Chemometric analysis of correlations between electronic absorption characteristics and structural and/or physicochemical parameters for ampholytic substances of biological and pharmaceutical relevance.

PubMed

Judycka-Proma, U; Bober, L; Gajewicz, A; Puzyn, T; Błażejowski, J

2015-03-05

Forty ampholytic compounds of biological and pharmaceutical relevance were subjected to chemometric analysis based on unsupervised and supervised learning algorithms. This enabled relations to be found between empirical spectral characteristics derived from electronic absorption data and structural and physicochemical parameters predicted by quantum chemistry methods or phenomenological relationships based on additivity rules. It was found that the energies of long wavelength absorption bands are correlated through multiparametric linear relationships with parameters reflecting the bulkiness features of the absorbing molecules as well as their nucleophilicity and electrophilicity. These dependences enable the quantitative analysis of spectral features of the compounds, as well as a comparison of their similarities and certain pharmaceutical and biological features. Three QSPR models to predict the energies of long-wavelength absorption in buffers with pH=2.5 and pH=7.0, as well as in methanol, were developed and validated in this study. These models can be further used to predict the long-wavelength absorption energies of untested substances (if they are structurally similar to the training compounds). Copyright © 2014 Elsevier B.V. All rights reserved.

Integrating Epistemological Perspectives on Chemistry in Chemical Education: The Cases of Concept Duality, Chemical Language, and Structural Explanations

ERIC Educational Resources Information Center

Kaya, Ebru; Erduran, Sibel

2013-01-01

In this paper, we trace the work of some philosophers of chemistry to draw some implications for the improvement of chemical education. We examine some key features of chemical knowledge, and how these features are relevant for school chemistry teaching and learning. In particular, we examine Laszlo's ("Foundations of Chemistry"…

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.

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

Revealing mesoscopic structural universality with diffusion.

PubMed

Novikov, Dmitry S; Jensen, Jens H; Helpern, Joseph A; Fieremans, Els

2014-04-08

Measuring molecular diffusion is widely used for characterizing materials and living organisms noninvasively. This characterization relies on relations between macroscopic diffusion metrics and structure at the mesoscopic scale commensurate with the diffusion length. Establishing such relations remains a fundamental challenge, hindering progress in materials science, porous media, and biomedical imaging. Here we show that the dynamical exponent in the time dependence of the diffusion coefficient distinguishes between the universality classes of the mesoscopic structural complexity. Our approach enables the interpretation of diffusion measurements by objectively selecting and modeling the most relevant structural features. As an example, the specific values of the dynamical exponent allow us to identify the relevant mesoscopic structure affecting MRI-measured water diffusion in muscles and in brain, and to elucidate the structural changes behind the decrease of diffusion coefficient in ischemic stroke.

Incidental and context-responsive activation of structure- and function-based action features during object identification

PubMed Central

Lee, Chia-lin; Middleton, Erica; Mirman, Daniel; Kalénine, Solène; Buxbaum, Laurel J.

2012-01-01

Previous studies suggest that action representations are activated during object processing, even when task-irrelevant. In addition, there is evidence that lexical-semantic context may affect such activation during object processing. Finally, prior work from our laboratory and others indicates that function-based (“use”) and structure-based (“move”) action subtypes may differ in their activation characteristics. Most studies assessing such effects, however, have required manual object-relevant motor responses, thereby plausibly influencing the activation of action representations. The present work utilizes eyetracking and a Visual World Paradigm task without object-relevant actions to assess the time course of activation of action representations, as well as their responsiveness to lexical-semantic context. In two experiments, participants heard a target word and selected its referent from an array of four objects. Gaze fixations on non-target objects signal activation of features shared between targets and non-targets. The experiments assessed activation of structure-based (Experiment 1) or function-based (Experiment 2) distractors, using neutral sentences (“S/he saw the …”) or sentences with a relevant action verb (Experiment 1: “S/he picked up the……”; Experiment 2: “S/he used the….”). We observed task-irrelevant activations of action information in both experiments. In neutral contexts, structure-based activation was relatively faster-rising but more transient than function-based activation. Additionally, action verb contexts reliably modified patterns of activation in both Experiments. These data provide fine-grained information about the dynamics of activation of function-based and structure-based actions in neutral and action-relevant contexts, in support of the “Two Action System” model of object and action processing (e.g., Buxbaum & Kalénine, 2010). PMID:22390294

Computational prediction of kink properties of helices in membrane proteins

NASA Astrophysics Data System (ADS)

Mai, T.-L.; Chen, C.-M.

2014-02-01

We have combined molecular dynamics simulations and fold identification procedures to investigate the structure of 696 kinked and 120 unkinked transmembrane (TM) helices in the PDBTM database. Our main aim of this study is to understand the formation of helical kinks by simulating their quasi-equilibrium heating processes, which might be relevant to the prediction of their structural features. The simulated structural features of these TM helices, including the position and the angle of helical kinks, were analyzed and compared with statistical data from PDBTM. From quasi-equilibrium heating processes of TM helices with four very different relaxation time constants, we found that these processes gave comparable predictions of the structural features of TM helices. Overall, 95 % of our best kink position predictions have an error of no more than two residues and 75 % of our best angle predictions have an error of less than 15°. Various structure assessments have been carried out to assess our predicted models of TM helices in PDBTM. Our results show that, in 696 predicted kinked helices, 70 % have a RMSD less than 2 Å, 71 % have a TM-score greater than 0.5, 69 % have a MaxSub score greater than 0.8, 60 % have a GDT-TS score greater than 85, and 58 % have a GDT-HA score greater than 70. For unkinked helices, our predicted models are also highly consistent with their crystal structure. These results provide strong supports for our assumption that kink formation of TM helices in quasi-equilibrium heating processes is relevant to predicting the structure of TM helices.

Prediction of Protein Modification Sites of Pyrrolidone Carboxylic Acid Using mRMR Feature Selection and Analysis

PubMed Central

Zheng, Lu-Lu; Niu, Shen; Hao, Pei; Feng, KaiYan; Cai, Yu-Dong; Li, Yixue

2011-01-01

Pyrrolidone carboxylic acid (PCA) is formed during a common post-translational modification (PTM) of extracellular and multi-pass membrane proteins. In this study, we developed a new predictor to predict the modification sites of PCA based on maximum relevance minimum redundancy (mRMR) and incremental feature selection (IFS). We incorporated 727 features that belonged to 7 kinds of protein properties to predict the modification sites, including sequence conservation, residual disorder, amino acid factor, secondary structure and solvent accessibility, gain/loss of amino acid during evolution, propensity of amino acid to be conserved at protein-protein interface and protein surface, and deviation of side chain carbon atom number. Among these 727 features, 244 features were selected by mRMR and IFS as the optimized features for the prediction, with which the prediction model achieved a maximum of MCC of 0.7812. Feature analysis showed that all feature types contributed to the modification process. Further site-specific feature analysis showed that the features derived from PCA's surrounding sites contributed more to the determination of PCA sites than other sites. The detailed feature analysis in this paper might provide important clues for understanding the mechanism of the PCA formation and guide relevant experimental validations. PMID:22174779

24 CFR 1000.162 - How will a recipient know that non-dwelling structures assisted under the IHBG program meet the...

Code of Federal Regulations, 2010 CFR

2010-04-01

..., design, features, amenities, performance or other factors. The standards for such structures must be able to support the reasonableness and necessity for these factors and to clearly identify the affordable... change; (vi) Cultural relevance of design; (vii) Size and scope supported by population and need; (viii...

Coping with Novelty and Human Intelligence: The Role of Counterfactual Reasoning

DTIC Science & Technology

1988-01-01

terminating relevance tests. Relevance is determined by checking whether the conceptual relation in the precue matches that in the item of the problem...found that experts tend to conceptualize dumain-related problems in abstract terms, whereas nonexperts apparently rely more on surface-level features...finally, the effects of the two surface-structural rule manipulations might for all subjects be partly due to perceptual, rather than conceptual

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.

Revealing mesoscopic structural universality with diffusion

PubMed Central

Novikov, Dmitry S.; Jensen, Jens H.; Helpern, Joseph A.; Fieremans, Els

2014-01-01

Measuring molecular diffusion is widely used for characterizing materials and living organisms noninvasively. This characterization relies on relations between macroscopic diffusion metrics and structure at the mesoscopic scale commensurate with the diffusion length. Establishing such relations remains a fundamental challenge, hindering progress in materials science, porous media, and biomedical imaging. Here we show that the dynamical exponent in the time dependence of the diffusion coefficient distinguishes between the universality classes of the mesoscopic structural complexity. Our approach enables the interpretation of diffusion measurements by objectively selecting and modeling the most relevant structural features. As an example, the specific values of the dynamical exponent allow us to identify the relevant mesoscopic structure affecting MRI-measured water diffusion in muscles and in brain, and to elucidate the structural changes behind the decrease of diffusion coefficient in ischemic stroke. PMID:24706873

Comprehensive analysis of line-edge and line-width roughness for EUV lithography

NASA Astrophysics Data System (ADS)

Bonam, Ravi; Liu, Chi-Chun; Breton, Mary; Sieg, Stuart; Seshadri, Indira; Saulnier, Nicole; Shearer, Jeffrey; Muthinti, Raja; Patlolla, Raghuveer; Huang, Huai

2017-03-01

Pattern transfer fidelity is always a major challenge for any lithography process and needs continuous improvement. Lithographic processes in semiconductor industry are primarily driven by optical imaging on photosensitive polymeric material (resists). Quality of pattern transfer can be assessed by quantifying multiple parameters such as, feature size uniformity (CD), placement, roughness, sidewall angles etc. Roughness in features primarily corresponds to variation of line edge or line width and has gained considerable significance, particularly due to shrinking feature sizes and variations of features in the same order. This has caused downstream processes (Etch (RIE), Chemical Mechanical Polish (CMP) etc.) to reconsider respective tolerance levels. A very important aspect of this work is relevance of roughness metrology from pattern formation at resist to subsequent processes, particularly electrical validity. A major drawback of current LER/LWR metric (sigma) is its lack of relevance across multiple downstream processes which effects material selection at various unit processes. In this work we present a comprehensive assessment of Line Edge and Line Width Roughness at multiple lithographic transfer processes. To simulate effect of roughness a pattern was designed with periodic jogs on the edges of lines with varying amplitudes and frequencies. There are numerous methodologies proposed to analyze roughness and in this work we apply them to programmed roughness structures to assess each technique's sensitivity. This work also aims to identify a relevant methodology to quantify roughness with relevance across downstream processes.

Enriching science, practice, and policy relevant to school psychology around the globe.

PubMed

Jimerson, Shane R

2016-03-01

This editorial provides a brief synthesis of the past, present, and future of School Psychology Quarterly, highlighting important contributions as an international resource to enrich, invigorate, enhance, and advance science, practice, and policy relevant to school psychology around the globe. Information herein highlights (a) the value of high quality and timely reviews, (b) publishing manuscripts that address a breadth of important topics relevant to school psychology, and (c) the structure and contributions of the special topic sections featured in School Psychology Quarterly. (c) 2016 APA, all rights reserved).

[Chronic periapical periodontitis of left maxillary first premolar with localized mineralized structure at periapical region: a case report].

PubMed

Dong, Wei; Li, Ren; Wen, Liming; Qi, Mengchun

2013-04-01

Chronic periapical periodontitis is characterized by destruction of periapical tissue and demonstrates translucent feature under X-ray examination. In this article, a localized mineralized structure, which showed high density under X-ray examination, was reported in a patient with chronic periapical periodontitis of left maxillary first premolar. Possible causes of the structure were analyzed and relevant literatures were reviewed.

Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder.

PubMed

Mwangi, Benson; Ebmeier, Klaus P; Matthews, Keith; Steele, J Douglas

2012-05-01

Quantitative abnormalities of brain structure in patients with major depressive disorder have been reported at a group level for decades. However, these structural differences appear subtle in comparison with conventional radiologically defined abnormalities, with considerable inter-subject variability. Consequently, it has not been possible to readily identify scans from patients with major depressive disorder at an individual level. Recently, machine learning techniques such as relevance vector machines and support vector machines have been applied to predictive classification of individual scans with variable success. Here we describe a novel hybrid method, which combines machine learning with feature selection and characterization, with the latter aimed at maximizing the accuracy of machine learning prediction. The method was tested using a multi-centre dataset of T(1)-weighted 'structural' scans. A total of 62 patients with major depressive disorder and matched controls were recruited from referred secondary care clinical populations in Aberdeen and Edinburgh, UK. The generalization ability and predictive accuracy of the classifiers was tested using data left out of the training process. High prediction accuracy was achieved (~90%). While feature selection was important for maximizing high predictive accuracy with machine learning, feature characterization contributed only a modest improvement to relevance vector machine-based prediction (~5%). Notably, while the only information provided for training the classifiers was T(1)-weighted scans plus a categorical label (major depressive disorder versus controls), both relevance vector machine and support vector machine 'weighting factors' (used for making predictions) correlated strongly with subjective ratings of illness severity. These results indicate that machine learning techniques have the potential to inform clinical practice and research, as they can make accurate predictions about brain scan data from individual subjects. Furthermore, machine learning weighting factors may reflect an objective biomarker of major depressive disorder illness severity, based on abnormalities of brain structure.

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

Dynamic data distributions in Vienna Fortran

NASA Technical Reports Server (NTRS)

Chapman, Barbara; Mehrotra, Piyush; Moritsch, Hans; Zima, Hans

1993-01-01

Vienna Fortran is a machine-independent language extension of Fortran, which is based upon the Single-Program-Multiple-Data (SPMD) paradigm and allows the user to write programs for distributed-memory systems using global addresses. The language features focus mainly on the issue of distributing data across virtual processor structures. Those features of Vienna Fortran that allow the data distributions of arrays to change dynamically, depending on runtime conditions are discussed. The relevant language features are discussed, their implementation is outlined, and how they may be used in applications is described.

Parallel object-oriented decision tree system

DOEpatents

Kamath,; Chandrika, Cantu-Paz [Dublin, CA; Erick, [Oakland, CA

2006-02-28

A data mining decision tree system that uncovers patterns, associations, anomalies, and other statistically significant structures in data by reading and displaying data files, extracting relevant features for each of the objects, and using a method of recognizing patterns among the objects based upon object features through a decision tree that reads the data, sorts the data if necessary, determines the best manner to split the data into subsets according to some criterion, and splits the data.

2,6-Diiminopiperidin-1-ol: an overlooked motif relevant to uranyl and transition metal binding on poly(amidoxime) adsorbents

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

Kennedy, Zachary C.; Cardenas, Allan Jay P.; Corbey, Jordan F.

2016-01-01

Glutardiamidoxime, a structural motif on sorbents used in uranium extraction from seawater, was discovered to cyclize in situ at room temperature to 2,6-diimino-piperidin-1-ol in the presence of uranyl nitrate. The new diimino motif was also generated when exposed to competing transition metals Cu(II) and Ni(II). Multinuclear μ-O bridged U(VI), Cu(II), and Ni(II) complexes featuring bound diimino ligands were isolated. A Cu(II) complex with the historically relevant cyclic imide dioxime motif is also reported for structural comparison to the reported diimino complexes.

Image Search Reranking With Hierarchical Topic Awareness.

PubMed

Tian, Xinmei; Yang, Linjun; Lu, Yijuan; Tian, Qi; Tao, Dacheng

2015-10-01

With much attention from both academia and industrial communities, visual search reranking has recently been proposed to refine image search results obtained from text-based image search engines. Most of the traditional reranking methods cannot capture both relevance and diversity of the search results at the same time. Or they ignore the hierarchical topic structure of search result. Each topic is treated equally and independently. However, in real applications, images returned for certain queries are naturally in hierarchical organization, rather than simple parallel relation. In this paper, a new reranking method "topic-aware reranking (TARerank)" is proposed. TARerank describes the hierarchical topic structure of search results in one model, and seamlessly captures both relevance and diversity of the image search results simultaneously. Through a structured learning framework, relevance and diversity are modeled in TARerank by a set of carefully designed features, and then the model is learned from human-labeled training samples. The learned model is expected to predict reranking results with high relevance and diversity for testing queries. To verify the effectiveness of the proposed method, we collect an image search dataset and conduct comparison experiments on it. The experimental results demonstrate that the proposed TARerank outperforms the existing relevance-based and diversified reranking methods.

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

Detecting nonsense for Chinese comments based on logistic regression

NASA Astrophysics Data System (ADS)

Zhuolin, Ren; Guang, Chen; Shu, Chen

2016-07-01

To understand cyber citizens' opinion accurately from Chinese news comments, the clear definition on nonsense is present, and a detection model based on logistic regression (LR) is proposed. The detection of nonsense can be treated as a binary-classification problem. Besides of traditional lexical features, we propose three kinds of features in terms of emotion, structure and relevance. By these features, we train an LR model and demonstrate its effect in understanding Chinese news comments. We find that each of proposed features can significantly promote the result. In our experiments, we achieve a prediction accuracy of 84.3% which improves the baseline 77.3% by 7%.

Use of Chemotypes for Profiling and Exploring the ToxCast Chemical-Assay Landscape (ACS Spring meeting)

EPA Science Inventory

EPA's ToxCast chemical library, currently exceeding 4000 unique chemicals, has successfully captured a broad diversity of chemical use-types, functionality, and structures and features potentially relevant to toxicity and environmental exposure landscapes. Chemical diversity in ...

ScrumPy: metabolic modelling with Python.

PubMed

Poolman, M G

2006-09-01

ScrumPy is a software package used for the definition and analysis of metabolic models. It is written using the Python programming language that is also used as a user interface. ScrumPy has features for both kinetic and structural modelling, but the emphasis is on structural modelling and those features of most relevance to analysis of large (genome-scale) models. The aim is at describing ScrumPy's functionality to readers with some knowledge of metabolic modelling, but implementation, programming and other computational details are omitted. ScrumPy is released under the Gnu Public Licence, and available for download from http://mudshark.brookes.ac.uk/ ScrumPy.

Features: Real-Time Adaptive Feature and Document Learning for Web Search.

ERIC Educational Resources Information Center

Chen, Zhixiang; Meng, Xiannong; Fowler, Richard H.; Zhu, Binhai

2001-01-01

Describes Features, an intelligent Web search engine that is able to perform real-time adaptive feature (i.e., keyword) and document learning. Explains how Features learns from users' document relevance feedback and automatically extracts and suggests indexing keywords relevant to a search query, and learns from users' keyword relevance feedback…

EPAs DSSTox Chemical Database: A Resource for the Non-Targeted Testing Community (EPA NTA workshop)

EPA Science Inventory

EPA’s DSSTox database project, which includes coverage of the ToxCast and Tox21 high-throughput testing inventories, provides high-quality chemical-structure files for inventories of toxicological and environmental relevance. A feature of the DSSTox project, which differentiates ...

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.

Competence-Based Knowledge Structures for Personalised Learning

ERIC Educational Resources Information Center

Heller, Jurgen; Steiner, Christina; Hockemeyer, Cord; Albert, Dietrich

2006-01-01

Competence-based extensions of Knowledge Space Theory are suggested as a formal framework for implementing key features of personalised learning in technology-enhanced learning. The approach links learning objects and assessment problems to the relevant skills that are taught or required. Various ways to derive these skills from domain ontologies…

Structure and Function in Homodimeric Enzymes: Simulations of Cooperative and Independent Functional Motions.

PubMed

Wells, Stephen A; van der Kamp, Marc W; McGeagh, John D; Mulholland, Adrian J

2015-01-01

Large-scale conformational change is a common feature in the catalytic cycles of enzymes. Many enzymes function as homodimers with active sites that contain elements from both chains. Symmetric and anti-symmetric cooperative motions in homodimers can potentially lead to correlated active site opening and/or closure, likely to be important for ligand binding and release. Here, we examine such motions in two different domain-swapped homodimeric enzymes: the DcpS scavenger decapping enzyme and citrate synthase. We use and compare two types of all-atom simulations: conventional molecular dynamics simulations to identify physically meaningful conformational ensembles, and rapid geometric simulations of flexible motion, biased along normal mode directions, to identify relevant motions encoded in the protein structure. The results indicate that the opening/closure motions are intrinsic features of both unliganded enzymes. In DcpS, conformational change is dominated by an anti-symmetric cooperative motion, causing one active site to close as the other opens; however a symmetric motion is also significant. In CS, we identify that both symmetric (suggested by crystallography) and asymmetric motions are features of the protein structure, and as a result the behaviour in solution is largely non-cooperative. The agreement between two modelling approaches using very different levels of theory indicates that the behaviours are indeed intrinsic to the protein structures. Geometric simulations correctly identify and explore large amplitudes of motion, while molecular dynamics simulations indicate the ranges of motion that are energetically feasible. Together, the simulation approaches are able to reveal unexpected functionally relevant motions, and highlight differences between enzymes.

Structure and Function in Homodimeric Enzymes: Simulations of Cooperative and Independent Functional Motions

PubMed Central

McGeagh, John D.; Mulholland, Adrian J.

2015-01-01

Large-scale conformational change is a common feature in the catalytic cycles of enzymes. Many enzymes function as homodimers with active sites that contain elements from both chains. Symmetric and anti-symmetric cooperative motions in homodimers can potentially lead to correlated active site opening and/or closure, likely to be important for ligand binding and release. Here, we examine such motions in two different domain-swapped homodimeric enzymes: the DcpS scavenger decapping enzyme and citrate synthase. We use and compare two types of all-atom simulations: conventional molecular dynamics simulations to identify physically meaningful conformational ensembles, and rapid geometric simulations of flexible motion, biased along normal mode directions, to identify relevant motions encoded in the protein structure. The results indicate that the opening/closure motions are intrinsic features of both unliganded enzymes. In DcpS, conformational change is dominated by an anti-symmetric cooperative motion, causing one active site to close as the other opens; however a symmetric motion is also significant. In CS, we identify that both symmetric (suggested by crystallography) and asymmetric motions are features of the protein structure, and as a result the behaviour in solution is largely non-cooperative. The agreement between two modelling approaches using very different levels of theory indicates that the behaviours are indeed intrinsic to the protein structures. Geometric simulations correctly identify and explore large amplitudes of motion, while molecular dynamics simulations indicate the ranges of motion that are energetically feasible. Together, the simulation approaches are able to reveal unexpected functionally relevant motions, and highlight differences between enzymes. PMID:26241964

Biological and functional relevance of CASP predictions

PubMed Central

Liu, Tianyun; Ish‐Shalom, Shirbi; Torng, Wen; Lafita, Aleix; Bock, Christian; Mort, Matthew; Cooper, David N; Bliven, Spencer; Capitani, Guido; Mooney, Sean D.

2017-01-01

Abstract Our goal is to answer the question: compared with experimental structures, how useful are predicted models for functional annotation? We assessed the functional utility of predicted models by comparing the performances of a suite of methods for functional characterization on the predictions and the experimental structures. We identified 28 sites in 25 protein targets to perform functional assessment. These 28 sites included nine sites with known ligand binding (holo‐sites), nine sites that are expected or suggested by experimental authors for small molecule binding (apo‐sites), and Ten sites containing important motifs, loops, or key residues with important disease‐associated mutations. We evaluated the utility of the predictions by comparing their microenvironments to the experimental structures. Overall structural quality correlates with functional utility. However, the best‐ranked predictions (global) may not have the best functional quality (local). Our assessment provides an ability to discriminate between predictions with high structural quality. When assessing ligand‐binding sites, most prediction methods have higher performance on apo‐sites than holo‐sites. Some servers show consistently high performance for certain types of functional sites. Finally, many functional sites are associated with protein‐protein interaction. We also analyzed biologically relevant features from the protein assemblies of two targets where the active site spanned the protein‐protein interface. For the assembly targets, we find that the features in the models are mainly determined by the choice of template. PMID:28975675

Mosaic tetraploidy in a liveborn infant with features of the DiGeorge anomaly.

PubMed

Wullich, B; Henn, W; Groterath, E; Ermis, A; Fuchs, S; Zankl, M

1991-11-01

We report on a liveborn male infant with mosaic tetraploidy who presented with multiple congenital anomalies including features of the DiGeorge anomaly (type I truncus arteriosus with other cardiovascular malformations, thymic hypoplasia, hypocalcemia). No structural chromosome aberrations, namely of chromosome 22, were detected. These findings contribute to the variability of symptoms of the polyploid phenotype. Additionally, the cytogenetic studies in our case emphasize the necessity of investigating fibroblasts in order to evaluate the relevant proportion of aberrant cells in mosaicism.

[Structural plasticity associated with drugs addiction].

PubMed

Zhu, Jie; Cao, Guo-fen; Dang, Yong-hui; Chen, Teng

2011-12-01

An essential feature of drug addiction is that an individual continues to use drug despite the threat of severely adverse physical or psychosocial consequences. Persistent changes in behavior and psychological function that occur as a function of drugs of abuse are thought to be due to the reorganization of synaptic connections (structural plasticity) in relevant brain circuits (especially the brains reward circuits). In this paper we summarized evidence that, indeed, exposure to amphetamine, cocaine, nicotine or morphine produced persistent changes in the structure of dendrites and dendritic spines on cells in relevant brain regions. We also approached the potential molecular mechanisms of these changes. It is suggested that structural plasticity associated with exposure to drugs of abuse reflects a reorganization of patterns of synaptic connectivity in these neural systems, a reorganization that alters their operation, thus contributing to some of the persistent sequela associated with drug use-including addiction.

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.

Designing attractive gamification features for collaborative storytelling websites.

PubMed

Hsu, Shang Hwa; Chang, Jen-Wei; Lee, Chun-Chia

2013-06-01

Gamification design is considered as the predictor of collaborative storytelling websites' success. Although aforementioned studies have mentioned a broad range of factors that may influence gamification, they neither depicted the actual design features nor relative attractiveness among them. This study aims to identify attractive gamification features for collaborative storytelling websites. We first constructed a hierarchical system structure of gamification design of collaborative storytelling websites and conducted a focus group interview with eighteen frequent users to identify 35gamification features. After that, this study determined the relative attractiveness of these gamification features by administrating an online survey to 6333 collaborative storytelling websites users. The results indicated that the top 10 most attractive gamification features could account for more than 50% of attractiveness among these 35 gamification features. The feature of unpredictable time pressure is important to website users, yet not revealed in previous relevant studies. Implications of the findings were discussed.

Network analysis of named entity co-occurrences in written texts

NASA Astrophysics Data System (ADS)

Amancio, Diego Raphael

2016-06-01

The use of methods borrowed from statistics and physics to analyze written texts has allowed the discovery of unprecedent patterns of human behavior and cognition by establishing links between models features and language structure. While current models have been useful to unveil patterns via analysis of syntactical and semantical networks, only a few works have probed the relevance of investigating the structure arising from the relationship between relevant entities such as characters, locations and organizations. In this study, we represent entities appearing in the same context as a co-occurrence network, where links are established according to a null model based on random, shuffled texts. Computational simulations performed in novels revealed that the proposed model displays interesting topological features, such as the small world feature, characterized by high values of clustering coefficient. The effectiveness of our model was verified in a practical pattern recognition task in real networks. When compared with traditional word adjacency networks, our model displayed optimized results in identifying unknown references in texts. Because the proposed representation plays a complementary role in characterizing unstructured documents via topological analysis of named entities, we believe that it could be useful to improve the characterization of written texts (and related systems), specially if combined with traditional approaches based on statistical and deeper paradigms.

Machine learning for autonomous crystal structure identification.

PubMed

Reinhart, Wesley F; Long, Andrew W; Howard, Michael P; Ferguson, Andrew L; Panagiotopoulos, Athanassios Z

2017-07-21

We present a machine learning technique to discover and distinguish relevant ordered structures from molecular simulation snapshots or particle tracking data. Unlike other popular methods for structural identification, our technique requires no a priori description of the target structures. Instead, we use nonlinear manifold learning to infer structural relationships between particles according to the topology of their local environment. This graph-based approach yields unbiased structural information which allows us to quantify the crystalline character of particles near defects, grain boundaries, and interfaces. We demonstrate the method by classifying particles in a simulation of colloidal crystallization, and show that our method identifies structural features that are missed by standard techniques.

Polysaccharides from the South African medicinal plant Artemisia afra: Structure and activity studies.

PubMed

Braünlich, Paula Marie; Inngjerdingen, Kari Tvete; Inngjerdingen, Marit; Johnson, Quinton; Paulsen, Berit Smestad; Mabusela, Wilfred

2018-01-01

Artemisia afra (Jacq. Ex. Willd), is an indigenous plant in South Africa and other parts of the African continent, where it is used as traditional medicine mostly for respiratory conditions. The objective of this study was to investigate the structural features of the polysaccharides from the leaves of this plant, as well as the biological activities of the polysaccharide fractions against the complement assay. Leaves of Artemisia afra were extracted sequentially with organic solvents (dichloromethane and methanol), 50% aqueous ethanol, and water at 50 and 100°C respectively. The polysaccharide extracts were fractionated by ion exchange chromatography and the resulting fractions were tested for biological activity against the complement fixation assay. Active fractions were further fractionated using gel filtration. Monosaccharide compositions and linkage analyses were determined for the relevant fractions. Polysaccharides were shown to be of the pectin type, and largely contain arabinogalactan, rhamnogalacturonan and homogalacturonan structural features. The presence of arabinogalactan type II features as suggested by methylation analysis was further confirmed by the ready precipitation of the relevant polysaccharides with the Yariv reagent. An unusual feature of some of these polysaccharides was the presence of relatively high levels of xylose as one of its monosaccharide constituents. Purified polysaccharide fractions were shown to possess higher biological activity than the selected standard in the complement assay. Digestion of these polysaccharides with an endo-polygalacturonase enzyme resulted in polymers with lower molecular weights as expected, but still with biological activity which exceeded that of the standard. Thus on the basis of these studies it may be suggested that immunomodulating properties probably contribute significantly to the health-promoting effects of this medicinal plant. Copyright © 2017 Elsevier B.V. All rights reserved.

Biological and functional relevance of CASP predictions.

PubMed

Liu, Tianyun; Ish-Shalom, Shirbi; Torng, Wen; Lafita, Aleix; Bock, Christian; Mort, Matthew; Cooper, David N; Bliven, Spencer; Capitani, Guido; Mooney, Sean D; Altman, Russ B

2018-03-01

Our goal is to answer the question: compared with experimental structures, how useful are predicted models for functional annotation? We assessed the functional utility of predicted models by comparing the performances of a suite of methods for functional characterization on the predictions and the experimental structures. We identified 28 sites in 25 protein targets to perform functional assessment. These 28 sites included nine sites with known ligand binding (holo-sites), nine sites that are expected or suggested by experimental authors for small molecule binding (apo-sites), and Ten sites containing important motifs, loops, or key residues with important disease-associated mutations. We evaluated the utility of the predictions by comparing their microenvironments to the experimental structures. Overall structural quality correlates with functional utility. However, the best-ranked predictions (global) may not have the best functional quality (local). Our assessment provides an ability to discriminate between predictions with high structural quality. When assessing ligand-binding sites, most prediction methods have higher performance on apo-sites than holo-sites. Some servers show consistently high performance for certain types of functional sites. Finally, many functional sites are associated with protein-protein interaction. We also analyzed biologically relevant features from the protein assemblies of two targets where the active site spanned the protein-protein interface. For the assembly targets, we find that the features in the models are mainly determined by the choice of template. © 2017 The Authors Proteins: Structure, Function and Bioinformatics Published by Wiley Periodicals, Inc.

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

Education at a Glance 2013: OECD Indicators

ERIC Educational Resources Information Center

OECD Publishing, 2013

2013-01-01

"Education at a Glance: OECD Indicators" is the authoritative source for accurate and relevant information on the state of education around the world. It provides data on the structure, finances, and performance of education systems in more than 40 countries, including OECD members and G20 partners. Featuring more than 100 charts, 200…

Anatomy and clinical significance of the uncinate process and uncovertebral joint: A comprehensive review.

PubMed

Hartman, Jeffrey

2014-04-01

The uncinate process and its associated uncovertebral articulation are features unique to the cervical spine. This review examines the morphology of these unique structures with particular emphasis on the regional anatomy, development and clinical significance. Five electronic databases were utilized in the literature search and additional relevant citations were retrieved from the references. A total of 74 citations were included for review. This literature review found that the uncinate processes and uncovertebral articulations are rudimentary at birth and develop and evolve with age. With degeneration they become clinically apparent with compression of related structures; most importantly affecting the spinal nerve root and vertebral artery. The articulations have also been found to precipitate torticollis when edematous and be acutely damaged in severe head and neck injuries. The uncinate processes are also important in providing stability and guiding the motion of the cervical spine. This review is intended to re-examine an often overlooked region of the cervical spine as not only an interesting anatomical feature but also a clinically relevant one. Copyright © 2014 Wiley Periodicals, Inc.

Efficient light emission from inorganic and organic semiconductor hybrid structures by energy-level tuning

PubMed Central

Schlesinger, R.; Bianchi, F.; Blumstengel, S.; Christodoulou, C.; Ovsyannikov, R.; Kobin, B.; Moudgil, K.; Barlow, S.; Hecht, S.; Marder, S.R.; Henneberger, F.; Koch, N.

2015-01-01

The fundamental limits of inorganic semiconductors for light emitting applications, such as holographic displays, biomedical imaging and ultrafast data processing and communication, might be overcome by hybridization with their organic counterparts, which feature enhanced frequency response and colour range. Innovative hybrid inorganic/organic structures exploit efficient electrical injection and high excitation density of inorganic semiconductors and subsequent energy transfer to the organic semiconductor, provided that the radiative emission yield is high. An inherent obstacle to that end is the unfavourable energy level offset at hybrid inorganic/organic structures, which rather facilitates charge transfer that quenches light emission. Here, we introduce a technologically relevant method to optimize the hybrid structure's energy levels, here comprising ZnO and a tailored ladder-type oligophenylene. The ZnO work function is substantially lowered with an organometallic donor monolayer, aligning the frontier levels of the inorganic and organic semiconductors. This increases the hybrid structure's radiative emission yield sevenfold, validating the relevance of our approach. PMID:25872919

Efficient light emission from inorganic and organic semiconductor hybrid structures by energy-level tuning.

PubMed

Schlesinger, R; Bianchi, F; Blumstengel, S; Christodoulou, C; Ovsyannikov, R; Kobin, B; Moudgil, K; Barlow, S; Hecht, S; Marder, S R; Henneberger, F; Koch, N

2015-04-15

The fundamental limits of inorganic semiconductors for light emitting applications, such as holographic displays, biomedical imaging and ultrafast data processing and communication, might be overcome by hybridization with their organic counterparts, which feature enhanced frequency response and colour range. Innovative hybrid inorganic/organic structures exploit efficient electrical injection and high excitation density of inorganic semiconductors and subsequent energy transfer to the organic semiconductor, provided that the radiative emission yield is high. An inherent obstacle to that end is the unfavourable energy level offset at hybrid inorganic/organic structures, which rather facilitates charge transfer that quenches light emission. Here, we introduce a technologically relevant method to optimize the hybrid structure's energy levels, here comprising ZnO and a tailored ladder-type oligophenylene. The ZnO work function is substantially lowered with an organometallic donor monolayer, aligning the frontier levels of the inorganic and organic semiconductors. This increases the hybrid structure's radiative emission yield sevenfold, validating the relevance of our approach.

An approach to functionally relevant clustering of the protein universe: Active site profile-based clustering of protein structures and sequences.

PubMed

Knutson, Stacy T; Westwood, Brian M; Leuthaeuser, Janelle B; Turner, Brandon E; Nguyendac, Don; Shea, Gabrielle; Kumar, Kiran; Hayden, Julia D; Harper, Angela F; Brown, Shoshana D; Morris, John H; Ferrin, Thomas E; Babbitt, Patricia C; Fetrow, Jacquelyn S

2017-04-01

Protein function identification remains a significant problem. Solving this problem at the molecular functional level would allow mechanistic determinant identification-amino acids that distinguish details between functional families within a superfamily. Active site profiling was developed to identify mechanistic determinants. DASP and DASP2 were developed as tools to search sequence databases using active site profiling. Here, TuLIP (Two-Level Iterative clustering Process) is introduced as an iterative, divisive clustering process that utilizes active site profiling to separate structurally characterized superfamily members into functionally relevant clusters. Underlying TuLIP is the observation that functionally relevant families (curated by Structure-Function Linkage Database, SFLD) self-identify in DASP2 searches; clusters containing multiple functional families do not. Each TuLIP iteration produces candidate clusters, each evaluated to determine if it self-identifies using DASP2. If so, it is deemed a functionally relevant group. Divisive clustering continues until each structure is either a functionally relevant group member or a singlet. TuLIP is validated on enolase and glutathione transferase structures, superfamilies well-curated by SFLD. Correlation is strong; small numbers of structures prevent statistically significant analysis. TuLIP-identified enolase clusters are used in DASP2 GenBank searches to identify sequences sharing functional site features. Analysis shows a true positive rate of 96%, false negative rate of 4%, and maximum false positive rate of 4%. F-measure and performance analysis on the enolase search results and comparison to GEMMA and SCI-PHY demonstrate that TuLIP avoids the over-division problem of these methods. Mechanistic determinants for enolase families are evaluated and shown to correlate well with literature results. © 2017 The Authors Protein Science published by Wiley Periodicals, Inc. on behalf of The Protein Society.

Change Detection in Uav Video Mosaics Combining a Feature Based Approach and Extended Image Differencing

NASA Astrophysics Data System (ADS)

Saur, Günter; Krüger, Wolfgang

2016-06-01

Change detection is an important task when using unmanned aerial vehicles (UAV) for video surveillance. We address changes of short time scale using observations in time distances of a few hours. Each observation (previous and current) is a short video sequence acquired by UAV in near-Nadir view. Relevant changes are, e.g., recently parked or moved vehicles. Examples for non-relevant changes are parallaxes caused by 3D structures of the scene, shadow and illumination changes, and compression or transmission artifacts. In this paper we present (1) a new feature based approach to change detection, (2) a combination with extended image differencing (Saur et al., 2014), and (3) the application to video sequences using temporal filtering. In the feature based approach, information about local image features, e.g., corners, is extracted in both images. The label "new object" is generated at image points, where features occur in the current image and no or weaker features are present in the previous image. The label "vanished object" corresponds to missing or weaker features in the current image and present features in the previous image. This leads to two "directed" change masks and differs from image differencing where only one "undirected" change mask is extracted which combines both label types to the single label "changed object". The combination of both algorithms is performed by merging the change masks of both approaches. A color mask showing the different contributions is used for visual inspection by a human image interpreter.

Infiltrative cervical lesions causing symptomatic occipital neuralgia.

PubMed

Sierra-Hidalgo, F; Ruíz, J; Morales-Cartagena, A; Martínez-Salio, A; Serna, J de la; Hernández-Gallego, J

2011-10-01

Occipital neuralgia is a well-recognized cause of posterior head and neck pain that may associate mild sensory changes in the cutaneous distribution of the occipital nerves, lacking a recognizable local structural aetiology in most cases. Atypical clinical features or an abnormal neurological examination are alerts for a potential underlying cause of pain, although cases of clinically typical occipital neuralgia as isolated manifestation of lesions of the cervical spinal cord, cervical roots, or occipital nerves have been increasingly reported. We describe two cases (one with typical and another one with atypical clinical features) of occipital neuralgia secondary to paravertebral pyomyositis and vertebral relapse of multiple myeloma in patients with relevant medical history that aroused the possibility of an underlying structural lesion. We discuss the need for cranio-cervical magnetic resonance imaging in all patients with occipital neuralgia, even when typical clinical features are present and neurological examination is completely normal.

