, after selecting the optimal set of features using stepwise logistic regression method. The best area under the receiver operating characteristic curve of 0.98 with an accuracy of is achieved with the mini-MIAS database, while the same for the DDSM database is 0.93 with accuracy .

Feature Selection based on Machine Learning in MRIs for Hippocampal Segmentation

NASA Astrophysics Data System (ADS)

Tangaro, Sabina; Amoroso, Nicola; Brescia, Massimo; Cavuoti, Stefano; Chincarini, Andrea; Errico, Rosangela; Paolo, Inglese; Longo, Giuseppe; Maglietta, Rosalia; Tateo, Andrea; Riccio, Giuseppe; Bellotti, Roberto

2015-01-01

Neurodegenerative diseases are frequently associated with structural changes in the brain. Magnetic resonance imaging (MRI) scans can show these variations and therefore can be used as a supportive feature for a number of neurodegenerative diseases. The hippocampus has been known to be a biomarker for Alzheimer disease and other neurological and psychiatric diseases. However, it requires accurate, robust, and reproducible delineation of hippocampal structures. Fully automatic methods are usually the voxel based approach; for each voxel a number of local features were calculated. In this paper, we compared four different techniques for feature selection from a set of 315 features extracted for each voxel: (i) filter method based on the Kolmogorov-Smirnov test; two wrapper methods, respectively, (ii) sequential forward selection and (iii) sequential backward elimination; and (iv) embedded method based on the Random Forest Classifier on a set of 10 T1-weighted brain MRIs and tested on an independent set of 25 subjects. The resulting segmentations were compared with manual reference labelling. By using only 23 feature for each voxel (sequential backward elimination) we obtained comparable state-of-the-art performances with respect to the standard tool FreeSurfer.

Task-relevant perceptual features can define categories in visual memory too.

PubMed

Antonelli, Karla B; Williams, Carrick C

2017-11-01

Although Konkle, Brady, Alvarez, and Oliva (2010, Journal of Experimental Psychology: General, 139(3), 558) claim that visual long-term memory (VLTM) is organized on underlying conceptual, not perceptual, information, visual memory results from visual search tasks are not well explained by this theory. We hypothesized that when viewing an object, any task-relevant visual information is critical to the organizational structure of VLTM. In two experiments, we examined the organization of VLTM by measuring the amount of retroactive interference created by objects possessing different combinations of task-relevant features. Based on task instructions, only the conceptual category was task relevant or both the conceptual category and a perceptual object feature were task relevant. Findings indicated that when made task relevant, perceptual object feature information, along with conceptual category information, could affect memory organization for objects in VLTM. However, when perceptual object feature information was task irrelevant, it did not contribute to memory organization; instead, memory defaulted to being organized around conceptual category information. These findings support the theory that a task-defined organizational structure is created in VLTM based on the relevance of particular object features and information.

Demersal fish assemblages on seamounts and other rugged features in the northeastern Caribbean

NASA Astrophysics Data System (ADS)

Quattrini, Andrea M.; Demopoulos, Amanda W. J.; Singer, Randal; Roa-Varon, Adela; Chaytor, Jason D.

2017-05-01

Recent investigations of demersal fish communities in deepwater (>50 m) habitats have considerably increased our knowledge of the factors that influence the assemblage structure of fishes across mesophotic to deep-sea depths. While different habitat types influence deepwater fish distribution, whether different types of rugged seafloor features provide functionally equivalent habitat for fishes is poorly understood. In the northeastern Caribbean, different types of rugged features (e.g., seamounts, banks, canyons) punctuate insular margins, and thus create a remarkable setting in which to compare demersal fish communities across various features. Concurrently, several water masses are vertically layered in the water column, creating strong stratification layers corresponding to specific abiotic conditions. In this study, we examined differences among fish assemblages across different features (e.g., seamount, canyon, bank/ridge) and water masses at depths ranging from 98 to 4060 m in the northeastern Caribbean. We conducted 26 remotely operated vehicle dives across 18 sites, identifying 156 species of which 42% of had not been previously recorded from particular depths or localities in the region. While rarefaction curves indicated fewer species at seamounts than at other features in the NE Caribbean, assemblage structure was similar among the different types of features. Thus, similar to seamount studies in other regions, seamounts in the Anegada Passage do not harbor distinct communities from other types of rugged features. Species assemblages, however, differed among depths, with zonation generally corresponding to water mass boundaries in the region. High species turnover occurred at depths <1200 m, and may be driven by changes in water mass characteristics including temperature (4.8-24.4 °C) and dissolved oxygen (2.2-9.5 mg per l). Our study suggests the importance of water masses in influencing community structure of benthic fauna, while considerably adding to the knowledge of mesophotic and deep-sea fish biogeography.

Demersal fish assemblages on seamounts and other rugged features in the northeastern Caribbean

USGS Publications Warehouse

Quattrini, Andrea M.; Demopoulos, Amanda W. J.; Singer, Randal; Roa-Varon, Adela; Chaytor, Jason D.

2017-01-01

Recent investigations of demersal fish communities in deepwater (>50 m) habitats have considerably increased our knowledge of the factors that influence the assemblage structure of fishes across mesophotic to deep-sea depths. While different habitat types influence deepwater fish distribution, whether different types of rugged seafloor features provide functionally equivalent habitat for fishes is poorly understood. In the northeastern Caribbean, different types of rugged features (e.g., seamounts, banks, canyons) punctuate insular margins, and thus create a remarkable setting in which to compare demersal fish communities across various features. Concurrently, several water masses are vertically layered in the water column, creating strong stratification layers corresponding to specific abiotic conditions. In this study, we examined differences among fish assemblages across different features (e.g., seamount, canyon, bank/ridge) and water masses at depths ranging from 98 to 4060 m in the northeastern Caribbean. We conducted 26 remotely operated vehicle dives across 18 sites, identifying 156 species of which 42% of had not been previously recorded from particular depths or localities in the region. While rarefaction curves indicated fewer species at seamounts than at other features in the NE Caribbean, assemblage structure was similar among the different types of features. Thus, similar to seamount studies in other regions, seamounts in the Anegada Passage do not harbor distinct communities from other types of rugged features. Species assemblages, however, differed among depths, with zonation generally corresponding to water mass boundaries in the region. High species turnover occurred at depths <1200 m, and may be driven by changes in water mass characteristics including temperature (4.8–24.4 °C) and dissolved oxygen (2.2–9.5 mg per l). Our study suggests the importance of water masses in influencing community structure of benthic fauna, while considerably adding to the knowledge of mesophotic and deep-sea fish biogeography.

Protein sectors: evolutionary units of three-dimensional structure

PubMed Central

Halabi, Najeeb; Rivoire, Olivier; Leibler, Stanislas; Ranganathan, Rama

2011-01-01

Proteins display a hierarchy of structural features at primary, secondary, tertiary, and higher-order levels, an organization that guides our current understanding of their biological properties and evolutionary origins. Here, we reveal a structural organization distinct from this traditional hierarchy by statistical analysis of correlated evolution between amino acids. Applied to the S1A serine proteases, the analysis indicates a decomposition of the protein into three quasi-independent groups of correlated amino acids that we term “protein sectors”. Each sector is physically connected in the tertiary structure, has a distinct functional role, and constitutes an independent mode of sequence divergence in the protein family. Functionally relevant sectors are evident in other protein families as well, suggesting that they may be general features of proteins. We propose that sectors represent a structural organization of proteins that reflects their evolutionary histories. PMID:19703402

Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder

PubMed Central

Zhao, Guangjun; Wang, Xuchu; Niu, Yanmin; Tan, Liwen; Zhang, Shao-Xiang

2016-01-01

Cryosection brain images in Chinese Visible Human (CVH) dataset contain rich anatomical structure information of tissues because of its high resolution (e.g., 0.167 mm per pixel). Fast and accurate segmentation of these images into white matter, gray matter, and cerebrospinal fluid plays a critical role in analyzing and measuring the anatomical structures of human brain. However, most existing automated segmentation methods are designed for computed tomography or magnetic resonance imaging data, and they may not be applicable for cryosection images due to the imaging difference. In this paper, we propose a supervised learning-based CVH brain tissues segmentation method that uses stacked autoencoder (SAE) to automatically learn the deep feature representations. Specifically, our model includes two successive parts where two three-layer SAEs take image patches as input to learn the complex anatomical feature representation, and then these features are sent to Softmax classifier for inferring the labels. Experimental results validated the effectiveness of our method and showed that it outperformed four other classical brain tissue detection strategies. Furthermore, we reconstructed three-dimensional surfaces of these tissues, which show their potential in exploring the high-resolution anatomical structures of human brain. PMID:27057543

Change Analysis in Structural Laser Scanning Point Clouds: The Baseline Method

PubMed Central

Shen, Yueqian; Lindenbergh, Roderik; Wang, Jinhu

2016-01-01

A method is introduced for detecting changes from point clouds that avoids registration. For many applications, changes are detected between two scans of the same scene obtained at different times. Traditionally, these scans are aligned to a common coordinate system having the disadvantage that this registration step introduces additional errors. In addition, registration requires stable targets or features. To avoid these issues, we propose a change detection method based on so-called baselines. Baselines connect feature points within one scan. To analyze changes, baselines connecting corresponding points in two scans are compared. As feature points either targets or virtual points corresponding to some reconstructable feature in the scene are used. The new method is implemented on two scans sampling a masonry laboratory building before and after seismic testing, that resulted in damages in the order of several centimeters. The centres of the bricks of the laboratory building are automatically extracted to serve as virtual points. Baselines connecting virtual points and/or target points are extracted and compared with respect to a suitable structural coordinate system. Changes detected from the baseline analysis are compared to a traditional cloud to cloud change analysis demonstrating the potential of the new method for structural analysis. PMID:28029121

Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder.

PubMed

Zhao, Guangjun; Wang, Xuchu; Niu, Yanmin; Tan, Liwen; Zhang, Shao-Xiang

2016-01-01

Cryosection brain images in Chinese Visible Human (CVH) dataset contain rich anatomical structure information of tissues because of its high resolution (e.g., 0.167 mm per pixel). Fast and accurate segmentation of these images into white matter, gray matter, and cerebrospinal fluid plays a critical role in analyzing and measuring the anatomical structures of human brain. However, most existing automated segmentation methods are designed for computed tomography or magnetic resonance imaging data, and they may not be applicable for cryosection images due to the imaging difference. In this paper, we propose a supervised learning-based CVH brain tissues segmentation method that uses stacked autoencoder (SAE) to automatically learn the deep feature representations. Specifically, our model includes two successive parts where two three-layer SAEs take image patches as input to learn the complex anatomical feature representation, and then these features are sent to Softmax classifier for inferring the labels. Experimental results validated the effectiveness of our method and showed that it outperformed four other classical brain tissue detection strategies. Furthermore, we reconstructed three-dimensional surfaces of these tissues, which show their potential in exploring the high-resolution anatomical structures of human brain.