Perceptual learning: toward a comprehensive theory.

PubMed

Watanabe, Takeo; Sasaki, Yuka

2015-01-03

Visual perceptual learning (VPL) is long-term performance increase resulting from visual perceptual experience. Task-relevant VPL of a feature results from training of a task on the feature relevant to the task. Task-irrelevant VPL arises as a result of exposure to the feature irrelevant to the trained task. At least two serious problems exist. First, there is the controversy over which stage of information processing is changed in association with task-relevant VPL. Second, no model has ever explained both task-relevant and task-irrelevant VPL. Here we propose a dual plasticity model in which feature-based plasticity is a change in a representation of the learned feature, and task-based plasticity is a change in processing of the trained task. Although the two types of plasticity underlie task-relevant VPL, only feature-based plasticity underlies task-irrelevant VPL. This model provides a new comprehensive framework in which apparently contradictory results could be explained.

Morphological characters of the thickbody skate Amblyraja frerichsi (Krefft 1968) (Rajiformes: Rajidae), with notes on its biology.

PubMed

Bustamante, Carlos; Lamilla, Julio; Concha, Francisco; Ebert, David A; Bennett, Michael B

2012-01-01

Detailed descriptions of morphological features, morphometrics, neurocranium anatomy, clasper structure and egg case descriptions are provided for the thickbody skate Amblyraja frerichsi; a rare, deep-water species from Chile, Argentina and Falkland Islands. The species diagnosis is complemented from new observations and aspects such as colour, size and distribution are described. Geographic and bathymetric distributional ranges are discussed as relevant features of this taxońs biology. Additionally, the conservation status is assessed including bycatch records from Chilean fisheries.

Morphological Characters of the Thickbody Skate Amblyraja frerichsi (Krefft 1968) (Rajiformes: Rajidae), with Notes on Its Biology

PubMed Central

Bustamante, Carlos; Lamilla, Julio; Concha, Francisco; Ebert, David A.; Bennett, Michael B.

2012-01-01

Detailed descriptions of morphological features, morphometrics, neurocranium anatomy, clasper structure and egg case descriptions are provided for the thickbody skate Amblyraja frerichsi; a rare, deep-water species from Chile, Argentina and Falkland Islands. The species diagnosis is complemented from new observations and aspects such as colour, size and distribution are described. Geographic and bathymetric distributional ranges are discussed as relevant features of this taxońs biology. Additionally, the conservation status is assessed including bycatch records from Chilean fisheries. PMID:22768186

Reply to David Kemmerer's "a critique of Mark D. Allen's 'the preservation of verb subcategory knowledge in a spoken language comprehension deficit'".

PubMed

Allen, Mark D; Owens, Tyler E

2008-07-01

Allen [Allen, M. D. (2005). The preservation of verb subcategory knowledge in a spoken language comprehension deficit. Brain and Language, 95, 255-264] presents evidence from a single patient, WBN, to motivate a theory of lexical processing and representation in which syntactic information may be encoded and retrieved independently of semantic information. In his critique, Kemmerer argues that because Allen depended entirely on preposition-based verb subcategory violations to test WBN's knowledge of correct argument structure, his results, at best, address a "strawman" theory. This argument rests on the assumption that preposition subcategory options are superficial syntactic phenomena which are not represented by argument structure proper. We demonstrate that preposition subcategory is in fact treated as semantically determined argument structure in the theories that Allen evaluated, and thus far from irrelevant. In further discussion of grammatically relevant versus irrelevant semantic features, Kemmerer offers a review of his own studies. However, due to an important design shortcoming in these experiments, we remain unconvinced. Reemphasizing the fact the Allen (2005) never claimed to rule out all semantic contributions to syntax, we propose an improvement in Kemmerer's approach that might provide more satisfactory evidence on the distinction between the kinds of relevant versus irrelevant features his studies have addressed.

Asymmetric Cultural Effects on Perceptual Expertise Underlie an Own-Race Bias for Voices

ERIC Educational Resources Information Center

Perrachione, Tyler K.; Chiao, Joan Y.; Wong, Patrick C. M.

2010-01-01

The own-race bias in memory for faces has been a rich source of empirical work on the mechanisms of person perception. This effect is thought to arise because the face-perception system differentially encodes the relevant structural dimensions of features and their configuration based on experiences with different groups of faces. However, the…

The Forest, the Trees, and the Leaves: Differences of Processing across Development

ERIC Educational Resources Information Center

Krakowski, Claire-Sara; Poirel, Nicolas; Vidal, Julie; Roëll, Margot; Pineau, Arlette; Borst, Grégoire; Houdé, Olivier

2016-01-01

To act and think, children and adults are continually required to ignore irrelevant visual information to focus on task-relevant items. As real-world visual information is organized into structures, we designed a feature visual search task containing 3-level hierarchical stimuli (i.e., local shapes that constituted intermediate shapes that formed…

Education at a Glance 2012: OECD Indicators

ERIC Educational Resources Information Center

OECD Publishing (NJ3), 2012

2012-01-01

"Education at a Glance: OECD Indicators" is the authoritative source for accurate and relevant information on the state of education around the world. It provides data on the structure, finances, and performance of education systems in the OECD's 34 member countries, as well as a number of non-member G20 nations. Featuring more than 140…

Structural features of microRNA (miRNA) precursors and their relevance to miRNA biogenesis and small interfering RNA/short hairpin RNA design.

PubMed

Krol, Jacek; Sobczak, Krzysztof; Wilczynska, Urszula; Drath, Maria; Jasinska, Anna; Kaczynska, Danuta; Krzyzosiak, Wlodzimierz J

2004-10-01

We have established the structures of 10 human microRNA (miRNA) precursors using biochemical methods. Eight of these structures turned out to be different from those that were computer-predicted. The differences localized in the terminal loop region and at the opposite side of the precursor hairpin stem. We have analyzed the features of these structures from the perspectives of miRNA biogenesis and active strand selection. We demonstrated the different thermodynamic stability profiles for pre-miRNA hairpins harboring miRNAs at their 5'- and 3'-sides and discussed their functional implications. Our results showed that miRNA prediction based on predicted precursor structures may give ambiguous results, and the success rate is significantly higher for the experimentally determined structures. On the other hand, the differences between the predicted and experimentally determined structures did not affect the stability of termini produced through "conceptual dicing." This result confirms the value of thermodynamic analysis based on mfold as a predictor of strand section by RNAi-induced silencing complex (RISC).

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.

Celluloid devils: a research study of male nurses in feature films.

PubMed

Stanley, David

2012-11-01

To report a study of how male nurses are portrayed in feature films. It was hypothesized that male nurses are frequently portrayed negatively or stereotypically in the film media, potentially having a negative impact on male nurse recruitment and the public's perception of male nurses. An interpretive, qualitative methodology guided by insights into hegemonic masculinity and structured around a set of collective case studies (films) was used to examine the portrayal of male nurses in feature films made in the Western world from 1900 to 2007. Over 36,000 feature film synopses were reviewed (via CINAHL, ProQuest and relevant movie-specific literature) for the keyword 'nurse' and 'nursing' with an additional search for films from 1900 to 2010 for the word 'male nurse'. Identified films were labelled as 'cases' and analysed collectively to determine key attributes related to men in nursing and explore them for the emergence of concepts and themes related to the image of male nurses in films. A total of 13 relevant cases (feature films) were identified with 12 being made in the USA. Most films portrayed male nurses negatively and in ways opposed to hegemonic masculinity, as effeminate, homosexual, homicidal, corrupt or incompetent. Few film images of male nurses show them in traditional masculine roles or as clinically competent or self-confident professionals.   Feature films predominantly portray male nurses negatively. Given the popularity of feature films, there may be negative effects on recruitment and on the public's perception of male nurses. © 2012 Blackwell Publishing Ltd.

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.

Iron chalcogenide superconductors at high magnetic fields

PubMed Central

Lei, Hechang; Wang, Kefeng; Hu, Rongwei; Ryu, Hyejin; Abeykoon, Milinda; Bozin, Emil S; Petrovic, Cedomir

2012-01-01

Iron chalcogenide superconductors have become one of the most investigated superconducting materials in recent years due to high upper critical fields, competing interactions and complex electronic and magnetic phase diagrams. The structural complexity, defects and atomic site occupancies significantly affect the normal and superconducting states in these compounds. In this work we review the vortex behavior, critical current density and high magnetic field pair-breaking mechanism in iron chalcogenide superconductors. We also point to relevant structural features and normal-state properties. PMID:27877518

A new method for constructing networks from binary data

NASA Astrophysics Data System (ADS)

van Borkulo, Claudia D.; Borsboom, Denny; Epskamp, Sacha; Blanken, Tessa F.; Boschloo, Lynn; Schoevers, Robert A.; Waldorp, Lourens J.

2014-08-01

Network analysis is entering fields where network structures are unknown, such as psychology and the educational sciences. A crucial step in the application of network models lies in the assessment of network structure. Current methods either have serious drawbacks or are only suitable for Gaussian data. In the present paper, we present a method for assessing network structures from binary data. Although models for binary data are infamous for their computational intractability, we present a computationally efficient model for estimating network structures. The approach, which is based on Ising models as used in physics, combines logistic regression with model selection based on a Goodness-of-Fit measure to identify relevant relationships between variables that define connections in a network. A validation study shows that this method succeeds in revealing the most relevant features of a network for realistic sample sizes. We apply our proposed method to estimate the network of depression and anxiety symptoms from symptom scores of 1108 subjects. Possible extensions of the model are discussed.

An approach to functionally relevant clustering of the protein universe: Active site profile‐based clustering of protein structures and sequences

PubMed Central

Knutson, Stacy T.; Westwood, Brian M.; Leuthaeuser, Janelle B.; Turner, Brandon E.; Nguyendac, Don; Shea, Gabrielle; Kumar, Kiran; Hayden, Julia D.; Harper, Angela F.; Brown, Shoshana D.; Morris, John H.; Ferrin, Thomas E.; Babbitt, Patricia C.

2017-01-01

Abstract Protein function identification remains a significant problem. Solving this problem at the molecular functional level would allow mechanistic determinant identification—amino acids that distinguish details between functional families within a superfamily. Active site profiling was developed to identify mechanistic determinants. DASP and DASP2 were developed as tools to search sequence databases using active site profiling. Here, TuLIP (Two‐Level Iterative clustering Process) is introduced as an iterative, divisive clustering process that utilizes active site profiling to separate structurally characterized superfamily members into functionally relevant clusters. Underlying TuLIP is the observation that functionally relevant families (curated by Structure‐Function Linkage Database, SFLD) self‐identify in DASP2 searches; clusters containing multiple functional families do not. Each TuLIP iteration produces candidate clusters, each evaluated to determine if it self‐identifies using DASP2. If so, it is deemed a functionally relevant group. Divisive clustering continues until each structure is either a functionally relevant group member or a singlet. TuLIP is validated on enolase and glutathione transferase structures, superfamilies well‐curated by SFLD. Correlation is strong; small numbers of structures prevent statistically significant analysis. TuLIP‐identified enolase clusters are used in DASP2 GenBank searches to identify sequences sharing functional site features. Analysis shows a true positive rate of 96%, false negative rate of 4%, and maximum false positive rate of 4%. F‐measure and performance analysis on the enolase search results and comparison to GEMMA and SCI‐PHY demonstrate that TuLIP avoids the over‐division problem of these methods. Mechanistic determinants for enolase families are evaluated and shown to correlate well with literature results. PMID:28054422

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.

Dorsal hippocampus is necessary for visual categorization in rats.

PubMed

Kim, Jangjin; Castro, Leyre; Wasserman, Edward A; Freeman, John H

2018-02-23

The hippocampus may play a role in categorization because of the need to differentiate stimulus categories (pattern separation) and to recognize category membership of stimuli from partial information (pattern completion). We hypothesized that the hippocampus would be more crucial for categorization of low-density (few relevant features) stimuli-due to the higher demand on pattern separation and pattern completion-than for categorization of high-density (many relevant features) stimuli. Using a touchscreen apparatus, rats were trained to categorize multiple abstract stimuli into two different categories. Each stimulus was a pentagonal configuration of five visual features; some of the visual features were relevant for defining the category whereas others were irrelevant. Two groups of rats were trained with either a high (dense, n = 8) or low (sparse, n = 8) number of category-relevant features. Upon reaching criterion discrimination (≥75% correct, on 2 consecutive days), bilateral cannulas were implanted in the dorsal hippocampus. The rats were then given either vehicle or muscimol infusions into the hippocampus just prior to various testing sessions. They were tested with: the previously trained stimuli (trained), novel stimuli involving new irrelevant features (novel), stimuli involving relocated features (relocation), and a single relevant feature (singleton). In training, the dense group reached criterion faster than the sparse group, indicating that the sparse task was more difficult than the dense task. In testing, accuracy of both groups was equally high for trained and novel stimuli. However, both groups showed impaired accuracy in the relocation and singleton conditions, with a greater deficit in the sparse group. The testing data indicate that rats encode both the relevant features and the spatial locations of the features. Hippocampal inactivation impaired visual categorization regardless of the density of the category-relevant features for the trained, novel, relocation, and singleton stimuli. Hippocampus-mediated pattern completion and pattern separation mechanisms may be necessary for visual categorization involving overlapping irrelevant features. © 2018 Wiley Periodicals, Inc.

Attentional Selection Can Be Predicted by Reinforcement Learning of Task-relevant Stimulus Features Weighted by Value-independent Stickiness.

PubMed

Balcarras, Matthew; Ardid, Salva; Kaping, Daniel; Everling, Stefan; Womelsdorf, Thilo

2016-02-01

Attention includes processes that evaluate stimuli relevance, select the most relevant stimulus against less relevant stimuli, and bias choice behavior toward the selected information. It is not clear how these processes interact. Here, we captured these processes in a reinforcement learning framework applied to a feature-based attention task that required macaques to learn and update the value of stimulus features while ignoring nonrelevant sensory features, locations, and action plans. We found that value-based reinforcement learning mechanisms could account for feature-based attentional selection and choice behavior but required a value-independent stickiness selection process to explain selection errors while at asymptotic behavior. By comparing different reinforcement learning schemes, we found that trial-by-trial selections were best predicted by a model that only represents expected values for the task-relevant feature dimension, with nonrelevant stimulus features and action plans having only a marginal influence on covert selections. These findings show that attentional control subprocesses can be described by (1) the reinforcement learning of feature values within a restricted feature space that excludes irrelevant feature dimensions, (2) a stochastic selection process on feature-specific value representations, and (3) value-independent stickiness toward previous feature selections akin to perseveration in the motor domain. We speculate that these three mechanisms are implemented by distinct but interacting brain circuits and that the proposed formal account of feature-based stimulus selection will be important to understand how attentional subprocesses are implemented in primate brain networks.

A numerical relativity scheme for cosmological simulations

NASA Astrophysics Data System (ADS)

Daverio, David; Dirian, Yves; Mitsou, Ermis

2017-12-01

Cosmological simulations involving the fully covariant gravitational dynamics may prove relevant in understanding relativistic/non-linear features and, therefore, in taking better advantage of the upcoming large scale structure survey data. We propose a new 3  +  1 integration scheme for general relativity in the case where the matter sector contains a minimally-coupled perfect fluid field. The original feature is that we completely eliminate the fluid components through the constraint equations, thus remaining with a set of unconstrained evolution equations for the rest of the fields. This procedure does not constrain the lapse function and shift vector, so it holds in arbitrary gauge and also works for arbitrary equation of state. An important advantage of this scheme is that it allows one to define and pass an adaptation of the robustness test to the cosmological context, at least in the case of pressureless perfect fluid matter, which is the relevant one for late-time cosmology.

Children's use of comparison and function in novel object categorization.

PubMed

Kimura, Katherine; Hunley, Samuel B; Namy, Laura L

2018-06-01

Although young children often rely on salient perceptual cues, such as shape, when categorizing novel objects, children eventually shift towards deeper relational reasoning about category membership. This study investigates what information young children use to classify novel instances of familiar categories. Specifically, we investigated two sources of information that have the potential to facilitate the classification of novel exemplars: (1) comparison of familiar category instances, and (2) attention to function information that might direct children's attention to functionally relevant perceptual features. Across two experiments, we found that comparing two perceptually similar category members-particularly when function information was also highlighted-led children to discover non-obvious relational features that supported their categorization of novel category instances. Together, these findings demonstrate that comparison may aid in novel object categorization by heightening the salience of less obvious, yet functionally relevant, relational structures that support conceptual reasoning. Copyright © 2018. Published by Elsevier Inc.

LANGUAGE EXPERIENCE SHAPES PROCESSING OF PITCH RELEVANT INFORMATION IN THE HUMAN BRAINSTEM AND AUDITORY CORTEX: ELECTROPHYSIOLOGICAL EVIDENCE.

PubMed

Krishnan, Ananthanarayan; Gandour, Jackson T

2014-12-01

Pitch is a robust perceptual attribute that plays an important role in speech, language, and music. As such, it provides an analytic window to evaluate how neural activity relevant to pitch undergo transformation from early sensory to later cognitive stages of processing in a well coordinated hierarchical network that is subject to experience-dependent plasticity. We review recent evidence of language experience-dependent effects in pitch processing based on comparisons of native vs. nonnative speakers of a tonal language from electrophysiological recordings in the auditory brainstem and auditory cortex. We present evidence that shows enhanced representation of linguistically-relevant pitch dimensions or features at both the brainstem and cortical levels with a stimulus-dependent preferential activation of the right hemisphere in native speakers of a tone language. We argue that neural representation of pitch-relevant information in the brainstem and early sensory level processing in the auditory cortex is shaped by the perceptual salience of domain-specific features. While both stages of processing are shaped by language experience, neural representations are transformed and fundamentally different at each biological level of abstraction. The representation of pitch relevant information in the brainstem is more fine-grained spectrotemporally as it reflects sustained neural phase-locking to pitch relevant periodicities contained in the stimulus. In contrast, the cortical pitch relevant neural activity reflects primarily a series of transient temporal neural events synchronized to certain temporal attributes of the pitch contour. We argue that experience-dependent enhancement of pitch representation for Chinese listeners most likely reflects an interaction between higher-level cognitive processes and early sensory-level processing to improve representations of behaviorally-relevant features that contribute optimally to perception. It is our view that long-term experience shapes this adaptive process wherein the top-down connections provide selective gating of inputs to both cortical and subcortical structures to enhance neural responses to specific behaviorally-relevant attributes of the stimulus. A theoretical framework for a neural network is proposed involving coordination between local, feedforward, and feedback components that can account for experience-dependent enhancement of pitch representations at multiple levels of the auditory pathway. The ability to record brainstem and cortical pitch relevant responses concurrently may provide a new window to evaluate the online interplay between feedback, feedforward, and local intrinsic components in the hierarchical processing of pitch relevant information.

LANGUAGE EXPERIENCE SHAPES PROCESSING OF PITCH RELEVANT INFORMATION IN THE HUMAN BRAINSTEM AND AUDITORY CORTEX: ELECTROPHYSIOLOGICAL EVIDENCE

PubMed Central

Krishnan, Ananthanarayan; Gandour, Jackson T.

2015-01-01

Pitch is a robust perceptual attribute that plays an important role in speech, language, and music. As such, it provides an analytic window to evaluate how neural activity relevant to pitch undergo transformation from early sensory to later cognitive stages of processing in a well coordinated hierarchical network that is subject to experience-dependent plasticity. We review recent evidence of language experience-dependent effects in pitch processing based on comparisons of native vs. nonnative speakers of a tonal language from electrophysiological recordings in the auditory brainstem and auditory cortex. We present evidence that shows enhanced representation of linguistically-relevant pitch dimensions or features at both the brainstem and cortical levels with a stimulus-dependent preferential activation of the right hemisphere in native speakers of a tone language. We argue that neural representation of pitch-relevant information in the brainstem and early sensory level processing in the auditory cortex is shaped by the perceptual salience of domain-specific features. While both stages of processing are shaped by language experience, neural representations are transformed and fundamentally different at each biological level of abstraction. The representation of pitch relevant information in the brainstem is more fine-grained spectrotemporally as it reflects sustained neural phase-locking to pitch relevant periodicities contained in the stimulus. In contrast, the cortical pitch relevant neural activity reflects primarily a series of transient temporal neural events synchronized to certain temporal attributes of the pitch contour. We argue that experience-dependent enhancement of pitch representation for Chinese listeners most likely reflects an interaction between higher-level cognitive processes and early sensory-level processing to improve representations of behaviorally-relevant features that contribute optimally to perception. It is our view that long-term experience shapes this adaptive process wherein the top-down connections provide selective gating of inputs to both cortical and subcortical structures to enhance neural responses to specific behaviorally-relevant attributes of the stimulus. A theoretical framework for a neural network is proposed involving coordination between local, feedforward, and feedback components that can account for experience-dependent enhancement of pitch representations at multiple levels of the auditory pathway. The ability to record brainstem and cortical pitch relevant responses concurrently may provide a new window to evaluate the online interplay between feedback, feedforward, and local intrinsic components in the hierarchical processing of pitch relevant information. PMID:25838636

The Effects of Goal Relevance and Perceptual Features on Emotional Items and Associative Memory

PubMed Central

Mao, Wei B.; An, Shu; Yang, Xiao F.

2017-01-01

Showing an emotional item in a neutral background scene often leads to enhanced memory for the emotional item and impaired associative memory for background details. Meanwhile, both top–down goal relevance and bottom–up perceptual features played important roles in memory binding. We conducted two experiments and aimed to further examine the effects of goal relevance and perceptual features on emotional items and associative memory. By manipulating goal relevance (asking participants to categorize only each item image as living or non-living or to categorize each whole composite picture consisted of item image and background scene as natural scene or manufactured scene) and perceptual features (controlling visual contrast and visual familiarity) in two experiments, we found that both high goal relevance and salient perceptual features (high salience of items vs. high familiarity of items) could promote emotional item memory, but they had different effects on associative memory for emotional items and neutral backgrounds. Specifically, high goal relevance and high perceptual-salience of items could jointly impair the associative memory for emotional items and neutral backgrounds, while the effect of item familiarity on associative memory for emotional items would be modulated by goal relevance. High familiarity of items could increase associative memory for negative items and neutral backgrounds only in the low goal relevance condition. These findings suggest the effect of emotion on associative memory is not only related to attentional capture elicited by emotion, but also can be affected by goal relevance and perceptual features of stimulus. PMID:28790943

The Effects of Goal Relevance and Perceptual Features on Emotional Items and Associative Memory.

PubMed

Mao, Wei B; An, Shu; Yang, Xiao F

2017-01-01

Showing an emotional item in a neutral background scene often leads to enhanced memory for the emotional item and impaired associative memory for background details. Meanwhile, both top-down goal relevance and bottom-up perceptual features played important roles in memory binding. We conducted two experiments and aimed to further examine the effects of goal relevance and perceptual features on emotional items and associative memory. By manipulating goal relevance (asking participants to categorize only each item image as living or non-living or to categorize each whole composite picture consisted of item image and background scene as natural scene or manufactured scene) and perceptual features (controlling visual contrast and visual familiarity) in two experiments, we found that both high goal relevance and salient perceptual features (high salience of items vs. high familiarity of items) could promote emotional item memory, but they had different effects on associative memory for emotional items and neutral backgrounds. Specifically, high goal relevance and high perceptual-salience of items could jointly impair the associative memory for emotional items and neutral backgrounds, while the effect of item familiarity on associative memory for emotional items would be modulated by goal relevance. High familiarity of items could increase associative memory for negative items and neutral backgrounds only in the low goal relevance condition. These findings suggest the effect of emotion on associative memory is not only related to attentional capture elicited by emotion, but also can be affected by goal relevance and perceptual features of stimulus.

The effect of alkaline pretreatment methods on cellulose structure and accessibility

DOE PAGES

Bali, Garima; Meng, Xianzhi; Deneff, Jacob I.; ...

2014-11-24

The effects of different alkaline pretreatments on cellulose structural features and accessibility are compared and correlated with the enzymatic hydrolysis of Populus. The pretreatments are shown to modify polysaccharides and lignin content to enhance the accessibility for cellulase enzymes. The highest increase in the cellulose accessibility was observed in dilute sodium hydroxide, followed by methods using ammonia soaking and lime (Ca(OH) 2). The biggest increase of cellulose accessibility occurs during the first 10 min of pretreatment, with further increases at a slower rate as severity increases. Low temperature ammonia soaking at longer residence times dissolved a major portion of hemicellulosemore » and exhibited higher cellulose accessibility than high temperature soaking. Moreover, the most significant reduction of degree of polymerization (DP) occurred for dilute sodium hydroxide (NaOH) and ammonia pretreated Populus samples. The study thus identifies important cellulose structural features and relevant parameters related to biomass recalcitrance.« less

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.

Computational modeling approaches to quantitative structure-binding kinetics relationships in drug discovery.

PubMed

De Benedetti, Pier G; Fanelli, Francesca

2018-03-21

Simple comparative correlation analyses and quantitative structure-kinetics relationship (QSKR) models highlight the interplay of kinetic rates and binding affinity as an essential feature in drug design and discovery. The choice of the molecular series, and their structural variations, used in QSKR modeling is fundamental to understanding the mechanistic implications of ligand and/or drug-target binding and/or unbinding processes. Here, we discuss the implications of linear correlations between kinetic rates and binding affinity constants and the relevance of the computational approaches to QSKR modeling. Copyright © 2018 Elsevier Ltd. All rights reserved.

Irrelevant reward and selection histories have different influences on task-relevant attentional selection.

PubMed

MacLean, Mary H; Giesbrecht, Barry

2015-07-01

Task-relevant and physically salient features influence visual selective attention. In the present study, we investigated the influence of task-irrelevant and physically nonsalient reward-associated features on visual selective attention. Two hypotheses were tested: One predicts that the effects of target-defining task-relevant and task-irrelevant features interact to modulate visual selection; the other predicts that visual selection is determined by the independent combination of relevant and irrelevant feature effects. These alternatives were tested using a visual search task that contained multiple targets, placing a high demand on the need for selectivity, and that was data-limited and required unspeeded responses, emphasizing early perceptual selection processes. One week prior to the visual search task, participants completed a training task in which they learned to associate particular colors with a specific reward value. In the search task, the reward-associated colors were presented surrounding targets and distractors, but were neither physically salient nor task-relevant. In two experiments, the irrelevant reward-associated features influenced performance, but only when they were presented in a task-relevant location. The costs induced by the irrelevant reward-associated features were greater when they oriented attention to a target than to a distractor. In a third experiment, we examined the effects of selection history in the absence of reward history and found that the interaction between task relevance and selection history differed, relative to when the features had previously been associated with reward. The results indicate that under conditions that demand highly efficient perceptual selection, physically nonsalient task-irrelevant and task-relevant factors interact to influence visual selective attention.

Predicting the performance of fingerprint similarity searching.

PubMed

Vogt, Martin; Bajorath, Jürgen

2011-01-01

Fingerprints are bit string representations of molecular structure that typically encode structural fragments, topological features, or pharmacophore patterns. Various fingerprint designs are utilized in virtual screening and their search performance essentially depends on three parameters: the nature of the fingerprint, the active compounds serving as reference molecules, and the composition of the screening database. It is of considerable interest and practical relevance to predict the performance of fingerprint similarity searching. A quantitative assessment of the potential that a fingerprint search might successfully retrieve active compounds, if available in the screening database, would substantially help to select the type of fingerprint most suitable for a given search problem. The method presented herein utilizes concepts from information theory to relate the fingerprint feature distributions of reference compounds to screening libraries. If these feature distributions do not sufficiently differ, active database compounds that are similar to reference molecules cannot be retrieved because they disappear in the "background." By quantifying the difference in feature distribution using the Kullback-Leibler divergence and relating the divergence to compound recovery rates obtained for different benchmark classes, fingerprint search performance can be quantitatively predicted.

The P600 in Implicit Artificial Grammar Learning

ERIC Educational Resources Information Center

Silva, Susana; Folia, Vasiliki; Hagoort, Peter; Petersson, Karl Magnus

2017-01-01

The suitability of the artificial grammar learning (AGL) paradigm to capture relevant aspects of the acquisition of linguistic structures has been empirically tested in a number of EEG studies. Some have shown a syntax-related P600 component, but it has not been ruled out that the AGL P600 effect is a response to surface features (e.g.,…

A Comparative Study on the Governance of Education for Older People in Japan and Korea

ERIC Educational Resources Information Center

Choi, Ilseon; Hori, Shigeo

2016-01-01

This paper compares the governance of education for older people in Japan and Korea. The findings revealed that the overall mechanisms of governance for the education of older people shared a number of similar features such as the structure of relevant laws, ministries, and policies. However, differences were also found regarding independence of…

Intentional attention switching in dichotic listening: exploring the efficiency of nonspatial and spatial selection.

PubMed

Lawo, Vera; Fels, Janina; Oberem, Josefa; Koch, Iring

2014-10-01

Using an auditory variant of task switching, we examined the ability to intentionally switch attention in a dichotic-listening task. In our study, participants responded selectively to one of two simultaneously presented auditory number words (spoken by a female and a male, one for each ear) by categorizing its numerical magnitude. The mapping of gender (female vs. male) and ear (left vs. right) was unpredictable. The to-be-attended feature for gender or ear, respectively, was indicated by a visual selection cue prior to auditory stimulus onset. In Experiment 1, explicitly cued switches of the relevant feature dimension (e.g., from gender to ear) and switches of the relevant feature within a dimension (e.g., from male to female) occurred in an unpredictable manner. We found large performance costs when the relevant feature switched, but switches of the relevant feature dimension incurred only small additional costs. The feature-switch costs were larger in ear-relevant than in gender-relevant trials. In Experiment 2, we replicated these findings using a simplified design (i.e., only within-dimension switches with blocked dimensions). In Experiment 3, we examined preparation effects by manipulating the cueing interval and found a preparation benefit only when ear was cued. Together, our data suggest that the large part of attentional switch costs arises from reconfiguration at the level of relevant auditory features (e.g., left vs. right) rather than feature dimensions (ear vs. gender). Additionally, our findings suggest that ear-based target selection benefits more from preparation time (i.e., time to direct attention to one ear) than gender-based target selection.

Directed-Assembly of Block Copolymers for Large-Scale, Three-Dimensional, Optical Metamaterials at Visible Wavelengths. Final LDRD Report

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

Hiszpanski, Anna M.

Metamaterials are composites with patterned subwavelength features where the choice of materials and subwavelength structuring bestows upon the metamaterials unique optical properties not found in nature, thereby enabling optical applications previously considered impossible. However, because the structure of optical metamaterials must be subwavelength, metamaterials operating at visible wavelengths require features on the order of 100 nm or smaller, and such resolution typically requires top-down lithographic fabrication techniques that are not easily scaled to device-relevant areas that are square centimeters in size. In this project, we developed a new fabrication route using block copolymers to make over large device-relevant areas opticalmore » metamaterials that operate at visible wavelengths. Our structures are smaller in size (sub-100 nm) and cover a larger area (cm 2) than what has been achieved with traditional nanofabrication routes. To guide our experimental efforts, we developed an algorithm to calculate the expected optical properties (specifically the index of refraction) of such metamaterials that predicts that we can achieve surprisingly large changes in optical properties with small changes in metamaterials’ structure. In the course of our work, we also found that the ordered metal nanowires meshes produced by our scalable fabrication route for making optical metamaterials may also possibly act as transparent electrodes, which are needed in electrical displays and solar cells. We explored the ordered metal nanowires meshes’ utility for this application and developed design guidelines to aide our experimental efforts.« less

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.

NSAID-derived γ-secretase modulation requires an acidic moiety on the carbazole scaffold.

PubMed

Zall, Andrea; Kieser, Daniel; Höttecke, Nicole; Naumann, Eva C; Thomaszewski, Binia; Schneider, Katrin; Steinbacher, Dirk T; Schubenel, Robert; Masur, Stefan; Baumann, Karlheinz; Schmidt, Boris

2011-08-15

Modulation of γ-secretase activity holds potential for the treatment of Alzheimer's disease. Most NSAID-derived γ-secretase modulators feature a carboxylic acid, which may impair blood-brain barrier permeation. The structure activity relationship of 33 carbazoles featuring diverse carboxylic acid isosteres or metabolic precursors thereof was established in a cellular amyloid secretion assay. The modulatory activity was observed for acidic moieties and metabolically labile esters only, which supports our hypothesis of an acid-lysine interaction to be relevant for this type of γ-secretase modulators. Copyright © 2011 Elsevier Ltd. All rights reserved.

Insights into Structural and Mechanistic Features of Viral IRES Elements

PubMed Central

Martinez-Salas, Encarnacion; Francisco-Velilla, Rosario; Fernandez-Chamorro, Javier; Embarek, Azman M.

2018-01-01

Internal ribosome entry site (IRES) elements are cis-acting RNA regions that promote internal initiation of protein synthesis using cap-independent mechanisms. However, distinct types of IRES elements present in the genome of various RNA viruses perform the same function despite lacking conservation of sequence and secondary RNA structure. Likewise, IRES elements differ in host factor requirement to recruit the ribosomal subunits. In spite of this diversity, evolutionarily conserved motifs in each family of RNA viruses preserve sequences impacting on RNA structure and RNA–protein interactions important for IRES activity. Indeed, IRES elements adopting remarkable different structural organizations contain RNA structural motifs that play an essential role in recruiting ribosomes, initiation factors and/or RNA-binding proteins using different mechanisms. Therefore, given that a universal IRES motif remains elusive, it is critical to understand how diverse structural motifs deliver functions relevant for IRES activity. This will be useful for understanding the molecular mechanisms beyond cap-independent translation, as well as the evolutionary history of these regulatory elements. Moreover, it could improve the accuracy to predict IRES-like motifs hidden in genome sequences. This review summarizes recent advances on the diversity and biological relevance of RNA structural motifs for viral IRES elements. PMID:29354113

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.

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.

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.

Identification of input variables for feature based artificial neural networks-saccade detection in EOG recordings.

PubMed

Tigges, P; Kathmann, N; Engel, R R

1997-07-01

Though artificial neural networks (ANN) are excellent tools for pattern recognition problems when signal to noise ratio is low, the identification of decision relevant features for ANN input data is still a crucial issue. The experience of the ANN designer and the existing knowledge and understanding of the problem seem to be the only links for a specific construction. In the present study a backpropagation ANN based on modified raw data inputs showed encouraging results. Investigating the specific influences of prototypical input patterns on a specially designed ANN led to a new sparse and efficient input data presentation. This data coding obtained by a semiautomatic procedure combining existing expert knowledge and the internal representation structures of the raw data based ANN yielded a list of feature vectors, each representing the relevant information for saccade identification. The feature based ANN produced a reduction of the error rate of nearly 40% compared with the raw data ANN. An overall correct classification of 92% of so far unknown data was realized. The proposed method of extracting internal ANN knowledge for the production of a better input data representation is not restricted to EOG recordings, and could be used in various fields of signal analysis.

Examining End-Of-Chapter Problems Across Editions of an Introductory Calculus-Based Physics Textbook

NASA Astrophysics Data System (ADS)

Xiao, Bin

End-Of-Chapter (EOC) problems have been part of many physics education studies. Typically, only problems "localized" as relevant to a single chapter were used. This work examines how well this type of problem represents all EOC problems and whether EOC problems found in leading textbooks have changed over the past several decades. To investigate whether EOC problems have connections between chapters, I solved all problems of the E&M; chapters of the most recent edition of a popular introductory level calculus-based textbook and coded the equations used to solve each problem. These results were compared to the first edition of the same text. Also, several relevant problem features were coded for those problems and results were compared for sample chapters across all editions. My findings include two parts. The result of equation usage shows that problems in the E&M; chapters do use equations from both other E&M; chapters and non-E&M; chapters. This out-of-chapter usage increased from the first edition to the last edition. Information about the knowledge structure of E&M; chapters was also revealed. The results of the problem feature study show that most EOC problems have common features but there was an increase of diversity in some of the problem features across editions.

Kernel-Based Relevance Analysis with Enhanced Interpretability for Detection of Brain Activity Patterns

PubMed Central

Alvarez-Meza, Andres M.; Orozco-Gutierrez, Alvaro; Castellanos-Dominguez, German

2017-01-01

We introduce Enhanced Kernel-based Relevance Analysis (EKRA) that aims to support the automatic identification of brain activity patterns using electroencephalographic recordings. EKRA is a data-driven strategy that incorporates two kernel functions to take advantage of the available joint information, associating neural responses to a given stimulus condition. Regarding this, a Centered Kernel Alignment functional is adjusted to learning the linear projection that best discriminates the input feature set, optimizing the required free parameters automatically. Our approach is carried out in two scenarios: (i) feature selection by computing a relevance vector from extracted neural features to facilitating the physiological interpretation of a given brain activity task, and (ii) enhanced feature selection to perform an additional transformation of relevant features aiming to improve the overall identification accuracy. Accordingly, we provide an alternative feature relevance analysis strategy that allows improving the system performance while favoring the data interpretability. For the validation purpose, EKRA is tested in two well-known tasks of brain activity: motor imagery discrimination and epileptic seizure detection. The obtained results show that the EKRA approach estimates a relevant representation space extracted from the provided supervised information, emphasizing the salient input features. As a result, our proposal outperforms the state-of-the-art methods regarding brain activity discrimination accuracy with the benefit of enhanced physiological interpretation about the task at hand. PMID:29056897

A systematic approach to infer biological relevance and biases of gene network structures.

PubMed

Antonov, Alexey V; Tetko, Igor V; Mewes, Hans W

2006-01-10

The development of high-throughput technologies has generated the need for bioinformatics approaches to assess the biological relevance of gene networks. Although several tools have been proposed for analysing the enrichment of functional categories in a set of genes, none of them is suitable for evaluating the biological relevance of the gene network. We propose a procedure and develop a web-based resource (BIOREL) to estimate the functional bias (biological relevance) of any given genetic network by integrating different sources of biological information. The weights of the edges in the network may be either binary or continuous. These essential features make our web tool unique among many similar services. BIOREL provides standardized estimations of the network biases extracted from independent data. By the analyses of real data we demonstrate that the potential application of BIOREL ranges from various benchmarking purposes to systematic analysis of the network biology.

Experience of light thin-walled structures improvement in construction

NASA Astrophysics Data System (ADS)

Frolovskaia, A. V.; Deordiev, S. V.; Falk, A.; Klinduh, N. Y.; Terehova, I. I.

2018-05-01

The authors on the basis of practical experience have analyzed low-rise construction with the use of energy-saving technologies. Characteristic features of possible variants of frame construction are looked at and described. The relevance of the paper consists in the improvement of the building frame design solution based on the analysis and elimination of disadvantages taking into account consumers’ point of view.

The LAILAPS search engine: a feature model for relevance ranking in life science databases.