Influence of Perceptual Saliency Hierarchy on Learning of Language Structures: An Artificial Language Learning Experiment

PubMed Central

Gong, Tao; Lam, Yau W.; Shuai, Lan

2016-01-01

Psychological experiments have revealed that in normal visual perception of humans, color cues are more salient than shape cues, which are more salient than textural patterns. We carried out an artificial language learning experiment to study whether such perceptual saliency hierarchy (color > shape > texture) influences the learning of orders regulating adjectives of involved visual features in a manner either congruent (expressing a salient feature in a salient part of the form) or incongruent (expressing a salient feature in a less salient part of the form) with that hierarchy. Results showed that within a few rounds of learning participants could learn the compositional segments encoding the visual features and the order between them, generalize the learned knowledge to unseen instances with the same or different orders, and show learning biases for orders that are congruent with the perceptual saliency hierarchy. Although the learning performances for both the biased and unbiased orders became similar given more learning trials, our study confirms that this type of individual perceptual constraint could contribute to the structural configuration of language, and points out that such constraint, as well as other factors, could collectively affect the structural diversity in languages. PMID:28066281

Change Analysis in Structural Laser Scanning Point Clouds: The Baseline Method.

PubMed

Shen, Yueqian; Lindenbergh, Roderik; Wang, Jinhu

2016-12-24

A method is introduced for detecting changes from point clouds that avoids registration. For many applications, changes are detected between two scans of the same scene obtained at different times. Traditionally, these scans are aligned to a common coordinate system having the disadvantage that this registration step introduces additional errors. In addition, registration requires stable targets or features. To avoid these issues, we propose a change detection method based on so-called baselines. Baselines connect feature points within one scan. To analyze changes, baselines connecting corresponding points in two scans are compared. As feature points either targets or virtual points corresponding to some reconstructable feature in the scene are used. The new method is implemented on two scans sampling a masonry laboratory building before and after seismic testing, that resulted in damages in the order of several centimeters. The centres of the bricks of the laboratory building are automatically extracted to serve as virtual points. Baselines connecting virtual points and/or target points are extracted and compared with respect to a suitable structural coordinate system. Changes detected from the baseline analysis are compared to a traditional cloud to cloud change analysis demonstrating the potential of the new method for structural analysis.

Venusian arachnoids revisited

NASA Astrophysics Data System (ADS)

Kostama, V.-P.; Tormanen, T.

The Venusian volcano-tectonic structures have been subject to many classification and characterisation schemes. Several structure-types have been identified (e.g. coronae, novae, arachnoids, calderas, and corona-novae). Of these groups, the relationship of arachnoids and coronae has been complicated, and is a subject to much debate. Some previous works and studies have fused these two categories together, and even promoted the view of non-existence of arachnoids at times. However, based on the recognisable differences in morphology and other characteristics (e.g. size, topography, volcanism), they should be treated as a separate class of structures. In our first global study of the volcano-tectonic features, we found 96 arachnoids [1, 2]. During the reanalysis of the features as a by-product of another study, the arachnoid population was re-evalueted, and more importantly, the identification criteria was rechecked. The revised population increases the arachnoid number to 130 features. The work also produced many examples of features that can be considered as transitional forms between different morphological groups. [1] Kostama, V.-P., M. Aittola, LPSC XXXII, Abstract#1185, 2001a. [2] Kostama, V.-P., M. Aittola, The Catalogue of Venusian Arachnoids, Coronae and Novae, http://cc.oulu.fi/tati/JR/Venus/volcanotectonics/catalogue.html, 2001b.

Influence of Perceptual Saliency Hierarchy on Learning of Language Structures: An Artificial Language Learning Experiment.

PubMed

Gong, Tao; Lam, Yau W; Shuai, Lan

2016-01-01

Psychological experiments have revealed that in normal visual perception of humans, color cues are more salient than shape cues, which are more salient than textural patterns. We carried out an artificial language learning experiment to study whether such perceptual saliency hierarchy (color > shape > texture) influences the learning of orders regulating adjectives of involved visual features in a manner either congruent (expressing a salient feature in a salient part of the form) or incongruent (expressing a salient feature in a less salient part of the form) with that hierarchy. Results showed that within a few rounds of learning participants could learn the compositional segments encoding the visual features and the order between them, generalize the learned knowledge to unseen instances with the same or different orders, and show learning biases for orders that are congruent with the perceptual saliency hierarchy. Although the learning performances for both the biased and unbiased orders became similar given more learning trials, our study confirms that this type of individual perceptual constraint could contribute to the structural configuration of language, and points out that such constraint, as well as other factors, could collectively affect the structural diversity in languages.

Applications of CPL mask technology for sub-65nm gate imaging

NASA Astrophysics Data System (ADS)

Litt, Lloyd C.; Conley, Will; Wu, Wei; Peters, Richie; Parker, Colita; Cobb, Jonathan; Kasprowicz, Bryan S.; van den Broeke, Doug; Park, J. C.; Karur-Shanmugam, Ramkumar

2005-05-01

The requirements for critical dimension control on gate layer for high performance products are increasingly demanding. Phase shift techniques provide aerial image enhancement, which can translate into improved process window performance and greater critical dimension (CD) control if properly applied. Unfortunately, the application of hard shifter technology to production requires significant effort in layout and optical proximity correction (OPC) application. Chromeless Phase Lithography (CPL) has several advantages over complementary phase mask (c:PSM) such as use of a single mask, and lack of phase placement 'coloring' conflicts and phase imbalance issues. CPL does have implementation issues that must be resolved before it can be used in full-scale production. CPL mask designs can be approached by separating features into three zones based on several parameters, including size relative to the lithographic resolution of the stepper lens, wavelength, and illumination conditions defined. Features are placed into buckets for different treatment zones. Zone 1 features are constructed with 100% transmission phase shifted structures and Zone 3 features are chrome (binary) structures. Features that fall into Zone 2, which are too wide to be defined using the 100% transmission of pure CPL (i.e. have negative mask error factor, MEEF) are the most troublesome and can be approached in several ways. The authors have investigated the application of zebra structures of various sizes to product type layouts. Previous work to investigate CPL using test structures set the groundwork for the more difficult task of applying CPL rules to actual random logic design layouts, which include many zone transitions. Mask making limitations have been identified that play a role in the zebra sizing that can be applied to Zone 2 features. The elimination of Zone 2 regions was also investigated in an effort to simplify the application of CPL and improve manufacturability of reticle through data enhancements.

Solution structure of Syrian hamster prion protein rPrP(90-231).

PubMed

Liu, H; Farr-Jones, S; Ulyanov, N B; Llinas, M; Marqusee, S; Groth, D; Cohen, F E; Prusiner, S B; James, T L

1999-04-27

NMR has been used to refine the structure of Syrian hamster (SHa) prion protein rPrP(90-231), which is commensurate with the infectious protease-resistant core of the scrapie prion protein PrPSc. The structure of rPrP(90-231), refolded to resemble the normal cellular isoform PrPC spectroscopically and immunologically, has been studied using multidimensional NMR; initial results were published [James et al. (1997) Proc. Natl. Acad. Sci. U.S.A. 94, 10086-10091]. We now report refinement with better definition revealing important structural and dynamic features which can be related to biological observations pertinent to prion diseases. Structure refinement was based on 2778 unambiguously assigned nuclear Overhauser effect (NOE) connectivities, 297 ambiguous NOE restraints, and 63 scalar coupling constants (3JHNHa). The structure is represented by an ensemble of 25 best-scoring structures from 100 structures calculated using ARIA/X-PLOR and further refined with restrained molecular dynamics using the AMBER 4.1 force field with an explicit shell of water molecules. The rPrP(90-231) structure features a core domain (residues 125-228), with a backbone atomic root-mean-square deviation (RMSD) of 0.67 A, consisting of three alpha-helices (residues 144-154, 172-193, and 200-227) and two short antiparallel beta-strands (residues 129-131 and 161-163). The N-terminus (residues 90-119) is largely unstructured despite some sparse and weak medium-range NOEs implying the existence of bends or turns. The transition region between the core domain and flexible N-terminus, i.e., residues 113-128, consists of hydrophobic residues or glycines and does not adopt any regular secondary structure in aqueous solution. There are about 30 medium- and long-range NOEs within this hydrophobic cluster, so it clearly manifests structure. Multiple discrete conformations are evident, implying the possible existence of one or more metastable states, which may feature in conversion of PrPC to PrPSc. To obtain a more comprehensive picture of rPrP(90-231), dynamics have been studied using amide hydrogen-deuterium exchange and 15N NMR relaxation times (T1 and T2) and 15N{1H} NOE measurements. Comparison of the structure with previous reports suggests sequence-dependent features that may be reflected in a species barrier to prion disease transmission.

A petrographic and geochemical investigation into the Gatun structure, a possible Tertiary impact structure near Gamboa, Republic de Panama

NASA Astrophysics Data System (ADS)

Tornabene, L. L.; Ryan, J. G.; Stewart, R. H.

2001-05-01

The Gatun Structure, (Latitude N 09 deg 05' 58.1", Longitude W 79 deg 47' 21.8", situated in the triple-canopy rainforest 10 km to the WSW of the Gamboa and about 2 km south of the Isle of Barbacoas, Republic de Panama), is a partially inundated, quasi-concentric surface feature 2.2km in diameter, which appears in aerial photographs and in radar imagery as an arcuate chain of islands with a raised central feature. Although deeply eroded, the structure possesses traits consistent with complex crater morphology: an elevated circular central uplift feature approximately 500-600 m in diameter and 50m high, and arcuate boundary ridges (a possible rim structure) ranging from 50-100 meters high. Within the central peak, highly altered and fractured siltstone of the Gatuncillo formation (Eocene) are uplifted and exposed through surrounding calcareous units of the Caimito formation (Oligocene), the major target rocks in the structure. The structure is crosscut by numerous dikes of unshocked basalt and basaltic andesite related to volcanism along the Panamanian segment of the Central American arc to the south. Analysis of mineral assemblages of units within the structure, and mineral compositions were measured via SEM-EDS and electron microprobe, using the JEOL SEM-Probe facility at the Center for the Study of Materials in Extreme Conditions (CeSMEC) at Florida International University. Bulk chemical and trace element analysis of whole rock samples were conducted via DC Plasma spectrometry at USF. Occurring concentrically within the structure, are limestones with anomalous spherical glass inclusions, both black and white hypocrystalline glasses (melt rocks?), lithic breccias, and melt-bearing breccias, some of which contain flow banding and evidence of selective melting. Three types of spherules (glass, fluid-drop and lithic), a pyroxene-quartz "necklace" disequilibrium structure and possibly shocked amphibole are all petrographic indicators of a possible impact event. In addition, the presence of maskelynite has been based on petrography, SEM-EDS and by RAMAN spectroscopy. RAMAN results indicated that many plagioclase grains in a blue-green clast bering breccia (suevite?) were highly disordered and amorphous. Considering the distance of the Gatun structure from the explosive volcanism of Panamanian arc (approximately 30 km away), and the presence of spherules, maskelynite and other disequilibrium shock features, an impact origin is our preferred interpretation for the Gatun structure.