PubMed

Lange, Matthias; Spies, Karl; Colmsee, Christian; Flemming, Steffen; Klapperstück, Matthias; Scholz, Uwe

2010-03-25

Efficient and effective information retrieval in life sciences is one of the most pressing challenge in bioinformatics. The incredible growth of life science databases to a vast network of interconnected information systems is to the same extent a big challenge and a great chance for life science research. The knowledge found in the Web, in particular in life-science databases, are a valuable major resource. In order to bring it to the scientist desktop, it is essential to have well performing search engines. Thereby, not the response time nor the number of results is important. The most crucial factor for millions of query results is the relevance ranking. In this paper, we present a feature model for relevance ranking in life science databases and its implementation in the LAILAPS search engine. Motivated by the observation of user behavior during their inspection of search engine result, we condensed a set of 9 relevance discriminating features. These features are intuitively used by scientists, who briefly screen database entries for potential relevance. The features are both sufficient to estimate the potential relevance, and efficiently quantifiable. The derivation of a relevance prediction function that computes the relevance from this features constitutes a regression problem. To solve this problem, we used artificial neural networks that have been trained with a reference set of relevant database entries for 19 protein queries. Supporting a flexible text index and a simple data import format, this concepts are implemented in the LAILAPS search engine. It can easily be used both as search engine for comprehensive integrated life science databases and for small in-house project databases. LAILAPS is publicly available for SWISSPROT data at http://lailaps.ipk-gatersleben.de.

Demands on attention and the role of response priming in visual discrimination of feature conjunctions.

PubMed

Fournier, Lisa R; Herbert, Rhonda J; Farris, Carrie

2004-10-01

This study examined how response mapping of features within single- and multiple-feature targets affects decision-based processing and attentional capacity demands. Observers judged the presence or absence of 1 or 2 target features within an object either presented alone or with distractors. Judging the presence of 2 features relative to the less discriminable of these features alone was faster (conjunction benefits) when the task-relevant features differed in discriminability and were consistently mapped to responses. Conjunction benefits were attributed to asynchronous decision priming across attended, task-relevant dimensions. A failure to find conjunction benefits for disjunctive conjunctions was attributed to increased memory demands and variable feature-response mapping for 2- versus single-feature targets. Further, attentional demands were similar between single- and 2-feature targets when response mapping, memory demands, and discriminability of the task-relevant features were equated between targets. Implications of the findings for recent attention models are discussed. (c) 2004 APA, all rights reserved

The geometrical structure of quantum theory as a natural generalization of information geometry

NASA Astrophysics Data System (ADS)

Reginatto, Marcel

2015-01-01

Quantum mechanics has a rich geometrical structure which allows for a geometrical formulation of the theory. This formalism was introduced by Kibble and later developed by a number of other authors. The usual approach has been to start from the standard description of quantum mechanics and identify the relevant geometrical features that can be used for the reformulation of the theory. Here this procedure is inverted: the geometrical structure of quantum theory is derived from information geometry, a geometrical structure that may be considered more fundamental, and the Hilbert space of the standard formulation of quantum mechanics is constructed using geometrical quantities. This suggests that quantum theory has its roots in information geometry.

Classifying Cognitive Profiles Using Machine Learning with Privileged Information in Mild Cognitive Impairment.

PubMed

Alahmadi, Hanin H; Shen, Yuan; Fouad, Shereen; Luft, Caroline Di B; Bentham, Peter; Kourtzi, Zoe; Tino, Peter

2016-01-01

Early diagnosis of dementia is critical for assessing disease progression and potential treatment. State-or-the-art machine learning techniques have been increasingly employed to take on this diagnostic task. In this study, we employed Generalized Matrix Learning Vector Quantization (GMLVQ) classifiers to discriminate patients with Mild Cognitive Impairment (MCI) from healthy controls based on their cognitive skills. Further, we adopted a "Learning with privileged information" approach to combine cognitive and fMRI data for the classification task. The resulting classifier operates solely on the cognitive data while it incorporates the fMRI data as privileged information (PI) during training. This novel classifier is of practical use as the collection of brain imaging data is not always possible with patients and older participants. MCI patients and healthy age-matched controls were trained to extract structure from temporal sequences. We ask whether machine learning classifiers can be used to discriminate patients from controls and whether differences between these groups relate to individual cognitive profiles. To this end, we tested participants in four cognitive tasks: working memory, cognitive inhibition, divided attention, and selective attention. We also collected fMRI data before and after training on a probabilistic sequence learning task and extracted fMRI responses and connectivity as features for machine learning classifiers. Our results show that the PI guided GMLVQ classifiers outperform the baseline classifier that only used the cognitive data. In addition, we found that for the baseline classifier, divided attention is the only relevant cognitive feature. When PI was incorporated, divided attention remained the most relevant feature while cognitive inhibition became also relevant for the task. Interestingly, this analysis for the fMRI GMLVQ classifier suggests that (1) when overall fMRI signal is used as inputs to the classifier, the post-training session is most relevant; and (2) when the graph feature reflecting underlying spatiotemporal fMRI pattern is used, the pre-training session is most relevant. Taken together these results suggest that brain connectivity before training and overall fMRI signal after training are both diagnostic of cognitive skills in MCI.

Which Features Make Illustrations in Multimedia Learning Interesting?

ERIC Educational Resources Information Center

Magner, Ulrike Irmgard Elisabeth; Glogger, Inga; Renkl, Alexander

2016-01-01

How can illustrations motivate learners in multimedia learning? Which features make illustrations interesting? Beside the theoretical relevance of addressing these questions, these issues are practically relevant when instructional designers are to decide which features of illustrations can trigger situational interest irrespective of individual…

Industry-relevant magnetron sputtering and cathodic arc ultra-high vacuum deposition system for in situ x-ray diffraction studies of thin film growth using high energy synchrotron radiation.

PubMed

Schroeder, J L; Thomson, W; Howard, B; Schell, N; Näslund, L-Å; Rogström, L; Johansson-Jõesaar, M P; Ghafoor, N; Odén, M; Nothnagel, E; Shepard, A; Greer, J; Birch, J

2015-09-01

We present an industry-relevant, large-scale, ultra-high vacuum (UHV) magnetron sputtering and cathodic arc deposition system purposefully designed for time-resolved in situ thin film deposition/annealing studies using high-energy (>50 keV), high photon flux (>10(12) ph/s) synchrotron radiation. The high photon flux, combined with a fast-acquisition-time (<1 s) two-dimensional (2D) detector, permits time-resolved in situ structural analysis of thin film formation processes. The high-energy synchrotron-radiation based x-rays result in small scattering angles (<11°), allowing large areas of reciprocal space to be imaged with a 2D detector. The system has been designed for use on the 1-tonne, ultra-high load, high-resolution hexapod at the P07 High Energy Materials Science beamline at PETRA III at the Deutsches Elektronen-Synchrotron in Hamburg, Germany. The deposition system includes standard features of a typical UHV deposition system plus a range of special features suited for synchrotron radiation studies and industry-relevant processes. We openly encourage the materials research community to contact us for collaborative opportunities using this unique and versatile scientific instrument.

Identification of DNA-Binding Proteins Using Mixed Feature Representation Methods.

PubMed

Qu, Kaiyang; Han, Ke; Wu, Song; Wang, Guohua; Wei, Leyi

2017-09-22

DNA-binding proteins play vital roles in cellular processes, such as DNA packaging, replication, transcription, regulation, and other DNA-associated activities. The current main prediction method is based on machine learning, and its accuracy mainly depends on the features extraction method. Therefore, using an efficient feature representation method is important to enhance the classification accuracy. However, existing feature representation methods cannot efficiently distinguish DNA-binding proteins from non-DNA-binding proteins. In this paper, a multi-feature representation method, which combines three feature representation methods, namely, K-Skip-N-Grams, Information theory, and Sequential and structural features (SSF), is used to represent the protein sequences and improve feature representation ability. In addition, the classifier is a support vector machine. The mixed-feature representation method is evaluated using 10-fold cross-validation and a test set. Feature vectors, which are obtained from a combination of three feature extractions, show the best performance in 10-fold cross-validation both under non-dimensional reduction and dimensional reduction by max-relevance-max-distance. Moreover, the reduced mixed feature method performs better than the non-reduced mixed feature technique. The feature vectors, which are a combination of SSF and K-Skip-N-Grams, show the best performance in the test set. Among these methods, mixed features exhibit superiority over the single features.

Quasiparticle scattering in type-II Weyl semimetal MoTe2

NASA Astrophysics Data System (ADS)

Lin, Chun-Liang; Arafune, Ryuichi; Minamitani, Emi; Kawai, Maki; Takagi, Noriaki

2018-03-01

The electronic structure of type-II Weyl semimetal molybdenum ditelluride (MoTe2) is studied by using scanning tunneling microscopy and density functional theory calculations. Through measuring energy-dependent quasiparticle interference (QPI) patterns with a cryogenic scanning tunneling microscope, several characteristic features are found in the QPI patterns. Two of them arise from the Weyl semimetal nature; one is the topological Fermi arc surface state and the other can be assigned to be a Weyl point. The remaining structures are derived from the scatterings relevant to the bulk electronic states. The findings lead to further understanding of the topological electronic structure of type-II Weyl semimetal MoTe2.

Quasiparticle scattering in type-II Weyl semimetal MoTe2.

PubMed

Lin, Chun-Liang; Arafune, Ryuichi; Minamitani, Emi; Kawai, Maki; Takagi, Noriaki

2018-02-15

The electronic structure of type-II Weyl semimetal molybdenum ditelluride (MoTe 2 ) is studied by using scanning tunneling microscopy and density functional theory calculations. Through measuring energy-dependent quasiparticle interference (QPI) patterns with a cryogenic scanning tunneling microscope, several characteristic features are found in the QPI patterns. Two of them arise from the Weyl semimetal nature; one is the topological Fermi arc surface state and the other can be assigned to be a Weyl point. The remaining structures are derived from the scatterings relevant to the bulk electronic states. The findings lead to further understanding of the topological electronic structure of type-II Weyl semimetal MoTe 2 .

Contingent attentional capture across multiple feature dimensions in a temporal search task.

PubMed

Ito, Motohiro; Kawahara, Jun I

2016-01-01

The present study examined whether attention can be flexibly controlled to monitor two different feature dimensions (shape and color) in a temporal search task. Specifically, we investigated the occurrence of contingent attentional capture (i.e., interference from task-relevant distractors) and resulting set reconfiguration (i.e., enhancement of single task-relevant set). If observers can restrict searches to a specific value for each relevant feature dimension independently, the capture and reconfiguration effect should only occur when the single relevant distractor in each dimension appears. Participants identified a target letter surrounded by a non-green square or a non-square green frame. The results revealed contingent attentional capture, as target identification accuracy was lower when the distractor contained a target-defining feature than when it contained a nontarget feature. Resulting set reconfiguration was also obtained in that accuracy was superior when the current target's feature (e.g., shape) corresponded to the defining feature of the present distractor (shape) than when the current target's feature did not match the distractor's feature (color). This enhancement was not due to perceptual priming. The present study demonstrated that the principles of contingent attentional capture and resulting set reconfiguration held even when multiple target feature dimensions were monitored. Copyright © 2015 Elsevier B.V. All rights reserved.

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.

Identifying the relevant features of the National Digital Cadastral Database (NDCDB) for spatial analysis by using the Delphi Technique

NASA Astrophysics Data System (ADS)

Halim, N. Z. A.; Sulaiman, S. A.; Talib, K.; Ng, E. G.

2018-02-01

This paper explains the process carried out in identifying the relevant features of the National Digital Cadastral Database (NDCDB) for spatial analysis. The research was initially a part of a larger research exercise to identify the significance of NDCDB from the legal, technical, role and land-based analysis perspectives. The research methodology of applying the Delphi technique is substantially discussed in this paper. A heterogeneous panel of 14 experts was created to determine the importance of NDCDB from the technical relevance standpoint. Three statements describing the relevant features of NDCDB for spatial analysis were established after three rounds of consensus building. It highlighted the NDCDB’s characteristics such as its spatial accuracy, functions, and criteria as a facilitating tool for spatial analysis. By recognising the relevant features of NDCDB for spatial analysis in this study, practical application of NDCDB for various analysis and purpose can be widely implemented.

Dynamic Integration of Task-Relevant Visual Features in Posterior Parietal Cortex

PubMed Central

Freedman, David J.

2014-01-01

Summary The primate visual system consists of multiple hierarchically organized cortical areas, each specialized for processing distinct aspects of the visual scene. For example, color and form are encoded in ventral pathway areas such as V4 and inferior temporal cortex, while motion is preferentially processed in dorsal pathway areas such as the middle temporal area. Such representations often need to be integrated perceptually to solve tasks which depend on multiple features. We tested the hypothesis that the lateral intraparietal area (LIP) integrates disparate task-relevant visual features by recording from LIP neurons in monkeys trained to identify target stimuli composed of conjunctions of color and motion features. We show that LIP neurons exhibit integrative representations of both color and motion features when they are task relevant, and task-dependent shifts of both direction and color tuning. This suggests that LIP plays a role in flexibly integrating task-relevant sensory signals. PMID:25199703

Identifying Bottom-Up and Top-Down Components of Attentional Weight by Experimental Analysis and Computational Modeling

ERIC Educational Resources Information Center

Nordfang, Maria; Dyrholm, Mads; Bundesen, Claus

2013-01-01

The attentional weight of a visual object depends on the contrast of the features of the object to its local surroundings (feature contrast) and the relevance of the features to one's goals (feature relevance). We investigated the dependency in partial report experiments with briefly presented stimuli but unspeeded responses. The task was to…

A Hierarchical Feature and Sample Selection Framework and Its Application for Alzheimer’s Disease Diagnosis

NASA Astrophysics Data System (ADS)

An, Le; Adeli, Ehsan; Liu, Mingxia; Zhang, Jun; Lee, Seong-Whan; Shen, Dinggang

2017-03-01

Classification is one of the most important tasks in machine learning. Due to feature redundancy or outliers in samples, using all available data for training a classifier may be suboptimal. For example, the Alzheimer’s disease (AD) is correlated with certain brain regions or single nucleotide polymorphisms (SNPs), and identification of relevant features is critical for computer-aided diagnosis. Many existing methods first select features from structural magnetic resonance imaging (MRI) or SNPs and then use those features to build the classifier. However, with the presence of many redundant features, the most discriminative features are difficult to be identified in a single step. Thus, we formulate a hierarchical feature and sample selection framework to gradually select informative features and discard ambiguous samples in multiple steps for improved classifier learning. To positively guide the data manifold preservation process, we utilize both labeled and unlabeled data during training, making our method semi-supervised. For validation, we conduct experiments on AD diagnosis by selecting mutually informative features from both MRI and SNP, and using the most discriminative samples for training. The superior classification results demonstrate the effectiveness of our approach, as compared with the rivals.

Spatial distribution of lacunarity of voxelized airborne LiDAR point clouds in various forest assemblages

NASA Astrophysics Data System (ADS)

Székely, Balázs; Kania, Adam; Standovár, Tibor; Heilmeier, Hermann

2015-04-01

Forest ecosystems have characteristic structure of features defined by various structural elements of different scales and vertical positions: shrub layers, understory vegetation, tree trunks, and branches. Furthermore in most of the cases there are superimposed structures in distributions (mosaic or island patterns) due to topography, soil variability, or even anthropogenic factors like past/present forest management activity. This multifaceted spatial context of the forests is relevant for many ecological issues, especially for maintaining forest biodiversity. Our aim in this study is twofold: (1) to quantify this structural variability laterally and vertically using lacunarity, and (2) to relate these results to relevant ecological features, i.e quantitatively described forest properties. Airborne LiDAR data of various quality and point density have been used for our study including a number of forested sites in Central and East Europe (partly Natura 2000 sites). The point clouds have been converted to voxel format and then converted to horizontal layers as images. These images were processed further for the lacunarity calculation. Areas of interest (AOIs) have been selected based on evaluation of the forested areas and auxiliary field information. The calculation has been performed for the AOIs for all available vertical data slices. The lacunarity function referring to a certain point and given vicinity varies horizontally and vertically, depending on the vegetation structure. Furthermore, the topography may also influence this property as the growth of plants, especially spacing and size of trees are influenced by the local topography and relief (e.g., slope, aspect). The comparisons of the flatland and hilly settings show interesting differences and the spatial patterns also vary differently. Because of the large amount of data resulting from these calculations, sophisticated methods are required to analyse the results. The large data amount then has been structured according to AOIs and relevant AOI pairs or small groups have been formed for comparative purposes. Change detection techniques have been applied to reveal fine differences. The spatial variation can be related to ecologically relevant forest characteristics. Data used in this study have been acquired in the framework of ChangeHabitat2 project (an IAPP Marie Curie Actions project of the European Union), in Hungarian-Slovakian Transnational Cooperation Programme 2007-2013, "Management of World Heritage Aggtelek Karst/Slovakian Karst Caves" (HUSK/1101/221/0180, Aggtelek NP). These studies were partly carried out in the project 'Multipurpose assessment serving forest biodiversity conservation in the Carpathian region of Hungary', Swiss-Hungarian Cooperation Programme (SH/4/13 Project). BS contributed as an Alexander von Humboldt Research Fellow.

[Development of methods and instruments for external quality assurance in inpatient parent-child rehabilitation and prevention].

PubMed

Neuderth, S; Lukasczik, M; Musekamp, G; Gerlich, C; Saupe-Heide, M; Löbmann, R; Vogel, H

2013-02-01

There so far is no standardized program for external quality assurance in inpatient parent-child prevention and rehabilitation in Germany. Therefore, instruments and methods of external quality assurance were developed and evaluated on behalf of the federal-level health insurance institutions. On the level of structure quality, a modular questionnaire for assessing structural features of rehabilitation/prevention centers, basic and allocation criteria as well as a checklist for visitations were developed. Structural data were collected in a nationwide survey of parent-child prevention and rehabilitation centers. Process and outcome quality data were collected in n=38 centers. Process quality was assessed using multiple methods (process-related structural features, case-related routine documentation, and incident-related patient questionnaires). Outcome quality was measured via patient questionnaires (n=1 799 patients). We used a multi-level modelling approach by adjusting relevant confounders on institutional and patient levels. The methods, instruments and analyzing procedures developed for measuring quality on the level of structure, processes and outcomes were adjusted in cooperation with all relevant stakeholders. Results are exemplarily presented for all quality assurance tools. For most of the risk-adjusted outcome parameters, we found no significant differences between institutions. For the first time, a comprehensive, standardized and generally applicable set of methods and instruments for routine use in comparative quality measurement of inpatient parent-child prevention and rehabilitation is available. However, it should be considered that the very heterogeneous field of family-oriented measures can not be covered entirely by an external quality assurance program. Therefore, methods and instruments have to be adapted continuously to the specifics of this area of health care and to new developments. © Georg Thieme Verlag KG Stuttgart · New York.

Prospecting Biotechnologically-Relevant Monooxygenases from Cold Sediment Metagenomes: An In Silico Approach

DOE PAGES

Musumeci, Matias A.; Lozada, Mariana; Rial, Daniela V.; ...

2017-04-09

The goal of this work was to identify sequences encoding monooxygenase biocatalysts with novel features by in silico mining an assembled metagenomic dataset of polar and subpolar marine sediments. The targeted enzyme sequences were Baeyer-Villiger and bacterial cytochrome P450 monooxygenases (CYP153). These enzymes have wide-ranging applications, from the synthesis of steroids, antibiotics, mycotoxins and pheromones to the synthesis of monomers for polymerization and anticancer precursors, due to their extraordinary enantio-, regio-, and chemo- selectivity that are valuable features for organic synthesis. Phylogenetic analyses were used to select the most divergent sequences affiliated to these enzyme families among the 264 putativemore » monooxygenases recovered from the ~14 million protein-coding sequences in the assembled metagenome dataset. Three-dimensional structure modeling and docking analysis suggested features useful in biotechnological applications in five metagenomic sequences, such as wide substrate range, novel substrate specificity or regioselectivity. Further analysis revealed structural features associated with psychrophilic enzymes, such as broader substrate accessibility, larger catalytic pockets or low domain interactions, suggesting that they could be applied in biooxidations at room or low temperatures, saving costs inherent to energy consumption. As a result, this work allowed the identification of putative enzyme candidates with promising features from metagenomes, providing a suitable starting point for further developments.« less

Prospecting Biotechnologically-Relevant Monooxygenases from Cold Sediment Metagenomes: An In Silico Approach

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

Musumeci, Matias A.; Lozada, Mariana; Rial, Daniela V.

The goal of this work was to identify sequences encoding monooxygenase biocatalysts with novel features by in silico mining an assembled metagenomic dataset of polar and subpolar marine sediments. The targeted enzyme sequences were Baeyer-Villiger and bacterial cytochrome P450 monooxygenases (CYP153). These enzymes have wide-ranging applications, from the synthesis of steroids, antibiotics, mycotoxins and pheromones to the synthesis of monomers for polymerization and anticancer precursors, due to their extraordinary enantio-, regio-, and chemo- selectivity that are valuable features for organic synthesis. Phylogenetic analyses were used to select the most divergent sequences affiliated to these enzyme families among the 264 putativemore » monooxygenases recovered from the ~14 million protein-coding sequences in the assembled metagenome dataset. Three-dimensional structure modeling and docking analysis suggested features useful in biotechnological applications in five metagenomic sequences, such as wide substrate range, novel substrate specificity or regioselectivity. Further analysis revealed structural features associated with psychrophilic enzymes, such as broader substrate accessibility, larger catalytic pockets or low domain interactions, suggesting that they could be applied in biooxidations at room or low temperatures, saving costs inherent to energy consumption. As a result, this work allowed the identification of putative enzyme candidates with promising features from metagenomes, providing a suitable starting point for further developments.« less

Prospecting Biotechnologically-Relevant Monooxygenases from Cold Sediment Metagenomes: An In Silico Approach.

PubMed

Musumeci, Matías A; Lozada, Mariana; Rial, Daniela V; Mac Cormack, Walter P; Jansson, Janet K; Sjöling, Sara; Carroll, JoLynn; Dionisi, Hebe M

2017-04-09

The goal of this work was to identify sequences encoding monooxygenase biocatalysts with novel features by in silico mining an assembled metagenomic dataset of polar and subpolar marine sediments. The targeted enzyme sequences were Baeyer-Villiger and bacterial cytochrome P450 monooxygenases (CYP153). These enzymes have wide-ranging applications, from the synthesis of steroids, antibiotics, mycotoxins and pheromones to the synthesis of monomers for polymerization and anticancer precursors, due to their extraordinary enantio-, regio-, and chemo- selectivity that are valuable features for organic synthesis. Phylogenetic analyses were used to select the most divergent sequences affiliated to these enzyme families among the 264 putative monooxygenases recovered from the ~14 million protein-coding sequences in the assembled metagenome dataset. Three-dimensional structure modeling and docking analysis suggested features useful in biotechnological applications in five metagenomic sequences, such as wide substrate range, novel substrate specificity or regioselectivity. Further analysis revealed structural features associated with psychrophilic enzymes, such as broader substrate accessibility, larger catalytic pockets or low domain interactions, suggesting that they could be applied in biooxidations at room or low temperatures, saving costs inherent to energy consumption. This work allowed the identification of putative enzyme candidates with promising features from metagenomes, providing a suitable starting point for further developments.

Prospecting Biotechnologically-Relevant Monooxygenases from Cold Sediment Metagenomes: An In Silico Approach

PubMed Central

Musumeci, Matías A.; Lozada, Mariana; Rial, Daniela V.; Mac Cormack, Walter P.; Jansson, Janet K.; Sjöling, Sara; Carroll, JoLynn; Dionisi, Hebe M.

2017-01-01

The goal of this work was to identify sequences encoding monooxygenase biocatalysts with novel features by in silico mining an assembled metagenomic dataset of polar and subpolar marine sediments. The targeted enzyme sequences were Baeyer–Villiger and bacterial cytochrome P450 monooxygenases (CYP153). These enzymes have wide-ranging applications, from the synthesis of steroids, antibiotics, mycotoxins and pheromones to the synthesis of monomers for polymerization and anticancer precursors, due to their extraordinary enantio-, regio-, and chemo- selectivity that are valuable features for organic synthesis. Phylogenetic analyses were used to select the most divergent sequences affiliated to these enzyme families among the 264 putative monooxygenases recovered from the ~14 million protein-coding sequences in the assembled metagenome dataset. Three-dimensional structure modeling and docking analysis suggested features useful in biotechnological applications in five metagenomic sequences, such as wide substrate range, novel substrate specificity or regioselectivity. Further analysis revealed structural features associated with psychrophilic enzymes, such as broader substrate accessibility, larger catalytic pockets or low domain interactions, suggesting that they could be applied in biooxidations at room or low temperatures, saving costs inherent to energy consumption. This work allowed the identification of putative enzyme candidates with promising features from metagenomes, providing a suitable starting point for further developments. PMID:28397770

Effective Dementia Education and Training for the Health and Social Care Workforce: A Systematic Review of the Literature

PubMed Central

Surr, Claire A.; Gates, Cara; Irving, Donna; Oyebode, Jan; Smith, Sarah Jane; Parveen, Sahdia; Drury, Michelle; Dennison, Alison

2017-01-01

Ensuring an informed and effective dementia workforce is of international concern; however, there remains limited understanding of how this can be achieved. This review aimed to identify features of effective dementia educational programs. Critical interpretive synthesis underpinned by Kirkpatrick’s return on investment model was applied. One hundred and fifty-two papers of variable quality were included. Common features of more efficacious educational programs included the need for educational programs to be relevant to participants’ role and experience, involve active face-to-face participation, underpin practice-based learning with theory, be delivered by an experienced facilitator, have a total duration of at least 8 hours with individual sessions of 90 minutes or more, support application of learning in practice, and provide a structured tool or guideline to guide care practice. Further robust research is required to develop the evidence base; however, the findings of this review have relevance for all working in workforce education. PMID:28989194

A novel method for unsteady flow field segmentation based on stochastic similarity of direction

NASA Astrophysics Data System (ADS)

Omata, Noriyasu; Shirayama, Susumu

2018-04-01

Recent developments in fluid dynamics research have opened up the possibility for the detailed quantitative understanding of unsteady flow fields. However, the visualization techniques currently in use generally provide only qualitative insights. A method for dividing the flow field into physically relevant regions of interest can help researchers quantify unsteady fluid behaviors. Most methods at present compare the trajectories of virtual Lagrangian particles. The time-invariant features of an unsteady flow are also frequently of interest, but the Lagrangian specification only reveals time-variant features. To address these challenges, we propose a novel method for the time-invariant spatial segmentation of an unsteady flow field. This segmentation method does not require Lagrangian particle tracking but instead quantitatively compares the stochastic models of the direction of the flow at each observed point. The proposed method is validated with several clustering tests for 3D flows past a sphere. Results show that the proposed method reveals the time-invariant, physically relevant structures of an unsteady flow.

Effective Dementia Education and Training for the Health and Social Care Workforce: A Systematic Review of the Literature.

PubMed

Surr, Claire A; Gates, Cara; Irving, Donna; Oyebode, Jan; Smith, Sarah Jane; Parveen, Sahdia; Drury, Michelle; Dennison, Alison

2017-10-01

Ensuring an informed and effective dementia workforce is of international concern; however, there remains limited understanding of how this can be achieved. This review aimed to identify features of effective dementia educational programs. Critical interpretive synthesis underpinned by Kirkpatrick's return on investment model was applied. One hundred and fifty-two papers of variable quality were included. Common features of more efficacious educational programs included the need for educational programs to be relevant to participants' role and experience, involve active face-to-face participation, underpin practice-based learning with theory, be delivered by an experienced facilitator, have a total duration of at least 8 hours with individual sessions of 90 minutes or more, support application of learning in practice, and provide a structured tool or guideline to guide care practice. Further robust research is required to develop the evidence base; however, the findings of this review have relevance for all working in workforce education.

Automatic Selection of Order Parameters in the Analysis of Large Scale Molecular Dynamics Simulations.

PubMed

Sultan, Mohammad M; Kiss, Gert; Shukla, Diwakar; Pande, Vijay S

2014-12-09

Given the large number of crystal structures and NMR ensembles that have been solved to date, classical molecular dynamics (MD) simulations have become powerful tools in the atomistic study of the kinetics and thermodynamics of biomolecular systems on ever increasing time scales. By virtue of the high-dimensional conformational state space that is explored, the interpretation of large-scale simulations faces difficulties not unlike those in the big data community. We address this challenge by introducing a method called clustering based feature selection (CB-FS) that employs a posterior analysis approach. It combines supervised machine learning (SML) and feature selection with Markov state models to automatically identify the relevant degrees of freedom that separate conformational states. We highlight the utility of the method in the evaluation of large-scale simulations and show that it can be used for the rapid and automated identification of relevant order parameters involved in the functional transitions of two exemplary cell-signaling proteins central to human disease states.

REPDOSE: A database on repeated dose toxicity studies of commercial chemicals--A multifunctional tool.

PubMed

Bitsch, A; Jacobi, S; Melber, C; Wahnschaffe, U; Simetska, N; Mangelsdorf, I

2006-12-01

A database for repeated dose toxicity data has been developed. Studies were selected by data quality. Review documents or risk assessments were used to get a pre-screened selection of available valid data. The structure of the chemicals should be rather simple for well defined chemical categories. The database consists of three core data sets for each chemical: (1) structural features and physico-chemical data, (2) data on study design, (3) study results. To allow consistent queries, a high degree of standardization categories and glossaries were developed for relevant parameters. At present, the database consists of 364 chemicals investigated in 1018 studies which resulted in a total of 6002 specific effects. Standard queries have been developed, which allow analyzing the influence of structural features or PC data on LOELs, target organs and effects. Furthermore, it can be used as an expert system. First queries have shown that the database is a very valuable tool.

Aesthetics-based classification of geological structures in outcrops for geotourism purposes: a tentative proposal

NASA Astrophysics Data System (ADS)

Mikhailenko, Anna V.; Nazarenko, Olesya V.; Ruban, Dmitry A.; Zayats, Pavel P.

2017-03-01

The current growth in geotourism requires an urgent development of classifications of geological features on the basis of criteria that are relevant to tourist perceptions. It appears that structure-related patterns are especially attractive for geotourists. Consideration of the main criteria by which tourists judge beauty and observations made in the geodiversity hotspot of the Western Caucasus allow us to propose a tentative aesthetics-based classification of geological structures in outcrops, with two classes and four subclasses. It is possible to distinguish between regular and quasi-regular patterns (i.e., striped and lined and contorted patterns) and irregular and complex patterns (paysage and sculptured patterns). Typical examples of each case are found both in the study area and on a global scale. The application of the proposed classification permits to emphasise features of interest to a broad range of tourists. Aesthetics-based (i.e., non-geological) classifications are necessary to take into account visions and attitudes of visitors.

Feature-selective Attention in Frontoparietal Cortex: Multivoxel Codes Adjust to Prioritize Task-relevant Information.

PubMed

Jackson, Jade; Rich, Anina N; Williams, Mark A; Woolgar, Alexandra

2017-02-01

Human cognition is characterized by astounding flexibility, enabling us to select appropriate information according to the objectives of our current task. A circuit of frontal and parietal brain regions, often referred to as the frontoparietal attention network or multiple-demand (MD) regions, are believed to play a fundamental role in this flexibility. There is evidence that these regions dynamically adjust their responses to selectively process information that is currently relevant for behavior, as proposed by the "adaptive coding hypothesis" [Duncan, J. An adaptive coding model of neural function in prefrontal cortex. Nature Reviews Neuroscience, 2, 820-829, 2001]. Could this provide a neural mechanism for feature-selective attention, the process by which we preferentially process one feature of a stimulus over another? We used multivariate pattern analysis of fMRI data during a perceptually challenging categorization task to investigate whether the representation of visual object features in the MD regions flexibly adjusts according to task relevance. Participants were trained to categorize visually similar novel objects along two orthogonal stimulus dimensions (length/orientation) and performed short alternating blocks in which only one of these dimensions was relevant. We found that multivoxel patterns of activation in the MD regions encoded the task-relevant distinctions more strongly than the task-irrelevant distinctions: The MD regions discriminated between stimuli of different lengths when length was relevant and between the same objects according to orientation when orientation was relevant. The data suggest a flexible neural system that adjusts its representation of visual objects to preferentially encode stimulus features that are currently relevant for behavior, providing a neural mechanism for feature-selective attention.

FLASHFLOOD: A 3D Field-based similarity search and alignment method for flexible molecules

NASA Astrophysics Data System (ADS)

Pitman, Michael C.; Huber, Wolfgang K.; Horn, Hans; Krämer, Andreas; Rice, Julia E.; Swope, William C.

2001-07-01

A three-dimensional field-based similarity search and alignment method for flexible molecules is introduced. The conformational space of a flexible molecule is represented in terms of fragments and torsional angles of allowed conformations. A user-definable property field is used to compute features of fragment pairs. Features are generalizations of CoMMA descriptors (Silverman, B.D. and Platt, D.E., J. Med. Chem., 39 (1996) 2129.) that characterize local regions of the property field by its local moments. The features are invariant under coordinate system transformations. Features taken from a query molecule are used to form alignments with fragment pairs in the database. An assembly algorithm is then used to merge the fragment pairs into full structures, aligned to the query. Key to the method is the use of a context adaptive descriptor scaling procedure as the basis for similarity. This allows the user to tune the weights of the various feature components based on examples relevant to the particular context under investigation. The property fields may range from simple, phenomenological fields, to fields derived from quantum mechanical calculations. We apply the method to the dihydrofolate/methotrexate benchmark system, and show that when one injects relevant contextual information into the descriptor scaling procedure, better results are obtained more efficiently. We also show how the method works and include computer times for a query from a database that represents approximately 23 million conformers of seventeen flexible molecules.

Synchrotron x-ray diffraction studies of the structural properties of electrode materials in operating battery cells

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

Thurston, T.R.; Jisrawi, N.M.; Mukerjee, S.

Hard x rays from a synchrotron source were utilized in diffraction experiments which probed the bulk of electrode materials while they were operating {ital in} {ital situ} in battery cells. Two technologically relevant electrode materials were examined; an {ital AB}{sub 2}-type anode in a nickel{endash}metal{endash}hydride cell and a LiMn{sub 2}O{sub 4} cathode in a Li-ion {open_quote}{open_quote}rocking chair{close_quote}{close_quote} cell. Structural features such as lattice expansions and contractions, phase transitions, and the formation of multiple phases were easily observed as either hydrogen or lithium was electrochemically intercalated in and out of the electrode materials. The relevance of this technique for future studiesmore » of battery electrode materials is discussed. {copyright} {ital 1996 American Institute of Physics.}« less

Reprint of: Combining theory and experiment for X-ray absorption spectroscopy and resonant X-ray scattering characterization of polymers

DOE PAGES

Su, Gregory M.; Cordova, Isvar A.; Brady, Michael A.; ...

2016-11-01

An improved understanding of fundamental chemistry, electronic structure, morphology, and dynamics in polymers and soft materials requires advanced characterization techniques that are amenable to in situ and operando studies. Soft X-ray methods are especially useful in their ability to non-destructively provide information on specific materials or chemical moieties. Analysis of these experiments, which can be very dependent on X-ray energy and polarization, can quickly become complex. Complementary modeling and predictive capabilities are required to properly probe these critical features. Here in this paper, we present relevant background on this emerging suite of techniques. We focus on how the combination ofmore » theory and experiment has been applied and can be further developed to drive our understanding of how these methods probe relevant chemistry, structure, and dynamics in soft materials.« less

Combining theory and experiment for X-ray absorption spectroscopy and resonant X-ray scattering characterization of polymers

DOE PAGES

Su, Gregory M.; Cordova, Isvar A.; Brady, Michael A.; ...

2016-07-04

We present that an improved understanding of fundamental chemistry, electronic structure, morphology, and dynamics in polymers and soft materials requires advanced characterization techniques that are amenable to in situ and operando studies. Soft X-ray methods are especially useful in their ability to non-destructively provide information on specific materials or chemical moieties. Analysis of these experiments, which can be very dependent on X-ray energy and polarization, can quickly become complex. Complementary modeling and predictive capabilities are required to properly probe these critical features. Here, we present relevant background on this emerging suite of techniques. Finally, we focus on how the combinationmore » of theory and experiment has been applied and can be further developed to drive our understanding of how these methods probe relevant chemistry, structure, and dynamics in soft materials.« less

Topological dimension tunes activity patterns in hierarchical modular networks

NASA Astrophysics Data System (ADS)

Safari, Ali; Moretti, Paolo; Muñoz, Miguel A.

2017-11-01

Connectivity patterns of relevance in neuroscience and systems biology can be encoded in hierarchical modular networks (HMNs). Recent studies highlight the role of hierarchical modular organization in shaping brain activity patterns, providing an excellent substrate to promote both segregation and integration of neural information. Here, we propose an extensive analysis of the critical spreading rate (or ‘epidemic’ threshold)—separating a phase with endemic persistent activity from one in which activity ceases—on diverse HMNs. By employing analytical and computational techniques we determine the nature of such a threshold and scrutinize how it depends on general structural features of the underlying HMN. We critically discuss the extent to which current graph-spectral methods can be applied to predict the onset of spreading in HMNs and, most importantly, we elucidate the role played by the network topological dimension as a relevant and unifying structural parameter, controlling the epidemic threshold.

Classification-free threat detection based on material-science-informed clustering

NASA Astrophysics Data System (ADS)

Yuan, Siyang; Wolter, Scott D.; Greenberg, Joel A.

2017-05-01

X-ray diffraction (XRD) is well-known for yielding composition and structural information about a material. However, in some applications (such as threat detection in aviation security), the properties of a material are more relevant to the task than is a detailed material characterization. Furthermore, the requirement that one first identify a material before determining its class may be difficult or even impossible for a sufficiently large pool of potentially present materials. We therefore seek to learn relevant composition-structure-property relationships between materials to enable material-identification-free classification. We use an expert-informed, data-driven approach operating on a library of XRD spectra from a broad array of stream of commerce materials. We investigate unsupervised learning techniques in order to learn about naturally emergent groupings, and apply supervised learning techniques to determine how well XRD features can be used to separate user-specified classes in the presence of different types and degrees of signal degradation.

Representation of complex vocalizations in the Lusitanian toadfish auditory system: evidence of fine temporal, frequency and amplitude discrimination

PubMed Central

Vasconcelos, Raquel O.; Fonseca, Paulo J.; Amorim, M. Clara P.; Ladich, Friedrich

2011-01-01

Many fishes rely on their auditory skills to interpret crucial information about predators and prey, and to communicate intraspecifically. Few studies, however, have examined how complex natural sounds are perceived in fishes. We investigated the representation of conspecific mating and agonistic calls in the auditory system of the Lusitanian toadfish Halobatrachus didactylus, and analysed auditory responses to heterospecific signals from ecologically relevant species: a sympatric vocal fish (meagre Argyrosomus regius) and a potential predator (dolphin Tursiops truncatus). Using auditory evoked potential (AEP) recordings, we showed that both sexes can resolve fine features of conspecific calls. The toadfish auditory system was most sensitive to frequencies well represented in the conspecific vocalizations (namely the mating boatwhistle), and revealed a fine representation of duration and pulsed structure of agonistic and mating calls. Stimuli and corresponding AEP amplitudes were highly correlated, indicating an accurate encoding of amplitude modulation. Moreover, Lusitanian toadfish were able to detect T. truncatus foraging sounds and A. regius calls, although at higher amplitudes. We provide strong evidence that the auditory system of a vocal fish, lacking accessory hearing structures, is capable of resolving fine features of complex vocalizations that are probably important for intraspecific communication and other relevant stimuli from the auditory scene. PMID:20861044

The Origins of Magnetic Structure in the Corona and Wind

NASA Technical Reports Server (NTRS)

Antiochos, Spiro K.

2010-01-01

One of the most important and most puzzling features of the coronal magnetic field is that it appears to have smooth magnetic structure with little evidence for non-potentiality except at two special locations: photospheric polarity inversions lines. (non-potentiality observed as a filament channel) and coronal hole boundaries, (observed as the slow solar wind). This characteristic feature of the closed-field corona is highly unexpected given that its magnetic field is continuously tangled by photospheric motions. Although reconnection can eliminate some of the injected structure, it cannot destroy the helicity, which should build up to produce observable complexity. I propose that an inverse cascade process transports the injected helicity from the interior of closed flux regions to their boundaries inversion lines and coronal holes, creating both filament channels and the slow wind. We describe how the helicity is injected and transported and calculate the relevant rates. I argue that one process, helicity transport, can explain both the observed lack and presence of structure in the coronal magnetic field. This work has been supported by the NASA HTP, SR&T, and LWS programs.

Non-iridescent Transmissive Structural Color Filter Featuring Highly Efficient Transmission and High Excitation Purity

PubMed Central

Shrestha, Vivek Raj; Lee, Sang-Shin; Kim, Eun-Soo; Choi, Duk-Yong

2014-01-01

Nanostructure based color filtering has been considered an attractive replacement for current colorant pigmentation in the display technologies, in view of its increased efficiencies, ease of fabrication and eco-friendliness. For such structural filtering, iridescence relevant to its angular dependency, which poses a detrimental barrier to the practical development of high performance display and sensing devices, should be mitigated. We report on a non-iridescent transmissive structural color filter, fabricated in a large area of 76.2 × 25.4 mm2, taking advantage of a stack of three etalon resonators in dielectric films based on a high-index cavity in amorphous silicon. The proposed filter features a high transmission above 80%, a high excitation purity of 0.93 and non-iridescence over a range of 160°, exhibiting no significant change in the center wavelength, dominant wavelength and excitation purity, which implies no change in hue and saturation of the output color. The proposed structure may find its potential applications to large-scale display and imaging sensor systems. PMID:24815530

Bimodal pair f-KdV dynamics in star-forming clouds

NASA Astrophysics Data System (ADS)

Karmakar, Pralay Kumar; Haloi, Archana; Roy, Supriya

2018-04-01

A theoretical formalism for investigating the bimodal conjugational mode dynamics of hybrid source, dictated by a unique pair of forced Korteweg-de Vries (f-KdV) equations in a complex turbo-magnetized star-forming cloud, is reported. It uses a standard multi-scale analysis executed over the cloud-governing equations in a closure form to derive the conjugated pair f-KdV system. We numerically see the structural features of two distinctive classes of eigenmode patterns stemming from the conjoint gravito-electrostatic interplay. The electrostatic compressive monotonic aperiodic shock-like patterns and gravitational compressive non-monotonic oscillatory shock-like structures are excitable. It is specifically revealed that the constitutive grain-charge (grain-mass) acts as electrostatic stabilizer (gravitational destabilizer) against the global cloud collapse dynamics. The basic features of the nonlinear coherent structures are confirmed in systematic phase-plane landscapes, indicating electrostatic irregular non-homoclinic open trajectories and gravitational atypical non-chaotic homoclinic fixed-point attractors. The relevance in the real astro-cosmic scenarios of the early phases of structure formation via wave-driven fluid-accretive transport processes is summarily emphasized.

Classification of document page images based on visual similarity of layout structures

NASA Astrophysics Data System (ADS)

Shin, Christian K.; Doermann, David S.

1999-12-01

Searching for documents by their type or genre is a natural way to enhance the effectiveness of document retrieval. The layout of a document contains a significant amount of information that can be used to classify a document's type in the absence of domain specific models. A document type or genre can be defined by the user based primarily on layout structure. Our classification approach is based on 'visual similarity' of the layout structure by building a supervised classifier, given examples of the class. We use image features, such as the percentages of tex and non-text (graphics, image, table, and ruling) content regions, column structures, variations in the point size of fonts, the density of content area, and various statistics on features of connected components which can be derived from class samples without class knowledge. In order to obtain class labels for training samples, we conducted a user relevance test where subjects ranked UW-I document images with respect to the 12 representative images. We implemented our classification scheme using the OC1, a decision tree classifier, and report our findings.

Giant surfactants provide a versatile platform for sub-10-nm nanostructure engineering

PubMed Central

Yu, Xinfei; Yue, Kan; Hsieh, I-Fan; Li, Yiwen; Dong, Xue-Hui; Liu, Chang; Xin, Yu; Wang, Hsiao-Fang; Shi, An-Chang; Newkome, George R.; Chen, Er-Qiang; Zhang, Wen-Bin; Cheng, Stephen Z. D.

2013-01-01

The engineering of structures across different length scales is central to the design of novel materials with controlled macroscopic properties. Herein, we introduce a unique class of self-assembling materials, which are built upon shape- and volume-persistent molecular nanoparticles and other structural motifs, such as polymers, and can be viewed as a size-amplified version of the corresponding small-molecule counterparts. Among them, “giant surfactants” with precise molecular structures have been synthesized by “clicking” compact and polar molecular nanoparticles to flexible polymer tails of various composition and architecture at specific sites. Capturing the structural features of small-molecule surfactants but possessing much larger sizes, giant surfactants bridge the gap between small-molecule surfactants and block copolymers and demonstrate a duality of both materials in terms of their self-assembly behaviors. The controlled structural variations of these giant surfactants through precision synthesis further reveal that their self-assemblies are remarkably sensitive to primary chemical structures, leading to highly diverse, thermodynamically stable nanostructures with feature sizes around 10 nm or smaller in the bulk, thin-film, and solution states, as dictated by the collective physical interactions and geometric constraints. The results suggest that this class of materials provides a versatile platform for engineering nanostructures with sub-10-nm feature sizes. These findings are not only scientifically intriguing in understanding the chemical and physical principles of the self-assembly, but also technologically relevant, such as in nanopatterning technology and microelectronics. PMID:23716680

Varying irrelevant phonetic features hinders learning of the feature being trained.

PubMed

Antoniou, Mark; Wong, Patrick C M

2016-01-01

Learning to distinguish nonnative words that differ in a critical phonetic feature can be difficult. Speech training studies typically employ methods that explicitly direct the learner's attention to the relevant nonnative feature to be learned. However, studies on vision have demonstrated that perceptual learning may occur implicitly, by exposing learners to stimulus features, even if they are irrelevant to the task, and it has recently been suggested that this task-irrelevant perceptual learning framework also applies to speech. In this study, subjects took part in a seven-day training regimen to learn to distinguish one of two nonnative features, namely, voice onset time or lexical tone, using explicit training methods consistent with most speech training studies. Critically, half of the subjects were exposed to stimuli that varied not only in the relevant feature, but in the irrelevant feature as well. The results showed that subjects who were trained with stimuli that varied in the relevant feature and held the irrelevant feature constant achieved the best learning outcomes. Varying both features hindered learning and generalization to new stimuli.

Automatic classification of protein structures using physicochemical parameters.

PubMed

Mohan, Abhilash; Rao, M Divya; Sunderrajan, Shruthi; Pennathur, Gautam

2014-09-01

Protein classification is the first step to functional annotation; SCOP and Pfam databases are currently the most relevant protein classification schemes. However, the disproportion in the number of three dimensional (3D) protein structures generated versus their classification into relevant superfamilies/families emphasizes the need for automated classification schemes. Predicting function of novel proteins based on sequence information alone has proven to be a major challenge. The present study focuses on the use of physicochemical parameters in conjunction with machine learning algorithms (Naive Bayes, Decision Trees, Random Forest and Support Vector Machines) to classify proteins into their respective SCOP superfamily/Pfam family, using sequence derived information. Spectrophores™, a 1D descriptor of the 3D molecular field surrounding a structure was used as a benchmark to compare the performance of the physicochemical parameters. The machine learning algorithms were modified to select features based on information gain for each SCOP superfamily/Pfam family. The effect of combining physicochemical parameters and spectrophores on classification accuracy (CA) was studied. Machine learning algorithms trained with the physicochemical parameters consistently classified SCOP superfamilies and Pfam families with a classification accuracy above 90%, while spectrophores performed with a CA of around 85%. Feature selection improved classification accuracy for both physicochemical parameters and spectrophores based machine learning algorithms. Combining both attributes resulted in a marginal loss of performance. Physicochemical parameters were able to classify proteins from both schemes with classification accuracy ranging from 90-96%. These results suggest the usefulness of this method in classifying proteins from amino acid sequences.

Advanced alerting features: displaying new relevant data and retracting alerts.

PubMed Central

Kuperman, G. J.; Hiltz, F. L.; Teich, J. M.

1997-01-01

We added two advanced features to our automated alerting system. The first feature identifies and displays, at the time an alert is reviewed, relevant data filed between the login time of a specimen leading to an alerting result and the time the alert is reviewed. Relevant data is defined as data of the same kind as generated the alert. The other feature retracts alerts when the alerting value is edited and no longer satisfies the alerting criteria. We evaluated the two features for a 14-week period (new relevant data) and a 6-week period (retraction). Of a total of 1104 alerts in the 14-week evaluation, 286 (25.9%) had new relevant data displayed at alert review time. Of the 286, 75.2% were due to additions of comments to the original piece of alerting data; 24.1% were due to new or pending laboratory results of the same type that generated the alert. Two alerts (out of 490) were retracted in a 6 week period. We conclude that in our system, new clinically relevant data is often added between the time of specimen login and the time that an alerting result from that specimen is reviewed. Retractions occur rarely but are important to detect and communicate. PMID:9357625

The geometrical structure of quantum theory as a natural generalization of information geometry

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

Reginatto, Marcel

2015-01-13

Quantum mechanics has a rich geometrical structure which allows for a geometrical formulation of the theory. This formalism was introduced by Kibble and later developed by a number of other authors. The usual approach has been to start from the standard description of quantum mechanics and identify the relevant geometrical features that can be used for the reformulation of the theory. Here this procedure is inverted: the geometrical structure of quantum theory is derived from information geometry, a geometrical structure that may be considered more fundamental, and the Hilbert space of the standard formulation of quantum mechanics is constructed usingmore » geometrical quantities. This suggests that quantum theory has its roots in information geometry.« less

Selection-for-action in visual search.

PubMed

Hannus, Aave; Cornelissen, Frans W; Lindemann, Oliver; Bekkering, Harold

2005-01-01

Grasping an object rather than pointing to it enhances processing of its orientation but not its color. Apparently, visual discrimination is selectively enhanced for a behaviorally relevant feature. In two experiments we investigated the limitations and targets of this bias. Specifically, in Experiment 1 we were interested to find out whether the effect is capacity demanding, therefore we manipulated the set-size of the display. The results indicated a clear cognitive processing capacity requirement, i.e. the magnitude of the effect decreased for a larger set size. Consequently, in Experiment 2, we investigated if the enhancement effect occurs only at the level of behaviorally relevant feature or at a level common to different features. Therefore we manipulated the discriminability of the behaviorally neutral feature (color). Again, results showed that this manipulation influenced the action enhancement of the behaviorally relevant feature. Particularly, the effect of the color manipulation on the action enhancement suggests that the action effect is more likely to bias the competition between different visual features rather than to enhance the processing of the relevant feature. We offer a theoretical account that integrates the action-intention effect within the biased competition model of visual selective attention.

Atomic structure of the GaAs(001)-c(4x4) surface: first-principles evidence for diversity of heterodimer motifs.

PubMed

Penev, E; Kratzer, P; Scheffler, M

2004-10-01

The GaAs(001)-c(4x4) surface was studied using ab initio atomistic thermodynamics based on density-functional theory calculations. We demonstrate that in a range of stoichiometries, between those of the conventional three As-dimer and the new three Ga-As-dimer models, there exists a diversity of atomic structures featuring Ga-As heterodimers. These results fully explain the experimental scanning tunneling microscopy images and are likely to be relevant also to the c(4x4)-reconstructed (001) surfaces of other III-V semiconductors.

Standardization of accelerator irradiation procedures for simulation of neutron induced damage in reactor structural materials

NASA Astrophysics Data System (ADS)

Shao, Lin; Gigax, Jonathan; Chen, Di; Kim, Hyosim; Garner, Frank A.; Wang, Jing; Toloczko, Mychailo B.

2017-10-01

Self-ion irradiation is widely used as a method to simulate neutron damage in reactor structural materials. Accelerator-based simulation of void swelling, however, introduces a number of neutron-atypical features which require careful data extraction and, in some cases, introduction of innovative irradiation techniques to alleviate these issues. We briefly summarize three such atypical features: defect imbalance effects, pulsed beam effects, and carbon contamination. The latter issue has just been recently recognized as being relevant to simulation of void swelling and is discussed here in greater detail. It is shown that carbon ions are entrained in the ion beam by Coulomb force drag and accelerated toward the target surface. Beam-contaminant interactions are modeled using molecular dynamics simulation. By applying a multiple beam deflection technique, carbon and other contaminants can be effectively filtered out, as demonstrated in an irradiation of HT-9 alloy by 3.5 MeV Fe ions.

The dielectric signature of glass density

NASA Astrophysics Data System (ADS)

Rams-Baron, M.; Wojnarowska, Z.; Knapik-Kowalczuk, J.; Jurkiewicz, K.; Burian, A.; Wojtyniak, M.; Pionteck, J.; Jaworska, M.; Rodríguez-Tinoco, C.; Paluch, M.

2017-09-01

At present, we are witnessing a renewed interest in the properties of densified glasses prepared by isobaric cooling of a liquid at elevated pressure. As high-pressure densification emerges as a promising approach in the development of glasses with customized features, understanding and controlling their unique properties represent a contemporary scientific and technological goal. The results presented herein indicate that the applied high-pressure preparation route leads to a glassy state with higher density (˜1%) and a reduced free volume of about 7%. We show that these subtle structural changes remarkably influence the dielectric response and spectral features of β-relaxation in etoricoxib glass. Our study, combining dynamical and structural techniques, reveal that β-relaxation in etoricoxib is extremely sensitive to the variations in molecular packing and can be used to probe the changes in glass density. Such connection is technologically relevant and may advance further progress in the field.

Feature relevance assessment for the semantic interpretation of 3D point cloud data

NASA Astrophysics Data System (ADS)

Weinmann, M.; Jutzi, B.; Mallet, C.

2013-10-01

The automatic analysis of large 3D point clouds represents a crucial task in photogrammetry, remote sensing and computer vision. In this paper, we propose a new methodology for the semantic interpretation of such point clouds which involves feature relevance assessment in order to reduce both processing time and memory consumption. Given a standard benchmark dataset with 1.3 million 3D points, we first extract a set of 21 geometric 3D and 2D features. Subsequently, we apply a classifier-independent ranking procedure which involves a general relevance metric in order to derive compact and robust subsets of versatile features which are generally applicable for a large variety of subsequent tasks. This metric is based on 7 different feature selection strategies and thus addresses different intrinsic properties of the given data. For the example of semantically interpreting 3D point cloud data, we demonstrate the great potential of smaller subsets consisting of only the most relevant features with 4 different state-of-the-art classifiers. The results reveal that, instead of including as many features as possible in order to compensate for lack of knowledge, a crucial task such as scene interpretation can be carried out with only few versatile features and even improved accuracy.

Does the choice of display system influence perception and visibility of clinically relevant features in digital pathology images?

NASA Astrophysics Data System (ADS)

Kimpe, Tom; Rostang, Johan; Avanaki, Ali; Espig, Kathryn; Xthona, Albert; Cocuranu, Ioan; Parwani, Anil V.; Pantanowitz, Liron

2014-03-01

Digital pathology systems typically consist of a slide scanner, processing software, visualization software, and finally a workstation with display for visualization of the digital slide images. This paper studies whether digital pathology images can look different when presenting them on different display systems, and whether these visual differences can result in different perceived contrast of clinically relevant features. By analyzing a set of four digital pathology images of different subspecialties on three different display systems, it was concluded that pathology images look different when visualized on different display systems. The importance of these visual differences is elucidated when they are located in areas of the digital slide that contain clinically relevant features. Based on a calculation of dE2000 differences between background and clinically relevant features, it was clear that perceived contrast of clinically relevant features is influenced by the choice of display system. Furthermore, it seems that the specific calibration target chosen for the display system has an important effect on the perceived contrast of clinically relevant features. Preliminary results suggest that calibrating to DICOM GSDF calibration performed slightly worse than sRGB, while a new experimental calibration target CSDF performed better than both DICOM GSDF and sRGB. This result is promising as it suggests that further research work could lead to better definition of an optimized calibration target for digital pathology images resulting in a positive effect on clinical performance.

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.

CCProf: exploring conformational change profile of proteins

PubMed Central

Chang, Che-Wei; Chou, Chai-Wei; Chang, Darby Tien-Hao

2016-01-01

In many biological processes, proteins have important interactions with various molecules such as proteins, ions or ligands. Many proteins undergo conformational changes upon these interactions, where regions with large conformational changes are critical to the interactions. This work presents the CCProf platform, which provides conformational changes of entire proteins, named conformational change profile (CCP) in the context. CCProf aims to be a platform where users can study potential causes of novel conformational changes. It provides 10 biological features, including conformational change, potential binding target site, secondary structure, conservation, disorder propensity, hydropathy propensity, sequence domain, structural domain, phosphorylation site and catalytic site. All these information are integrated into a well-aligned view, so that researchers can capture important relevance between different biological features visually. The CCProf contains 986 187 protein structure pairs for 3123 proteins. In addition, CCProf provides a 3D view in which users can see the protein structures before and after conformational changes as well as binding targets that induce conformational changes. All information (e.g. CCP, binding targets and protein structures) shown in CCProf, including intermediate data are available for download to expedite further analyses. Database URL: http://zoro.ee.ncku.edu.tw/ccprof/ PMID:27016699

Chimaeric sounds reveal dichotomies in auditory perception

PubMed Central

Smith, Zachary M.; Delgutte, Bertrand; Oxenham, Andrew J.

2008-01-01

By Fourier's theorem1, signals can be decomposed into a sum of sinusoids of different frequencies. This is especially relevant for hearing, because the inner ear performs a form of mechanical Fourier transform by mapping frequencies along the length of the cochlear partition. An alternative signal decomposition, originated by Hilbert2, is to factor a signal into the product of a slowly varying envelope and a rapidly varying fine time structure. Neurons in the auditory brainstem3–6 sensitive to these features have been found in mammalian physiological studies. To investigate the relative perceptual importance of envelope and fine structure, we synthesized stimuli that we call ‘auditory chimaeras’, which have the envelope of one sound and the fine structure of another. Here we show that the envelope is most important for speech reception, and the fine structure is most important for pitch perception and sound localization. When the two features are in conflict, the sound of speech is heard at a location determined by the fine structure, but the words are identified according to the envelope. This finding reveals a possible acoustic basis for the hypothesized ‘what’ and ‘where’ pathways in the auditory cortex7–10. PMID:11882898

Using complex networks for text classification: Discriminating informative and imaginative documents

NASA Astrophysics Data System (ADS)

de Arruda, Henrique F.; Costa, Luciano da F.; Amancio, Diego R.

2016-01-01

Statistical methods have been widely employed in recent years to grasp many language properties. The application of such techniques have allowed an improvement of several linguistic applications, such as machine translation and document classification. In the latter, many approaches have emphasised the semantical content of texts, as is the case of bag-of-word language models. These approaches have certainly yielded reasonable performance. However, some potential features such as the structural organization of texts have been used only in a few studies. In this context, we probe how features derived from textual structure analysis can be effectively employed in a classification task. More specifically, we performed a supervised classification aiming at discriminating informative from imaginative documents. Using a networked model that describes the local topological/dynamical properties of function words, we achieved an accuracy rate of up to 95%, which is much higher than similar networked approaches. A systematic analysis of feature relevance revealed that symmetry and accessibility measurements are among the most prominent network measurements. Our results suggest that these measurements could be used in related language applications, as they play a complementary role in characterising texts.

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.

Cascade detection for the extraction of localized sequence features; specificity results for HIV-1 protease and structure-function results for the Schellman loop.

PubMed

Newell, Nicholas E

2011-12-15

The extraction of the set of features most relevant to function from classified biological sequence sets is still a challenging problem. A central issue is the determination of expected counts for higher order features so that artifact features may be screened. Cascade detection (CD), a new algorithm for the extraction of localized features from sequence sets, is introduced. CD is a natural extension of the proportional modeling techniques used in contingency table analysis into the domain of feature detection. The algorithm is successfully tested on synthetic data and then applied to feature detection problems from two different domains to demonstrate its broad utility. An analysis of HIV-1 protease specificity reveals patterns of strong first-order features that group hydrophobic residues by side chain geometry and exhibit substantial symmetry about the cleavage site. Higher order results suggest that favorable cooperativity is weak by comparison and broadly distributed, but indicate possible synergies between negative charge and hydrophobicity in the substrate. Structure-function results for the Schellman loop, a helix-capping motif in proteins, contain strong first-order features and also show statistically significant cooperativities that provide new insights into the design of the motif. These include a new 'hydrophobic staple' and multiple amphipathic and electrostatic pair features. CD should prove useful not only for sequence analysis, but also for the detection of multifactor synergies in cross-classified data from clinical studies or other sources. Windows XP/7 application and data files available at: https://sites.google.com/site/cascadedetect/home. nacnewell@comcast.net Supplementary information is available at Bioinformatics online.

A unified framework for image retrieval using keyword and visual features.

PubMed

Jing, Feng; Li, Mingling; Zhang, Hong-Jiang; Zhang, Bo

2005-07-01

In this paper, a unified image retrieval framework based on both keyword annotations and visual features is proposed. In this framework, a set of statistical models are built based on visual features of a small set of manually labeled images to represent semantic concepts and used to propagate keywords to other unlabeled images. These models are updated periodically when more images implicitly labeled by users become available through relevance feedback. In this sense, the keyword models serve the function of accumulation and memorization of knowledge learned from user-provided relevance feedback. Furthermore, two sets of effective and efficient similarity measures and relevance feedback schemes are proposed for query by keyword scenario and query by image example scenario, respectively. Keyword models are combined with visual features in these schemes. In particular, a new, entropy-based active learning strategy is introduced to improve the efficiency of relevance feedback for query by keyword. Furthermore, a new algorithm is proposed to estimate the keyword features of the search concept for query by image example. It is shown to be more appropriate than two existing relevance feedback algorithms. Experimental results demonstrate the effectiveness of the proposed framework.

Learning to rank using user clicks and visual features for image retrieval.

PubMed

Yu, Jun; Tao, Dacheng; Wang, Meng; Rui, Yong

2015-04-01

The inconsistency between textual features and visual contents can cause poor image search results. To solve this problem, click features, which are more reliable than textual information in justifying the relevance between a query and clicked images, are adopted in image ranking model. However, the existing ranking model cannot integrate visual features, which are efficient in refining the click-based search results. In this paper, we propose a novel ranking model based on the learning to rank framework. Visual features and click features are simultaneously utilized to obtain the ranking model. Specifically, the proposed approach is based on large margin structured output learning and the visual consistency is integrated with the click features through a hypergraph regularizer term. In accordance with the fast alternating linearization method, we design a novel algorithm to optimize the objective function. This algorithm alternately minimizes two different approximations of the original objective function by keeping one function unchanged and linearizing the other. We conduct experiments on a large-scale dataset collected from the Microsoft Bing image search engine, and the results demonstrate that the proposed learning to rank models based on visual features and user clicks outperforms state-of-the-art algorithms.

Household Energy Consumption Segmentation Using Hourly Data

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

Kwac, J; Flora, J; Rajagopal, R

2014-01-01

The increasing US deployment of residential advanced metering infrastructure (AMI) has made hourly energy consumption data widely available. Using CA smart meter data, we investigate a household electricity segmentation methodology that uses an encoding system with a pre-processed load shape dictionary. Structured approaches using features derived from the encoded data drive five sample program and policy relevant energy lifestyle segmentation strategies. We also ensure that the methodologies developed scale to large data sets.

Attention to Distinct Goal-relevant Features Differentially Guides Semantic Knowledge Retrieval.

PubMed

Hanson, Gavin K; Chrysikou, Evangelia G

2017-07-01

A critical aspect of conceptual knowledge is the selective activation of goal-relevant aspects of meaning. Although the contributions of ventrolateral prefrontal and posterior temporal areas to semantic cognition are well established, the precise role of posterior parietal cortex in semantic control remains unknown. Here, we examined whether this region modulates attention to goal-relevant features within semantic memory according to the same principles that determine the salience of task-relevant object properties during visual attention. Using multivoxel pattern analysis, we decoded attentional referents during a semantic judgment task, in which participants matched an object cue to a target according to concrete (i.e., color, shape) or abstract (i.e., function, thematic context) semantic features. The goal-relevant semantic feature participants attended to (e.g., color or shape, function or theme) could be decoded from task-associated cortical activity with above-chance accuracy, a pattern that held for both concrete and abstract semantic features. A Bayesian confusion matrix analysis further identified differential contributions to representing attentional demands toward specific object properties across lateral prefrontal, posterior temporal, and inferior parietal regions, with the dorsolateral pFC supporting distinctions between higher-order properties and the left intraparietal sulcus being the only region supporting distinctions across all semantic features. These results are the first to demonstrate that patterns of neural activity in the parietal cortex are sensitive to which features of a concept are attended to, thus supporting the contributions of posterior parietal cortex to semantic control.

Version 3 of the historical-clinical-risk management-20 (HCR-20V3): relevance to violence risk assessment and management in forensic conditional release contexts.

PubMed

Douglas, Kevin S

2014-09-01

The conditional release of insanity acquittees requires decisions both about community risk level and the contextual factors that may mitigate or aggravate risk. This article discusses the potential role of the newly revised Historical-Clinical-Risk Management-20 (HCR-20, Version 3) within the conditional release context. A brief review of the structured professional judgment (SPJ) approach to violence risk assessment and management is provided. Version 2 of the HCR-20, which has been broadly adopted and evaluated, is briefly described. New features of Version 3 of the HCR-20 with particular relevance to conditional release decision-making are reviewed, including: item indicators; ratings of the relevance of risk factors to an individual's violence; risk formulation; scenario planning; and risk management planning. Version 3 of the HCR-20 includes a number of features that should assist evaluators and decision-makers to determine risk level, as well as to anticipate and specify community conditions and contexts that may mitigate or aggravate risk. Research on the HCR-20 Version 3 using approximately 800 participants across three settings (forensic psychiatric, civil psychiatric, correctional) and eight countries is reviewed. Copyright © 2014 John Wiley & Sons, Ltd.

Informing the Human Plasma Protein Binding of ...

EPA Pesticide Factsheets

The free fraction of a xenobiotic in plasma (Fub) is an important determinant of chemical adsorption, distribution, metabolism, elimination, and toxicity, yet experimental plasma protein binding data is scarce for environmentally relevant chemicals. The presented work explores the merit of utilizing available pharmaceutical data to predict Fub for environmentally relevant chemicals via machine learning techniques. Quantitative structure-activity relationship (QSAR) models were constructed with k nearest neighbors (kNN), support vector machines (SVM), and random forest (RF) machine learning algorithms from a training set of 1045 pharmaceuticals. The models were then evaluated with independent test sets of pharmaceuticals (200 compounds) and environmentally relevant ToxCast chemicals (406 total, in two groups of 238 and 168 compounds). The selection of a minimal feature set of 10-15 2D molecular descriptors allowed for both informative feature interpretation and practical applicability domain assessment via a bounded box of descriptor ranges and principal component analysis. The diverse pharmaceutical and environmental chemical sets exhibit similarities in terms of chemical space (99-82% overlap), as well as comparable bias and variance in constructed learning curves. All the models exhibit significant predictability with mean absolute errors (MAE) in the range of 0.10-0.18 Fub. The models performed best for highly bound chemicals (MAE 0.07-0.12), neutrals (MAE 0

Comparative analyses of structural features and scaffold diversity for purchasable compound libraries.

PubMed

Shang, Jun; Sun, Huiyong; Liu, Hui; Chen, Fu; Tian, Sheng; Pan, Peichen; Li, Dan; Kong, Dexin; Hou, Tingjun

2017-04-21

Large purchasable screening libraries of small molecules afforded by commercial vendors are indispensable sources for virtual screening (VS). Selecting an optimal screening library for a specific VS campaign is quite important to improve the success rates and avoid wasting resources in later experimental phases. Analysis of the structural features and molecular diversity for different screening libraries can provide valuable information to the decision making process when selecting screening libraries for VS. In this study, the structural features and scaffold diversity of eleven purchasable screening libraries and Traditional Chinese Medicine Compound Database (TCMCD) were analyzed and compared. Their scaffold diversity represented by the Murcko frameworks and Level 1 scaffolds was characterized by the scaffold counts and cumulative scaffold frequency plots, and visualized by Tree Maps and SAR Maps. The analysis demonstrates that, based on the standardized subsets with similar molecular weight distributions, Chembridge, ChemicalBlock, Mucle, TCMCD and VitasM are more structurally diverse than the others. Compared with all purchasable screening libraries, TCMCD has the highest structural complexity indeed but more conservative molecular scaffolds. Moreover, we found that some representative scaffolds were important components of drug candidates against different drug targets, such as kinases and guanosine-binding protein coupled receptors, and therefore the molecules containing pharmacologically important scaffolds found in screening libraries might be potential inhibitors against the relevant targets. This study may provide valuable perspective on which purchasable compound libraries are better for you to screen. Graphical abstract Selecting diverse compound libraries with scaffold analyses.

Identifying marker genes in transcription profiling data using a mixture of feature relevance experts.

PubMed

Chow, M L; Moler, E J; Mian, I S

2001-03-08

Transcription profiling experiments permit the expression levels of many genes to be measured simultaneously. Given profiling data from two types of samples, genes that most distinguish the samples (marker genes) are good candidates for subsequent in-depth experimental studies and developing decision support systems for diagnosis, prognosis, and monitoring. This work proposes a mixture of feature relevance experts as a method for identifying marker genes and illustrates the idea using published data from samples labeled as acute lymphoblastic and myeloid leukemia (ALL, AML). A feature relevance expert implements an algorithm that calculates how well a gene distinguishes samples, reorders genes according to this relevance measure, and uses a supervised learning method [here, support vector machines (SVMs)] to determine the generalization performances of different nested gene subsets. The mixture of three feature relevance experts examined implement two existing and one novel feature relevance measures. For each expert, a gene subset consisting of the top 50 genes distinguished ALL from AML samples as completely as all 7,070 genes. The 125 genes at the union of the top 50s are plausible markers for a prototype decision support system. Chromosomal aberration and other data support the prediction that the three genes at the intersection of the top 50s, cystatin C, azurocidin, and adipsin, are good targets for investigating the basic biology of ALL/AML. The same data were employed to identify markers that distinguish samples based on their labels of T cell/B cell, peripheral blood/bone marrow, and male/female. Selenoprotein W may discriminate T cells from B cells. Results from analysis of transcription profiling data from tumor/nontumor colon adenocarcinoma samples support the general utility of the aforementioned approach. Theoretical issues such as choosing SVM kernels and their parameters, training and evaluating feature relevance experts, and the impact of potentially mislabeled samples on marker identification (feature selection) are discussed.

Dynamic deformable models for 3D MRI heart segmentation

NASA Astrophysics Data System (ADS)

Zhukov, Leonid; Bao, Zhaosheng; Gusikov, Igor; Wood, John; Breen, David E.

2002-05-01

Automated or semiautomated segmentation of medical images decreases interstudy variation, observer bias, and postprocessing time as well as providing clincally-relevant quantitative data. In this paper we present a new dynamic deformable modeling approach to 3D segmentation. It utilizes recently developed dynamic remeshing techniques and curvature estimation methods to produce high-quality meshes. The approach has been implemented in an interactive environment that allows a user to specify an initial model and identify key features in the data. These features act as hard constraints that the model must not pass through as it deforms. We have employed the method to perform semi-automatic segmentation of heart structures from cine MRI data.

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

Joint Concept Correlation and Feature-Concept Relevance Learning for Multilabel Classification.

PubMed

Zhao, Xiaowei; Ma, Zhigang; Li, Zhi; Li, Zhihui

2018-02-01

In recent years, multilabel classification has attracted significant attention in multimedia annotation. However, most of the multilabel classification methods focus only on the inherent correlations existing among multiple labels and concepts and ignore the relevance between features and the target concepts. To obtain more robust multilabel classification results, we propose a new multilabel classification method aiming to capture the correlations among multiple concepts by leveraging hypergraph that is proved to be beneficial for relational learning. Moreover, we consider mining feature-concept relevance, which is often overlooked by many multilabel learning algorithms. To better show the feature-concept relevance, we impose a sparsity constraint on the proposed method. We compare the proposed method with several other multilabel classification methods and evaluate the classification performance by mean average precision on several data sets. The experimental results show that the proposed method outperforms the state-of-the-art methods.

Discriminative prediction of mammalian enhancers from DNA sequence

PubMed Central

Lee, Dongwon; Karchin, Rachel; Beer, Michael A.

2011-01-01

Accurately predicting regulatory sequences and enhancers in entire genomes is an important but difficult problem, especially in large vertebrate genomes. With the advent of ChIP-seq technology, experimental detection of genome-wide EP300/CREBBP bound regions provides a powerful platform to develop predictive tools for regulatory sequences and to study their sequence properties. Here, we develop a support vector machine (SVM) framework which can accurately identify EP300-bound enhancers using only genomic sequence and an unbiased set of general sequence features. Moreover, we find that the predictive sequence features identified by the SVM classifier reveal biologically relevant sequence elements enriched in the enhancers, but we also identify other features that are significantly depleted in enhancers. The predictive sequence features are evolutionarily conserved and spatially clustered, providing further support of their functional significance. Although our SVM is trained on experimental data, we also predict novel enhancers and show that these putative enhancers are significantly enriched in both ChIP-seq signal and DNase I hypersensitivity signal in the mouse brain and are located near relevant genes. Finally, we present results of comparisons between other EP300/CREBBP data sets using our SVM and uncover sequence elements enriched and/or depleted in the different classes of enhancers. Many of these sequence features play a role in specifying tissue-specific or developmental-stage-specific enhancer activity, but our results indicate that some features operate in a general or tissue-independent manner. In addition to providing a high confidence list of enhancer targets for subsequent experimental investigation, these results contribute to our understanding of the general sequence structure of vertebrate enhancers. PMID:21875935

Structure-property relation and relevance of beam theories for microtubules: a coupled molecular and continuum mechanics study.

PubMed

Li, Si; Wang, Chengyuan; Nithiarasu, Perumal

2018-04-01

Quasi-one-dimensional microtubules (MTs) in cells enjoy high axial rigidity but large transverse flexibility due to the inter-protofilament (PF) sliding. This study aims to explore the structure-property relation for MTs and examine the relevance of the beam theories to their unique features. A molecular structural mechanics (MSM) model was used to identify the origin of the inter-PF sliding and its role in bending and vibration of MTs. The beam models were then fitted to the MSM to reveal how they cope with the distinct mechanical responses induced by the inter-PF sliding. Clear evidence showed that the inter-PF sliding is due to the soft inter-PF bonds and leads to the length-dependent bending stiffness. The Euler beam theory is found to adequately describe MT deformation when the inter-PF sliding is largely prohibited. Nevertheless, neither shear deformation nor the nonlocal effect considered in the 'more accurate' beam theories can fully capture the effect of the inter-PF sliding. This reflects the distinct deformation mechanisms between an MT and its equivalent continuous body.

Crystal Structure of Thrombin Bound to the Uncleaved Extracellular Fragment of PAR1

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

Gandhi, Prafull S.; Chen, Zhiwei; Di Cera, Enrico

2010-05-11

Abundant structural information exists on how thrombin recognizes ligands at the active site or at exosites separate from the active site region, but remarkably little is known about how thrombin recognizes substrates that bridge both the active site and exosite I. The case of the protease-activated receptor PAR1 is particularly relevant in view of the plethora of biological effects associated with its activation by thrombin. Here, we present the 1.8 {angstrom} resolution structure of thrombin S195A in complex with a 30-residue long uncleaved extracellular fragment of PAR1 that documents for the first time a productive binding mode bridging the activemore » site and exosite I. The structure reveals two unexpected features of the thrombin-PAR1 interaction. The acidic P3 residue of PAR1, Asp{sup 39}, does not hinder binding to the active site and actually makes favorable interactions with Gly{sup 219} of thrombin. The tethered ligand domain shows a considerable degree of disorder even when bound to thrombin. The results fill a significant gap in our understanding of the molecular mechanisms of recognition by thrombin in ways that are relevant to other physiological substrates.« less

Combining functional and structural tests improves the diagnostic accuracy of relevance vector machine classifiers

PubMed Central

Racette, Lyne; Chiou, Christine Y.; Hao, Jiucang; Bowd, Christopher; Goldbaum, Michael H.; Zangwill, Linda M.; Lee, Te-Won; Weinreb, Robert N.; Sample, Pamela A.

2009-01-01

Purpose To investigate whether combining optic disc topography and short-wavelength automated perimetry (SWAP) data improves the diagnostic accuracy of relevance vector machine (RVM) classifiers for detecting glaucomatous eyes compared to using each test alone. Methods One eye of 144 glaucoma patients and 68 healthy controls from the Diagnostic Innovations in Glaucoma Study were included. RVM were trained and tested with cross-validation on optimized (backward elimination) SWAP features (thresholds plus age; pattern deviation (PD); total deviation (TD)) and on Heidelberg Retina Tomograph II (HRT) optic disc topography features, independently and in combination. RVM performance was also compared to two HRT linear discriminant functions (LDF) and to SWAP mean deviation (MD) and pattern standard deviation (PSD). Classifier performance was measured by the area under the receiver operating characteristic curves (AUROCs) generated for each feature set and by the sensitivities at set specificities of 75%, 90% and 96%. Results RVM trained on combined HRT and SWAP thresholds plus age had significantly higher AUROC (0.93) than RVM trained on HRT (0.88) and SWAP (0.76) alone. AUROCs for the SWAP global indices (MD: 0.68; PSD: 0.72) offered no advantage over SWAP thresholds plus age, while the LDF AUROCs were significantly lower than RVM trained on the combined SWAP and HRT feature set and on HRT alone feature set. Conclusions Training RVM on combined optimized HRT and SWAP data improved diagnostic accuracy compared to training on SWAP and HRT parameters alone. Future research may identify other combinations of tests and classifiers that can also improve diagnostic accuracy. PMID:19528827

Modeling the Evolution of Beliefs Using an Attentional Focus Mechanism

PubMed Central

Marković, Dimitrije; Gläscher, Jan; Bossaerts, Peter; O’Doherty, John; Kiebel, Stefan J.