Characterizing trabecular bone structure for assessing vertebral fracture risk on volumetric quantitative computed tomography

NASA Astrophysics Data System (ADS)

Nagarajan, Mahesh B.; Checefsky, Walter A.; Abidin, Anas Z.; Tsai, Halley; Wang, Xixi; Hobbs, Susan K.; Bauer, Jan S.; Baum, Thomas; Wismüller, Axel

2015-03-01

While the proximal femur is preferred for measuring bone mineral density (BMD) in fracture risk estimation, the introduction of volumetric quantitative computed tomography has revealed stronger associations between BMD and spinal fracture status. In this study, we propose to capture properties of trabecular bone structure in spinal vertebrae with advanced second-order statistical features for purposes of fracture risk assessment. For this purpose, axial multi-detector CT (MDCT) images were acquired from 28 spinal vertebrae specimens using a whole-body 256-row CT scanner with a dedicated calibration phantom. A semi-automated method was used to annotate the trabecular compartment in the central vertebral slice with a circular region of interest (ROI) to exclude cortical bone; pixels within were converted to values indicative of BMD. Six second-order statistical features derived from gray-level co-occurrence matrices (GLCM) and the mean BMD within the ROI were then extracted and used in conjunction with a generalized radial basis functions (GRBF) neural network to predict the failure load of the specimens; true failure load was measured through biomechanical testing. Prediction performance was evaluated with a root-mean-square error (RMSE) metric. The best prediction performance was observed with GLCM feature `correlation' (RMSE = 1.02 ± 0.18), which significantly outperformed all other GLCM features (p < 0.01). GLCM feature correlation also significantly outperformed MDCTmeasured mean BMD (RMSE = 1.11 ± 0.17) (p< 10-4). These results suggest that biomechanical strength prediction in spinal vertebrae can be significantly improved through characterization of trabecular bone structure with GLCM-derived texture features.

Structural features, substrate specificity, kinetic properties of insect α-amylase and specificity of plant α-amylase inhibitors.

PubMed

Kaur, Rimaljeet; Kaur, Narinder; Gupta, Anil Kumar

2014-11-01

α-Amylase is an important digestive enzyme required for the optimal growth and development of insects. Several insect α-amylases had been purified and their physical and chemical properties were characterized. Insect α-amylases of different orders display variability in structure, properties and substrate specificity. Such diverse properties of amylases could be due to different feeding habits and gut environment of insects. In this review, structural features and properties of several insect α-amylases were compared. This could be helpful in exploring the diversity in characteristics of α-amylase between the members of the same class (insecta). Properties like pH optima are reflected in enzyme structural features. In plants, α-amylase inhibitors (α-AIs) occur as part of natural defense mechanisms against pests by interfering in their digestion process and thus could also provide access to new pest management strategies. AIs are quite specific in their action; therefore, these could be employed according to their effectiveness against target amylases. Potential of transgenics with α-AIs has also been discussed for insect resistance and controlling infestation. The differences in structural features of insect α-amylases provided reasons for their efficient functioning at different pH and the specificity towards various substrates. Various proteinaceous and non-proteinaceous inhibitors discussed could be helpful in controlling pest infestation. In depth detailed studies are required on proteinaceous α-AI-α-amylase interaction at different pH's as well as the insect proteinase action on these inhibitors before selecting the α-AI for making transgenics resistant to particular insect. Copyright © 2014 Elsevier Inc. All rights reserved.

Effect of hydrogen peroxide pretreatment on the structural features and the enzymatic hydrolysis of rice straw.

PubMed

Wei, C J; Cheng, C Y

1985-10-01

Assessment was made to evaluate the effect of hydrogen peroxide pretreatment on the change of the structural features and the enzymatic hydrolysis of rice straw. Changes in the lignin content, weight loss, accessibility for Cadoxen, water holding capacity, and crystallinity of straw were measured during pretreatment to express the modification of the lignocellulosic structure of straw. The rates and the extents of enzymatic hydrolysis, cellulase adsorption, and cellobiose accumulation in the initial stage of hydrolysis were determined to study the pretreatment effect on hydrolysis. Pretreatment at 60 degrees C for 5 h in a solution with 1% (w/w) H(2)O(2) and NaOH resulted in 60% delignification, 40% weight loss, a fivefold increase in the accessibility for Cadoxen, an one times increase in the water-holding capacity, and only a slight decrease in crystallinity as compared with that of the untreated straw. Improvement on the pretreatment effect could be made by increasing the initial alkalinity and the pretreatment temperature of hydrogen peroxide solution. A saturated improvement on the structural features was found when the weight ratio of hydrogen peroxide to straw was above 0.25 g H(2)O(2)/g straw in an alkaline H(2)O(2) solution with 1% (w/w) NaOH at 32 degrees C. The initial rates and extents of hydrolysis, cellulase adsorption, and cellobiose accumulation in hydrolysis were enhanced in accordance with the improved structural features of straw pretreated. A four times increase in the extent of the enzymatic hydrolysis of straw for 24 h was attributed to the alkaline hydrogen peroxide pretreatment.

Multi-threshold white matter structural networks fusion for accurate diagnosis of Tourette syndrome children

NASA Astrophysics Data System (ADS)

Wen, Hongwei; Liu, Yue; Wang, Shengpei; Li, Zuoyong; Zhang, Jishui; Peng, Yun; He, Huiguang

2017-03-01

Tourette syndrome (TS) is a childhood-onset neurobehavioral disorder. To date, TS is still misdiagnosed due to its varied presentation and lacking of obvious clinical symptoms. Therefore, studies of objective imaging biomarkers are of great importance for early TS diagnosis. As tic generation has been linked to disturbed structural networks, and many efforts have been made recently to investigate brain functional or structural networks using machine learning methods, for the purpose of disease diagnosis. However, few studies were related to TS and some drawbacks still existed in them. Therefore, we propose a novel classification framework integrating a multi-threshold strategy and a network fusion scheme to address the preexisting drawbacks. Here we used diffusion MRI probabilistic tractography to construct the structural networks of 44 TS children and 48 healthy children. We ameliorated the similarity network fusion algorithm specially to fuse the multi-threshold structural networks. Graph theoretical analysis was then implemented, and nodal degree, nodal efficiency and nodal betweenness centrality were selected as features. Finally, support vector machine recursive feature extraction (SVM-RFE) algorithm was used for feature selection, and then optimal features are fed into SVM to automatically discriminate TS children from controls. We achieved a high accuracy of 89.13% evaluated by a nested cross validation, demonstrated the superior performance of our framework over other comparison methods. The involved discriminative regions for classification primarily located in the basal ganglia and frontal cortico-cortical networks, all highly related to the pathology of TS. Together, our study may provide potential neuroimaging biomarkers for early-stage TS diagnosis.

Guiding exploration in conformational feature space with Lipschitz underestimation for ab-initio protein structure prediction.

PubMed

Hao, Xiaohu; Zhang, Guijun; Zhou, Xiaogen

2018-04-01

Computing conformations which are essential to associate structural and functional information with gene sequences, is challenging due to the high dimensionality and rugged energy surface of the protein conformational space. Consequently, the dimension of the protein conformational space should be reduced to a proper level, and an effective exploring algorithm should be proposed. In this paper, a plug-in method for guiding exploration in conformational feature space with Lipschitz underestimation (LUE) for ab-initio protein structure prediction is proposed. The conformational space is converted into ultrafast shape recognition (USR) feature space firstly. Based on the USR feature space, the conformational space can be further converted into Underestimation space according to Lipschitz estimation theory for guiding exploration. As a consequence of the use of underestimation model, the tight lower bound estimate information can be used for exploration guidance, the invalid sampling areas can be eliminated in advance, and the number of energy function evaluations can be reduced. The proposed method provides a novel technique to solve the exploring problem of protein conformational space. LUE is applied to differential evolution (DE) algorithm, and metropolis Monte Carlo(MMC) algorithm which is available in the Rosetta; When LUE is applied to DE and MMC, it will be screened by the underestimation method prior to energy calculation and selection. Further, LUE is compared with DE and MMC by testing on 15 small-to-medium structurally diverse proteins. Test results show that near-native protein structures with higher accuracy can be obtained more rapidly and efficiently with the use of LUE. Copyright © 2018 Elsevier Ltd. All rights reserved.

Semantic data association for planar features in outdoor 6D-SLAM using lidar

NASA Astrophysics Data System (ADS)

Ulas, C.; Temeltas, H.

2013-05-01

Simultaneous Localization and Mapping (SLAM) is a fundamental problem of the autonomous systems in GPS (Global Navigation System) denied environments. The traditional probabilistic SLAM methods uses point features as landmarks and hold all the feature positions in their state vector in addition to the robot pose. The bottleneck of the point-feature based SLAM methods is the data association problem, which are mostly based on a statistical measure. The data association performance is very critical for a robust SLAM method since all the filtering strategies are applied after a known correspondence. For point-features, two different but very close landmarks in the same scene might be confused while giving the correspondence decision when their positions and error covariance matrix are solely taking into account. Instead of using the point features, planar features can be considered as an alternative landmark model in the SLAM problem to be able to provide a more consistent data association. Planes contain rich information for the solution of the data association problem and can be distinguished easily with respect to point features. In addition, planar maps are very compact since an environment has only very limited number of planar structures. The planar features does not have to be large structures like building wall or roofs; the small plane segments can also be used as landmarks like billboards, traffic posts and some part of the bridges in urban areas. In this paper, a probabilistic plane-feature extraction method from 3DLiDAR data and the data association based on the extracted semantic information of the planar features is introduced. The experimental results show that the semantic data association provides very satisfactory result in outdoor 6D-SLAM.