2015-01-01

For making decisions in everyday life we often have first to infer the set of environmental features that are relevant for the current task. Here we investigated the computational mechanisms underlying the evolution of beliefs about the relevance of environmental features in a dynamical and noisy environment. For this purpose we designed a probabilistic Wisconsin card sorting task (WCST) with belief solicitation, in which subjects were presented with stimuli composed of multiple visual features. At each moment in time a particular feature was relevant for obtaining reward, and participants had to infer which feature was relevant and report their beliefs accordingly. To test the hypothesis that attentional focus modulates the belief update process, we derived and fitted several probabilistic and non-probabilistic behavioral models, which either incorporate a dynamical model of attentional focus, in the form of a hierarchical winner-take-all neuronal network, or a diffusive model, without attention-like features. We used Bayesian model selection to identify the most likely generative model of subjects’ behavior and found that attention-like features in the behavioral model are essential for explaining subjects’ responses. Furthermore, we demonstrate a method for integrating both connectionist and Bayesian models of decision making within a single framework that allowed us to infer hidden belief processes of human subjects. PMID:26495984

Integrative topological analysis of mass spectrometry data reveals molecular features with clinical relevance in esophageal squamous cell carcinoma

PubMed Central

Gao, She-Gan; Liu, Rui-Min; Zhao, Yun-Gang; Wang, Pei; Ward, Douglas G.; Wang, Guang-Chao; Guo, Xiang-Qian; Gu, Juan; Niu, Wan-Bin; Zhang, Tian; Martin, Ashley; Guo, Zhi-Peng; Feng, Xiao-Shan; Qi, Yi-Jun; Ma, Yuan-Fang

2016-01-01

Combining MS-based proteomic data with network and topological features of such network would identify more clinically relevant molecules and meaningfully expand the repertoire of proteins derived from MS analysis. The integrative topological indexes representing 95.96% information of seven individual topological measures of node proteins were calculated within a protein-protein interaction (PPI) network, built using 244 differentially expressed proteins (DEPs) identified by iTRAQ 2D-LC-MS/MS. Compared with DEPs, differentially expressed genes (DEGs) and comprehensive features (CFs), structurally dominant nodes (SDNs) based on integrative topological index distribution produced comparable classification performance in three different clinical settings using five independent gene expression data sets. The signature molecules of SDN-based classifier for distinction of early from late clinical TNM stages were enriched in biological traits of protein synthesis, intracellular localization and ribosome biogenesis, which suggests that ribosome biogenesis represents a promising therapeutic target for treating ESCC. In addition, ITGB1 expression selected exclusively by integrative topological measures correlated with clinical stages and prognosis, which was further validated with two independent cohorts of ESCC samples. Thus the integrative topological analysis of PPI networks proposed in this study provides an alternative approach to identify potential biomarkers and therapeutic targets from MS/MS data with functional insights in ESCC. PMID:26898710

Visualizing spatial correlation: structural and electronic orders in iron-based superconductors on atomic scale

NASA Astrophysics Data System (ADS)

Maksov, Artem; Ziatdinov, Maxim; Li, Li; Sefat, Athena; Maksymovych, Petro; Kalinin, Sergei

Crystalline matter on the nanoscale level often exhibits strongly inhomogeneous structural and electronic orders, which have a profound effect on macroscopic properties. This may be caused by subtle interplay between chemical disorder, strain, magnetic, and structural order parameters. We present a novel approach based on combination of high resolution scanning tunneling microscopy/spectroscopy (STM/S) and deep data style analysis for automatic separation, extraction, and correlation of structural and electronic behavior which might lead us to uncovering the underlying sources of inhomogeneity in in iron-based family of superconductors (FeSe, BaFe2As2) . We identify STS spectral features using physically robust Bayesian linear unmixing, and show their direct relevance to the fundamental physical properties of the system, including electronic states associated with individual defects and impurities. We collect structural data from individual unit cells on the crystalline lattice, and calculate both global and local indicators of spatial correlation with electronic features, demonstrating, for the first time, a direct quantifiable connection between observed structural order parameters extracted from the STM data and electronic order parameters identified within the STS data. This research was sponsored by the Division of Materials Sciences and Engineering, Office of Science, Basic Energy Sciences, US DOE.

A bootstrap based Neyman-Pearson test for identifying variable importance.

PubMed

Ditzler, Gregory; Polikar, Robi; Rosen, Gail

2015-04-01

Selection of most informative features that leads to a small loss on future data are arguably one of the most important steps in classification, data analysis and model selection. Several feature selection (FS) algorithms are available; however, due to noise present in any data set, FS algorithms are typically accompanied by an appropriate cross-validation scheme. In this brief, we propose a statistical hypothesis test derived from the Neyman-Pearson lemma for determining if a feature is statistically relevant. The proposed approach can be applied as a wrapper to any FS algorithm, regardless of the FS criteria used by that algorithm, to determine whether a feature belongs in the relevant set. Perhaps more importantly, this procedure efficiently determines the number of relevant features given an initial starting point. We provide freely available software implementations of the proposed methodology.

Mammogram classification scheme using 2D-discrete wavelet and local binary pattern for detection of breast cancer

NASA Astrophysics Data System (ADS)

Adi Putra, Januar

2018-04-01

In this paper, we propose a new mammogram classification scheme to classify the breast tissues as normal or abnormal. Feature matrix is generated using Local Binary Pattern to all the detailed coefficients from 2D-DWT of the region of interest (ROI) of a mammogram. Feature selection is done by selecting the relevant features that affect the classification. Feature selection is used to reduce the dimensionality of data and features that are not relevant, in this paper the F-test and Ttest will be performed to the results of the feature extraction dataset to reduce and select the relevant feature. The best features are used in a Neural Network classifier for classification. In this research we use MIAS and DDSM database. In addition to the suggested scheme, the competent schemes are also simulated for comparative analysis. It is observed that the proposed scheme has a better say with respect to accuracy, specificity and sensitivity. Based on experiments, the performance of the proposed scheme can produce high accuracy that is 92.71%, while the lowest accuracy obtained is 77.08%.

A case study of the use of GPR for rehabilitation of a classified Art Deco building: The InovaDomus house

NASA Astrophysics Data System (ADS)

Barraca, Nuno; Almeida, Miguel; Varum, Humberto; Almeida, Fernando; Matias, Manuel Senos

2016-04-01

Ancient buildings in historical town centers can be protected by Cultural Heritage legislation, thus implying that any rehabilitation must respect their main architectural features. These concerns also apply to Modern and Contemporary buildings, in particular if they are important examples of architectural styles from those periods. These extra problems, or motivations, add to the inherent structural delicacy of ancient building restoration that requires detailed knowledge of the building foundations, characteristics and materials, modification history, infrastructure mapping, current pathologies, etc., all relevant information for an informed rehabilitation project. Such knowledge is seldom available before the actual rehabilitation works begin, and the usual invasive preliminary surveys are frequently expensive, time-consuming and likely significantly alter/damage the building's main features or structural integrity. Hence, the current demand for indirect, non-invasive, reliable and high resolution imagery techniques able to produce relevant information at the early stages of a rehabilitation project. The present work demonstrates that Ground Penetrating Radar (GPR or Georadar) surveys can provide a priori knowledge on the structure, construction techniques, materials, history and pathologies in a classified Modern Age building. It is also shown that the use of GPR on these projects requires carefully designed surveys, taking into account the known information, spatial constraints, environmental noise, nature and dimensions of the expected targets and suitable data processing sequences. Thus, if properly applied, GPR produces high-resolution results crucial for sound engineering/architectural interventions aiming to restore and renovate Modern and Contemporary buildings, with (1) focus on the overall quality of the end-result, (2) no damage inflicted to the existing structure, (3) respect of the building's historical coherence and architectural elements and characteristics, that is, its Cultural Heritage value. Most of the findings and applications discussed in this work can be seen as an approximation to model studies, so that, relevant information can be drawn from the different investigated situations. Therefore, owing to the nature and the range of the problems encountered in this case study, it is also expected that the presented GPR data and interpretation will provide important clues and guidance in the planning and investigation of similar projects and problems.

Selecting relevant 3D image features of margin sharpness and texture for lung nodule retrieval.

PubMed

Ferreira, José Raniery; de Azevedo-Marques, Paulo Mazzoncini; Oliveira, Marcelo Costa

2017-03-01

Lung cancer is the leading cause of cancer-related deaths in the world. Its diagnosis is a challenge task to specialists due to several aspects on the classification of lung nodules. Therefore, it is important to integrate content-based image retrieval methods on the lung nodule classification process, since they are capable of retrieving similar cases from databases that were previously diagnosed. However, this mechanism depends on extracting relevant image features in order to obtain high efficiency. The goal of this paper is to perform the selection of 3D image features of margin sharpness and texture that can be relevant on the retrieval of similar cancerous and benign lung nodules. A total of 48 3D image attributes were extracted from the nodule volume. Border sharpness features were extracted from perpendicular lines drawn over the lesion boundary. Second-order texture features were extracted from a cooccurrence matrix. Relevant features were selected by a correlation-based method and a statistical significance analysis. Retrieval performance was assessed according to the nodule's potential malignancy on the 10 most similar cases and by the parameters of precision and recall. Statistical significant features reduced retrieval performance. Correlation-based method selected 2 margin sharpness attributes and 6 texture attributes and obtained higher precision compared to all 48 extracted features on similar nodule retrieval. Feature space dimensionality reduction of 83 % obtained higher retrieval performance and presented to be a computationaly low cost method of retrieving similar nodules for the diagnosis of lung cancer.

Citrate bridges between mineral platelets in bone

PubMed Central

Davies, Erika; Müller, Karin H.; Wong, Wai Ching; Pickard, Chris J.; Reid, David G.; Skepper, Jeremy N.; Duer, Melinda J.

2014-01-01

We provide evidence that citrate anions bridge between mineral platelets in bone and hypothesize that their presence acts to maintain separate platelets with disordered regions between them rather than gradual transformations into larger, more ordered blocks of mineral. To assess this hypothesis, we take as a model for a citrate bridging between layers of calcium phosphate mineral a double salt octacalcium phosphate citrate (OCP-citrate). We use a combination of multinuclear solid-state NMR spectroscopy, powder X-ray diffraction, and first principles electronic structure calculations to propose a quantitative structure for this material, in which citrate anions reside in a hydrated layer, bridging between apatitic layers. To assess the relevance of such a structure in native bone mineral, we present for the first time, to our knowledge, 17O NMR data on bone and compare them with 17O NMR data for OCP-citrate and other calcium phosphate minerals relevant to bone. The proposed structural model that we deduce from this work for bone mineral is a layered structure with thin apatitic platelets sandwiched between OCP-citrate–like hydrated layers. Such a structure can explain a number of known structural features of bone mineral: the thin, plate-like morphology of mature bone mineral crystals, the presence of significant quantities of strongly bound water molecules, and the relatively high concentration of hydrogen phosphate as well as the maintenance of a disordered region between mineral platelets. PMID:24706850

Statistical molecular design of balanced compound libraries for QSAR modeling.

PubMed

Linusson, A; Elofsson, M; Andersson, I E; Dahlgren, M K

2010-01-01

A fundamental step in preclinical drug development is the computation of quantitative structure-activity relationship (QSAR) models, i.e. models that link chemical features of compounds with activities towards a target macromolecule associated with the initiation or progression of a disease. QSAR models are computed by combining information on the physicochemical and structural features of a library of congeneric compounds, typically assembled from two or more building blocks, and biological data from one or more in vitro assays. Since the models provide information on features affecting the compounds' biological activity they can be used as guides for further optimization. However, in order for a QSAR model to be relevant to the targeted disease, and drug development in general, the compound library used must contain molecules with balanced variation of the features spanning the chemical space believed to be important for interaction with the biological target. In addition, the assays used must be robust and deliver high quality data that are directly related to the function of the biological target and the associated disease state. In this review, we discuss and exemplify the concept of statistical molecular design (SMD) in the selection of building blocks and final synthetic targets (i.e. compounds to synthesize) to generate information-rich, balanced libraries for biological testing and computation of QSAR models.

Use cases and DEMO: aligning functional features of ICT-infrastructure to business processes.

PubMed

Maij, E; Toussaint, P J; Kalshoven, M; Poerschke, M; Zwetsloot-Schonk, J H M

2002-11-12

The proper alignment of functional features of the ICT-infrastructure to business processes is a major challenge in health care organisations. This alignment takes into account that the organisational structure not only shapes the ICT-infrastructure, but that the inverse also holds. To solve the alignment problem, relevant features of the ICT-infrastructure should be derived from the organisational structure and the influence of this envisaged ICT to the work practices should be pointed out. The objective of our study was to develop a method to solve this alignment problem. In a previous study we demonstrated the appropriateness of the business process modelling methodology Dynamic Essential Modelling of Organizations (DEMO). A proven and widely used modelling language for expressing functional features is Unified Modelling Language (UML). In the context of a specific case study at the University Medical Centre Utrecht in the Netherlands we investigated if the combined use of DEMO and UML could solve the alignment problem. The study demonstrated that the DEMO models were suited as a starting point in deriving system functionality by using the use case concept of UML. Further, the case study demonstrated that in using this approach for the alignment problem, insight is gained into the mutual influence of ICT-infrastructure and organisation structure: (a) specification of independent, re-usable components-as a set of related functionalities-is realised, and (b) a helpful representation of the current and future work practice is provided for in relation to the envisaged ICT support.

Antimicrobial peptides: a new class of antimalarial drugs?

PubMed Central

Vale, Nuno; Aguiar, Luísa; Gomes, Paula

2014-01-01

A range of antimicrobial peptides (AMP) exhibit activity on malaria parasites, Plasmodium spp., in their blood or mosquito stages, or both. These peptides include a diverse array of both natural and synthetic molecules varying greatly in size, charge, hydrophobicity, and secondary structure features. Along with an overview of relevant literature reports regarding AMP that display antiplasmodial activity, this review makes a few considerations about those molecules as a potential new class of antimalarial drugs. PMID:25566072

Relevance of deterministic chaos theory to studies in functioning of dynamical systems

NASA Astrophysics Data System (ADS)

Glagolev, S. N.; Bukhonova, S. M.; Chikina, E. D.

2018-03-01

The paper considers chaotic behavior of dynamical systems typical for social and economic processes. Approaches to analysis and evaluation of system development processes are studies from the point of view of controllability and determinateness. Explanations are given for necessity to apply non-standard mathematical tools to explain states of dynamical social and economic systems on the basis of fractal theory. Features of fractal structures, such as non-regularity, self-similarity, dimensionality and fractionality are considered.

An optimal transportation approach for nuclear structure-based pathology.

PubMed

Wang, Wei; Ozolek, John A; Slepčev, Dejan; Lee, Ann B; Chen, Cheng; Rohde, Gustavo K

2011-03-01

Nuclear morphology and structure as visualized from histopathology microscopy images can yield important diagnostic clues in some benign and malignant tissue lesions. Precise quantitative information about nuclear structure and morphology, however, is currently not available for many diagnostic challenges. This is due, in part, to the lack of methods to quantify these differences from image data. We describe a method to characterize and contrast the distribution of nuclear structure in different tissue classes (normal, benign, cancer, etc.). The approach is based on quantifying chromatin morphology in different groups of cells using the optimal transportation (Kantorovich-Wasserstein) metric in combination with the Fisher discriminant analysis and multidimensional scaling techniques. We show that the optimal transportation metric is able to measure relevant biological information as it enables automatic determination of the class (e.g., normal versus cancer) of a set of nuclei. We show that the classification accuracies obtained using this metric are, on average, as good or better than those obtained utilizing a set of previously described numerical features. We apply our methods to two diagnostic challenges for surgical pathology: one in the liver and one in the thyroid. Results automatically computed using this technique show potentially biologically relevant differences in nuclear structure in liver and thyroid cancers.

An optimal transportation approach for nuclear structure-based pathology

PubMed Central

Wang, Wei; Ozolek, John A.; Slepčev, Dejan; Lee, Ann B.; Chen, Cheng; Rohde, Gustavo K.

2012-01-01

Nuclear morphology and structure as visualized from histopathology microscopy images can yield important diagnostic clues in some benign and malignant tissue lesions. Precise quantitative information about nuclear structure and morphology, however, is currently not available for many diagnostic challenges. This is due, in part, to the lack of methods to quantify these differences from image data. We describe a method to characterize and contrast the distribution of nuclear structure in different tissue classes (normal, benign, cancer, etc.). The approach is based on quantifying chromatin morphology in different groups of cells using the optimal transportation (Kantorovich-Wasserstein) metric in combination with the Fisher discriminant analysis and multidimensional scaling techniques. We show that the optimal transportation metric is able to measure relevant biological information as it enables automatic determination of the class (e.g. normal vs. cancer) of a set of nuclei. We show that the classification accuracies obtained using this metric are, on average, as good or better than those obtained utilizing a set of previously described numerical features. We apply our methods to two diagnostic challenges for surgical pathology: one in the liver and one in the thyroid. Results automatically computed using this technique show potentially biologically relevant differences in nuclear structure in liver and thyroid cancers. PMID:20977984

Attention improves encoding of task-relevant features in the human visual cortex

PubMed Central

Jehee, Janneke F.M.; Brady, Devin K.; Tong, Frank

2011-01-01

When spatial attention is directed towards a particular stimulus, increased activity is commonly observed in corresponding locations of the visual cortex. Does this attentional increase in activity indicate improved processing of all features contained within the attended stimulus, or might spatial attention selectively enhance the features relevant to the observer’s task? We used fMRI decoding methods to measure the strength of orientation-selective activity patterns in the human visual cortex while subjects performed either an orientation or contrast discrimination task, involving one of two laterally presented gratings. Greater overall BOLD activation with spatial attention was observed in areas V1-V4 for both tasks. However, multivariate pattern analysis revealed that orientation-selective responses were enhanced by attention only when orientation was the task-relevant feature, and not when the grating’s contrast had to be attended. In a second experiment, observers discriminated the orientation or color of a specific lateral grating. Here, orientation-selective responses were enhanced in both tasks but color-selective responses were enhanced only when color was task-relevant. In both experiments, task-specific enhancement of feature-selective activity was not confined to the attended stimulus location, but instead spread to other locations in the visual field, suggesting the concurrent involvement of a global feature-based attentional mechanism. These results suggest that attention can be remarkably selective in its ability to enhance particular task-relevant features, and further reveal that increases in overall BOLD amplitude are not necessarily accompanied by improved processing of stimulus information. PMID:21632942

Attention improves encoding of task-relevant features in the human visual cortex.

PubMed

Jehee, Janneke F M; Brady, Devin K; Tong, Frank

2011-06-01

When spatial attention is directed toward a particular stimulus, increased activity is commonly observed in corresponding locations of the visual cortex. Does this attentional increase in activity indicate improved processing of all features contained within the attended stimulus, or might spatial attention selectively enhance the features relevant to the observer's task? We used fMRI decoding methods to measure the strength of orientation-selective activity patterns in the human visual cortex while subjects performed either an orientation or contrast discrimination task, involving one of two laterally presented gratings. Greater overall BOLD activation with spatial attention was observed in visual cortical areas V1-V4 for both tasks. However, multivariate pattern analysis revealed that orientation-selective responses were enhanced by attention only when orientation was the task-relevant feature and not when the contrast of the grating had to be attended. In a second experiment, observers discriminated the orientation or color of a specific lateral grating. Here, orientation-selective responses were enhanced in both tasks, but color-selective responses were enhanced only when color was task relevant. In both experiments, task-specific enhancement of feature-selective activity was not confined to the attended stimulus location but instead spread to other locations in the visual field, suggesting the concurrent involvement of a global feature-based attentional mechanism. These results suggest that attention can be remarkably selective in its ability to enhance particular task-relevant features and further reveal that increases in overall BOLD amplitude are not necessarily accompanied by improved processing of stimulus information.

[Morphometric features of the structure of the central nucleus of the amygdala in men and women].

PubMed

Antyukhov, A D

2015-01-01

To identify the interhemispheric asymmetry in the structure of the central nucleus of the amygdala in men and women. Morphometric features of the structure of neurons of the central nucleus amygdala complex were studied in histological sections of the brain of 6 men and 6 women (24 hemispheres), aged 19 to 55 years, with no lifetime diagnosis of mental or neurological disease. The value of the profile fields of neurons of the central nucleus amygdala complex in the left and right hemispheres of the brain were investigated. In women, the average value of neurons in the left hemisphere was somewhat greater than in the right hemisphere, while in men this value was greater in the right hemisphere. The interhemispheric morphometric differences were not significant regardless of gender. In addition, the quantity of relevant fields of neurons in the central nucleus of the amygdala in women was significantly larger than that of men in both hemispheres. The authors attempted to associate the results obtained in the study with emotional perception in men and women.

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 white matter asymmetries in relation to functional asymmetries during speech perception and production.

PubMed

Ocklenburg, Sebastian; Hugdahl, Kenneth; Westerhausen, René

2013-12-01

Functional hemispheric asymmetries of speech production and perception are a key feature of the human language system, but their neurophysiological basis is still poorly understood. Using a combined fMRI and tract-based spatial statistics approach, we investigated the relation of microstructural asymmetries in language-relevant white matter pathways and functional activation asymmetries during silent verb generation and passive listening to spoken words. Tract-based spatial statistics revealed several leftward asymmetric clusters in the arcuate fasciculus and uncinate fasciculus that were differentially related to activation asymmetries in the two functional tasks. Frontal and temporal activation asymmetries during silent verb generation were positively related to the strength of specific microstructural white matter asymmetries in the arcuate fasciculus. In contrast, microstructural uncinate fasciculus asymmetries were related to temporal activation asymmetries during passive listening. These findings suggest that white matter asymmetries may indeed be one of the factors underlying functional hemispheric asymmetries. Moreover, they also show that specific localized white matter asymmetries might be of greater relevance for functional activation asymmetries than microstructural features of whole pathways. © 2013.

Clinical anatomy of the elbow and shoulder.

PubMed

Villaseñor-Ovies, Pablo; Vargas, Angélica; Chiapas-Gasca, Karla; Canoso, Juan J; Hernández-Díaz, Cristina; Saavedra, Miguel Ángel; Navarro-Zarza, José Eduardo; Kalish, Robert A

The elbow patients herein discussed feature common soft tissue conditions such as tennis elbow, golfers' elbow and olecranon bursitis. Relevant anatomical structures for these conditions can easily be identified and demonstrated by cross examination by instructors and participants. Patients usually present rotator cuff tendinopathy, frozen shoulder, axillary neuropathy and suprascapular neuropathy. The structures involved in tendinopathy and frozen shoulder can be easily identified and demonstrated under normal conditions. The axillary and the suprascapular nerves have surface landmarks but cannot be palpated. In neuropathy however, physical findings in both neuropathies are pathognomonic and will be discussed. Copyright © 2012 Elsevier España, S.L. All rights reserved.

The preparation and characterization of a lithium borate glass prepared by the gel technique

NASA Technical Reports Server (NTRS)

Weinberg, M. C.; Neilson, G. F.; Smith, G. L.; Dunn, B.; Moore, G. S.; Mackenzie, J. D.

1985-01-01

The preparation of an amorphous lithium borate gel by the metal organic procedure is described. In addition, a preliminary evaluation of the behavior of the gel upon heating is given. In particular the crystallization tendency of the gel is studied with the aid of DTA and X-ray diffraction, and the structural changes in the gel are monitored with the aid of IR spectroscopy. The glass produced from the lithium borate gel is compared to both the gel precursor material and a glass of similar composition prepared by conventional techniques. Specifically, the relevant water contents, crystallization behavior, and structural features are contrasted.

Molecular Dynamics Simulations and Structural Analysis of Giardia duodenalis 14-3-3 Protein-Protein Interactions.

PubMed

Cau, Ylenia; Fiorillo, Annarita; Mori, Mattia; Ilari, Andrea; Botta, Maurizo; Lalle, Marco

2015-12-28

Giardiasis is a gastrointestinal diarrheal illness caused by the protozoan parasite Giardia duodenalis, which affects annually over 200 million people worldwide. The limited antigiardial drug arsenal and the emergence of clinical cases refractory to standard treatments dictate the need for new chemotherapeutics. The 14-3-3 family of regulatory proteins, extensively involved in protein-protein interactions (PPIs) with pSer/pThr clients, represents a highly promising target. Despite homology with human counterparts, the single 14-3-3 of G. duodenalis (g14-3-3) is characterized by a constitutive phosphorylation in a region critical for target binding, thus affecting the function and the conformation of g14-3-3/clients interaction. However, to approach the design of specific small molecule modulators of g14-3-3 PPIs, structural elucidations are required. Here, we present a detailed computational and crystallographic study exploring the implications of g14-3-3 phosphorylation on protein structure and target binding. Self-Guided Langevin Dynamics and classical molecular dynamics simulations show that phosphorylation affects locally and globally g14-3-3 conformation, inducing a structural rearrangement more suitable for target binding. Profitable features for g14-3-3/clients interaction were highlighted using a hydrophobicity-based descriptor to characterize g14-3-3 client peptides. Finally, the X-ray structure of g14-3-3 in complex with a mode-1 prototype phosphopeptide was solved and combined with structure-based simulations to identify molecular features relevant for clients binding to g14-3-3. The data presented herein provide a further and structural understanding of g14-3-3 features and set the basis for drug design studies.

Theoretical Methods of Domain Structures in Ultrathin Ferroelectric Films: A Review

PubMed Central

Liu, Jianyi; Chen, Weijin; Wang, Biao; Zheng, Yue

2014-01-01

This review covers methods and recent developments of the theoretical study of domain structures in ultrathin ferroelectric films. The review begins with an introduction to some basic concepts and theories (e.g., polarization and its modern theory, ferroelectric phase transition, domain formation, and finite size effects, etc.) that are relevant to the study of domain structures in ultrathin ferroelectric films. Basic techniques and recent progress of a variety of important approaches for domain structure simulation, including first-principles calculation, molecular dynamics, Monte Carlo simulation, effective Hamiltonian approach and phase field modeling, as well as multiscale simulation are then elaborated. For each approach, its important features and relative merits over other approaches for modeling domain structures in ultrathin ferroelectric films are discussed. Finally, we review recent theoretical studies on some important issues of domain structures in ultrathin ferroelectric films, with an emphasis on the effects of interfacial electrostatics, boundary conditions and external loads. PMID:28788198

Extreme brain events: Higher-order statistics of brain resting activity and its relation with structural connectivity

NASA Astrophysics Data System (ADS)

Amor, T. A.; Russo, R.; Diez, I.; Bharath, P.; Zirovich, M.; Stramaglia, S.; Cortes, J. M.; de Arcangelis, L.; Chialvo, D. R.

2015-09-01

The brain exhibits a wide variety of spatiotemporal patterns of neuronal activity recorded using functional magnetic resonance imaging as the so-called blood-oxygenated-level-dependent (BOLD) signal. An active area of work includes efforts to best describe the plethora of these patterns evolving continuously in the brain. Here we explore the third-moment statistics of the brain BOLD signals in the resting state as a proxy to capture extreme BOLD events. We find that the brain signal exhibits typically nonzero skewness, with positive values for cortical regions and negative values for subcortical regions. Furthermore, the combined analysis of structural and functional connectivity demonstrates that relatively more connected regions exhibit activity with high negative skewness. Overall, these results highlight the relevance of recent results emphasizing that the spatiotemporal location of the relatively large-amplitude events in the BOLD time series contains relevant information to reproduce a number of features of the brain dynamics during resting state in health and disease.

Clinical features of olfactory disorders in patients seeking medical consultation

PubMed Central

Chen, Guowei; Wei, Yongxiang; Miao, Xutao; Li, Kunyan; Ren, Yuanyuan; Liu, Jia

2013-01-01

Background Olfactory disorders are common complaints in ENT clinics. We investigated causes and relevant features of olfactory disorders and the need for gustatory testing in patients with olfactory dysfunction. Material/Methods A total of 140 patients seeking medical consultations were enrolled. All patients were asked about their olfactory disorders in a structured interview of medical history and underwent thorough otolaryngologic examinations and imaging of the head. Results Causes of olfactory disorders were classified as: upper respiratory tract infection (URTI), sinonasal diseases (NSD), head trauma, idiopathic, endoscopic sinus surgery, congenital anosmia, and other causes. Each of the various causes of olfactory dysfunction had its own distinct clinical features. Nineteen of 54 patients whose gustation was assessed had gustatory disorders. Conclusions The leading causes of olfactory dysfunction were URTI, NSD, head trauma, and idiopathic causes. Gustatory disorders were fairly common in patients with olfactory dysfunction. High priority should be given to complaints of olfactory disorders. PMID:23748259

Scanning electron microscope cathodoluminescence imaging of subgrain boundaries, twins and planar deformation features in quartz

NASA Astrophysics Data System (ADS)

Hamers, M. F.; Pennock, G. M.; Drury, M. R.

2017-04-01

The study of deformation features has been of great importance to determine deformation mechanisms in quartz. Relevant microstructures in both growth and deformation processes include dislocations, subgrains, subgrain boundaries, Brazil and Dauphiné twins and planar deformation features (PDFs). Dislocations and twin boundaries are most commonly imaged using a transmission electron microscope (TEM), because these cannot directly be observed using light microscopy, in contrast to PDFs. Here, we show that red-filtered cathodoluminescence imaging in a scanning electron microscope (SEM) is a useful method to visualise subgrain boundaries, Brazil and Dauphiné twin boundaries. Because standard petrographic thin sections can be studied in the SEM, the observed structures can be directly and easily correlated to light microscopy studies. In contrast to TEM preparation methods, SEM techniques are non-destructive to the area of interest on a petrographic thin section.

Is filtering difficulty the basis of attentional deficits in schizophrenia?

PubMed

Ravizza, Susan M; Robertson, Lynn C; Carter, Cameron S; Nordahl, Thomas E; Salo, Ruth E

2007-06-30

The distractibility that schizophrenia patients display may be the result of a deficiency in filtering out irrelevant information. The aim of the current study was to assess whether patients with schizophrenia exhibit greater difficulty when task-irrelevant features change compared to healthy participants. Thirteen medicated outpatients with a diagnosis of schizophrenia and thirteen age- and parental education-matched controls performed a target selection task in which the task-relevant letter or the task-irrelevant features of color, and/or location repeated or switched. Participants were required to respond by pressing the appropriate key associated with the target letter. These patients with schizophrenia were slower when the task-relevant target letter switched than when it repeated. In contrast, schizophrenia patients performed similarly to controls when task-irrelevant information changed. Thus, we found no evidence that patients with schizophrenia were impaired in inhibiting irrelevant perceptual features. In contrast, changes in task-relevant features were problematic for patients relative to control participants. These results suggest that medicated outpatients who are mild to moderately symptomatic do not exhibit global impairments of feature processing. Instead, impairments are restricted to situations when task-relevant features vary. The current findings also suggest that when a course of action is not implied by an irrelevant feature, outpatients' behavior is not modulated by extraneous visual information any more than in healthy controls.

The Fabric of Meaning and Subjective Effects in LSD-Induced States Depend on Serotonin 2A Receptor Activation.

PubMed

Preller, Katrin H; Herdener, Marcus; Pokorny, Thomas; Planzer, Amanda; Kraehenmann, Rainer; Stämpfli, Philipp; Liechti, Matthias E; Seifritz, Erich; Vollenweider, Franz X

2017-02-06

A core aspect of the human self is the attribution of personal relevance to everyday stimuli enabling us to experience our environment as meaningful [1]. However, abnormalities in the attribution of personal relevance to sensory experiences are also critical features of many psychiatric disorders [2, 3]. Despite their clinical relevance, the neurochemical and anatomical substrates enabling meaningful experiences are largely unknown. Therefore, we investigated the neuropharmacology of personal relevance processing in humans by combining fMRI and the administration of the mixed serotonin (5-HT) and dopamine receptor (R) agonist lysergic acid diethylamide (LSD), well known to alter the subjective meaning of percepts, with and without pretreatment with the 5-HT 2A R antagonist ketanserin. General subjective LSD effects were fully blocked by ketanserin. In addition, ketanserin inhibited the LSD-induced attribution of personal relevance to previously meaningless stimuli and modulated the processing of meaningful stimuli in cortical midline structures. These findings point to the crucial role of the 5-HT 2A R subtype and cortical midline regions in the generation and attribution of personal relevance. Our results thus increase our mechanistic understanding of personal relevance processing and reveal potential targets for the treatment of psychiatric illnesses characterized by alterations in personal relevance attribution. Copyright © 2017 Elsevier Ltd. All rights reserved.

Analysis of underlying causes of inter-expert disagreement in retinopathy of prematurity diagnosis. Application of machine learning principles.

PubMed

Ataer-Cansizoglu, E; Kalpathy-Cramer, J; You, S; Keck, K; Erdogmus, D; Chiang, M F

2015-01-01

Inter-expert variability in image-based clinical diagnosis has been demonstrated in many diseases including retinopathy of prematurity (ROP), which is a disease affecting low birth weight infants and is a major cause of childhood blindness. In order to better understand the underlying causes of variability among experts, we propose a method to quantify the variability of expert decisions and analyze the relationship between expert diagnoses and features computed from the images. Identification of these features is relevant for development of computer-based decision support systems and educational systems in ROP, and these methods may be applicable to other diseases where inter-expert variability is observed. The experiments were carried out on a dataset of 34 retinal images, each with diagnoses provided independently by 22 experts. Analysis was performed using concepts of Mutual Information (MI) and Kernel Density Estimation. A large set of structural features (a total of 66) were extracted from retinal images. Feature selection was utilized to identify the most important features that correlated to actual clinical decisions by the 22 study experts. The best three features for each observer were selected by an exhaustive search on all possible feature subsets and considering joint MI as a relevance criterion. We also compared our results with the results of Cohen's Kappa [36] as an inter-rater reliability measure. The results demonstrate that a group of observers (17 among 22) decide consistently with each other. Mean and second central moment of arteriolar tortuosity is among the reasons of disagreement between this group and the rest of the observers, meaning that the group of experts consider amount of tortuosity as well as the variation of tortuosity in the image. Given a set of image-based features, the proposed analysis method can identify critical image-based features that lead to expert agreement and disagreement in diagnosis of ROP. Although tree-based features and various statistics such as central moment are not popular in the literature, our results suggest that they are important for diagnosis.

Predicting hot spots in protein interfaces based on protrusion index, pseudo hydrophobicity and electron-ion interaction pseudopotential features

PubMed Central

Xia, Junfeng; Yue, Zhenyu; Di, Yunqiang; Zhu, Xiaolei; Zheng, Chun-Hou

2016-01-01

The identification of hot spots, a small subset of protein interfaces that accounts for the majority of binding free energy, is becoming more important for the research of drug design and cancer development. Based on our previous methods (APIS and KFC2), here we proposed a novel hot spot prediction method. For each hot spot residue, we firstly constructed a wide variety of 108 sequence, structural, and neighborhood features to characterize potential hot spot residues, including conventional ones and new one (pseudo hydrophobicity) exploited in this study. We then selected 3 top-ranking features that contribute the most in the classification by a two-step feature selection process consisting of minimal-redundancy-maximal-relevance algorithm and an exhaustive search method. We used support vector machines to build our final prediction model. When testing our model on an independent test set, our method showed the highest F1-score of 0.70 and MCC of 0.46 comparing with the existing state-of-the-art hot spot prediction methods. Our results indicate that these features are more effective than the conventional features considered previously, and that the combination of our and traditional features may support the creation of a discriminative feature set for efficient prediction of hot spots in protein interfaces. PMID:26934646

About the composition of self-relevance: Conjunctions not features are bound to the self.

PubMed

Schäfer, Sarah; Frings, Christian; Wentura, Dirk

2016-06-01

Sui and colleagues (Journal of Experimental Psychology: Human Perception and Performance, 38, 1105-1117, 2012) introduced a matching paradigm to investigate prioritized processing of instructed self-relevance. They arbitrarily assigned simple geometric shapes to the participant and two other persons. Subsequently, the task was to judge whether label-shape pairings matched or not. The authors found a remarkable self-prioritization effect, that is, for matching self-related trials verification was very fast and accurate in comparison to the non-matching conditions. We analyzed whether single features or feature conjunctions are tagged to the self. In particular, we assigned colored shapes to the labels and included partial-matching trials (i.e., trials in which only one feature matched the label, whereas the other feature did not match the label). If single features are tagged to the self, partial matches would result in interference, whereas they should elicit the same data pattern as non-matching trials if only feature conjunctions are tagged to the self. Our data suggest the latter; only feature conjunctions are tagged to the self and are processed in a prioritized manner. This result emphasizes the functionality of self-relevance as a selection mechanism.

Conformational features of cepacian: the exopolysaccharide produced by clinical strains of Burkholderia cepacia.

PubMed

Nogueira, Carlos E Sampaio; Ruggiero, Jose R; Sist, Paola; Cescutti, Paola; Urbani, Ranieri; Rizzo, Roberto

2005-04-11

Conformational energy calculations and molecular dynamics investigations, both in water and in dimethyl sulfoxide, were carried out on the exopolysaccharide cepacian produced by the majority of the clinical strains of Burkholderia cepacia, an opportunistic pathogen causing serious lung infection in patients affected by cystic fibrosis, The investigation was aimed at defining the structural and conformational features, which might be relevant for clarification of the structure-function relationships of the polymer. The molecular dynamics calculations were carried out by Ramachandran-type energy plots of the disaccharides that constitute the polymer repeating unit. The dynamics of an oligomer composed of three repeating units were investigated in water and in Me2SO, a non-aggregating solvent. Analysis of the time persistence of hydrogen bonds showed the presence of a large number of favourable interactions in water, which were less evident in Me2SO. The calculations on the cepacian chain indicated that polymer conformational features in water were affected by the lateral chains, but were also largely dictated by the presence of solvent. Moreover, the large number of intra-chain hydrogen bonds in water disappeared in Me2SO solution, increasing the average dimension of the polymer chains.

Nanoscale organization in the fluorinated room temperature ionic liquid: Tetraethyl ammonium (trifluoromethanesulfonyl)(nonafluorobutylsulfonyl)imide

NASA Astrophysics Data System (ADS)

Lo Celso, F.; Appetecchi, G. B.; Jafta, C. J.; Gontrani, L.; Canongia Lopes, J. N.; Triolo, A.; Russina, O.

2018-05-01

Fluorinated Room Temperature Ionic Liquids (FRTILs) are a branch of ionic liquids that is the object of growing interest for a wide range of potential applications, due to the synergic combination of specifically ionic features and those properties that stem from fluorous tails. So far limited experimental work exists on the micro- and mesoscopic structural organization in this class of compounds. Such a work is however necessary to fully understand morphological details at atomistic level that would have strong implications in terms of bulk properties. Here we use the synergy between X-ray and neutron scattering together with molecular dynamics simulations to access structural details of a technologically relevant FRTIL that is characterised by an anion bearing a long enough fluorinated tail to develop specific morphological features. In particular, we find the first experimental evidence that in FRTILs bearing an asymmetric bis(perfluoroalkyl)sulfonyl-imide anion, fluorous side chains tend to be spatially segregated into nm-scale spatial heterogeneities. This feature together with the well-established micro-segregation of side alkyl chains in conventional RTILs leads to the concept of triphilic ILs, whose technological applications are yet to be fully developed.

Towards boron neutron capture therapy: the formulation and preliminary in vitro evaluation of liposomal vehicles for the therapeutic delivery of the dequalinium salt of bis-nido-carborane.

PubMed

Theodoropoulos, Dimitrios; Rova, Aikaterini; Smith, James R; Barbu, Eugen; Calabrese, Gianpiero; Vizirianakis, Ioannis S; Tsibouklis, John; Fatouros, Dimitrios G

2013-11-15

Liposomes of phosphatidylcholine or of dimyristoylphosphatidylcholine that incorporate bis-nido-carborane dequalinium salt are stable in physiologically relevant media and have in vitro toxicity profiles that appear to be compatible with potential therapeutic applications. These features render the structures suitable candidate boron-delivery vehicles for evaluation in the boron neutron capture therapy of cancer. Copyright © 2013 Elsevier Ltd. All rights reserved.

Parkinson’s disease and pesticides: a toxicological perspective

PubMed Central

Hatcher, Jaime M.; Pennell, Kurt D.; Miller, Gary W.

2017-01-01

Environmental factors have been shown to contribute to the incidence of Parkinson’s disease (PD). Pesticides, which represent one of the primary classes of environmental agents associated with PD, share the common feature of being intentionally released into the environment to control or eliminate pests. Pesticides consist of multiple classes and subclasses of insecticides, herbicides, rodenticides, fungicides, fumigants and others and exhibit a vast array of chemically diverse structures. In this review we examine the evidence regarding the ability of each of the major pesticide subclasses to increase the incidence of PD. We propose that, from a toxicological perspective, it would be beneficial to identify specific subclasses, common structural features and the propensity for widespread human exposure when considering the potential role in PD, rather than using the overly broad term of ‘pesticides’ to describe this diverse group of chemicals. Furthermore, these chemicals and their environmentally relevant combinations should be evaluated for their ability to promote or accelerate PD and not merely for being singular causative agents. PMID:18453001

Comparing solvophobic and multivalent induced collapse in polyelectrolyte brushes

DOE PAGES

Jackson, Nicholas E.; Brettmann, Blair K.; Vishwanath, Venkatram; ...

2017-02-03

Here, coarse-grained molecular dynamics enhanced by free-energy sampling methods is used to examine the roles of solvophobicity and multivalent salts on polyelectrolyte brush collapse. Specifically, we demonstrate that while ostensibly similar, solvophobic collapsed brushes and multivalent-ion collapsed brushes exhibit distinct mechanistic and structural features. Notably, multivalent-induced heterogeneous brush collapse is observed under good solvent polymer backbone conditions, demonstrating that the mechanism of multivalent collapse is not contingent upon a solvophobic backbone. Umbrella sampling of the potential of mean-force (PMF) between two individual brush strands confirms this analysis, revealing starkly different PMFs under solvophobic and multivalent conditions, suggesting the role ofmore » multivalent “bridging” as the discriminating feature in trivalent collapse. Structurally, multivalent ions show a propensity for nucleating order within collapsed brushes, whereas poor-solvent collapsed brushes are more disordered; this difference is traced to the existence of a metastable PMF minimum for poor solvent conditions, and a global PMF minimum for trivalent systems, under experimentally relevant conditions.« less

High resolution PFPE-based molding High resolution PFPE-based molding High resolution PFPE-based molding techniques for nanofabrication of high pattern density sub-20 nm features: A fundamental materials approach

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

Williams, Stuart S; Samulski, Edward; Lopez, Renee

2010-01-01

ABSTRACT. Described herein is the development and investigation of PFPE-based elastomers for high resolution replica molding applications. The modulus of the elastomeric materials was increased through synthetic and additive approaches while maintaining relatively low surface energies (<25 mN/m). Using practically relevant large area master templates, we show that the resolution of the molds is strongly dependant upon the elastomeric mold modulus. A composite mold approach was used to form flexible molds out of stiff, high modulus materials that allow for replication of sub-20 nm post structures. Sub-100 nm line grating master templates, formed using e-beam lithography, were used to determinemore » the experimental stability of the molding materials. It was observed that as the feature spacing decreased, high modulus composite molds were able to effectively replicate the nano-grating structures without cracking or tear-out defects that typically occur with high modulus elastomers.« less

Comparing solvophobic and multivalent induced collapse in polyelectrolyte brushes

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

Jackson, Nicholas E.; Brettmann, Blair K.; Vishwanath, Venkatram

Here, coarse-grained molecular dynamics enhanced by free-energy sampling methods is used to examine the roles of solvophobicity and multivalent salts on polyelectrolyte brush collapse. Specifically, we demonstrate that while ostensibly similar, solvophobic collapsed brushes and multivalent-ion collapsed brushes exhibit distinct mechanistic and structural features. Notably, multivalent-induced heterogeneous brush collapse is observed under good solvent polymer backbone conditions, demonstrating that the mechanism of multivalent collapse is not contingent upon a solvophobic backbone. Umbrella sampling of the potential of mean-force (PMF) between two individual brush strands confirms this analysis, revealing starkly different PMFs under solvophobic and multivalent conditions, suggesting the role ofmore » multivalent “bridging” as the discriminating feature in trivalent collapse. Structurally, multivalent ions show a propensity for nucleating order within collapsed brushes, whereas poor-solvent collapsed brushes are more disordered; this difference is traced to the existence of a metastable PMF minimum for poor solvent conditions, and a global PMF minimum for trivalent systems, under experimentally relevant conditions.« less

Animal Mitochondrial DNA Replication

PubMed Central

Ciesielski, Grzegorz L.; Oliveira, Marcos T.; Kaguni, Laurie S.

2016-01-01

Recent advances in the field of mitochondrial DNA (mtDNA) replication highlight the diversity of both the mechanisms utilized and the structural and functional organization of the proteins at mtDNA replication fork, despite the simplicity of the animal mtDNA genome. DNA polymerase γ, mtDNA helicase and mitochondrial single-stranded DNA-binding protein- the key replisome proteins, have evolved distinct structural features and biochemical properties. These appear to be correlated with mtDNA genomic features in different metazoan taxa and with their modes of DNA replication, although a substantial integrative research is warranted to establish firmly these links. To date, several modes of mtDNA replication have been described for animals: rolling circle, theta, strand-displacement, and RITOLS/bootlace. Resolution of a continuing controversy relevant to mtDNA replication in mammals/vertebrates will have a direct impact on the mechanistic interpretation of mtDNA-related human diseases. Here we review these subjects, integrating earlier and recent data to provide a perspective on the major challenges for future research. PMID:27241933

Standardization of accelerator irradiation procedures for simulation of neutron induced damage in reactor structural materials

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

Shao, Lin; Gigax, Jonathan; Chen, Di

Self-ion irradiation is widely used as a method to simulate neutron damage in reactor structural materials. Accelerator-based simulation of void swelling, however, introduces a number of neutron-atypical features which require careful data extraction and in some cases introduction of innovative irradiation techniques to alleviate these issues. We briefly summarize three such atypical features: defect imbalance effects, pulsed beam effects, and carbon contamination. The latter issue has just been recently recognized as being relevant to simulation of void swelling and is discussed here in greater detail. It is shown that carbon ions are entrained in the ion beam by Coulomb forcemore » drag and accelerated toward the target surface. Beam-contaminant interactions are modeled using molecular dynamics simulation. By applying a multiple beam deflection technique, carbon and other contaminants can be effectively filtered out, as demonstrated in an irradiation of HT-9 alloy by 3.5 MeV Fe ions.« less

Standardization of accelerator irradiation procedures for simulation of neutron induced damage in reactor structural materials

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

Shao, Lin; Gigax, Jonathan; Chen, Di

Self-ion irradiation is widely used as a method to simulate neutron damage in reactor structural materials. Accelerator-based simulation of void swelling, however, introduces a number of neutron-atypical features which require careful data extraction and, in some cases, introduction of innovative irradiation techniques to alleviate these issues. In this paper, we briefly summarize three such atypical features: defect imbalance effects, pulsed beam effects, and carbon contamination. The latter issue has just been recently recognized as being relevant to simulation of void swelling and is discussed here in greater detail. It is shown that carbon ions are entrained in the ion beammore » by Coulomb force drag and accelerated toward the target surface. Beam-contaminant interactions are modeled using molecular dynamics simulation. Finally, by applying a multiple beam deflection technique, carbon and other contaminants can be effectively filtered out, as demonstrated in an irradiation of HT-9 alloy by 3.5 MeV Fe ions.« less

Standardization of accelerator irradiation procedures for simulation of neutron induced damage in reactor structural materials

DOE PAGES

Shao, Lin; Gigax, Jonathan; Chen, Di; ...

2017-06-12

Self-ion irradiation is widely used as a method to simulate neutron damage in reactor structural materials. Accelerator-based simulation of void swelling, however, introduces a number of neutron-atypical features which require careful data extraction and, in some cases, introduction of innovative irradiation techniques to alleviate these issues. In this paper, we briefly summarize three such atypical features: defect imbalance effects, pulsed beam effects, and carbon contamination. The latter issue has just been recently recognized as being relevant to simulation of void swelling and is discussed here in greater detail. It is shown that carbon ions are entrained in the ion beammore » by Coulomb force drag and accelerated toward the target surface. Beam-contaminant interactions are modeled using molecular dynamics simulation. Finally, by applying a multiple beam deflection technique, carbon and other contaminants can be effectively filtered out, as demonstrated in an irradiation of HT-9 alloy by 3.5 MeV Fe ions.« less

Evaluation of food-relevant chemicals in the ToxCast high ...

EPA Pesticide Factsheets

There are thousands of chemicals that are directly added to or come in contact with food, many of which have undergone little to no toxicological evaluation. The ToxCast high-throughput screening (HTS) program has evaluated over 1,800 chemicals in concentration-response across ~820 assay endpoints and continues to grow; with all data completely available to the public, this resource serves as a unique opportunity to evaluate the bioactivity of chemicals in vitro. This study investigated the chemical landscape of the food-relevant chemical universe using cheminformatics analyses, and subsequently evaluated the bioactivity of food-relevant chemicals included in the ToxCast HTS program. Initially, a list of 9,437 food-relevant chemicals was compiled by comprehensively mining publicly available sources for direct food additives, food contact substances, indirect food additives, and pesticides. Of these food-relevant chemicals, 4,638 were associated with curated structure definition files amenable to defining physical/chemical features used to generate chemical fingerprints. Clustering was conducted based on the chemical fingerprints using a self-organizing map approach. This revealed that pesticides, food contact substances, and direct food additives generally clustered apart from one another, supporting that these categories reflect not only different uses but also distinct chemistries. Subsequently, 967 of the 9,437 food-relevant chemicals were identified in the T

Engineering the electronic structure of graphene superlattices via Fermi velocity modulation

NASA Astrophysics Data System (ADS)

Lima, Jonas R. F.

2017-01-01

Graphene superlattices have attracted much research interest in the last years, since it is possible to manipulate the electronic properties of graphene in these structures. It has been verified that extra Dirac points appear in the electronic structure of the system. The electronic structure in the vicinity of these points has been studied for a gapless and gapped graphene superlattice and for a graphene superlattice with a spatially modulated energy gap. In each case a different behavior was obtained. In this work we show that via Fermi velocity engineering it is possible to tune the electronic properties of a graphene superlattice to match all the previous cases studied. We also obtained new features of the system never observed before, reveling that the electronic structure of graphene is very sensitive to the modulation of the Fermi velocity. The results obtained here are relevant for the development of novel graphene-based electronic devices.

An in-depth understanding of biomass recalcitrance using natural poplar variants as the feedstock

DOE PAGES

Meng, Xianzhi; Pu, Yunqiao; Yoo, Chang Geun; ...

2016-12-12

Here, in an effort to better understand the biomass recalcitrance, six natural poplar variants were selected as feedstocks based on previous sugar release analysis. Compositional analysis and physicochemical characterizations of these poplars were performed and the correlations between these physicochemical properties and enzymatic hydrolysis yield were investigated. Gel permeation chromatography (GPC) and 13C solid state NMR were used to determine the degree of polymerization (DP) and crystallinity index (CrI) of cellulose, and the results along with the sugar release study indicated that cellulose DP likely played a more important role in enzymatic hydrolysis. Simons’ stain revealed that the accessible surface area of substrate significantly varied among these variants from 17.3 to 33.2 mg gmore » $$–1\\atop{biomass}$$ as reflected by dye adsorption, and cellulose accessibility was shown as one of the major factors governing substrates digestibility. HSQC and 31P NMR analysis detailed the structural features of poplar lignin variants. Overall, cellulose relevant factors appeared to have a stronger correlation with glucose release, if any, than lignin structural features. Lignin structural features, such as a phenolic hydroxyl group and the ratio of syringyl and guaiacyl (S/G), were found to have a more convincing impact on xylose release. Low lignin content, low cellulose DP, and high cellulose accessibility generally favor enzymatic hydrolysis; however, recalcitrance cannot be simply judged on any single substrate factor.« less

An in-depth understanding of biomass recalcitrance using natural poplar variants as the feedstock

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

Meng, Xianzhi; Pu, Yunqiao; Yoo, Chang Geun

Here, in an effort to better understand the biomass recalcitrance, six natural poplar variants were selected as feedstocks based on previous sugar release analysis. Compositional analysis and physicochemical characterizations of these poplars were performed and the correlations between these physicochemical properties and enzymatic hydrolysis yield were investigated. Gel permeation chromatography (GPC) and 13C solid state NMR were used to determine the degree of polymerization (DP) and crystallinity index (CrI) of cellulose, and the results along with the sugar release study indicated that cellulose DP likely played a more important role in enzymatic hydrolysis. Simons’ stain revealed that the accessible surface area of substrate significantly varied among these variants from 17.3 to 33.2 mg gmore » $$–1\\atop{biomass}$$ as reflected by dye adsorption, and cellulose accessibility was shown as one of the major factors governing substrates digestibility. HSQC and 31P NMR analysis detailed the structural features of poplar lignin variants. Overall, cellulose relevant factors appeared to have a stronger correlation with glucose release, if any, than lignin structural features. Lignin structural features, such as a phenolic hydroxyl group and the ratio of syringyl and guaiacyl (S/G), were found to have a more convincing impact on xylose release. Low lignin content, low cellulose DP, and high cellulose accessibility generally favor enzymatic hydrolysis; however, recalcitrance cannot be simply judged on any single substrate factor.« less

Generic Features of Tertiary Chromatin Structure as Detected in Natural Chromosomes

PubMed Central

Müller, Waltraud G.; Rieder, Dietmar; Kreth, Gregor; Cremer, Christoph; Trajanoski, Zlatko; McNally, James G.

2004-01-01

Knowledge of tertiary chromatin structure in mammalian interphase chromosomes is largely derived from artificial tandem arrays. In these model systems, light microscope images reveal fibers or beaded fibers after high-density targeting of transactivators to insertional domains spanning several megabases. These images of fibers have lent support to chromonema fiber models of tertiary structure. To assess the relevance of these studies to natural mammalian chromatin, we identified two different ∼400-kb regions on human chromosomes 6 and 22 and then examined light microscope images of interphase tertiary chromatin structure when the regions were transcriptionally active and inactive. When transcriptionally active, these natural chromosomal regions elongated, yielding images characterized by a series of adjacent puncta or “beads”, referred to hereafter as beaded images. These elongated structures required transcription for their maintenance. Thus, despite marked differences in the density and the mode of transactivation, the natural and artificial systems showed similarities, suggesting that beaded images are generic features of transcriptionally active tertiary chromatin. We show here, however, that these images do not necessarily favor chromonema fiber models but can also be explained by a radial-loop model or even a simple nucleosome affinity, random-chain model. Thus, light microscope images of tertiary structure cannot distinguish among competing models, although they do impose key constraints: chromatin must be clustered to yield beaded images and then packaged within each cluster to enable decondensation into adjacent clusters. PMID:15485905

Full-Scale Test and Analysis Results of a PRSEUS Fuselage Panel to Assess Damage Containment Features

NASA Technical Reports Server (NTRS)

Bergan, Andrew; Bakuckas, John G., Jr.; Lovejoy, Andrew; Jegley, Dawn; Linton, Kim; Neal, Bert; Korkosz, Gregory; Awerbuch, Jonathan; Tan, Tein-Min

2012-01-01

Integrally stitched composite technology is an area that shows promise in enhancing the structural integrity of aircraft and aerospace structures. The most recent generation of this technology is the Pultruded Rod Stitched Efficient Unitized Structure (PRSEUS) concept. The goal of the PRSEUS concept relevant to this test is to provide damage containment capability for composite structures while reducing overall structural weight. The National Aeronautics and Space Administration (NASA), the Federal Aviation Administration (FAA), and The Boeing Company have partnered in an effort to assess the damage containment features of a full-scale curved PRSEUS panel using the FAA Full-Scale Aircraft Structural Test Evaluation and Research (FASTER) facility. A single PRSEUS test panel was subjected to axial tension, internal pressure, and combined axial tension and internal pressure loads. The test results showed excellent performance of the PRSEUS concept. No growth of Barely Visible Impact Damage (BVID) was observed after ultimate loads were applied. With a two-bay notch severing the central stringer, damage was contained within the two-bay region well above the required limit load conditions. Catastrophic failure was well above the ultimate load level. Information describing the test panel and procedure has been previously presented, so this paper focuses on the experimental procedure, test results, nondestructive inspection results, and preliminary test and analysis correlation.

DemQSAR: predicting human volume of distribution and clearance of drugs

NASA Astrophysics Data System (ADS)

Demir-Kavuk, Ozgur; Bentzien, Jörg; Muegge, Ingo; Knapp, Ernst-Walter

2011-12-01

In silico methods characterizing molecular compounds with respect to pharmacologically relevant properties can accelerate the identification of new drugs and reduce their development costs. Quantitative structure-activity/-property relationship (QSAR/QSPR) correlate structure and physico-chemical properties of molecular compounds with a specific functional activity/property under study. Typically a large number of molecular features are generated for the compounds. In many cases the number of generated features exceeds the number of molecular compounds with known property values that are available for learning. Machine learning methods tend to overfit the training data in such situations, i.e. the method adjusts to very specific features of the training data, which are not characteristic for the considered property. This problem can be alleviated by diminishing the influence of unimportant, redundant or even misleading features. A better strategy is to eliminate such features completely. Ideally, a molecular property can be described by a small number of features that are chemically interpretable. The purpose of the present contribution is to provide a predictive modeling approach, which combines feature generation, feature selection, model building and control of overtraining into a single application called DemQSAR. DemQSAR is used to predict human volume of distribution (VDss) and human clearance (CL). To control overtraining, quadratic and linear regularization terms were employed. A recursive feature selection approach is used to reduce the number of descriptors. The prediction performance is as good as the best predictions reported in the recent literature. The example presented here demonstrates that DemQSAR can generate a model that uses very few features while maintaining high predictive power. A standalone DemQSAR Java application for model building of any user defined property as well as a web interface for the prediction of human VDss and CL is available on the webpage of DemPRED: http://agknapp.chemie.fu-berlin.de/dempred/.

DemQSAR: predicting human volume of distribution and clearance of drugs.

PubMed

Demir-Kavuk, Ozgur; Bentzien, Jörg; Muegge, Ingo; Knapp, Ernst-Walter

2011-12-01

In silico methods characterizing molecular compounds with respect to pharmacologically relevant properties can accelerate the identification of new drugs and reduce their development costs. Quantitative structure-activity/-property relationship (QSAR/QSPR) correlate structure and physico-chemical properties of molecular compounds with a specific functional activity/property under study. Typically a large number of molecular features are generated for the compounds. In many cases the number of generated features exceeds the number of molecular compounds with known property values that are available for learning. Machine learning methods tend to overfit the training data in such situations, i.e. the method adjusts to very specific features of the training data, which are not characteristic for the considered property. This problem can be alleviated by diminishing the influence of unimportant, redundant or even misleading features. A better strategy is to eliminate such features completely. Ideally, a molecular property can be described by a small number of features that are chemically interpretable. The purpose of the present contribution is to provide a predictive modeling approach, which combines feature generation, feature selection, model building and control of overtraining into a single application called DemQSAR. DemQSAR is used to predict human volume of distribution (VD(ss)) and human clearance (CL). To control overtraining, quadratic and linear regularization terms were employed. A recursive feature selection approach is used to reduce the number of descriptors. The prediction performance is as good as the best predictions reported in the recent literature. The example presented here demonstrates that DemQSAR can generate a model that uses very few features while maintaining high predictive power. A standalone DemQSAR Java application for model building of any user defined property as well as a web interface for the prediction of human VD(ss) and CL is available on the webpage of DemPRED: http://agknapp.chemie.fu-berlin.de/dempred/ .

Smokers' Views on Personal Carbon Monoxide Monitors, Associated Apps, and Their Use: An Interview and Think-Aloud Study.

PubMed

Herbeć, Aleksandra; Perski, Olga; Shahab, Lion; West, Robert

2018-02-07

Smartphone-based personal carbon monoxide (CO) monitors and associated apps, or "CO Smartphone Systems" (CSSs) for short, could enable smokers to independently monitor their smoking and quitting. This study explored views and preferences regarding CSSs and their use among 16 adult, UK-based smokers. First, semi-structured interviews explored participants' expectations of CSSs. Secondly, a think-aloud study identified participants' reactions to a personal CO monitor and to existing or prototype apps. Framework Analysis identified five themes: (1) General views, needs, and motivation to use CSSs; (2) Views on the personal CO monitor; (3) Practicalities of CSS use; (4) Desired features in associated apps; and (5) Factors affecting preferences for CSSs and their use. Participants had high expectations of CSSs and their potential to increase motivation. Priority app features included: easy CO testing journeys, relevant and motivating feedback, and recording of contextual data. Appearance and usability of the personal CO monitor, and accuracy and relevance of CO testing were considered important for engagement. Participants differed in their motivation to use and preferences for CSSs features and use, which might have non-trivial impact on evaluation efforts. Personal CO monitors and associated apps may be attractive tools for smokers, but making CSSs easy to use and evaluating these among different groups of smokers may be challenging.

Feature saliency and feedback information interactively impact visual category learning

PubMed Central

Hammer, Rubi; Sloutsky, Vladimir; Grill-Spector, Kalanit

2015-01-01

Visual category learning (VCL) involves detecting which features are most relevant for categorization. VCL relies on attentional learning, which enables effectively redirecting attention to object’s features most relevant for categorization, while ‘filtering out’ irrelevant features. When features relevant for categorization are not salient, VCL relies also on perceptual learning, which enables becoming more sensitive to subtle yet important differences between objects. Little is known about how attentional learning and perceptual learning interact when VCL relies on both processes at the same time. Here we tested this interaction. Participants performed VCL tasks in which they learned to categorize novel stimuli by detecting the feature dimension relevant for categorization. Tasks varied both in feature saliency (low-saliency tasks that required perceptual learning vs. high-saliency tasks), and in feedback information (tasks with mid-information, moderately ambiguous feedback that increased attentional load, vs. tasks with high-information non-ambiguous feedback). We found that mid-information and high-information feedback were similarly effective for VCL in high-saliency tasks. This suggests that an increased attentional load, associated with the processing of moderately ambiguous feedback, has little effect on VCL when features are salient. In low-saliency tasks, VCL relied on slower perceptual learning; but when the feedback was highly informative participants were able to ultimately attain the same performance as during the high-saliency VCL tasks. However, VCL was significantly compromised in the low-saliency mid-information feedback task. We suggest that such low-saliency mid-information learning scenarios are characterized by a ‘cognitive loop paradox’ where two interdependent learning processes have to take place simultaneously. PMID:25745404

The devil is in the detail: brain dynamics in preparation for a global-local task.

PubMed

Leaver, Echo E; Low, Kathy A; DiVacri, Assunta; Merla, Arcangelo; Fabiani, Monica; Gratton, Gabriele

2015-08-01

When analyzing visual scenes, it is sometimes important to determine the relevant "grain" size. Attention control mechanisms may help direct our processing to the intended grain size. Here we used the event-related optical signal, a method possessing high temporal and spatial resolution, to examine the involvement of brain structures within the dorsal attention network (DAN) and the visual processing network (VPN) in preparation for the appropriate level of analysis. Behavioral data indicate that the small features of a hierarchical stimulus (local condition) are more difficult to process than the large features (global condition). Consistent with this finding, cues predicting a local trial were associated with greater DAN activation. This activity was bilateral but more pronounced in the left hemisphere, where it showed a frontal-to-parietal progression over time. Furthermore, the amount of DAN activation, especially in the left hemisphere and in parietal regions, was predictive of subsequent performance. Although local cues elicited left-lateralized DAN activity, no preponderantly right activity was observed for global cues; however, the data indicated an interaction between level of analysis (local vs. global) and hemisphere in VPN. They further showed that local processing involves structures in the ventral VPN, whereas global processing involves structures in the dorsal VPN. These results indicate that in our study preparation for analyzing different size features is an asymmetric process, in which greater preparation is required to focus on small rather than large features, perhaps because of their lesser salience. This preparation involves the same DAN used for other attention control operations.

Barbiturates Bind in the GLIC Ion Channel Pore and Cause Inhibition by Stabilizing a Closed State*♦

PubMed Central

Fourati, Zaineb; Ruza, Reinis Reinholds; Laverty, Duncan; Drège, Emmanuelle; Delarue-Cochin, Sandrine; Joseph, Delphine; Koehl, Patrice; Smart, Trevor; Delarue, Marc

2017-01-01

Barbiturates induce anesthesia by modulating the activity of anionic and cationic pentameric ligand-gated ion channels (pLGICs). Despite more than a century of use in clinical practice, the prototypic binding site for this class of drugs within pLGICs is yet to be described. In this study, we present the first X-ray structures of barbiturates bound to GLIC, a cationic prokaryotic pLGIC with excellent structural homology to other relevant channels sensitive to general anesthetics and, as shown here, to barbiturates, at clinically relevant concentrations. Several derivatives of barbiturates containing anomalous scatterers were synthesized, and these derivatives helped us unambiguously identify a unique barbiturate binding site within the central ion channel pore in a closed conformation. In addition, docking calculations around the observed binding site for all three states of the receptor, including a model of the desensitized state, showed that barbiturates preferentially stabilize the closed state. The identification of this pore binding site sheds light on the mechanism of barbiturate inhibition of cationic pLGICs and allows the rationalization of several structural and functional features previously observed for barbiturates. PMID:27986812

3D facial expression recognition using maximum relevance minimum redundancy geometrical features

NASA Astrophysics Data System (ADS)

Rabiu, Habibu; Saripan, M. Iqbal; Mashohor, Syamsiah; Marhaban, Mohd Hamiruce

2012-12-01

In recent years, facial expression recognition (FER) has become an attractive research area, which besides the fundamental challenges, it poses, finds application in areas, such as human-computer interaction, clinical psychology, lie detection, pain assessment, and neurology. Generally the approaches to FER consist of three main steps: face detection, feature extraction and expression recognition. The recognition accuracy of FER hinges immensely on the relevance of the selected features in representing the target expressions. In this article, we present a person and gender independent 3D facial expression recognition method, using maximum relevance minimum redundancy geometrical features. The aim is to detect a compact set of features that sufficiently represents the most discriminative features between the target classes. Multi-class one-against-one SVM classifier was employed to recognize the seven facial expressions; neutral, happy, sad, angry, fear, disgust, and surprise. The average recognition accuracy of 92.2% was recorded. Furthermore, inter database homogeneity was investigated between two independent databases the BU-3DFE and UPM-3DFE the results showed a strong homogeneity between the two databases.

Creating and Using a Consumer Chemical Molecular Graphics Database: The "Molecule of the Day" - A Great Way To Begin Your Lecture

NASA Astrophysics Data System (ADS)

Scharberg, Maureen A.; Cox, Oran E.; Barelli, Carl A.

1997-07-01

"The Molecule of the Day" consumer chemical database has been created to allow introductory chemistry students to explore molecular structures of chemicals in household products, and to provide opportunities in molecular modeling for undergraduate chemistry students. Before class begins, an overhead transparency is displayed which shows a three-dimensional molecular structure of a household chemical, and lists relevant features and uses of this chemical. Within answers to questionnaires, students have commented that this molecular graphics database has helped them to visually connect the microscopic structure of a molecule with its physical and chemical properties, as well as its uses in consumer products. It is anticipated that this database will be incorporated into a navigational software package such as Netscape.

Quasiparticle Scattering in Type-II Weyl semimetal MoTe₂.

PubMed

Lin, Chun-Liang; Arafune, Ryuichi; Minamitani, Emi; Kawai, Maki; Takagi, Noriaki

2018-01-30

The electronic structure of type-II Weyl semimetal molybdenum ditelluride (MoTe₂) is studied by using scanning tunneling microscopy and density functional theory calculations. Through measuring energy-dependent quasiparticle interference (QPI) patterns with a cryogenic scanning tunneling microscope, several characteristic features are found in the QPI patterns. Two of them arise from the Weyl semimetal nature; one is the topological Fermi arc surface state and the other can be assigned to be a Weyl point. The remaining structures are derived from the scatterings relevant to the bulk electronic states. The findings lead to thorough understanding of the topological electronic structure of type-II Weyl semimetal MoTe₂. © 2018 IOP Publishing Ltd.

Metal Transport across Biomembranes: Emerging Models for a Distinct Chemistry*

PubMed Central

Argüello, José M.; Raimunda, Daniel; González-Guerrero, Manuel

2012-01-01

Transition metals are essential components of important biomolecules, and their homeostasis is central to many life processes. Transmembrane transporters are key elements controlling the distribution of metals in various compartments. However, due to their chemical properties, transition elements require transporters with different structural-functional characteristics from those of alkali and alkali earth ions. Emerging structural information and functional studies have revealed distinctive features of metal transport. Among these are the relevance of multifaceted events involving metal transfer among participating proteins, the importance of coordination geometry at transmembrane transport sites, and the presence of the largely irreversible steps associated with vectorial transport. Here, we discuss how these characteristics shape novel transition metal ion transport models. PMID:22389499

Metal transport across biomembranes: emerging models for a distinct chemistry.

PubMed

Argüello, José M; Raimunda, Daniel; González-Guerrero, Manuel

2012-04-20

Transition metals are essential components of important biomolecules, and their homeostasis is central to many life processes. Transmembrane transporters are key elements controlling the distribution of metals in various compartments. However, due to their chemical properties, transition elements require transporters with different structural-functional characteristics from those of alkali and alkali earth ions. Emerging structural information and functional studies have revealed distinctive features of metal transport. Among these are the relevance of multifaceted events involving metal transfer among participating proteins, the importance of coordination geometry at transmembrane transport sites, and the presence of the largely irreversible steps associated with vectorial transport. Here, we discuss how these characteristics shape novel transition metal ion transport models.

LS-SNP/PDB: annotated non-synonymous SNPs mapped to Protein Data Bank structures.

PubMed

Ryan, Michael; Diekhans, Mark; Lien, Stephanie; Liu, Yun; Karchin, Rachel

2009-06-01

LS-SNP/PDB is a new WWW resource for genome-wide annotation of human non-synonymous (amino acid changing) SNPs. It serves high-quality protein graphics rendered with UCSF Chimera molecular visualization software. The system is kept up-to-date by an automated, high-throughput build pipeline that systematically maps human nsSNPs onto Protein Data Bank structures and annotates several biologically relevant features. LS-SNP/PDB is available at (http://ls-snp.icm.jhu.edu/ls-snp-pdb) and via links from protein data bank (PDB) biology and chemistry tabs, UCSC Genome Browser Gene Details and SNP Details pages and PharmGKB Gene Variants Downloads/Cross-References pages.

An online database of nuclear electromagnetic moments

NASA Astrophysics Data System (ADS)

Mertzimekis, T. J.; Stamou, K.; Psaltis, A.

2016-01-01

Measurements of nuclear magnetic dipole and electric quadrupole moments are considered quite important for the understanding of nuclear structure both near and far from the valley of stability. The recent advent of radioactive beams has resulted in a plethora of new, continuously flowing, experimental data on nuclear structure - including nuclear moments - which hinders the information management. A new, dedicated, public and user friendly online database (http://magneticmoments.info) has been created comprising experimental data of nuclear electromagnetic moments. The present database supersedes existing printed compilations, including also non-evaluated series of data and relevant meta-data, while putting strong emphasis on bimonthly updates. The scope, features and extensions of the database are reported.

Decahydrobenzoquinolin-5-one sigma receptor ligands: Divergent development of both sigma 1 and sigma 2 receptor selective examples.

PubMed

McLeod, Michael C; Aubé, Jeffrey; Frankowski, Kevin J

2016-12-01

Analogues of the decahydrobenzoquinolin-5-one class of sigma (σ) receptor ligands were used to probe the structure-activity relationship trends for this recently discovered series of σ ligands. In all, 29 representatives were tested for σ and opioid receptor affinity, leading to the identification of compounds possessing improved σ 1 selectivity and, for the first time in this series, examples possessing preferential σ 2 affinity. Several structural features associated with these selectivity trends have been identified. Two analogues of improved selectivity were evaluated in a binding panel of 43 CNS-relevant targets to confirm their sigma receptor preference. Copyright © 2016 Elsevier Ltd. All rights reserved.

Optical properties of graphene nanoflakes: Shape matters.

PubMed

Mansilla Wettstein, Candela; Bonafé, Franco P; Oviedo, M Belén; Sánchez, Cristián G

2016-06-14

In recent years there has been significant debate on whether the edge type of graphene nanoflakes (GNFs) or graphene quantum dots (GQDs) are relevant for their electronic structure, thermal stability, and optical properties. Using computer simulations, we have proven that there is a fundamental difference in the absorption spectra between samples of the same shape, similar size but different edge type, namely, armchair or zigzag edges. These can be explained by the presence of electronic structures near the Fermi level which are localized on the edges. These features are also evident from the dependence of band gap on the GNF size, which shows three very distinct trends for different shapes and edge geometries.

Optical properties of graphene nanoflakes: Shape matters

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

Mansilla Wettstein, Candela; Bonafé, Franco P.; Sánchez, Cristián G., E-mail: cgsanchez@fcq.unc.edu.ar

In recent years there has been significant debate on whether the edge type of graphene nanoflakes (GNFs) or graphene quantum dots (GQDs) are relevant for their electronic structure, thermal stability, and optical properties. Using computer simulations, we have proven that there is a fundamental difference in the absorption spectra between samples of the same shape, similar size but different edge type, namely, armchair or zigzag edges. These can be explained by the presence of electronic structures near the Fermi level which are localized on the edges. These features are also evident from the dependence of band gap on the GNFmore » size, which shows three very distinct trends for different shapes and edge geometries.« less

Elements of the Chicxulub Impact Structure as revealed in SRTM and surface GPS topographic data

NASA Astrophysics Data System (ADS)

Kobrick, M.; Kinsland, G. L.; Sanchez, G.; Cardador, M. H.

2003-04-01

Pope et al have utilized elevations from the Petroleos Mexicanos (PEMEX) gravity data files to show that the main component of the surface expression of the Chicxu-lub Impact Structure is a roughly semi-circular, low-relief depression about 90 km in diameter. They also identified other topographic features and the elements of the buried impact which possibly led to the development of these features. Kinsland et al presented a connection between these topographic anomalies, small gravity anomalies and buried structure of the impact. Shaded relief images from recently acquired SRTM elevation data clearly show the circular depression of the crater and the moat/cenote ring. In addition we can readily identify Inner trough 1, Inner trough 2 and Outer trough as defined by Pope et al. The agreement between the topographic maps of Pope et al, Kinsland et al and SRTM data are remarkable considering that the distribution and types of data in the sets are so different. We also have ground topographic data collected with a special "autonomous differ-ential GPS" system during summer 2002. Profiles from these data generally agree with both the gravity data based topographic maps and profiles extracted from the SRTM data. Preliminary analyses of our new data, SRTM and GPS, have uncovered features not previously recognized: 1) as shown by the GPS data the moat/cenote ring consists of two distinct depressions separated by about 10 km...perhaps separate ring faults, 2) in the SRTM data over the southern part of the crater and on southward for perhaps 20 km beyond the moat/ cenote ring there exists a pattern, as yet unexplained, of roughly concentric topographic features whose center lies at about 21deg 40min N and 89deg 25min W, about 50km NNE of the moat/cenote ring center. The corroboration and better definition of the previously recognized topographic features yielded by the two new forms of data strengthens the cases for these fea-tures and for their relevance to the underlying collapsed crater structure.

Prediction of lysine glutarylation sites by maximum relevance minimum redundancy feature selection.

PubMed

Ju, Zhe; He, Jian-Jun

2018-06-01

Lysine glutarylation is new type of protein acylation modification in both prokaryotes and eukaryotes. To better understand the molecular mechanism of glutarylation, it is important to identify glutarylated substrates and their corresponding glutarylation sites accurately. In this study, a novel bioinformatics tool named GlutPred is developed to predict glutarylation sites by using multiple feature extraction and maximum relevance minimum redundancy feature selection. On the one hand, amino acid factors, binary encoding, and the composition of k-spaced amino acid pairs features are incorporated to encode glutarylation sites. And the maximum relevance minimum redundancy method and the incremental feature selection algorithm are adopted to remove the redundant features. On the other hand, a biased support vector machine algorithm is used to handle the imbalanced problem in glutarylation sites training dataset. As illustrated by 10-fold cross-validation, the performance of GlutPred achieves a satisfactory performance with a Sensitivity of 64.80%, a Specificity of 76.60%, an Accuracy of 74.90% and a Matthew's correlation coefficient of 0.3194. Feature analysis shows that some k-spaced amino acid pair features play the most important roles in the prediction of glutarylation sites. The conclusions derived from this study might provide some clues for understanding the molecular mechanisms of glutarylation. Copyright © 2018 Elsevier Inc. All rights reserved.

Chemically Active, Porous 3D-Printed Thermoplastic Composites.

PubMed

Evans, Kent A; Kennedy, Zachary C; Arey, Bruce W; Christ, Josef F; Schaef, Herbert T; Nune, Satish K; Erikson, Rebecca L

2018-05-02

Metal-organic frameworks (MOFs) exhibit exceptional properties and are widely investigated because of their structural and functional versatility relevant to catalysis, separations, and sensing applications. However, their commercial or large-scale application is often limited by their powder forms which make integration into devices challenging. Here, we report the production of MOF-thermoplastic polymer composites in well-defined and customizable forms and with complex internal structural features accessed via a standard three-dimensional (3D) printer. MOFs (zeolitic imidazolate framework; ZIF-8) were incorporated homogeneously into both poly(lactic acid) (PLA) and thermoplastic polyurethane (TPU) matrices at high loadings (up to 50% by mass), extruded into filaments, and utilized for on-demand access to 3D structures by fused deposition modeling. Printed, rigid PLA/MOF composites display a large surface area (SA avg = 531 m 2 g -1 ) and hierarchical pore features, whereas flexible TPU/MOF composites achieve a high surface area (SA avg = 706 m 2 g -1 ) by employing a simple method developed to expose obstructed micropores postprinting. Critically, embedded particles in the plastic matrices retain their ability to participate in chemical interactions characteristic of the parent framework. The fabrication strategies were extended to other MOFs and illustrate the potential of 3D printing to create unique porous and high surface area chemically active structures.