Graduating to Postdoc: Information-Sharing in Support of Organizational Structures and Needs

NASA Technical Reports Server (NTRS)

Keller, Richard M.; Lucas, Paul J.; Compton, Michael M.; Stewart, Helen J.; Baya, Vinod; DelAlto, Martha

1999-01-01

The deployment of information-sharing systems in large organizations can significantly impact existing policies and procedures with regard to authority and control over information. Unless information-sharing systems explicitly support organizational structures and needs, these systems will be rejected summarily. The Postdoc system is a deployed Web-based information-sharing system created specifically to address organizational needs. Postdoc contains various organizational support features including a shared, globally navigable document space, as well as specialized access control, distributed administration, and mailing list features built around the key notion of hierarchical group structures. We review successes and difficulties in supporting organizational needs with Postdoc

Manifold structure preservative for hyperspectral target detection

NASA Astrophysics Data System (ADS)

Imani, Maryam

2018-05-01

A nonparametric method termed as manifold structure preservative (MSP) is proposed in this paper for hyperspectral target detection. MSP transforms the feature space of data to maximize the separation between target and background signals. Moreover, it minimizes the reconstruction error of targets and preserves the topological structure of data in the projected feature space. MSP does not need to consider any distribution for target and background data. So, it can achieve accurate results in real scenarios due to avoiding unreliable assumptions. The proposed MSP detector is compared to several popular detectors and the experiments on a synthetic data and two real hyperspectral images indicate the superior ability of it in target detection.

The fine structure of spermatozoa of Hydrolagus colliei (Chondrichthyes, Holocephali).

PubMed

Stanley, H P

1983-05-01

The ultrastructure of spermatozoa in Hydrolagus colliei is described. Basic similarities of structure to the sperm of the related elasmobranch fish are noted. The most significant features of sperm structure in Hydrolagus that differ from those of elasmobranch fish occur in the tail. The axoneme is eccentrically located and forms a double helix with a single longitudinal column. A second longitudinal column is reduced to a short remnant at the base of the tail. Microtubules within the axoneme are also helically disposed, a feature that is consistent with the rotating motion of the sperm. Abundant glycogen reserves are stored along the length of the tail.

Specific features of the structural and magnetic states of a Zn1 - x Ni x Se crystal ( x = 0.0025) at low temperatures

NASA Astrophysics Data System (ADS)

Dubinin, S. F.; Sokolov, V. I.; Parkhomenko, V. D.; Teploukhov, S. G.; Gruzdev, N. B.

2008-12-01

The magnetic state and the structure of a Zn1 - x Ni x Se ( x = 0.0025) bulk crystal were studied at low temperatures. It is revealed that the magnetic and crystal structures below T ≅ 15 K are dependent on the cooling rate of this dilute semiconductor. For example, on fast cooling to 4.2 K, about 10% hexagonal ferromagnetic phase is formed in the crystal. During heating, the phase disappears at T ≅ 15 K. The results obtained are discussed with allowance for the specific features of the Jahn-Teller distortions in this compound.

Classification and Lateralization of Temporal Lobe Epilepsies with and without Hippocampal Atrophy Based on Whole-Brain Automatic MRI Segmentation

PubMed Central

Keihaninejad, Shiva; Heckemann, Rolf A.; Gousias, Ioannis S.; Hajnal, Joseph V.; Duncan, John S.; Aljabar, Paul; Rueckert, Daniel; Hammers, Alexander

2012-01-01

Brain images contain information suitable for automatically sorting subjects into categories such as healthy controls and patients. We sought to identify morphometric criteria for distinguishing controls (n = 28) from patients with unilateral temporal lobe epilepsy (TLE), 60 with and 20 without hippocampal atrophy (TLE-HA and TLE-N, respectively), and for determining the presumed side of seizure onset. The framework employs multi-atlas segmentation to estimate the volumes of 83 brain structures. A kernel-based separability criterion was then used to identify structures whose volumes discriminate between the groups. Next, we applied support vector machines (SVM) to the selected set for classification on the basis of volumes. We also computed pairwise similarities between all subjects and used spectral analysis to convert these into per-subject features. SVM was again applied to these feature data. After training on a subgroup, all TLE-HA patients were correctly distinguished from controls, achieving an accuracy of 96 ± 2% in both classification schemes. For TLE-N patients, the accuracy was 86 ± 2% based on structural volumes and 91 ± 3% using spectral analysis. Structures discriminating between patients and controls were mainly localized ipsilaterally to the presumed seizure focus. For the TLE-HA group, they were mainly in the temporal lobe; for the TLE-N group they included orbitofrontal regions, as well as the ipsilateral substantia nigra. Correct lateralization of the presumed seizure onset zone was achieved using hippocampi and parahippocampal gyri in all TLE-HA patients using either classification scheme; in the TLE-N patients, lateralization was accurate based on structural volumes in 86 ± 4%, and in 94 ± 4% with the spectral analysis approach. Unilateral TLE has imaging features that can be identified automatically, even when they are invisible to human experts. Such morphometric image features may serve as classification and lateralization criteria. The technique also detects unsuspected distinguishing features like the substantia nigra, warranting further study. PMID:22523539

Structural Element Testing in Support of the Design of the NASA Composite Crew Module

NASA Technical Reports Server (NTRS)

Kellas, Sotiris; Jackson, Wade C.; Thesken, John C.; Schleicher, Eric; Wagner, Perry; Kirsch, Michael T.

2012-01-01

In January 2007, the NASA Administrator and Associate Administrator for the Exploration Systems Mission Directorate chartered the NASA Engineering and Safety Center (NESC) to design, build, and test a full-scale Composite Crew Module (CCM). For the design and manufacturing of the CCM, the team adopted the building block approach where design and manufacturing risks were mitigated through manufacturing trials and structural testing at various levels of complexity. Following NASA's Structural Design Verification Requirements, a further objective was the verification of design analysis methods and the provision of design data for critical structural features. Test articles increasing in complexity from basic material characterization coupons through structural feature elements and large structural components, to full-scale structures were evaluated. This paper discusses only four elements tests three of which include joints and one that includes a tapering honeycomb core detail. For each test series included are specimen details, instrumentation, test results, a brief analysis description, test analysis correlation and conclusions.

Similar Ring Structures on Mars and Tibetan Plateau confirm recent tectonism on Martian Northern polar region

NASA Astrophysics Data System (ADS)

Anglés, A.; Li, Y. L.

2017-10-01

The polar regions of Mars feature layered deposits, some of which exist as enclosed zoning structures. These deposits raised strong interest since their discovery and still remain one of the most controversial features on Mars. Zoning structures that are enclosed only appear in the Northern polar region, where the disappearance of water bodies may have left behind huge deposits of evaporate salts. The origin of the layered deposits has been widely debated. Here we propose that the enclosed nature of the zoning structures indicates the result of recent tectonism. We compared similar structures at an analogue site located in the western Qaidam Basin of Tibetan Plateau, a unique tectonic setting with abundant saline deposits. The enclosed structures, which we term Ring Structures, in both the analogue site and in the Northern polar region of Mars, were formed by uplift induced pressurization and buoyancy of salts as the result of recent tectonic activity.

Elemental and Microscopic Analysis of Naturally Occurring C-O-Si Hetero-Fullerene-Like Structures.

PubMed

Hullavarad, Nilima V; Hullavarad, Shiva S; Fochesatto, Javier

2015-03-01

Carbon exhibits an ability to form a wide range of structures in nature. Under favorable conditions, carbon condenses to form hollow, spheroid fullerenes in an inert atmosphere. Using high resolution FESEM, we have concealed the existence of giant hetero-fullerene like structures in the natural form. Clear, distinct features of connected hexagons and pentagons were observed. Energy dispersive X-ray analysis depth-profile of natural fullerene structures indicates that Russian-doll-like configurations composed of C, 0, and Si rings exist in nature. The analysis is based on an outstanding molecular feature found in the size fraction of aerosols having diameters 150 nm to 1.0 µm. The fullerene like structures, which are ~ 150 nm in diameter, are observed in large numbers. To the best of our knowledge, this is the first direct detailed observation of natural fullerene-like structures. This article reports inadvertent observation of naturally occurring hetero-fullerene-like structures in the Arctic.

The post-rigor structure of myosin VI and implications for the recovery stroke

PubMed Central

Ménétrey, Julie; Llinas, Paola; Cicolari, Jérome; Squires, Gaëlle; Liu, Xiaoyan; Li, Anna; Sweeney, H Lee; Houdusse, Anne

2008-01-01

Myosin VI has an unexpectedly large swing of its lever arm (powerstroke) that optimizes its unique reverse direction movement. The basis for this is an unprecedented rearrangement of the subdomain to which the lever arm is attached, referred to as the converter. It is unclear at what point(s) in the myosin VI ATPase cycle rearrangements in the converter occur, and how this would effect lever arm position. We solved the structure of myosin VI with an ATP analogue (ADP.BeF3) bound in its nucleotide-binding pocket. The structure reveals that no rearrangement in the converter occur upon ATP binding. Based on previously solved myosin structures, our structure suggests that no reversal of the powerstroke occurs during detachment of myosin VI from actin. The structure also reveals novel features of the myosin VI motor that may be important in maintaining the converter conformation during detachment from actin, and other features that may promote rapid rearrangements in the structure following actin detachment that enable hydrolysis of ATP. PMID:18046460

Mineralogy of Cretaceous/Tertiary boundary clays in the Chicxulub structure in northern Yucatan

NASA Technical Reports Server (NTRS)

Ming, D. W.; Sharpton, Virgil L.; Schuraytz, B. C.

1991-01-01

The Cretaceous/Tertiary (K/T) boundary clay layer is thought to be derived from ejecta material from meteorite impact, based on the anomalous concentrations of noble metals in the layer. Because of recent findings of a half-meter thick ejecta deposit at the K/T boundary in Haiti, efforts have focused on locating a large impact feature in the Caribbean and the Gulf of Mexico. One of the leading candidates for the site of a large impact is the Chicxulub structure located on the northern Yucatan Peninsula in Mexico. The Chicxulub structure is a subsurface zone of upper Cretaceous igneous rocks, carbonates, and breccias. The structure has been interpreted to be a 200 km diameter; however, there is some question to the size of the structure or to the fact that it even is an impact feature. Little is known about the mineralogy of this structure; the objective of this study was to determine the clay mineralogy of core samples from within the Chicxulub structure.

Prospects and limitations of full-text index structures in genome analysis

PubMed Central

Vyverman, Michaël; De Baets, Bernard; Fack, Veerle; Dawyndt, Peter

2012-01-01

The combination of incessant advances in sequencing technology producing large amounts of data and innovative bioinformatics approaches, designed to cope with this data flood, has led to new interesting results in the life sciences. Given the magnitude of sequence data to be processed, many bioinformatics tools rely on efficient solutions to a variety of complex string problems. These solutions include fast heuristic algorithms and advanced data structures, generally referred to as index structures. Although the importance of index structures is generally known to the bioinformatics community, the design and potency of these data structures, as well as their properties and limitations, are less understood. Moreover, the last decade has seen a boom in the number of variant index structures featuring complex and diverse memory-time trade-offs. This article brings a comprehensive state-of-the-art overview of the most popular index structures and their recently developed variants. Their features, interrelationships, the trade-offs they impose, but also their practical limitations, are explained and compared. PMID:22584621

Electron Microscopy and Analytical X-ray Characterization of Compositional and Nanoscale Structural Changes in Fossil Bone

NASA Astrophysics Data System (ADS)

Boatman, Elizabeth Marie

The nanoscale structure of compact bone contains several features that are direct indicators of bulk tissue mechanical properties. Fossil bone tissues represent unique opportunities to understand the compact bone structure/property relationships from a deep time perspective, offering a possible array of new insights into bone diseases, biomimicry of composite materials, and basic knowledge of bioapatite composition and nanoscale bone structure. To date, most work with fossil bone has employed microscale techniques and has counter-indicated the survival of bioapatite and other nanoscale structural features. The obvious disconnect between the use of microscale techniques and the discernment of nanoscale structure has prompted this work. The goal of this study was to characterize the nanoscale constituents of fossil compact bone by applying a suite of diffraction, microscopy, and spectrometry techniques, representing the highest levels of spatial and energy resolution available today, and capable of complementary structural and compositional characterization from the micro- to the nanoscale. Fossil dinosaur and crocodile long bone specimens, as well as modern ratite and crocodile femurs, were acquired from the UC Museum of Paleontology. Preserved physiological features of significance were documented with scanning electron microscopy back-scattered imaging. Electron microprobe wavelength-dispersive X-ray spectroscopy (WDS) revealed fossil bone compositions enriched in fluorine with a complementary loss of oxygen. X-ray diffraction analyses demonstrated that all specimens were composed of apatite. Transmission electron microscopy (TEM) imaging revealed preserved nanocrystallinity in the fossil bones and electron diffraction studies further identified these nanocrystallites as apatite. Tomographic analyses of nanoscale elements imaged by TEM and small angle X-ray scattering were performed, with the results of each analysis further indicating that nanoscale structure is highly conserved in these four fossil specimens. Finally, the results of this study indicate that bioapatite can be preserved in even the most ancient vertebrate specimens, further supporting the idea that fossilization is a preservational process. This work also underlines the importance of using appropriately selected characterization and analytical techniques for the study of fossil bone, especially from the perspective of spatial resolution and the scale of the bone structural features in question.

Blurred Palmprint Recognition Based on Stable-Feature Extraction Using a Vese–Osher Decomposition Model

PubMed Central

Hong, Danfeng; Su, Jian; Hong, Qinggen; Pan, Zhenkuan; Wang, Guodong

2014-01-01

As palmprints are captured using non-contact devices, image blur is inevitably generated because of the defocused status. This degrades the recognition performance of the system. To solve this problem, we propose a stable-feature extraction method based on a Vese–Osher (VO) decomposition model to recognize blurred palmprints effectively. A Gaussian defocus degradation model is first established to simulate image blur. With different degrees of blurring, stable features are found to exist in the image which can be investigated by analyzing the blur theoretically. Then, a VO decomposition model is used to obtain structure and texture layers of the blurred palmprint images. The structure layer is stable for different degrees of blurring (this is a theoretical conclusion that needs to be further proved via experiment). Next, an algorithm based on weighted robustness histogram of oriented gradients (WRHOG) is designed to extract the stable features from the structure layer of the blurred palmprint image. Finally, a normalized correlation coefficient is introduced to measure the similarity in the palmprint features. We also designed and performed a series of experiments to show the benefits of the proposed method. The experimental results are used to demonstrate the theoretical conclusion that the structure layer is stable for different blurring scales. The WRHOG method also proves to be an advanced and robust method of distinguishing blurred palmprints. The recognition results obtained using the proposed method and data from two palmprint databases (PolyU and Blurred–PolyU) are stable and superior in comparison to previous high-performance methods (the equal error rate is only 0.132%). In addition, the authentication time is less than 1.3 s, which is fast enough to meet real-time demands. Therefore, the proposed method is a feasible way of implementing blurred palmprint recognition. PMID:24992328

Blurred palmprint recognition based on stable-feature extraction using a Vese-Osher decomposition model.

PubMed

Hong, Danfeng; Su, Jian; Hong, Qinggen; Pan, Zhenkuan; Wang, Guodong

2014-01-01

As palmprints are captured using non-contact devices, image blur is inevitably generated because of the defocused status. This degrades the recognition performance of the system. To solve this problem, we propose a stable-feature extraction method based on a Vese-Osher (VO) decomposition model to recognize blurred palmprints effectively. A Gaussian defocus degradation model is first established to simulate image blur. With different degrees of blurring, stable features are found to exist in the image which can be investigated by analyzing the blur theoretically. Then, a VO decomposition model is used to obtain structure and texture layers of the blurred palmprint images. The structure layer is stable for different degrees of blurring (this is a theoretical conclusion that needs to be further proved via experiment). Next, an algorithm based on weighted robustness histogram of oriented gradients (WRHOG) is designed to extract the stable features from the structure layer of the blurred palmprint image. Finally, a normalized correlation coefficient is introduced to measure the similarity in the palmprint features. We also designed and performed a series of experiments to show the benefits of the proposed method. The experimental results are used to demonstrate the theoretical conclusion that the structure layer is stable for different blurring scales. The WRHOG method also proves to be an advanced and robust method of distinguishing blurred palmprints. The recognition results obtained using the proposed method and data from two palmprint databases (PolyU and Blurred-PolyU) are stable and superior in comparison to previous high-performance methods (the equal error rate is only 0.132%). In addition, the authentication time is less than 1.3 s, which is fast enough to meet real-time demands. Therefore, the proposed method is a feasible way of implementing blurred palmprint recognition.

Feature and Statistical Model Development in Structural Health Monitoring

NASA Astrophysics Data System (ADS)

Kim, Inho

All structures suffer wear and tear because of impact, excessive load, fatigue, corrosion, etc. in addition to inherent defects during their manufacturing processes and their exposure to various environmental effects. These structural degradations are often imperceptible, but they can severely affect the structural performance of a component, thereby severely decreasing its service life. Although previous studies of Structural Health Monitoring (SHM) have revealed extensive prior knowledge on the parts of SHM processes, such as the operational evaluation, data processing, and feature extraction, few studies have been conducted from a systematical perspective, the statistical model development. The first part of this dissertation, the characteristics of inverse scattering problems, such as ill-posedness and nonlinearity, reviews ultrasonic guided wave-based structural health monitoring problems. The distinctive features and the selection of the domain analysis are investigated by analytically searching the conditions of the uniqueness solutions for ill-posedness and are validated experimentally. Based on the distinctive features, a novel wave packet tracing (WPT) method for damage localization and size quantification is presented. This method involves creating time-space representations of the guided Lamb waves (GLWs), collected at a series of locations, with a spatially dense distribution along paths at pre-selected angles with respect to the direction, normal to the direction of wave propagation. The fringe patterns due to wave dispersion, which depends on the phase velocity, are selected as the primary features that carry information, regarding the wave propagation and scattering. The following part of this dissertation presents a novel damage-localization framework, using a fully automated process. In order to construct the statistical model for autonomous damage localization deep-learning techniques, such as restricted Boltzmann machine and deep belief network, are trained and utilized to interpret nonlinear far-field wave patterns. Next, a novel bridge scour estimation approach that comprises advantages of both empirical and data-driven models is developed. Two field datasets from the literature are used, and a Support Vector Machine (SVM), a machine-learning algorithm, is used to fuse the field data samples and classify the data with physical phenomena. The Fast Non-dominated Sorting Genetic Algorithm (NSGA-II) is evaluated on the model performance objective functions to search for Pareto optimal fronts.

A Target-Less Vision-Based Displacement Sensor Based on Image Convex Hull Optimization for Measuring the Dynamic Response of Building Structures.

PubMed

Choi, Insub; Kim, JunHee; Kim, Donghyun

2016-12-08

Existing vision-based displacement sensors (VDSs) extract displacement data through changes in the movement of a target that is identified within the image using natural or artificial structure markers. A target-less vision-based displacement sensor (hereafter called "TVDS") is proposed. It can extract displacement data without targets, which then serve as feature points in the image of the structure. The TVDS can extract and track the feature points without the target in the image through image convex hull optimization, which is done to adjust the threshold values and to optimize them so that they can have the same convex hull in every image frame and so that the center of the convex hull is the feature point. In addition, the pixel coordinates of the feature point can be converted to physical coordinates through a scaling factor map calculated based on the distance, angle, and focal length between the camera and target. The accuracy of the proposed scaling factor map was verified through an experiment in which the diameter of a circular marker was estimated. A white-noise excitation test was conducted, and the reliability of the displacement data obtained from the TVDS was analyzed by comparing the displacement data of the structure measured with a laser displacement sensor (LDS). The dynamic characteristics of the structure, such as the mode shape and natural frequency, were extracted using the obtained displacement data, and were compared with the numerical analysis results. TVDS yielded highly reliable displacement data and highly accurate dynamic characteristics, such as the natural frequency and mode shape of the structure. As the proposed TVDS can easily extract the displacement data even without artificial or natural markers, it has the advantage of extracting displacement data from any portion of the structure in the image.

New features in Saturn's atmosphere revealed by high-resolution thermal infrared images

NASA Technical Reports Server (NTRS)

Gezari, D. Y.; Mumma, M. J.; Espenak, F.; Deming, D.; Bjoraker, G.; Woods, L.; Folz, W.

1989-01-01

Observations of the stratospheric IR emission structure on Saturn are presented. The high-spatial-resolution global images show a variety of new features, including a narrow equatorial belt of enhanced emission at 7.8 micron, a prominent symmetrical north polar hotspot at all three wavelengths, and a midlatitude structure which is asymmetrically brightened at the east limb. The results confirm the polar brightening and reversal in position predicted by recent models for seasonal thermal variations of Saturn's stratosphere.