Feature-Based Statistical Analysis of Combustion Simulation Data

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

Bennett, J; Krishnamoorthy, V; Liu, S

2011-11-18

We present a new framework for feature-based statistical analysis of large-scale scientific data and demonstrate its effectiveness by analyzing features from Direct Numerical Simulations (DNS) of turbulent combustion. Turbulent flows are ubiquitous and account for transport and mixing processes in combustion, astrophysics, fusion, and climate modeling among other disciplines. They are also characterized by coherent structure or organized motion, i.e. nonlocal entities whose geometrical features can directly impact molecular mixing and reactive processes. While traditional multi-point statistics provide correlative information, they lack nonlocal structural information, and hence, fail to provide mechanistic causality information between organized fluid motion and mixing andmore » reactive processes. Hence, it is of great interest to capture and track flow features and their statistics together with their correlation with relevant scalar quantities, e.g. temperature or species concentrations. In our approach we encode the set of all possible flow features by pre-computing merge trees augmented with attributes, such as statistical moments of various scalar fields, e.g. temperature, as well as length-scales computed via spectral analysis. The computation is performed in an efficient streaming manner in a pre-processing step and results in a collection of meta-data that is orders of magnitude smaller than the original simulation data. This meta-data is sufficient to support a fully flexible and interactive analysis of the features, allowing for arbitrary thresholds, providing per-feature statistics, and creating various global diagnostics such as Cumulative Density Functions (CDFs), histograms, or time-series. We combine the analysis with a rendering of the features in a linked-view browser that enables scientists to interactively explore, visualize, and analyze the equivalent of one terabyte of simulation data. We highlight the utility of this new framework for combustion science; however, it is applicable to many other science domains.« less

Parallel object-oriented data mining system

DOEpatents

Kamath, Chandrika; Cantu-Paz, Erick

2004-01-06

A data mining system uncovers patterns, associations, anomalies and other statistically significant structures in data. Data files are read and displayed. Objects in the data files are identified. Relevant features for the objects are extracted. Patterns among the objects are recognized based upon the features. Data from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) sky survey was used to search for bent doubles. This test was conducted on data from the Very Large Array in New Mexico which seeks to locate a special type of quasar (radio-emitting stellar object) called bent doubles. The FIRST survey has generated more than 32,000 images of the sky to date. Each image is 7.1 megabytes, yielding more than 100 gigabytes of image data in the entire data set.

Multi-spacecraft studies of the auroral acceleration region: From cluster to nanosatellites

NASA Astrophysics Data System (ADS)

Sadeghi, S.; Emami, M. R.

2017-03-01

This paper discusses the utilization of multiple Cubesats in various formations for studies in the auroral acceleration region. The focus is on the quasi-static properties, spatio-temporal features, electric potential structures, field-aligned currents, and their relationships, all of which are fundamentally important for an understanding of the magnetosphere-ionosphere coupling. It is argued that a multitude of nanosatellites can address some of the relevant outstanding questions in a broader range of spatial, temporal, and geometrical features, with higher redundancy and data consistency, potentially resulting in a shorter mission period and a higher chance of mission success. A number of mission concepts consisting of a cluster of 6-12 Cubesats with their specific onboard payloads are suggested for such missions over a period of as short as two months.

Neural evidence reveals the rapid effects of reward history on selective attention.

PubMed

MacLean, Mary H; Giesbrecht, Barry

2015-05-05

Selective attention is often framed as being primarily driven by two factors: task-relevance and physical salience. However, factors like selection and reward history, which are neither currently task-relevant nor physically salient, can reliably and persistently influence visual selective attention. The current study investigated the nature of the persistent effects of irrelevant, physically non-salient, reward-associated features. These features affected one of the earliest reliable neural indicators of visual selective attention in humans, the P1 event-related potential, measured one week after the reward associations were learned. However, the effects of reward history were moderated by current task demands. The modulation of visually evoked activity supports the hypothesis that reward history influences the innate salience of reward associated features, such that even when no longer relevant, nor physically salient, these features have a rapid, persistent, and robust effect on early visual selective attention. Copyright © 2015 Elsevier B.V. All rights reserved.

Multiple mechanisms in the perception of face gender: Effect of sex-irrelevant features.

PubMed

Komori, Masashi; Kawamura, Satoru; Ishihara, Shigekazu

2011-06-01

Effects of sex-relevant and sex-irrelevant facial features on the evaluation of facial gender were investigated. Participants rated masculinity of 48 male facial photographs and femininity of 48 female facial photographs. Eighty feature points were measured on each of the facial photographs. Using a generalized Procrustes analysis, facial shapes were converted into multidimensional vectors, with the average face as a starting point. Each vector was decomposed into a sex-relevant subvector and a sex-irrelevant subvector which were, respectively, parallel and orthogonal to the main male-female axis. Principal components analysis (PCA) was performed on the sex-irrelevant subvectors. One principal component was negatively correlated with both perceived masculinity and femininity, and another was correlated only with femininity, though both components were orthogonal to the male-female dimension (and thus by definition sex-irrelevant). These results indicate that evaluation of facial gender depends on sex-irrelevant as well as sex-relevant facial features.

An Optimization-Based Method for Feature Ranking in Nonlinear Regression Problems.

PubMed

Bravi, Luca; Piccialli, Veronica; Sciandrone, Marco

2017-04-01

In this paper, we consider the feature ranking problem, where, given a set of training instances, the task is to associate a score with the features in order to assess their relevance. Feature ranking is a very important tool for decision support systems, and may be used as an auxiliary step of feature selection to reduce the high dimensionality of real-world data. We focus on regression problems by assuming that the process underlying the generated data can be approximated by a continuous function (for instance, a feedforward neural network). We formally state the notion of relevance of a feature by introducing a minimum zero-norm inversion problem of a neural network, which is a nonsmooth, constrained optimization problem. We employ a concave approximation of the zero-norm function, and we define a smooth, global optimization problem to be solved in order to assess the relevance of the features. We present the new feature ranking method based on the solution of instances of the global optimization problem depending on the available training data. Computational experiments on both artificial and real data sets are performed, and point out that the proposed feature ranking method is a valid alternative to existing methods in terms of effectiveness. The obtained results also show that the method is costly in terms of CPU time, and this may be a limitation in the solution of large-dimensional problems.

Classification of chemical substances, reactions, and interactions: The effect of expertise

NASA Astrophysics Data System (ADS)

Stains, Marilyne Nicole Olivia

2007-12-01

This project explored the strategies that undergraduate and graduate chemistry students engaged in when solving classification tasks involving microscopic (particulate) representations of chemical substances and microscopic and symbolic representations of different chemical reactions. We were specifically interested in characterizing the basic features to which students pay attention while classifying, identifying the patterns of reasoning that they follow, and comparing the performance of students with different levels of preparation in the discipline. In general, our results suggest that advanced levels of expertise in chemical classification do not necessarily evolve in a linear and continuous way with academic training. Novice students had a tendency to reduce the cognitive demand of the task and rely on common-sense reasoning; they had difficulties differentiating concepts (conceptual undifferentiation) and based their classification decisions on only one variable (reduction). These ways of thinking lead them to consider extraneous features, pay more attention to explicit or surface features than implicit features and to overlook important and relevant features. However, unfamiliar levels of representations (microscopic level) seemed to trigger deeper and more meaningful thinking processes. On the other hand, expert students classified entities using a specific set of rules that they applied throughout the classification tasks. They considered a larger variety of implicit features and the unfamiliarity with the microscopic level of representation did not affect their reasoning processes. Consequently, novices created numerous small groups, few of them being chemically meaningful, while experts created few but large chemically meaningful groups. Novices also had difficulties correctly classifying entities in chemically meaningful groups. Finally, expert chemists in our study used classification schemes that are not necessarily traditionally taught in classroom chemistry (e.g. the structure of substances is more relevant to them than their composition when classifying substances as compounds or elements). This result suggests that practice in the field may develop different types of knowledge framework than those usually presented in chemistry textbooks.

X-ray Absorption and Emission Spectroscopy of CrIII (Hydr)Oxides: Analysis of the K-Pre-Edge Region

NASA Astrophysics Data System (ADS)

Frommer, Jakob; Nachtegaal, Maarten; Czekaj, Izabela; Weng, Tsu-Chien; Kretzschmar, Ruben

2009-10-01

Pre-edge spectral features below the main X-ray absorption K-edge of transition metals show a pronounced chemical sensitivity and are promising sources of structural information. Nevertheless, the use of pre-edge analysis in applied research is limited because of the lack of definite theoretical peak-assignments. The aim of this study was to determine the factors affecting the chromium K-pre-edge features in trivalent chromium-bearing oxides and oxyhydroxides. The selected phases varied in the degree of octahedral polymerization and the degree of iron-for-chromium substitution in the crystal structure. We investigated the pre-edge fine structure by means of high-energy-resolution fluorescence detected X-ray absorption spectroscopy and by 1s2p resonant X-ray emission spectroscopy. Multiplet theory and full multiple-scattering calculations were used to analyze the experimental data. We show that the chromium K-pre-edge contains localized and nonlocalized transitions. Contributions arising from nonlocalized metal-metal transitions are sensitive to the nearest metal type and to the linkage mode between neighboring metal octahedra. Analyzing these transitions opens up new opportunities for investigating the local coordination environment of chromium in poorly ordered solids of environmental relevance.

Analysis of breast thermograms using Gabor wavelet anisotropy index.

PubMed

Suganthi, S S; Ramakrishnan, S

2014-09-01

In this study, an attempt is made to distinguish the normal and abnormal tissues in breast thermal images using Gabor wavelet transform. Thermograms having normal, benign and malignant tissues are considered in this study and are obtained from public online database. Segmentation of breast tissues is performed by multiplying raw image and ground truth mask. Left and right breast regions are separated after removing the non-breast regions from the segmented image. Based on the pathological conditions, the separated breast regions are grouped as normal and abnormal tissues. Gabor features such as energy and amplitude in different scales and orientations are extracted. Anisotropy and orientation measures are calculated from the extracted features and analyzed. A distinctive variation is observed among different orientations of the extracted features. It is found that the anisotropy measure is capable of differentiating the structural changes due to varied metabolic conditions. Further, the Gabor features also showed relative variations among different pathological conditions. It appears that these features can be used efficiently to identify normal and abnormal tissues and hence, improve the relevance of breast thermography in early detection of breast cancer and content based image retrieval.

Characterization and extraction of the synaptic apposition surface for synaptic geometry analysis

PubMed Central

Morales, Juan; Rodríguez, Angel; Rodríguez, José-Rodrigo; DeFelipe, Javier; Merchán-Pérez, Angel

2013-01-01

Geometrical features of chemical synapses are relevant to their function. Two critical components of the synaptic junction are the active zone (AZ) and the postsynaptic density (PSD), as they are related to the probability of synaptic release and the number of postsynaptic receptors, respectively. Morphological studies of these structures are greatly facilitated by the use of recent electron microscopy techniques, such as combined focused ion beam milling and scanning electron microscopy (FIB/SEM), and software tools that permit reconstruction of large numbers of synapses in three dimensions. Since the AZ and the PSD are in close apposition and have a similar surface area, they can be represented by a single surface—the synaptic apposition surface (SAS). We have developed an efficient computational technique to automatically extract this surface from synaptic junctions that have previously been three-dimensionally reconstructed from actual tissue samples imaged by automated FIB/SEM. Given its relationship with the release probability and the number of postsynaptic receptors, the surface area of the SAS is a functionally relevant measure of the size of a synapse that can complement other geometrical features like the volume of the reconstructed synaptic junction, the equivalent ellipsoid size and the Feret's diameter. PMID:23847474

Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems.

PubMed

Lê Cao, Kim-Anh; Boitard, Simon; Besse, Philippe

2011-06-22

Variable selection on high throughput biological data, such as gene expression or single nucleotide polymorphisms (SNPs), becomes inevitable to select relevant information and, therefore, to better characterize diseases or assess genetic structure. There are different ways to perform variable selection in large data sets. Statistical tests are commonly used to identify differentially expressed features for explanatory purposes, whereas Machine Learning wrapper approaches can be used for predictive purposes. In the case of multiple highly correlated variables, another option is to use multivariate exploratory approaches to give more insight into cell biology, biological pathways or complex traits. A simple extension of a sparse PLS exploratory approach is proposed to perform variable selection in a multiclass classification framework. sPLS-DA has a classification performance similar to other wrapper or sparse discriminant analysis approaches on public microarray and SNP data sets. More importantly, sPLS-DA is clearly competitive in terms of computational efficiency and superior in terms of interpretability of the results via valuable graphical outputs. sPLS-DA is available in the R package mixOmics, which is dedicated to the analysis of large biological data sets.

Measurement of food-related approach-avoidance biases: Larger biases when food stimuli are task relevant.

PubMed

Lender, Anja; Meule, Adrian; Rinck, Mike; Brockmeyer, Timo; Blechert, Jens

2018-06-01

Strong implicit responses to food have evolved to avoid energy depletion but contribute to overeating in today's affluent environments. The Approach-Avoidance Task (AAT) supposedly assesses implicit biases in response to food stimuli: Participants push pictures on a monitor "away" or pull them "near" with a joystick that controls a corresponding image zoom. One version of the task couples movement direction with image content-independent features, for example, pulling blue-framed images and pushing green-framed images regardless of content ('irrelevant feature version'). However, participants might selectively attend to this feature and ignore image content and, thus, such a task setup might underestimate existing biases. The present study tested this attention account by comparing two irrelevant feature versions of the task with either a more peripheral (image frame color: green vs. blue) or central (small circle vs. cross overlaid over the image content) image feature as response instruction to a 'relevant feature version', in which participants responded to the image content, thus making it impossible to ignore that content. Images of chocolate-containing foods and of objects were used, and several trait and state measures were acquired to validate the obtained biases. Results revealed a robust approach bias towards food only in the relevant feature condition. Interestingly, a positive correlation with state chocolate craving during the task was found when all three conditions were combined, indicative of criterion validity of all three versions. However, no correlations were found with trait chocolate craving. Results provide a strong case for the relevant feature version of the AAT for bias measurement. They also point to several methodological avenues for future research around selective attention in the irrelevant versions and task validity regarding trait vs. state variables. Copyright © 2018 Elsevier Ltd. All rights reserved.

Planning to avoid trouble in the operating room: experts' formulation of the preoperative plan.

PubMed

Zilbert, Nathan R; St-Martin, Laurent; Regehr, Glenn; Gallinger, Steven; Moulton, Carol-Anne

2015-01-01

The purpose of this study was to capture the preoperative plans of expert hepato-pancreato-biliary (HPB) surgeons with the goal of finding consistent aspects of the preoperative planning process. HPB surgeons were asked to think aloud when reviewing 4 preoperative computed tomography scans of patients with distal pancreatic tumors. The imaging features they identified and the planned actions they proposed were tabulated. Surgeons viewed the tabulated list of imaging features for each case and rated the relevance of each feature for their subsequent preoperative plan. Average rater intraclass correlation coefficients were calculated for each type of data collected (imaging features detected, planned actions reported, and relevance of each feature) to establish whether the surgeons were consistent with one another in their responses. Average rater intraclass correlation coefficient values greater than 0.7 were considered indicative of consistency. Division of General Surgery, University of Toronto. HPB surgeons affiliated with the University of Toronto. A total of 11 HPB surgeons thought aloud when reviewing 4 computed tomography scans. Surgeons were consistent in the imaging features they detected but inconsistent in the planned actions they reported. Of the HPB surgeons, 8 completed the assessment of feature relevance. For 3 of the 4 cases, the surgeons were consistent in rating the relevance of specific imaging features on their preoperative plans. These results suggest that HPB surgeons are consistent in some aspects of the preoperative planning process but not others. The findings further our understanding of the preoperative planning process and will guide future research on the best ways to incorporate the teaching and evaluation of preoperative planning into surgical training. Copyright © 2014 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.

Resonances and bound states in the continuum on periodic arrays of slightly noncircular cylinders

NASA Astrophysics Data System (ADS)

Hu, Zhen; Lu, Ya Yan

2018-02-01

Optical bound states in the continuum (BICs), especially those on periodic structures, have interesting properties and potentially important applications. Existing theoretical and numerical studies for optical BICs are mostly for idealized structures with simple and perfect geometric features, such as circular holes, rectangular cylinders and spheres. Since small distortions are always present in actual fabricated structures, we perform a high accuracy numerical study for BICs and resonances on a simple periodic structure with small distortions, i.e., periodic arrays of slightly noncircular cylinders. Our numerical results confirm that symmetries are important not only for the so-called symmetry-protected BICs, but also for the majority of propagating BICs which do not have a symmetry mismatch with the outgoing radiation waves. Typically, the BICs continue to exist if the small distortions keep the relevant symmetries, and they become resonant modes with finite quality factors if the small distortions break a required symmetry.

A Multidimensional Diversity‐Oriented Synthesis Strategy for Structurally Diverse and Complex Macrocycles

PubMed Central

Nie, Feilin; Kunciw, Dominique L.; Wilcke, David; Stokes, Jamie E.; Galloway, Warren R. J. D.; Bartlett, Sean; Sore, Hannah F.

2016-01-01

Abstract Synthetic macrocycles are an attractive area in drug discovery. However, their use has been hindered by a lack of versatile platforms for the generation of structurally (and thus shape) diverse macrocycle libraries. Herein, we describe a new concept in library synthesis, termed multidimensional diversity‐oriented synthesis, and its application towards macrocycles. This enabled the step‐efficient generation of a library of 45 novel, structurally diverse, and highly‐functionalized macrocycles based around a broad range of scaffolds and incorporating a wide variety of biologically relevant structural motifs. The synthesis strategy exploited the diverse reactivity of aza‐ylides and imines, and featured eight different macrocyclization methods, two of which were novel. Computational analyses reveal a broad coverage of molecular shape space by the library and provides insight into how the various diversity‐generating steps of the synthesis strategy impact on molecular shape. PMID:27484830

Association between MRI structural features and cognitive measures in pediatric multiple sclerosis

NASA Astrophysics Data System (ADS)

Amoroso, N.; Bellotti, R.; Fanizzi, A.; Lombardi, A.; Monaco, A.; Liguori, M.; Margari, L.; Simone, M.; Viterbo, R. G.; Tangaro, S.

2017-09-01

Multiple sclerosis (MS) is an inflammatory and demyelinating disease associated with neurodegenerative processes that lead to brain structural changes. The disease affects mostly young adults, but 3-5% of cases has a pediatric onset (POMS). Magnetic Resonance Imaging (MRI) is generally used for diagnosis and follow-up in MS patients, however the most common MRI measures (e.g. new or enlarging T2-weighted lesions, T1-weighted gadolinium- enhancing lesions) have often failed as surrogate markers of MS disability and progression. MS is clinically heterogenous with symptoms that can include both physical changes (such as visual loss or walking difficulties) and cognitive impairment. 30-50% of POMS experience prominent cognitive dysfunction. In order to investigate the association between cognitive measures and brain morphometry, in this work we present a fully automated pipeline for processing and analyzing MRI brain scans. Relevant anatomical structures are segmented with FreeSurfer; besides, statistical features are computed. Thus, we describe the data referred to 12 patients with early POMS (mean age at MRI: 15.5 +/- 2.7 years) with a set of 181 structural features. The major cognitive abilities measured are verbal and visuo-spatial learning, expressive language and complex attention. Data was collected at the Department of Basic Sciences, Neurosciences and Sense Organs, University of Bari, and exploring different abilities like the verbal and visuo-spatial learning, expressive language and complex attention. Different regression models and parameter configurations are explored to assess the robustness of the results, in particular Generalized Linear Models, Bayes Regression, Random Forests, Support Vector Regression and Artificial Neural Networks are discussed.

Homology modeling of parasite histone deacetylases to guide the structure-based design of selective inhibitors.

PubMed

Melesina, Jelena; Robaa, Dina; Pierce, Raymond J; Romier, Christophe; Sippl, Wolfgang

2015-11-01

Histone deacetylases (HDACs) are promising epigenetic targets for the treatment of various diseases, including cancer and neurodegenerative disorders. There is evidence that they can also be addressed to treat parasitic infections. Recently, the first X-ray structure of a parasite HDAC was published, Schistosoma mansoni HDAC8, giving structural insights into its inhibition. However, most of the targets from parasites of interest still lack this structural information. Therefore, we prepared homology models of relevant parasitic HDACs and compared them to human and S. mansoni HDACs. The information about known S. mansoni HDAC8 inhibitors and compounds that affect the growth of Trypanosoma, Leishmania and Plasmodium species was used to validate the models by docking and molecular dynamics studies. Our results provide analysis of structural features of parasitic HDACs and should be helpful for selecting promising candidates for biological testing and for structure-based optimisation of parasite-specific inhibitors. Copyright © 2015 Elsevier Inc. All rights reserved.

Structural and Molecular Modeling Features of P2X Receptors

PubMed Central

Alves, Luiz Anastacio; da Silva, João Herminio Martins; Ferreira, Dinarte Neto Moreira; Fidalgo-Neto, Antonio Augusto; Teixeira, Pedro Celso Nogueira; de Souza, Cristina Alves Magalhães; Caffarena, Ernesto Raúl; de Freitas, Mônica Santos

2014-01-01

Currently, adenosine 5′-triphosphate (ATP) is recognized as the extracellular messenger that acts through P2 receptors. P2 receptors are divided into two subtypes: P2Y metabotropic receptors and P2X ionotropic receptors, both of which are found in virtually all mammalian cell types studied. Due to the difficulty in studying membrane protein structures by X-ray crystallography or NMR techniques, there is little information about these structures available in the literature. Two structures of the P2X4 receptor in truncated form have been solved by crystallography. Molecular modeling has proven to be an excellent tool for studying ionotropic receptors. Recently, modeling studies carried out on P2X receptors have advanced our knowledge of the P2X receptor structure-function relationships. This review presents a brief history of ion channel structural studies and shows how modeling approaches can be used to address relevant questions about P2X receptors. PMID:24637936

Shape Adaptive, Robust Iris Feature Extraction from Noisy Iris Images

PubMed Central

Ghodrati, Hamed; Dehghani, Mohammad Javad; Danyali, Habibolah

2013-01-01

In the current iris recognition systems, noise removing step is only used to detect noisy parts of the iris region and features extracted from there will be excluded in matching step. Whereas depending on the filter structure used in feature extraction, the noisy parts may influence relevant features. To the best of our knowledge, the effect of noise factors on feature extraction has not been considered in the previous works. This paper investigates the effect of shape adaptive wavelet transform and shape adaptive Gabor-wavelet for feature extraction on the iris recognition performance. In addition, an effective noise-removing approach is proposed in this paper. The contribution is to detect eyelashes and reflections by calculating appropriate thresholds by a procedure called statistical decision making. The eyelids are segmented by parabolic Hough transform in normalized iris image to decrease computational burden through omitting rotation term. The iris is localized by an accurate and fast algorithm based on coarse-to-fine strategy. The principle of mask code generation is to assign the noisy bits in an iris code in order to exclude them in matching step is presented in details. An experimental result shows that by using the shape adaptive Gabor-wavelet technique there is an improvement on the accuracy of recognition rate. PMID:24696801

Shape adaptive, robust iris feature extraction from noisy iris images.

PubMed

Ghodrati, Hamed; Dehghani, Mohammad Javad; Danyali, Habibolah

2013-10-01

In the current iris recognition systems, noise removing step is only used to detect noisy parts of the iris region and features extracted from there will be excluded in matching step. Whereas depending on the filter structure used in feature extraction, the noisy parts may influence relevant features. To the best of our knowledge, the effect of noise factors on feature extraction has not been considered in the previous works. This paper investigates the effect of shape adaptive wavelet transform and shape adaptive Gabor-wavelet for feature extraction on the iris recognition performance. In addition, an effective noise-removing approach is proposed in this paper. The contribution is to detect eyelashes and reflections by calculating appropriate thresholds by a procedure called statistical decision making. The eyelids are segmented by parabolic Hough transform in normalized iris image to decrease computational burden through omitting rotation term. The iris is localized by an accurate and fast algorithm based on coarse-to-fine strategy. The principle of mask code generation is to assign the noisy bits in an iris code in order to exclude them in matching step is presented in details. An experimental result shows that by using the shape adaptive Gabor-wavelet technique there is an improvement on the accuracy of recognition rate.

The effect of category learning on attentional modulation of visual cortex.

PubMed

Folstein, Jonathan R; Fuller, Kelly; Howard, Dorothy; DePatie, Thomas

2017-09-01

Learning about visual object categories causes changes in the way we perceive those objects. One likely mechanism by which this occurs is the application of attention to potentially relevant objects. Here we test the hypothesis that category membership influences the allocation of attention, allowing attention to be applied not only to object features, but to entire categories. Participants briefly learned to categorize a set of novel cartoon animals after which EEG was recorded while participants distinguished between a target and non-target category. A second identical EEG session was conducted after two sessions of categorization practice. The category structure and task design allowed parametric manipulation of number of target features while holding feature frequency and category membership constant. We found no evidence that category membership influenced attentional selection: a postero-lateral negative component, labeled the selection negativity/N250, increased over time and was sensitive to number of target features, not target categories. In contrast, the right hemisphere N170 was not sensitive to target features. The P300 appeared sensitive to category in the first session, but showed a graded sensitivity to number of target features in the second session, possibly suggesting a transition from rule-based to similarity based categorization. Copyright © 2017. Published by Elsevier Ltd.

Diarylpyrimidine-dihydrobenzyloxopyrimidine hybrids: new, wide-spectrum anti-HIV-1 agents active at (sub)-nanomolar level.

PubMed

Rotili, Dante; Tarantino, Domenico; Artico, Marino; Nawrozkij, Maxim B; Gonzalez-Ortega, Emmanuel; Clotet, Bonaventura; Samuele, Alberta; Esté, José A; Maga, Giovanni; Mai, Antonello

2011-04-28

Here, we describe a novel small series of non-nucleoside reverse transcriptase inhibitors (NNRTIs) that combine peculiar structural features of diarylpyrimidines (DAPYs) and dihydro-alkoxy-benzyl-oxopyrimidines (DABOs). These DAPY-DABO hybrids (1-4) showed a characteristic SAR profile and a nanomolar anti-HIV-1 activity at both enzymatic and cellular level. In particular, the two compounds 4d and 2d, with a (sub)nanomolar activity against wild-type and clinically relevant HIV-1 mutant strains, were selected as lead compounds for next optimization studies.

Mutational robustness accelerates the origin of novel RNA phenotypes through phenotypic plasticity.

PubMed

Wagner, Andreas

2014-02-18

Novel phenotypes can originate either through mutations in existing genotypes or through phenotypic plasticity, the ability of one genotype to form multiple phenotypes. From molecules to organisms, plasticity is a ubiquitous feature of life, and a potential source of exaptations, adaptive traits that originated for nonadaptive reasons. Another ubiquitous feature is robustness to mutations, although it is unknown whether such robustness helps or hinders the origin of new phenotypes through plasticity. RNA is ideal to address this question, because it shows extensive plasticity in its secondary structure phenotypes, a consequence of their continual folding and unfolding, and these phenotypes have important biological functions. Moreover, RNA is to some extent robust to mutations. This robustness structures RNA genotype space into myriad connected networks of genotypes with the same phenotype, and it influences the dynamics of evolving populations on a genotype network. In this study I show that both effects help accelerate the exploration of novel phenotypes through plasticity. My observations are based on many RNA molecules sampled at random from RNA sequence space, and on 30 biological RNA molecules. They are thus not only a generic feature of RNA sequence space but are relevant for the molecular evolution of biological RNA. Copyright © 2014 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Stress-driven buckling patterns in spheroidal core/shell structures.

PubMed

Yin, Jie; Cao, Zexian; Li, Chaorong; Sheinman, Izhak; Chen, Xi

2008-12-09

Many natural fruits and vegetables adopt an approximately spheroidal shape and are characterized by their distinct undulating topologies. We demonstrate that various global pattern features can be reproduced by anisotropic stress-driven buckles on spheroidal core/shell systems, which implies that the relevant mechanical forces might provide a template underpinning the topological conformation in some fruits and plants. Three dimensionless parameters, the ratio of effective size/thickness, the ratio of equatorial/polar radii, and the ratio of core/shell moduli, primarily govern the initiation and formation of the patterns. A distinct morphological feature occurs only when these parameters fall within certain ranges: In a prolate spheroid, reticular buckles take over longitudinal ridged patterns when one or more parameters become large. Our results demonstrate that some universal features of fruit/vegetable patterns (e.g., those observed in Korean melons, silk gourds, ribbed pumpkins, striped cavern tomatoes, and cantaloupes, etc.) may be related to the spontaneous buckling from mechanical perspectives, although the more complex biological or biochemical processes are involved at deep levels.

Characteristic Features of Hanging: A Study in Rural District of Central India.

PubMed

Ambade, Vipul Namdeorao; Kolpe, Dayanand; Tumram, Nilesh; Meshram, Satin; Pawar, Mohan; Kukde, Hemant

2015-09-01

The ligature mark is the most relevant feature of hanging. This study was undertaken with a view to determine the characteristic features of hanging and its association with ligature material or mode of suspension. Of a total medicolegal deaths reported at an Apex Medical Centre, hanging was noted in 4.1% cases, all suicidal with mortality rate of 1.5 per 100,000 population per year. The hanging was complete in 67.7% with nylon rope as the commonest type of ligature material used for ligation. The hanging mark was usually single, situated above thyroid cartilage, incomplete, prominent, and directed toward nape of neck. The mark of dribbling of saliva was seen in 11.8% cases. Facial congestion, petechial hemorrhage, and cyanosis were significantly seen in partial hanging. Though occasionally reported, the argent line was noted in 78.7% hanging deaths with neck muscle hemorrhage in 23.6% cases. Fracture of neck structure was predominant in complete hanging. © 2015 American Academy of Forensic Sciences.

New Science in Plain Sight: Optical Manifestations of Coupled Subauroral Features Documented by Citizen Scientists

NASA Astrophysics Data System (ADS)

MacDonald, E.; Heavner, M.; Kosar, B.; Case, N.; Donovan, E.; Spanswick, E.; Nishimura, Y.; Gallardo-Lacourt, B.

2017-12-01

Aurora has been observed and recorded by people for thousands of years. Recently, citizen scientists captured features of aurora-like arc events not previously described in the literature at subauroral latitudes. Amateur photo sequences show unusual flow, unstable composition changes, and field aligned structures. Observations from the Swarm satellite crossing the arc reveals thermal enhancement, density depletion, and strong westward ion flow. These signatures resemble features previously described from in situ observation however the optical manifestation is surprising and contains rich, unstable signatures as well. The relevant observations have presented important implications on a variety of open questions, including the fundamental definition of aurora, and limitations of jargon and subfield distinctions. This paper covers the discovery, its context, and the significant implications for the application of public participation measurement modes to the natural sciences whereby they can form a disruptive gap to expose new observing perspectives. Photo Credit: Notanee Bourassa, Alberta Aurora Chasers

[The GIPSY-RECPAM model: a versatile approach for integrated evaluation in cardiologic care].

PubMed

Carinci, F

2009-01-01

Tree-structured methodology applied for the GISSI-PSICOLOGIA project, although performed in the framework of earliest GISSI studies, represents a powerful tool to analyze different aspects of cardiologic care. The GISSI-PSICOLOGIA project has delivered a novel methodology based on the joint application of psychometric tools and sophisticated statistical techniques. Its prospective use could allow building effective epidemiological models relevant to the prognosis of the cardiologic patient. The various features of the RECPAM method allow a versatile use in the framework of modern e-health projects. The study used the Cognitive Behavioral Assessment H Form (CBA-H) psychometrics scales. The potential for its future application in the framework of Italian cardiology is relevant and particularly indicated to assist planning of systems for integrated care and routine evaluation of the cardiologic patient.

The modal surface interpolation method for damage localization

NASA Astrophysics Data System (ADS)

Pina Limongelli, Maria

2017-05-01

The Interpolation Method (IM) has been previously proposed and successfully applied for damage localization in plate like structures. The method is based on the detection of localized reductions of smoothness in the Operational Deformed Shapes (ODSs) of the structure. The IM can be applied to any type of structure provided the ODSs are estimated accurately in the original and in the damaged configurations. If the latter circumstance fails to occur, for example when the structure is subjected to an unknown input(s) or if the structural responses are strongly corrupted by noise, both false and missing alarms occur when the IM is applied to localize a concentrated damage. In order to overcome these drawbacks a modification of the method is herein investigated. An ODS is the deformed shape of a structure subjected to a harmonic excitation: at resonances the ODS are dominated by the relevant mode shapes. The effect of noise at resonance is usually lower with respect to other frequency values hence the relevant ODS are estimated with higher reliability. Several methods have been proposed to reliably estimate modal shapes in case of unknown input. These two circumstances can be exploited to improve the reliability of the IM. In order to reduce or eliminate the drawbacks related to the estimation of the ODSs in case of noisy signals, in this paper is investigated a modified version of the method based on a damage feature calculated considering the interpolation error relevant only to the modal shapes and not to all the operational shapes in the significant frequency range. Herein will be reported the comparison between the results of the IM in its actual version (with the interpolation error calculated summing up the contributions of all the operational shapes) and in the new proposed version (with the estimation of the interpolation error limited to the modal shapes).

Remembering Complex Objects in Visual Working Memory: Do Capacity Limits Restrict Objects or Features?

PubMed Central

Hardman, Kyle; Cowan, Nelson

2014-01-01

Visual working memory stores stimuli from our environment as representations that can be accessed by high-level control processes. This study addresses a longstanding debate in the literature about whether storage limits in visual working memory include a limit to the complexity of discrete items. We examined the issue with a number of change-detection experiments that used complex stimuli which possessed multiple features per stimulus item. We manipulated the number of relevant features of the stimulus objects in order to vary feature load. In all of our experiments, we found that increased feature load led to a reduction in change-detection accuracy. However, we found that feature load alone could not account for the results, but that a consideration of the number of relevant objects was also required. This study supports capacity limits for both feature and object storage in visual working memory. PMID:25089739

Feature-based attentional modulations in the absence of direct visual stimulation.

PubMed

Serences, John T; Boynton, Geoffrey M

2007-07-19

When faced with a crowded visual scene, observers must selectively attend to behaviorally relevant objects to avoid sensory overload. Often this selection process is guided by prior knowledge of a target-defining feature (e.g., the color red when looking for an apple), which enhances the firing rate of visual neurons that are selective for the attended feature. Here, we used functional magnetic resonance imaging and a pattern classification algorithm to predict the attentional state of human observers as they monitored a visual feature (one of two directions of motion). We find that feature-specific attention effects spread across the visual field-even to regions of the scene that do not contain a stimulus. This spread of feature-based attention to empty regions of space may facilitate the perception of behaviorally relevant stimuli by increasing sensitivity to attended features at all locations in the visual field.

Integrated Composite Analyzer (ICAN): Users and programmers manual

NASA Technical Reports Server (NTRS)

Murthy, P. L. N.; Chamis, C. C.

1986-01-01

The use of and relevant equations programmed in a computer code designed to carry out a comprehensive linear analysis of multilayered fiber composites is described. The analysis contains the essential features required to effectively design structural components made from fiber composites. The inputs to the code are constituent material properties, factors reflecting the fabrication process, and composite geometry. The code performs micromechanics, macromechanics, and laminate analysis, including the hygrothermal response of fiber composites. The code outputs are the various ply and composite properties, composite structural response, and composite stress analysis results with details on failure. The code is in Fortran IV and can be used efficiently as a package in complex structural analysis programs. The input-output format is described extensively through the use of a sample problem. The program listing is also included. The code manual consists of two parts.

Electroencephalogram Signal Classification for Automated Epileptic Seizure Detection Using Genetic Algorithm

PubMed Central

Nanthini, B. Suguna; Santhi, B.

2017-01-01

Background: Epilepsy causes when the repeated seizure occurs in the brain. Electroencephalogram (EEG) test provides valuable information about the brain functions and can be useful to detect brain disorder, especially for epilepsy. In this study, application for an automated seizure detection model has been introduced successfully. Materials and Methods: The EEG signals are decomposed into sub-bands by discrete wavelet transform using db2 (daubechies) wavelet. The eight statistical features, the four gray level co-occurrence matrix and Renyi entropy estimation with four different degrees of order, are extracted from the raw EEG and its sub-bands. Genetic algorithm (GA) is used to select eight relevant features from the 16 dimension features. The model has been trained and tested using support vector machine (SVM) classifier successfully for EEG signals. The performance of the SVM classifier is evaluated for two different databases. Results: The study has been experimented through two different analyses and achieved satisfactory performance for automated seizure detection using relevant features as the input to the SVM classifier. Conclusion: Relevant features using GA give better accuracy performance for seizure detection. PMID:28781480

Macromolecular Traits in the African Rice Oryza glaberrima and in Glaberrima/Sativa Crosses, and Their Relevance to Processing.

PubMed

Marengo, Mauro; Barbiroli, Alberto; Bonomi, Francesco; Casiraghi, Maria Cristina; Marti, Alessandra; Pagani, Maria Ambrogina; Manful, John; Graham-Acquaah, Seth; Ragg, Enzio; Fessas, Dimitrios; Hogenboom, Johannes A; Iametti, Stefania

2017-10-01

Molecular properties of proteins and starch were investigated in 2 accessions of Oryza glaberrima and Oryza sativa, and in one NERICA cross between the 2 species, to assess traits that could be relevant to transformation into specific foods. Protein nature and organization in O. glaberrima were different from those in O. sativa and in NERICA. Despite the similar cysteine content in all samples, thiol accessibility in O. glaberrima proteins was higher than in NERICA or in O. sativa. Inter-protein disulphide bonds were important for the formation of protein aggregates in O. glaberrima, whereas non-covalent protein-protein interactions were relevant in NERICA and O. sativa. DSC and NMR studies indicated only minor differences in the structure of starch in these species, as also made evident by their microstructural features. Nevertheless, starch gelatinization in O. glaberrima was very different from what was observed in O. sativa and NERICA. The content of soluble species in gelatinized starch from the various species in the presence/absence of treatments with specific enzymes indicated that release of small starch breakdown products was lowest in O. glaberrima, in particular from the amylopectin component. These findings may explain the low glycemic index of O. glaberrima, and provide a rationale for extending the use of O. glaberrima in the production of specific rice-based products, thus improving the economic value and the market appeal of African crops. The structural features of proteins and starch in O. glaberrima are very different from those in O. sativa and in the NERICA cross. These results appear useful as for extending the use of O. glaberrima cultivars in the design and production of specific rice-based products (for example, pasta), that might, in turn, improve the economic value and the market appeal of locally sourced raw materials, by introducing added-value products on the African market. © 2017 Institute of Food Technologists®.

nRC: non-coding RNA Classifier based on structural features.