“Addition” and “Subtraction”: Selectivity Design for Type II Maternal Embryonic Leucine Zipper Kinase Inhibitors

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

Chen, Xin; Giraldes, John; Sprague, Elizabeth R.

2017-02-17

While adding the structural features that are more favored by on-target activity is the more common strategy in selectivity optimization, the opposite strategy of subtracting the structural features that contribute more to off-target activity can also be very effective. Reported here is our successful effort of improving the kinase selectivity of type II maternal embryonic leucine zipper kinase inhibitors by applying these two complementary approaches together, which clearly demonstrates the powerful synergy between them.

Extracting and identifying concrete structural defects in GPR images

NASA Astrophysics Data System (ADS)

Ye, Qiling; Jiao, Liangbao; Liu, Chuanxin; Cao, Xuehong; Huston, Dryver; Xia, Tian

2018-03-01

Traditionally most GPR data interpretations are performed manually. With the advancement of computing technologies, how to automate GPR data interpretation to achieve high efficiency and accuracy has become an active research subject. In this paper, analytical characterizations of major defects in concrete structures, including delamination, air void and moisture in GPR images, are performed. In the study, the image features of different defects are compared. Algorithms are developed for defect feature extraction and identification. For validations, both simulation results and field test data are utilized.

Atomic Structure

NASA Astrophysics Data System (ADS)

Whelan, Colm T.

2018-04-01

A knowledge of atomic theory should be an essential part of every physicist's and chemist's toolkit. This book provides an introduction to the basic ideas that govern our understanding of microscopic matter, and the essential features of atomic structure and spectra are presented in a direct and easily accessible manner. Semi-classical ideas are reviewed and an introduction to the quantum mechanics of one and two electron systems and their interaction with external electromagnetic fields is featured. Multielectron atoms are also introduced, and the key methods for calculating their properties reviewed.

Charge-Dependent Atomic-Scale Structures of High-Index and (110) Gold Electrode Surfaces as Revealed by Scanning Tunneling Microscopy

DTIC Science & Technology

1994-02-01

known gold atomic diameter of 2.89 A. Within a given domain, featuring adjacent terrace strings separated by monoatomic steps, the measured unit-cell...to utilize high-index gold faces in exploring the influence of monoatomic steps and related structural features on surface electrochemical phenomena...110) Gold Electrode Surfaces D1 T IC as Revealed by Scanning Tunneling Microscopy FLECTE MAR 10 19941 by E Xiaoping Gao, Gregory J. Edens, Antoinette

Determination of Structural Parameters from EXAFS (Extended X-Ray Absorption Fine Structure): Application to Solutions and Catalysts.

DTIC Science & Technology

1984-05-23

the disorder was accurately known. Inverse Transform To isolate the EAFS contribution due to a single feature in the Fourier transform, the inverse ...is associated with setting the "fold" components to 27 zero in r-space. An inverse transform (real part) of the major feature of the Fig. 4 Fourier...phase of the resulting inverse transform represents only any differences between the material being studied and the reference. This residual is

Proportional plus integral MIMO controller for regulation and tracking with anti-wind-up features

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

Puleston, P.F.; Mantz, R.J.

1993-11-01

A proportional plus integral matrix control structure for MIMO systems is proposed. Based on a standard optimal control structure with integral action, it permits a greater degree of independence of the design and tuning of the regulating and tracking features, without considerably increasing the controller complexity. Fast recovery from load disturbances is achieved, while large overshoots associated with set-point changes and reset wind-up problems can be reduced. A simple effective procedure for practical tuning is introduced.

New data concerning the age and specific features of magmatism of timanides in the southern part of the Lyapin structure (Northern Urals)

NASA Astrophysics Data System (ADS)

Petrov, G. A.; Ronkin, Yu. L.; Gerdes, A.; Maslov, A. V.

2017-10-01

New data on composition and age of Precambrian granites and volcanic rocks in the southern part of the Lyapin structure (Northern Urals) are considered. The geochemical features of the igneous rocks are similar to those of the rocks formed in both divergent and convergent environments. In the Late Precambrian (583-553 Ma), the investigated area is assumed to have been a part of the active margin above the mantle plume.

GATOR: Requirements capturing of telephony features

NASA Technical Reports Server (NTRS)

Dankel, Douglas D., II; Walker, Wayne; Schmalz, Mark

1992-01-01

We are developing a natural language-based, requirements gathering system called GATOR (for the GATherer Of Requirements). GATOR assists in the development of more accurate and complete specifications of new telephony features. GATOR interacts with a feature designer who describes a new feature, set of features, or capability to be implemented. The system aids this individual in the specification process by asking for clarifications when potential ambiguities are present, by identifying potential conflicts with other existing features, and by presenting its understanding of the feature to the designer. Through user interaction with a model of the existing telephony feature set, GATOR constructs a formal representation of the new, 'to be implemented' feature. Ultimately GATOR will produce a requirements document and will maintain an internal representation of this feature to aid in future design and specification. This paper consists of three sections that describe (1) the structure of GATOR, (2) POND, GATOR's internal knowledge representation language, and (3) current research issues.

EARLY-TYPE GALAXIES WITH TIDAL DEBRIS AND THEIR SCALING RELATIONS IN THE SPITZER SURVEY OF STELLAR STRUCTURE IN GALAXIES (S{sup 4}G)

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

Kim, Taehyun; Sheth, Kartik; Munoz-Mateos, Juan-Carlos

2012-07-01

Tidal debris around galaxies can yield important clues on their evolution. We have identified tidal debris in 11 early-type galaxies (T {<=} 0) from a sample of 65 early types drawn from the Spitzer Survey of Stellar Structure in Galaxies (S{sup 4}G). The tidal debris includes features such as shells, ripples, and tidal tails. A variety of techniques, including two-dimensional decomposition of galactic structures, were used to quantify the residual tidal features. The tidal debris contributes {approx}3%-10% to the total 3.6 {mu}m luminosity of the host galaxy. Structural parameters of the galaxies were estimated using two-dimensional profile fitting. We investigatemore » the locations of galaxies with tidal debris in the fundamental plane and Kormendy relation. We find that galaxies with tidal debris lie within the scatter of early-type galaxies without tidal features. Assuming that the tidal debris is indicative of recent gravitational interaction or merger, this suggests that these galaxies have either undergone minor merging events so that the overall structural properties of the galaxies are not significantly altered, or they have undergone a major merging events but already have experienced sufficient relaxation and phase mixing so that their structural properties become similar to those of the non-interacting early-type galaxies.« less

Distribution of genotype network sizes in sequence-to-structure genotype-phenotype maps.

PubMed

Manrubia, Susanna; Cuesta, José A

2017-04-01

An essential quantity to ensure evolvability of populations is the navigability of the genotype space. Navigability, understood as the ease with which alternative phenotypes are reached, relies on the existence of sufficiently large and mutually attainable genotype networks. The size of genotype networks (e.g. the number of RNA sequences folding into a particular secondary structure or the number of DNA sequences coding for the same protein structure) is astronomically large in all functional molecules investigated: an exhaustive experimental or computational study of all RNA folds or all protein structures becomes impossible even for moderately long sequences. Here, we analytically derive the distribution of genotype network sizes for a hierarchy of models which successively incorporate features of increasingly realistic sequence-to-structure genotype-phenotype maps. The main feature of these models relies on the characterization of each phenotype through a prototypical sequence whose sites admit a variable fraction of letters of the alphabet. Our models interpolate between two limit distributions: a power-law distribution, when the ordering of sites in the prototypical sequence is strongly constrained, and a lognormal distribution, as suggested for RNA, when different orderings of the same set of sites yield different phenotypes. Our main result is the qualitative and quantitative identification of those features of sequence-to-structure maps that lead to different distributions of genotype network sizes. © 2017 The Author(s).

Blue gum gaming machine: an evaluation of responsible gambling features.

PubMed

Blaszczynski, Alexander; Gainsbury, Sally; Karlov, Lisa

2014-09-01

Structural characteristics of gaming machines contribute to persistence in play and excessive losses. The purpose of this study was to evaluate the effectiveness of five proposed responsible gaming features: responsible gaming messages; a bank meter quarantining winnings until termination of play; alarm clock facilitating setting time-reminders; demo mode allowing play without money; and a charity donation feature where residual amounts can be donated rather than played to zero credits. A series of ten modified gaming machines were located in five Australian gambling venues. The sample comprised 300 patrons attending the venue and who played the gaming machines. Participants completed a structured interview eliciting gambling and socio-demographic data and information on their perceptions and experience of play on the index machines. Results showed that one-quarter of participants considered that these features would contribute to preventing recreational gamblers from developing problems. Just under half of the participants rated these effects to be at least moderate or significant. The promising results suggest that further refinements to several of these features could represent a modest but effective approach to minimising excessive gambling on gaming machines.

Using Gaussian windows to explore a multivariate data set

NASA Technical Reports Server (NTRS)

Jaeckel, Louis A.

1991-01-01

In an earlier paper, I recounted an exploratory analysis, using Gaussian windows, of a data set derived from the Infrared Astronomical Satellite. Here, my goals are to develop strategies for finding structural features in a data set in a many-dimensional space, and to find ways to describe the shape of such a data set. After a brief review of Gaussian windows, I describe the current implementation of the method. I give some ways of describing features that we might find in the data, such as clusters and saddle points, and also extended structures such as a 'bar', which is an essentially one-dimensional concentration of data points. I then define a distance function, which I use to determine which data points are 'associated' with a feature. Data points not associated with any feature are called 'outliers'. I then explore the data set, giving the strategies that I used and quantitative descriptions of the features that I found, including clusters, bars, and a saddle point. I tried to use strategies and procedures that could, in principle, be used in any number of dimensions.

Discrimination of artificial categories structured by family resemblances: a comparative study in people (Homo sapiens) and pigeons (Columba livia).

PubMed

Makino, Hiroshi; Jitsumori, Masako

2007-02-01

Adult humans (Homo sapiens) and pigeons (Columba livia) were trained to discriminate artificial categories that the authors created by mimicking 2 properties of natural categories. One was a family resemblance relationship: The highly variable exemplars, including those that did not have features in common, were structured by a similarity network with the features correlating to one another in each category. The other was a polymorphous rule: No single feature was essential for distinguishing the categories, and all the features overlapped between the categories. Pigeons learned the categories with ease and then showed a prototype effect in accord with the degrees of family resemblance for novel stimuli. Some evidence was also observed for interactive effects of learning of individual exemplars and feature frequencies. Humans had difficulty in learning the categories. The participants who learned the categories generally responded to novel stimuli in an all-or-none fashion on the basis of their acquired classification decision rules. The processes that underlie the classification performances of the 2 species are discussed.

Exploiting Acoustic and Syntactic Features for Automatic Prosody Labeling in a Maximum Entropy Framework

PubMed Central

Sridhar, Vivek Kumar Rangarajan; Bangalore, Srinivas; Narayanan, Shrikanth S.

2009-01-01

In this paper, we describe a maximum entropy-based automatic prosody labeling framework that exploits both language and speech information. We apply the proposed framework to both prominence and phrase structure detection within the Tones and Break Indices (ToBI) annotation scheme. Our framework utilizes novel syntactic features in the form of supertags and a quantized acoustic–prosodic feature representation that is similar to linear parameterizations of the prosodic contour. The proposed model is trained discriminatively and is robust in the selection of appropriate features for the task of prosody detection. The proposed maximum entropy acoustic–syntactic model achieves pitch accent and boundary tone detection accuracies of 86.0% and 93.1% on the Boston University Radio News corpus, and, 79.8% and 90.3% on the Boston Directions corpus. The phrase structure detection through prosodic break index labeling provides accuracies of 84% and 87% on the two corpora, respectively. The reported results are significantly better than previously reported results and demonstrate the strength of maximum entropy model in jointly modeling simple lexical, syntactic, and acoustic features for automatic prosody labeling. PMID:19603083

Flight State Identification of a Self-Sensing Wing via an Improved Feature Selection Method and Machine Learning Approaches

PubMed Central

Chen, Xi; Wu, Qi; Ren, He; Chang, Fu-Kuo

2018-01-01

In this work, a data-driven approach for identifying the flight state of a self-sensing wing structure with an embedded multi-functional sensing network is proposed. The flight state is characterized by the structural vibration signals recorded from a series of wind tunnel experiments under varying angles of attack and airspeeds. A large feature pool is created by extracting potential features from the signals covering the time domain, the frequency domain as well as the information domain. Special emphasis is given to feature selection in which a novel filter method is developed based on the combination of a modified distance evaluation algorithm and a variance inflation factor. Machine learning algorithms are then employed to establish the mapping relationship from the feature space to the practical state space. Results from two case studies demonstrate the high identification accuracy and the effectiveness of the model complexity reduction via the proposed method, thus providing new perspectives of self-awareness towards the next generation of intelligent air vehicles. PMID:29710832

3D High Resolution Mesh Deformation Based on Multi Library Wavelet Neural Network Architecture

NASA Astrophysics Data System (ADS)

Dhibi, Naziha; Elkefi, Akram; Bellil, Wajdi; Amar, Chokri Ben

2016-12-01

This paper deals with the features of a novel technique for large Laplacian boundary deformations using estimated rotations. The proposed method is based on a Multi Library Wavelet Neural Network structure founded on several mother wavelet families (MLWNN). The objective is to align features of mesh and minimize distortion with a fixed feature that minimizes the sum of the distances between all corresponding vertices. New mesh deformation method worked in the domain of Region of Interest (ROI). Our approach computes deformed ROI, updates and optimizes it to align features of mesh based on MLWNN and spherical parameterization configuration. This structure has the advantage of constructing the network by several mother wavelets to solve high dimensions problem using the best wavelet mother that models the signal better. The simulation test achieved the robustness and speed considerations when developing deformation methodologies. The Mean-Square Error and the ratio of deformation are low compared to other works from the state of the art. Our approach minimizes distortions with fixed features to have a well reconstructed object.

Mirjana Dimitrievska | NREL

Science.gov Websites

understanding the structure-dependent vibrational properties and reorientational behavior of different alkali Sad, Serbia Featured Publications M. Dimitrievska et al., "Structure-dependent vibrational : Structure and luminescence," J. Phys. Chem. C 120(33), 18887-18894 (2016). DOI: http://dx.doi.org

Structural hierarchy of autism spectrum disorder symptoms: an integrative framework.

PubMed

Kim, Hyunsik; Keifer, Cara M; Rodriguez-Seijas, Craig; Eaton, Nicholas R; Lerner, Matthew D; Gadow, Kenneth D

2018-01-01

In an attempt to resolve questions regarding the symptom classification of autism spectrum disorder (ASD), previous research generally aimed to demonstrate superiority of one model over another. Rather than adjudicating which model may be optimal, we propose an alternative approach that integrates competing models using Goldberg's bass-ackwards method, providing a comprehensive understanding of the underlying symptom structure of ASD. The study sample comprised 3,825 individuals, consecutive referrals to a university hospital developmental disabilities specialty clinic or a child psychiatry outpatient clinic. This study analyzed DSM-IV-referenced ASD symptom statements from parent and teacher versions of the Child and Adolescent Symptom Inventory-4R. A series of exploratory structural equation models was conducted in order to produce interpretable latent factors that account for multivariate covariance. Results indicated that ASD symptoms were structured into an interpretable hierarchy across multiple informants. This hierarchy includes five levels; key features of ASD bifurcate into different constructs with increasing specificity. This is the first study to examine an underlying structural hierarchy of ASD symptomatology using the bass-ackwards method. This hierarchy demonstrates how core features of ASD relate at differing levels of resolution, providing a model for conceptualizing ASD heterogeneity and a structure for integrating divergent theories of cognitive processes and behavioral features that define the disorder. These findings suggest that a more coherent and complete understanding of the structure of ASD symptoms may be reflected in a metastructure rather than at one level of resolution. © 2017 Association for Child and Adolescent Mental Health.

One-step synthesis and structural features of CdS/montmorillonite nanocomposites.

PubMed

Han, Zhaohui; Zhu, Huaiyong; Bulcock, Shaun R; Ringer, Simon P

2005-02-24

A novel synthesis method was introduced for the nanocomposites of cadmium sulfide and montmorillonite. This method features the combination of an ion exchange process and an in situ hydrothermal decomposition process of a complex precursor, which is simple in contrast to the conventional synthesis methods that comprise two separate steps for similar nanocomposite materials. Cadmium sulfide species in the composites exist in the forms of pillars and nanoparticles, the crystallized sulfide particles are in the hexagonal phase, and the sizes change when the amount of the complex for the synthesis is varied. Structural features of the nanocomposites are similar to those of the clay host but changed because of the introduction of the sulfide into the clay.

Background feature descriptor for offline handwritten numeral recognition

NASA Astrophysics Data System (ADS)

Ming, Delie; Wang, Hao; Tian, Tian; Jie, Feiran; Lei, Bo

2011-11-01

This paper puts forward an offline handwritten numeral recognition method based on background structural descriptor (sixteen-value numerical background expression). Through encoding the background pixels in the image according to a certain rule, 16 different eigenvalues were generated, which reflected the background condition of every digit, then reflected the structural features of the digits. Through pattern language description of images by these features, automatic segmentation of overlapping digits and numeral recognition can be realized. This method is characterized by great deformation resistant ability, high recognition speed and easy realization. Finally, the experimental results and conclusions are presented. The experimental results of recognizing datasets from various practical application fields reflect that with this method, a good recognition effect can be achieved.

Common structural features of cholesterol binding sites in crystallized soluble proteins

PubMed Central

Bukiya, Anna N.; Dopico, Alejandro M.

2017-01-01

Cholesterol-protein interactions are essential for the architectural organization of cell membranes and for lipid metabolism. While cholesterol-sensing motifs in transmembrane proteins have been identified, little is known about cholesterol recognition by soluble proteins. We reviewed the structural characteristics of binding sites for cholesterol and cholesterol sulfate from crystallographic structures available in the Protein Data Bank. This analysis unveiled key features of cholesterol-binding sites that are present in either all or the majority of sites: i) the cholesterol molecule is generally positioned between protein domains that have an organized secondary structure; ii) the cholesterol hydroxyl/sulfo group is often partnered by Asn, Gln, and/or Tyr, while the hydrophobic part of cholesterol interacts with Leu, Ile, Val, and/or Phe; iii) cholesterol hydrogen-bonding partners are often found on α-helices, while amino acids that interact with cholesterol’s hydrophobic core have a slight preference for β-strands and secondary structure-lacking protein areas; iv) the steroid’s C21 and C26 constitute the “hot spots” most often seen for steroid-protein hydrophobic interactions; v) common “cold spots” are C8–C10, C13, and C17, at which contacts with the proteins were not detected. Several common features we identified for soluble protein-steroid interaction appear evolutionarily conserved. PMID:28420706

Improving the Performance of the Structure-Based Connectionist Network for Diagnosis of Helicopter Gearboxes

NASA Technical Reports Server (NTRS)

Jammu, Vinay B.; Danai, Koroush; Lewicki, David G.

1996-01-01

A diagnostic method is introduced for helicopter gearboxes that uses knowledge of the gear-box structure and characteristics of the 'features' of vibration to define the influences of faults on features. The 'structural influences' in this method are defined based on the root mean square value of vibration obtained from a simplified lumped-mass model of the gearbox. The structural influences are then converted to fuzzy variables, to account for the approximate nature of the lumped-mass model, and used as the weights of a connectionist network. Diagnosis in this Structure-Based Connectionist Network (SBCN) is performed by propagating the abnormal vibration features through the weights of SBCN to obtain fault possibility values for each component in the gearbox. Upon occurrence of misdiagnoses, the SBCN also has the ability to improve its diagnostic performance. For this, a supervised training method is presented which adapts the weights of SBCN to minimize the number of misdiagnoses. For experimental evaluation of the SBCN, vibration data from a OH-58A helicopter gearbox collected at NASA Lewis Research Center is used. Diagnostic results indicate that the SBCN is able to diagnose about 80% of the faults without training, and is able to improve its performance to nearly 100% after training.

Feature engineering for MEDLINE citation categorization with MeSH.

PubMed

Jimeno Yepes, Antonio Jose; Plaza, Laura; Carrillo-de-Albornoz, Jorge; Mork, James G; Aronson, Alan R

2015-04-08

Research in biomedical text categorization has mostly used the bag-of-words representation. Other more sophisticated representations of text based on syntactic, semantic and argumentative properties have been less studied. In this paper, we evaluate the impact of different text representations of biomedical texts as features for reproducing the MeSH annotations of some of the most frequent MeSH headings. In addition to unigrams and bigrams, these features include noun phrases, citation meta-data, citation structure, and semantic annotation of the citations. Traditional features like unigrams and bigrams exhibit strong performance compared to other feature sets. Little or no improvement is obtained when using meta-data or citation structure. Noun phrases are too sparse and thus have lower performance compared to more traditional features. Conceptual annotation of the texts by MetaMap shows similar performance compared to unigrams, but adding concepts from the UMLS taxonomy does not improve the performance of using only mapped concepts. The combination of all the features performs largely better than any individual feature set considered. In addition, this combination improves the performance of a state-of-the-art MeSH indexer. Concerning the machine learning algorithms, we find that those that are more resilient to class imbalance largely obtain better performance. We conclude that even though traditional features such as unigrams and bigrams have strong performance compared to other features, it is possible to combine them to effectively improve the performance of the bag-of-words representation. We have also found that the combination of the learning algorithm and feature sets has an influence in the overall performance of the system. Moreover, using learning algorithms resilient to class imbalance largely improves performance. However, when using a large set of features, consideration needs to be taken with algorithms due to the risk of over-fitting. Specific combinations of learning algorithms and features for individual MeSH headings could further increase the performance of an indexing system.

Structural Features of Algebraic Quantum Notations

ERIC Educational Resources Information Center

Gire, Elizabeth; Price, Edward

2015-01-01

The formalism of quantum mechanics includes a rich collection of representations for describing quantum systems, including functions, graphs, matrices, histograms of probabilities, and Dirac notation. The varied features of these representations affect how computations are performed. For example, identifying probabilities of measurement outcomes…

Development of lower Triassic wrinkle structures: implications for the search for life on other planets.

PubMed

Mata, Scott A; Bottjer, David J

2009-11-01

Wrinkle structures are microbially mediated sedimentary structures that are a common feature of Proterozoic and earliest Phanerozoic siliciclastic seafloors on Earth and occur only rarely in post-Cambrian strata. These macroscopic microbially induced sedimentary structures are readily identifiable at the outcrop scale, and their recognition on other planetary bodies by landed missions may suggest the presence of past microbial life. Wrinkle structures of the Lower Triassic (Spathian) Virgin Limestone Member of the Moenkopi Formation in the western United States record an occurrence of widespread microbialite formation in the wake of the end-Permian mass extinction, the largest biotic crisis of the Phanerozoic. Wrinkle structures occur on proximal sandy tempestites deposited within the offshore transition. Storm layers appear to have been rapidly colonized by microbial mats and were subsequently buried by mud during fair-weather conditions. Wrinkle structures exhibit flat-topped crests and sinuous troughs, with associated mica grains oriented parallel to bedding, suggestive of trapping and binding activity. Although Lower Triassic wrinkle structures postdate the widespread occurrence of these features during the Proterozoic and Cambrian, they exhibit many of the same characteristics and environmental trends, which suggests a conservation of microbial formational and preservational processes in subtidal siliciclastic settings on Earth from the Precambrian into the Phanerozoic. In the search for extraterrestrial life, it may be these conservative characteristics that prove to be the most useful and robust for recognizing microbial features on other planetary bodies, and may add to an ever-growing foundation of knowledge for directing future explorations aimed at seeking out macroscopic microbial signatures.

Strong nonadditivity as a key structure-activity relationship feature: distinguishing structural changes from assay artifacts.

PubMed

Kramer, Christian; Fuchs, Julian E; Liedl, Klaus R

2015-03-23

Nonadditivity in protein-ligand affinity data represents highly instructive structure-activity relationship (SAR) features that indicate structural changes and have the potential to guide rational drug design. At the same time, nonadditivity is a challenge for both basic SAR analysis as well as many ligand-based data analysis techniques such as Free-Wilson Analysis and Matched Molecular Pair analysis, since linear substituent contribution models inherently assume additivity and thus do not work in such cases. While structural causes for nonadditivity have been analyzed anecdotally, no systematic approaches to interpret and use nonadditivity prospectively have been developed yet. In this contribution, we lay the statistical framework for systematic analysis of nonadditivity in a SAR series. First, we develop a general metric to quantify nonadditivity. Then, we demonstrate the non-negligible impact of experimental uncertainty that creates apparent nonadditivity, and we introduce techniques to handle experimental uncertainty. Finally, we analyze public SAR data sets for strong nonadditivity and use recourse to the original publications and available X-ray structures to find structural explanations for the nonadditivity observed. We find that all cases of strong nonadditivity (ΔΔpKi and ΔΔpIC50 > 2.0 log units) with sufficient structural information to generate reasonable hypothesis involve changes in binding mode. With the appropriate statistical basis, nonadditivity analysis offers a variety of new attempts for various areas in computer-aided drug design, including the validation of scoring functions and free energy perturbation approaches, binding pocket classification, and novel features in SAR analysis tools.

A feature-based developmental model of the infant brain in structural MRI.

PubMed

Toews, Matthew; Wells, William M; Zöllei, Lilla

2012-01-01

In this paper, anatomical development is modeled as a collection of distinctive image patterns localized in space and time. A Bayesian posterior probability is defined over a random variable of subject age, conditioned on data in the form of scale-invariant image features. The model is automatically learned from a large set of images exhibiting significant variation, used to discover anatomical structure related to age and development, and fit to new images to predict age. The model is applied to a set of 230 infant structural MRIs of 92 subjects acquired at multiple sites over an age range of 8-590 days. Experiments demonstrate that the model can be used to identify age-related anatomical structure, and to predict the age of new subjects with an average error of 72 days.

WONKA: objective novel complex analysis for ensembles of protein-ligand structures.

PubMed

Bradley, A R; Wall, I D; von Delft, F; Green, D V S; Deane, C M; Marsden, B D

2015-10-01

WONKA is a tool for the systematic analysis of an ensemble of protein-ligand structures. It makes the identification of conserved and unusual features within such an ensemble straightforward. WONKA uses an intuitive workflow to process structural co-ordinates. Ligand and protein features are summarised and then presented within an interactive web application. WONKA's power in consolidating and summarising large amounts of data is described through the analysis of three bromodomain datasets. Furthermore, and in contrast to many current methods, WONKA relates analysis to individual ligands, from which we find unusual and erroneous binding modes. Finally the use of WONKA as an annotation tool to share observations about structures is demonstrated. WONKA is freely available to download and install locally or can be used online at http://wonka.sgc.ox.ac.uk.

Domain-specific learning of grammatical structure in musical and phonological sequences.

PubMed

Bly, Benjamin Martin; Carrión, Ricardo E; Rasch, Björn

2009-01-01

Artificial grammar learning depends on acquisition of abstract structural representations rather than domain-specific representational constraints, or so many studies tell us. Using an artificial grammar task, we compared learning performance in two stimulus domains in which respondents have differing tacit prior knowledge. We found that despite grammatically identical sequence structures, learning was better for harmonically related chord sequences than for letter name sequences or harmonically unrelated chord sequences. We also found transfer effects within the musical and letter name tasks, but not across the domains. We conclude that knowledge acquired in implicit learning depends not only on abstract features of structured stimuli, but that the learning of regularities is in some respects domain-specific and strongly linked to particular features of the stimulus domain.

The crustal tectonics and history of Europa: A structural, morphological, and comparative analysis. M.S. Thesis

NASA Technical Reports Server (NTRS)

Schenk, P. M.

1984-01-01

An evaluation of surface features and structures on the Galilean moon Europa is made using the available high resolution Voyager imagery, low resolution support imaging, and what understanding of ice structure and mechanical behavior science has that is applicable to the problem. A general discussion of the history of Europa studies and the fundamental global morphology is undertaken. The visible lineament and terrain patterns are described, and possible origins discussed. Observations of faulting and block rotation previously described are amplified. A comparison of Europa's structures to terrestrial sea ice and lava lake crust features is also included. Finally, an attempt is made at synthesizing a unified model for the evolution of Europa's crust is presented which is compared with models developed by others.

A method of plane geometry primitive presentation

NASA Astrophysics Data System (ADS)

Jiao, Anbo; Luo, Haibo; Chang, Zheng; Hui, Bin

2014-11-01

Point feature and line feature are basic elements in object feature sets, and they play an important role in object matching and recognition. On one hand, point feature is sensitive to noise; on the other hand, there are usually a huge number of point features in an image, which makes it complex for matching. Line feature includes straight line segment and curve. One difficulty in straight line segment matching is the uncertainty of endpoint location, the other is straight line segment fracture problem or short straight line segments joined to form long straight line segment. While for the curve, in addition to the above problems, there is another difficulty in how to quantitatively describe the shape difference between curves. Due to the problems of point feature and line feature, the robustness and accuracy of target description will be affected; in this case, a method of plane geometry primitive presentation is proposed to describe the significant structure of an object. Firstly, two types of primitives are constructed, they are intersecting line primitive and blob primitive. Secondly, a line segment detector (LSD) is applied to detect line segment, and then intersecting line primitive is extracted. Finally, robustness and accuracy of the plane geometry primitive presentation method is studied. This method has a good ability to obtain structural information of the object, even if there is rotation or scale change of the object in the image. Experimental results verify the robustness and accuracy of this method.

Feature-oriented regional modeling and simulations in the Gulf of Maine and Georges Bank

NASA Astrophysics Data System (ADS)

Gangopadhyay, Avijit; Robinson, Allan R.; Haley, Patrick J.; Leslie, Wayne G.; Lozano, Carlos J.; Bisagni, James J.; Yu, Zhitao

2003-03-01

The multiscale synoptic circulation system in the Gulf of Maine and Georges Bank (GOMGB) region is presented using a feature-oriented approach. Prevalent synoptic circulation structures, or 'features', are identified from previous observational studies. These features include the buoyancy-driven Maine Coastal Current, the Georges Bank anticyclonic frontal circulation system, the basin-scale cyclonic gyres (Jordan, Georges and Wilkinson), the deep inflow through the Northeast Channel (NEC), the shallow outflow via the Great South Channel (GSC), and the shelf-slope front (SSF). Their synoptic water-mass ( T- S) structures are characterized and parameterized in a generalized formulation to develop temperature-salinity feature models. A synoptic initialization scheme for feature-oriented regional modeling and simulation (FORMS) of the circulation in the coastal-to-deep region of the GOMGB system is then developed. First, the temperature and salinity feature-model profiles are placed on a regional circulation template and then objectively analyzed with appropriate background climatology in the coastal region. Furthermore, these fields are melded with adjacent deep-ocean regional circulation (Gulf Stream Meander and Ring region) along and across the SSF. These initialization fields are then used for dynamical simulations via the primitive equation model. Simulation results are analyzed to calibrate the multiparameter feature-oriented modeling system. Experimental short-term synoptic simulations are presented for multiple resolutions in different regions with and without atmospheric forcing. The presented 'generic and portable' methodology demonstrates the potential of applying similar FORMS in many other regions of the Global Coastal Ocean.