PubMed

Fiannaca, Antonino; La Rosa, Massimo; La Paglia, Laura; Rizzo, Riccardo; Urso, Alfonso

2017-01-01

Non-coding RNA (ncRNA) are small non-coding sequences involved in gene expression regulation of many biological processes and diseases. The recent discovery of a large set of different ncRNAs with biologically relevant roles has opened the way to develop methods able to discriminate between the different ncRNA classes. Moreover, the lack of knowledge about the complete mechanisms in regulative processes, together with the development of high-throughput technologies, has required the help of bioinformatics tools in addressing biologists and clinicians with a deeper comprehension of the functional roles of ncRNAs. In this work, we introduce a new ncRNA classification tool, nRC (non-coding RNA Classifier). Our approach is based on features extraction from the ncRNA secondary structure together with a supervised classification algorithm implementing a deep learning architecture based on convolutional neural networks. We tested our approach for the classification of 13 different ncRNA classes. We obtained classification scores, using the most common statistical measures. In particular, we reach an accuracy and sensitivity score of about 74%. The proposed method outperforms other similar classification methods based on secondary structure features and machine learning algorithms, including the RNAcon tool that, to date, is the reference classifier. nRC tool is freely available as a docker image at https://hub.docker.com/r/tblab/nrc/. The source code of nRC tool is also available at https://github.com/IcarPA-TBlab/nrc.

LINKING LUNG AIRWAY STRUCTURE TO PULMONARY FUNCTION VIA COMPOSITE BRIDGE REGRESSION

PubMed Central

Chen, Kun; Hoffman, Eric A.; Seetharaman, Indu; Jiao, Feiran; Lin, Ching-Long; Chan, Kung-Sik

2017-01-01

The human lung airway is a complex inverted tree-like structure. Detailed airway measurements can be extracted from MDCT-scanned lung images, such as segmental wall thickness, airway diameter, parent-child branch angles, etc. The wealth of lung airway data provides a unique opportunity for advancing our understanding of the fundamental structure-function relationships within the lung. An important problem is to construct and identify important lung airway features in normal subjects and connect these to standardized pulmonary function test results such as FEV1%. Among other things, the problem is complicated by the fact that a particular airway feature may be an important (relevant) predictor only when it pertains to segments of certain generations. Thus, the key is an efficient, consistent method for simultaneously conducting group selection (lung airway feature types) and within-group variable selection (airway generations), i.e., bi-level selection. Here we streamline a comprehensive procedure to process the lung airway data via imputation, normalization, transformation and groupwise principal component analysis, and then adopt a new composite penalized regression approach for conducting bi-level feature selection. As a prototype of composite penalization, the proposed composite bridge regression method is shown to admit an efficient algorithm, enjoy bi-level oracle properties, and outperform several existing methods. We analyze the MDCT lung image data from a cohort of 132 subjects with normal lung function. Our results show that, lung function in terms of FEV1% is promoted by having a less dense and more homogeneous lung comprising an airway whose segments enjoy more heterogeneity in wall thicknesses, larger mean diameters, lumen areas and branch angles. These data hold the potential of defining more accurately the “normal” subject population with borderline atypical lung functions that are clearly influenced by many genetic and environmental factors. PMID:28280520

Autoantibodies Against the Node of Ranvier in Seropositive Chronic Inflammatory Demyelinating Polyneuropathy: Diagnostic, Pathogenic, and Therapeutic Relevance

PubMed Central

Vural, Atay; Doppler, Kathrin; Meinl, Edgar

2018-01-01

Discovery of disease-associated autoantibodies has transformed the clinical management of a variety of neurological disorders. Detection of autoantibodies aids diagnosis and allows patient stratification resulting in treatment optimization. In the last years, a set of autoantibodies against proteins located at the node of Ranvier has been identified in patients with chronic inflammatory demyelinating polyneuropathy (CIDP). These antibodies target neurofascin, contactin1, or contactin-associated protein 1, and we propose to name CIDP patients with these antibodies collectively as seropositive. They have unique clinical characteristics that differ from seronegative CIDP. Moreover, there is compelling evidence that autoantibodies are relevant for the pathogenesis. In this article, we review the current knowledge on the characteristics of autoantibodies against the node of Ranvier proteins and their clinical relevance in CIDP. We start with a description of the structure of the node of Ranvier followed by a summary of assays used to identify seropositive patients; and then, we describe clinical features and characteristics linked to seropositivity. We review knowledge on the role of these autoantibodies for the pathogenesis with relevance for the emerging concept of nodopathy/paranodopathy and summarize the treatment implications. PMID:29867996

Autoantibodies Against the Node of Ranvier in Seropositive Chronic Inflammatory Demyelinating Polyneuropathy: Diagnostic, Pathogenic, and Therapeutic Relevance.

PubMed

Vural, Atay; Doppler, Kathrin; Meinl, Edgar

2018-01-01

Discovery of disease-associated autoantibodies has transformed the clinical management of a variety of neurological disorders. Detection of autoantibodies aids diagnosis and allows patient stratification resulting in treatment optimization. In the last years, a set of autoantibodies against proteins located at the node of Ranvier has been identified in patients with chronic inflammatory demyelinating polyneuropathy (CIDP). These antibodies target neurofascin, contactin1, or contactin-associated protein 1, and we propose to name CIDP patients with these antibodies collectively as seropositive. They have unique clinical characteristics that differ from seronegative CIDP. Moreover, there is compelling evidence that autoantibodies are relevant for the pathogenesis. In this article, we review the current knowledge on the characteristics of autoantibodies against the node of Ranvier proteins and their clinical relevance in CIDP. We start with a description of the structure of the node of Ranvier followed by a summary of assays used to identify seropositive patients; and then, we describe clinical features and characteristics linked to seropositivity. We review knowledge on the role of these autoantibodies for the pathogenesis with relevance for the emerging concept of nodopathy/paranodopathy and summarize the treatment implications.

The cost of selective attention in category learning: Developmental differences between adults and infants

PubMed Central

Best, Catherine A.; Yim, Hyungwook; Sloutsky, Vladimir M.

2013-01-01

Selective attention plays an important role in category learning. However, immaturities of top-down attentional control during infancy coupled with successful category learning suggest that early category learning is achieved without attending selectively. Research presented here examines this possibility by focusing on category learning in infants (6–8 months old) and adults. Participants were trained on a novel visual category. Halfway through the experiment, unbeknownst to participants, the to-be-learned category switched to another category, where previously relevant features became irrelevant and previously irrelevant features became relevant. If participants attend selectively to the relevant features of the first category, they should incur a cost of selective attention immediately after the unknown category switch. Results revealed that adults demonstrated a cost, as evidenced by a decrease in accuracy and response time on test trials as well as a decrease in visual attention to newly relevant features. In contrast, infants did not demonstrate a similar cost of selective attention as adults despite evidence of learning both to-be-learned categories. Findings are discussed as supporting multiple systems of category learning and as suggesting that learning mechanisms engaged by adults may be different from those engaged by infants. PMID:23773914

Structures of Pseudomonas aeruginosa β-ketoacyl-(acyl-carrier-protein) synthase II (FabF) and a C164Q mutant provide templates for antibacterial drug discovery and identify a buried potassium ion and a ligand-binding site that is an artefact of the crystal form

PubMed Central

Baum, Bernhard; Lecker, Laura S. M.; Zoltner, Martin; Jaenicke, Elmar; Schnell, Robert; Hunter, William N.; Brenk, Ruth

2015-01-01

Bacterial infections remain a serious health concern, in particular causing life-threatening infections of hospitalized and immunocompromised patients. The situation is exacerbated by the rise in antibacterial drug resistance, and new treatments are urgently sought. In this endeavour, accurate structures of molecular targets can support early-stage drug discovery. Here, crystal structures, in three distinct forms, of recombinant Pseudomonas aeruginosa β-ketoacyl-(acyl-carrier-protein) synthase II (FabF) are presented. This enzyme, which is involved in fatty-acid biosynthesis, has been validated by genetic and chemical means as an antibiotic target in Gram-positive bacteria and represents a potential target in Gram-negative bacteria. The structures of apo FabF, of a C164Q mutant in which the binding site is altered to resemble the substrate-bound state and of a complex with 3-(benzoylamino)-2-hydroxybenzoic acid are reported. This compound mimics aspects of a known natural product inhibitor, platensimycin, and surprisingly was observed binding outside the active site, interacting with a symmetry-related molecule. An unusual feature is a completely buried potassium-binding site that was identified in all three structures. Comparisons suggest that this may represent a conserved structural feature of FabF relevant to fold stability. The new structures provide templates for structure-based ligand design and, together with the protocols and reagents, may underpin a target-based drug-discovery project for urgently needed antibacterials. PMID:26249693

Informing the Human Plasma Protein Binding of Environmental Chemicals by Machine Learning in the Pharmaceutical Space: Applicability Domain and Limits of Predictability.

PubMed

Ingle, Brandall L; Veber, Brandon C; Nichols, John W; Tornero-Velez, Rogelio

2016-11-28

The free fraction of a xenobiotic in plasma (F ub ) is an important determinant of chemical adsorption, distribution, metabolism, elimination, and toxicity, yet experimental plasma protein binding data are scarce for environmentally relevant chemicals. The presented work explores the merit of utilizing available pharmaceutical data to predict F ub for environmentally relevant chemicals via machine learning techniques. Quantitative structure-activity relationship (QSAR) models were constructed with k nearest neighbors (kNN), support vector machines (SVM), and random forest (RF) machine learning algorithms from a training set of 1045 pharmaceuticals. The models were then evaluated with independent test sets of pharmaceuticals (200 compounds) and environmentally relevant ToxCast chemicals (406 total, in two groups of 238 and 168 compounds). The selection of a minimal feature set of 10-15 2D molecular descriptors allowed for both informative feature interpretation and practical applicability domain assessment via a bounded box of descriptor ranges and principal component analysis. The diverse pharmaceutical and environmental chemical sets exhibit similarities in terms of chemical space (99-82% overlap), as well as comparable bias and variance in constructed learning curves. All the models exhibit significant predictability with mean absolute errors (MAE) in the range of 0.10-0.18F ub . The models performed best for highly bound chemicals (MAE 0.07-0.12), neutrals (MAE 0.11-0.14), and acids (MAE 0.14-0.17). A consensus model had the highest accuracy across both pharmaceuticals (MAE 0.151-0.155) and environmentally relevant chemicals (MAE 0.110-0.131). The inclusion of the majority of the ToxCast test sets within the AD of the consensus model, coupled with high prediction accuracy for these chemicals, indicates the model provides a QSAR for F ub that is broadly applicable to both pharmaceuticals and environmentally relevant chemicals.

Joint Estimation of Effective Brain Wave Activation Modes Using EEG/MEG Sensor Arrays and Multimodal MRI Volumes.

PubMed

Galinsky, Vitaly L; Martinez, Antigona; Paulus, Martin P; Frank, Lawrence R

2018-04-13

In this letter, we present a new method for integration of sensor-based multifrequency bands of electroencephalography and magnetoencephalography data sets into a voxel-based structural-temporal magnetic resonance imaging analysis by utilizing the general joint estimation using entropy regularization (JESTER) framework. This allows enhancement of the spatial-temporal localization of brain function and the ability to relate it to morphological features and structural connectivity. This method has broad implications for both basic neuroscience research and clinical neuroscience focused on identifying disease-relevant biomarkers by enhancing the spatial-temporal resolution of the estimates derived from current neuroimaging modalities, thereby providing a better picture of the normal human brain in basic neuroimaging experiments and variations associated with disease states.

Self-Assembly in Systems Containing Silicone Compounds

NASA Astrophysics Data System (ADS)

Ferreira, Maira Silva; Loh, Watson

2009-01-01

Chemical systems formed by silicone solvents and surfactants have potential applications in a variety of industrial products. In spite of their technological relevance, there are few reports on the scientific literature that focus on characterizing such ternary systems. In this work, we have aimed to develop a general, structural investigation on the phase diagram of one system that typically comprises silicone-based chemicals, by means of the SAXS (small-angle X-ray scattering) technique. Important features such as the presence of diverse aggregation states in the overall system, either on their own or in equilibrium with other structures, have been detected. As a result, optically isotropic chemical systems (direct and/or reversed microemulsions) and liquid crystals with lamellar or hexagonal packing have been identified and characterized.

Band structure analysis of a thin plate with periodic arrangements of slender beams

NASA Astrophysics Data System (ADS)

Serrano, Ó.; Zaera, R.; Fernández-Sáez, J.

2018-04-01

This work analyzes the wave propagation in structures composed of a periodic arrangement of vertical beams rigidly joined to a plate substrate. Three different configurations for the distribution of the beams have been analyzed: square, triangular, and hexagonal. A dimensional analysis of the problem indicates the presence of three dimensionless groups of parameters controlling the response of the system. The main features of the wave propagation have been found using numerical procedures based on the Finite Element Method, through the application of the Bloch's theorem for the corresponding primitive unit cells. Illustrative examples of the effect of the different dimensionless parameters on the dynamic behavior of the system are presented, providing information relevant for design.

First principles study of the electronic and magnetic structures and bonding properties of UCoC2 ternary, characteristic of C-C units

NASA Astrophysics Data System (ADS)

Matar, Samir F.

2013-03-01

The electronic structure of UCoC2, a di-carbide with the C-C units is examined from ab initio with an assessment of the properties of chemical bonding. The energy-volume equation of state shows large anisotropy effects due to C-C alignment along tetragonal c-axis leading to high linear incompressibility. Relevant features of selective bonding of uranium and cobalt with carbon at two different Wyckoff sites and strong C-C interactions are remarkable. The vibrational frequencies for C⋯C stretching modes indicate closer behavior to aliphatic C-C rather than Cdbnd C double bond. A ferromagnetic ground state is proposed from the calculations.

Spectroscopic characterization of cell membranes and their constituents of the plant-associated soil bacterium Azospirillum brasilense

NASA Astrophysics Data System (ADS)

Kamnev, A. A.; Antonyuk, L. P.; Matora, L. Yu.; Serebrennikova, O. B.; Sumaroka, M. V.; Colina, M.; Renou-Gonnord, M.-F.; Ignatov, V. V.

1999-05-01

Structural and compositional features of bacterial membranes and some of their isolated constituents (cell surface lipopolysaccharide, phospholipids) of the plant-growth-promoting diazotrophic rhizobacterium Azospirillum brasilense (wild-type strain Sp245) were characterized using Fourier transform infrared (FTIR) spectroscopy and some other techniques. FTIR spectra of the cell membranes were shown to comprise the main vibration modes of the relevant lipopolysaccharide and protein components which are believed to be involved in associative plant-bacterium interactions, as well as of phospholipid constituents. The role and functions of metal cations in the structural organization and physicochemical properties of bacterial cell membranes are also discussed considering their accumulation in the membranes from the culture medium.

Randomized clinical trials in implant therapy: relationships among methodological, statistical, clinical, paratextual features and number of citations.

PubMed

Nieri, Michele; Clauser, Carlo; Franceschi, Debora; Pagliaro, Umberto; Saletta, Daniele; Pini-Prato, Giovanpaolo

2007-08-01

The aim of the present study was to investigate the relationships among reported methodological, statistical, clinical and paratextual variables of randomized clinical trials (RCTs) in implant therapy, and their influence on subsequent research. The material consisted of the RCTs in implant therapy published through the end of the year 2000. Methodological, statistical, clinical and paratextual features of the articles were assessed and recorded. The perceived clinical relevance was subjectively evaluated by an experienced clinician on anonymous abstracts. The impact on research was measured by the number of citations found in the Science Citation Index. A new statistical technique (Structural learning of Bayesian Networks) was used to assess the relationships among the considered variables. Descriptive statistics revealed that the reported methodology and statistics of RCTs in implant therapy were defective. Follow-up of the studies was generally short. The perceived clinical relevance appeared to be associated with the objectives of the studies and with the number of published images in the original articles. The impact on research was related to the nationality of the involved institutions and to the number of published images. RCTs in implant therapy (until 2000) show important methodological and statistical flaws and may not be appropriate for guiding clinicians in their practice. The methodological and statistical quality of the studies did not appear to affect their impact on practice and research. Bayesian Networks suggest new and unexpected relationships among the methodological, statistical, clinical and paratextual features of RCTs.

[Prediction of occult carcinoma in contralateral nodules based on the ultrasonic features of unilateral papillary thyroid carcinoma].

PubMed

Yang, L M; Li, Q; Zhao, B W; Lyu, J G; Xu, H S; Xu, L L; Li, S Y; Gao, L; Zhu, J

2017-04-07

Objective: To investigate the occurrence of occult carcinoma in contralateral lobes based on the ultrasonic features of unilateral papillary thyroid carcinoma. Methods: The study included 202 consecutives cases of unilateral papillary thyroid carcinoma with benign nodules in the contralateral lobe identified by preoperative ultrasound or fine-needle aspiration from June 2014 to December 2015. All patients received total thyroidectomies, and with postoperative pathological examination they were divided into two groups, one including 60 cases with positive occult cancer and another one consisting of 142 cases with negative occult cancer. Univariate and multivariate analyses were performed to analyze the sonographic features of unilateral papillary thyroid carcinoma relevant to the occurrence of occult carcinoma in the contralateral nodules. Results: Univariate analysis indicated occult carcinoma in the contralateral lobes was associated with Hashimoto's thyroiditis(χ(2)=3.955, P =0.047), unclear border (χ(2)=4.375, P =0.036)and multifocality in the ipsilateral(χ(2)=7.375, P =0.007), but not with tumors maximum size, location, A/T, shape, internal structure, internal echo, acoustic halo, calcification, capsular invasion and blood flow signal in the lobe with carcinoma on another side. Multivariate analysis showed unclear border ( OR =2.727, P =0.010) and multifocality in the ipsilateral( OR =2.807, P =0.005)of carcinoma were independent predictive factor for contralateral occult PTC. Conclusions: Unclear border and multifocality of PTC in the ipsilateral were closely relevant to the occurrence of occult carcinoma in the contralateral nodules.

Species-environment relationships and potential for distribution modelling in coastal waters

NASA Astrophysics Data System (ADS)

Snickars, M.; Gullström, M.; Sundblad, G.; Bergström, U.; Downie, A.-L.; Lindegarth, M.; Mattila, J.

2014-01-01

Due to increasing pressure on the marine environment there is a growing need to understand species-environment relationships. To provide background for prioritising among variables (predictors) for use in distribution models, the relevance of predictors for benthic species was reviewed using the coastal Baltic Sea as a case-study area. Significant relationships for three response groups (fish, macroinvertebrates, macrovegetation) and six predictor categories (bottom topography, biotic features, hydrography, wave exposure, substrate and spatiotemporal variability) were extracted from 145 queried peer-reviewed field-studies covering three decades and six subregions. In addition, the occurrence of interaction among predictors was analysed. Hydrography was most often found in significant relationships, had low level of interaction with other predictors, but also had the most non-significant relationships. Depth and wave exposure were important in all subregions and are readily available, increasing their applicability for cross-regional modelling efforts. Otherwise, effort to model species distributions may prove challenging at larger scale as the relevance of predictors differed among both response groups and regions. Fish and hard bottom macrovegetation have the largest modelling potential, as they are structured by a set of predictors that at the same time are accurately mapped. A general importance of biotic features implies that these need to be accounted for in distribution modelling, but the mapping of most biotic features is challenging, which currently lowers the applicability. The presence of interactions suggests that predictive methods allowing for interactive effects are preferable. Detailing these complexities is important for future distribution modelling.

Inference of Functionally-Relevant N-acetyltransferase Residues Based on Statistical Correlations.

PubMed

Neuwald, Andrew F; Altschul, Stephen F

2016-12-01

Over evolutionary time, members of a superfamily of homologous proteins sharing a common structural core diverge into subgroups filling various functional niches. At the sequence level, such divergence appears as correlations that arise from residue patterns distinct to each subgroup. Such a superfamily may be viewed as a population of sequences corresponding to a complex, high-dimensional probability distribution. Here we model this distribution as hierarchical interrelated hidden Markov models (hiHMMs), which describe these sequence correlations implicitly. By characterizing such correlations one may hope to obtain information regarding functionally-relevant properties that have thus far evaded detection. To do so, we infer a hiHMM distribution from sequence data using Bayes' theorem and Markov chain Monte Carlo (MCMC) sampling, which is widely recognized as the most effective approach for characterizing a complex, high dimensional distribution. Other routines then map correlated residue patterns to available structures with a view to hypothesis generation. When applied to N-acetyltransferases, this reveals sequence and structural features indicative of functionally important, yet generally unknown biochemical properties. Even for sets of proteins for which nothing is known beyond unannotated sequences and structures, this can lead to helpful insights. We describe, for example, a putative coenzyme-A-induced-fit substrate binding mechanism mediated by arginine residue switching between salt bridge and π-π stacking interactions. A suite of programs implementing this approach is available (psed.igs.umaryland.edu).

Symmetry breaking, mixing, instability, and low-frequency variability in a minimal Lorenz-like system.

PubMed

Lucarini, Valerio; Fraedrich, Klaus

2009-08-01

Starting from the classical Saltzman two-dimensional convection equations, we derive via a severe spectral truncation a minimal 10 ODE system which includes the thermal effect of viscous dissipation. Neglecting this process leads to a dynamical system which includes a decoupled generalized Lorenz system. The consideration of this process breaks an important symmetry and couples the dynamics of fast and slow variables, with the ensuing modifications to the structural properties of the attractor and of the spectral features. When the relevant nondimensional number (Eckert number Ec) is different from zero, an additional time scale of O(Ec(-1)) is introduced in the system, as shown with standard multiscale analysis and made clear by several numerical evidences. Moreover, the system is ergodic and hyperbolic, the slow variables feature long-term memory with 1/f(3/2) power spectra, and the fast variables feature amplitude modulation. Increasing the strength of the thermal-viscous feedback has a stabilizing effect, as both the metric entropy and the Kaplan-Yorke attractor dimension decrease monotonically with Ec. The analyzed system features very rich dynamics: it overcomes some of the limitations of the Lorenz system and might have prototypical value in relevant processes in complex systems dynamics, such as the interaction between slow and fast variables, the presence of long-term memory, and the associated extreme value statistics. This analysis shows how neglecting the coupling of slow and fast variables only on the basis of scale analysis can be catastrophic. In fact, this leads to spurious invariances that affect essential dynamical properties (ergodicity, hyperbolicity) and that cause the model losing ability in describing intrinsically multiscale processes.

Hierarchical Feedback Modules and Reaction Hubs in Cell Signaling Networks

PubMed Central

Xu, Jianfeng; Lan, Yueheng

2015-01-01

Despite much effort, identification of modular structures and study of their organizing and functional roles remain a formidable challenge in molecular systems biology, which, however, is essential in reaching a systematic understanding of large-scale cell regulation networks and hence gaining capacity of exerting effective interference to cell activity. Combining graph theoretic methods with available dynamics information, we successfully retrieved multiple feedback modules of three important signaling networks. These feedbacks are structurally arranged in a hierarchical way and dynamically produce layered temporal profiles of output signals. We found that global and local feedbacks act in very different ways and on distinct features of the information flow conveyed by signal transduction but work highly coordinately to implement specific biological functions. The redundancy embodied with multiple signal-relaying channels and feedback controls bestow great robustness and the reaction hubs seated at junctions of different paths announce their paramount importance through exquisite parameter management. The current investigation reveals intriguing general features of the organization of cell signaling networks and their relevance to biological function, which may find interesting applications in analysis, design and control of bio-networks. PMID:25951347

Wireless AE Event and Environmental Monitoring for Wind Turbine Blades at Low Sampling Rates

NASA Astrophysics Data System (ADS)

Bouzid, Omar M.; Tian, Gui Y.; Cumanan, K.; Neasham, J.

Integration of acoustic wireless technology in structural health monitoring (SHM) applications introduces new challenges due to requirements of high sampling rates, additional communication bandwidth, memory space, and power resources. In order to circumvent these challenges, this chapter proposes a novel solution through building a wireless SHM technique in conjunction with acoustic emission (AE) with field deployment on the structure of a wind turbine. This solution requires a low sampling rate which is lower than the Nyquist rate. In addition, features extracted from aliased AE signals instead of reconstructing the original signals on-board the wireless nodes are exploited to monitor AE events, such as wind, rain, strong hail, and bird strike in different environmental conditions in conjunction with artificial AE sources. Time feature extraction algorithm, in addition to the principal component analysis (PCA) method, is used to extract and classify the relevant information, which in turn is used to classify or recognise a testing condition that is represented by the response signals. This proposed novel technique yields a significant data reduction during the monitoring process of wind turbine blades.

Evaluating molecular cobalt complexes for the conversion of N2 to NH3.

PubMed

Del Castillo, Trevor J; Thompson, Niklas B; Suess, Daniel L M; Ung, Gaël; Peters, Jonas C

2015-10-05

Well-defined molecular catalysts for the reduction of N2 to NH3 with protons and electrons remain very rare despite decades of interest and are currently limited to systems featuring molybdenum or iron. This report details the synthesis of a molecular cobalt complex that generates superstoichiometric yields of NH3 (>200% NH3 per Co-N2 precursor) via the direct reduction of N2 with protons and electrons. While the NH3 yields reported herein are modest by comparison to those of previously described iron and molybdenum systems, they intimate that other metals are likely to be viable as molecular N2 reduction catalysts. Additionally, a comparison of the featured tris(phosphine)borane Co-N2 complex with structurally related Co-N2 and Fe-N2 species shows how remarkably sensitive the N2 reduction performance of potential precatalysts is. These studies enable consideration of the structural and electronic effects that are likely relevant to N2 conversion activity, including the π basicity, charge state, and geometric flexibility.

What amyloidoses may tell us about normal protein folding: The Alzheimer's disease story

NASA Astrophysics Data System (ADS)

Teplow, David B.

2002-03-01

Alzheimer's disease (AD) is a progressive, neurodegenerative disorder characterized by severe neuronal injury and death. A prominent histopathologic feature of AD is disseminated parenchymal and vascular amyloid deposition. The fibrils in these deposits are composed of the amyloid β-protein (Aβ), a peptide of 4 kDa mass. In vitro and in vivo studies of Aβ fibril formation have shown that both oligomeric and polymeric Aβ assemblies have neurotoxic activity. Understanding how these assemblies form thus could be of direct therapeutic relevance. However, the aggregation and fibril-forming propensities of Aβ have complicated structure determination. Nevertheless, careful morphologic, spectroscopic, protein chemical, and physiologic analyses of the time-dependent changes in Aβ conformation, assembly state, and biological activity which occur during fibrillogenesis have significantly advanced our understanding of this clinically important process. Here, I will discuss recent findings about the pathway(s) of Aβ folding and assembly and about key structural features of Aβ which control the associated kinetics. Interestingly, the amyloidogenic folding pathway of Aβ is in some respects the mirror image of that through which natively folded amyloidogenic proteins proceed.

Self-assembled hierarchically structured organic-inorganic composite systems.

PubMed

Tritschler, Ulrich; Cölfen, Helmut

2016-05-13

Designing bio-inspired, multifunctional organic-inorganic composite materials is one of the most popular current research objectives. Due to the high complexity of biocomposite structures found in nacre and bone, for example, a one-pot scalable and versatile synthesis approach addressing structural key features of biominerals and affording bio-inspired, multifunctional organic-inorganic composites with advanced physical properties is highly challenging. This article reviews recent progress in synthesizing organic-inorganic composite materials via various self-assembly techniques and in this context highlights a recently developed bio-inspired synthesis concept for the fabrication of hierarchically structured, organic-inorganic composite materials. This one-step self-organization concept based on simultaneous liquid crystal formation of anisotropic inorganic nanoparticles and a functional liquid crystalline polymer turned out to be simple, fast, scalable and versatile, leading to various (multi-)functional composite materials, which exhibit hierarchical structuring over several length scales. Consequently, this synthesis approach is relevant for further progress and scientific breakthrough in the research field of bio-inspired and biomimetic materials.

Bismuth Sodium Titanate Based Materials for Piezoelectric Actuators

PubMed Central

Reichmann, Klaus; Feteira, Antonio; Li, Ming

2015-01-01

The ban of lead in many electronic products and the expectation that, sooner or later, this ban will include the currently exempt piezoelectric ceramics based on Lead-Zirconate-Titanate has motivated many research groups to look for lead-free substitutes. After a short overview on different classes of lead-free piezoelectric ceramics with large strain, this review will focus on Bismuth-Sodium-Titanate and its solid solutions. These compounds exhibit extraordinarily high strain, due to a field induced phase transition, which makes them attractive for actuator applications. The structural features of these materials and the origin of the field-induced strain will be revised. Technologies for texturing, which increases the useable strain, will be introduced. Finally, the features that are relevant for the application of these materials in a multilayer design will be summarized. PMID:28793724

Structure-based classification and ontology in chemistry

PubMed Central

2012-01-01

Background Recent years have seen an explosion in the availability of data in the chemistry domain. With this information explosion, however, retrieving relevant results from the available information, and organising those results, become even harder problems. Computational processing is essential to filter and organise the available resources so as to better facilitate the work of scientists. Ontologies encode expert domain knowledge in a hierarchically organised machine-processable format. One such ontology for the chemical domain is ChEBI. ChEBI provides a classification of chemicals based on their structural features and a role or activity-based classification. An example of a structure-based class is 'pentacyclic compound' (compounds containing five-ring structures), while an example of a role-based class is 'analgesic', since many different chemicals can act as analgesics without sharing structural features. Structure-based classification in chemistry exploits elegant regularities and symmetries in the underlying chemical domain. As yet, there has been neither a systematic analysis of the types of structural classification in use in chemistry nor a comparison to the capabilities of available technologies. Results We analyze the different categories of structural classes in chemistry, presenting a list of patterns for features found in class definitions. We compare these patterns of class definition to tools which allow for automation of hierarchy construction within cheminformatics and within logic-based ontology technology, going into detail in the latter case with respect to the expressive capabilities of the Web Ontology Language and recent extensions for modelling structured objects. Finally we discuss the relationships and interactions between cheminformatics approaches and logic-based approaches. Conclusion Systems that perform intelligent reasoning tasks on chemistry data require a diverse set of underlying computational utilities including algorithmic, statistical and logic-based tools. For the task of automatic structure-based classification of chemical entities, essential to managing the vast swathes of chemical data being brought online, systems which are capable of hybrid reasoning combining several different approaches are crucial. We provide a thorough review of the available tools and methodologies, and identify areas of open research. PMID:22480202

RNA-TVcurve: a Web server for RNA secondary structure comparison based on a multi-scale similarity of its triple vector curve representation.

PubMed

Li, Ying; Shi, Xiaohu; Liang, Yanchun; Xie, Juan; Zhang, Yu; Ma, Qin

2017-01-21

RNAs have been found to carry diverse functionalities in nature. Inferring the similarity between two given RNAs is a fundamental step to understand and interpret their functional relationship. The majority of functional RNAs show conserved secondary structures, rather than sequence conservation. Those algorithms relying on sequence-based features usually have limitations in their prediction performance. Hence, integrating RNA structure features is very critical for RNA analysis. Existing algorithms mainly fall into two categories: alignment-based and alignment-free. The alignment-free algorithms of RNA comparison usually have lower time complexity than alignment-based algorithms. An alignment-free RNA comparison algorithm was proposed, in which novel numerical representations RNA-TVcurve (triple vector curve representation) of RNA sequence and corresponding secondary structure features are provided. Then a multi-scale similarity score of two given RNAs was designed based on wavelet decomposition of their numerical representation. In support of RNA mutation and phylogenetic analysis, a web server (RNA-TVcurve) was designed based on this alignment-free RNA comparison algorithm. It provides three functional modules: 1) visualization of numerical representation of RNA secondary structure; 2) detection of single-point mutation based on secondary structure; and 3) comparison of pairwise and multiple RNA secondary structures. The inputs of the web server require RNA primary sequences, while corresponding secondary structures are optional. For the primary sequences alone, the web server can compute the secondary structures using free energy minimization algorithm in terms of RNAfold tool from Vienna RNA package. RNA-TVcurve is the first integrated web server, based on an alignment-free method, to deliver a suite of RNA analysis functions, including visualization, mutation analysis and multiple RNAs structure comparison. The comparison results with two popular RNA comparison tools, RNApdist and RNAdistance, showcased that RNA-TVcurve can efficiently capture subtle relationships among RNAs for mutation detection and non-coding RNA classification. All the relevant results were shown in an intuitive graphical manner, and can be freely downloaded from this server. RNA-TVcurve, along with test examples and detailed documents, are available at: http://ml.jlu.edu.cn/tvcurve/ .

Infrared microspectroscopy of live cells in microfluidic devices (MD-IRMS): toward a powerful label-free cell-based assay.

PubMed

Vaccari, L; Birarda, G; Businaro, L; Pacor, S; Grenci, G

2012-06-05

Until nowadays most infrared microspectroscopy (IRMS) experiments on biological specimens (i.e., tissues or cells) have been routinely carried out on fixed or dried samples in order to circumvent water absorption problems. In this paper, we demonstrate the possibility to widen the range of in-vitro IRMS experiments to vibrational analysis of live cellular samples, thanks to the development of novel biocompatible IR-visible transparent microfluidic devices (MD). In order to highlight the biological relevance of IRMS in MD (MD-IRMS), we performed a systematic exploration of the biochemical alterations induced by different fixation protocols, ethanol 70% and formaldehyde solution 4%, as well as air-drying on U937 leukemic monocytes by comparing their IR vibrational features with the live U937 counterpart. Both fixation and air-drying procedures affected lipid composition and order as well as protein structure at a different extent while they both induced structural alterations in nucleic acids. Therefore, only IRMS of live cells can provide reliable information on both DNA and RNA structure and on their cellular dynamic. In summary, we show that MD-IRMS of live cells is feasible, reliable, and biologically relevant to be recognized as a label-free cell-based assay.

Extraction of object skeletons in multispectral imagery by the orthogonal regression fitting

NASA Astrophysics Data System (ADS)

Palenichka, Roman M.; Zaremba, Marek B.

2003-03-01

Accurate and automatic extraction of skeletal shape of objects of interest from satellite images provides an efficient solution to such image analysis tasks as object detection, object identification, and shape description. The problem of skeletal shape extraction can be effectively solved in three basic steps: intensity clustering (i.e. segmentation) of objects, extraction of a structural graph of the object shape, and refinement of structural graph by the orthogonal regression fitting. The objects of interest are segmented from the background by a clustering transformation of primary features (spectral components) with respect to each pixel. The structural graph is composed of connected skeleton vertices and represents the topology of the skeleton. In the general case, it is a quite rough piecewise-linear representation of object skeletons. The positions of skeleton vertices on the image plane are adjusted by means of the orthogonal regression fitting. It consists of changing positions of existing vertices according to the minimum of the mean orthogonal distances and, eventually, adding new vertices in-between if a given accuracy if not yet satisfied. Vertices of initial piecewise-linear skeletons are extracted by using a multi-scale image relevance function. The relevance function is an image local operator that has local maximums at the centers of the objects of interest.

Reward- and attention-related biasing of sensory selection in visual cortex.

PubMed

Buschschulte, Antje; Boehler, Carsten N; Strumpf, Hendrik; Stoppel, Christian; Heinze, Hans-Jochen; Schoenfeld, Mircea A; Hopf, Jens-Max

2014-05-01

Attention to task-relevant features leads to a biasing of sensory selection in extrastriate cortex. Features signaling reward seem to produce a similar bias, but how modulatory effects due to reward and attention relate to each other is largely unexplored. To address this issue, it is critical to separate top-down settings defining reward relevance from those defining attention. To this end, we used a visual search paradigm in which the target's definition (attention to color) was dissociated from reward relevance by delivering monetary reward on search frames where a certain task-irrelevant color was combined with the target-defining color to form the target object. We assessed the state of neural biasing for the attended and reward-relevant color by analyzing the neuromagnetic brain response to asynchronously presented irrelevant distractor probes drawn in the target-defining color, the reward-relevant color, and a completely irrelevant color as a reference. We observed that for the prospect of moderate rewards, the target-defining color but not the reward-relevant color produced a selective enhancement of the neuromagnetic response between 180 and 280 msec in ventral extrastriate visual cortex. Increasing reward prospect caused a delayed attenuation (220-250 msec) of the response to reward probes, which followed a prior (160-180 msec) response enhancement in dorsal ACC. Notably, shorter latency responses in dorsal ACC were associated with stronger attenuation in extrastriate visual cortex. Finally, an analysis of the brain response to the search frames revealed that the presence of the reward-relevant color in search distractors elicited an enhanced response that was abolished after increasing reward size. The present data together indicate that when top-down definitions of reward relevance and attention are separated, the behavioral significance of reward-associated features is still rapidly coded in higher-level cortex areas, thereby commanding effective top-down inhibitory control to counter a selection bias for those features in extrastriate visual cortex.

Teachers' Source Evaluation Self-Efficacy Predicts Their Use of Relevant Source Features When Evaluating the Trustworthiness of Web Sources on Special Education

ERIC Educational Resources Information Center

Andreassen, Rune; Bråten, Ivar

2013-01-01

Building on prior research and theory concerning source evaluation and the role of self-efficacy in the context of online learning, this study investigated the relationship between teachers' beliefs about their capability to evaluate the trustworthiness of sources and their reliance on relevant source features when judging the trustworthiness…

Amygdala and auditory cortex exhibit distinct sensitivity to relevant acoustic features of auditory emotions.

PubMed

Pannese, Alessia; Grandjean, Didier; Frühholz, Sascha

2016-12-01

Discriminating between auditory signals of different affective value is critical to successful social interaction. It is commonly held that acoustic decoding of such signals occurs in the auditory system, whereas affective decoding occurs in the amygdala. However, given that the amygdala receives direct subcortical projections that bypass the auditory cortex, it is possible that some acoustic decoding occurs in the amygdala as well, when the acoustic features are relevant for affective discrimination. We tested this hypothesis by combining functional neuroimaging with the neurophysiological phenomena of repetition suppression (RS) and repetition enhancement (RE) in human listeners. Our results show that both amygdala and auditory cortex responded differentially to physical voice features, suggesting that the amygdala and auditory cortex decode the affective quality of the voice not only by processing the emotional content from previously processed acoustic features, but also by processing the acoustic features themselves, when these are relevant to the identification of the voice's affective value. Specifically, we found that the auditory cortex is sensitive to spectral high-frequency voice cues when discriminating vocal anger from vocal fear and joy, whereas the amygdala is sensitive to vocal pitch when discriminating between negative vocal emotions (i.e., anger and fear). Vocal pitch is an instantaneously recognized voice feature, which is potentially transferred to the amygdala by direct subcortical projections. These results together provide evidence that, besides the auditory cortex, the amygdala too processes acoustic information, when this is relevant to the discrimination of auditory emotions. Copyright © 2016 Elsevier Ltd. All rights reserved.

Feature-Selective Attention Adaptively Shifts Noise Correlations in Primary Auditory Cortex.

PubMed

Downer, Joshua D; Rapone, Brittany; Verhein, Jessica; O'Connor, Kevin N; Sutter, Mitchell L

2017-05-24

Sensory environments often contain an overwhelming amount of information, with both relevant and irrelevant information competing for neural resources. Feature attention mediates this competition by selecting the sensory features needed to form a coherent percept. How attention affects the activity of populations of neurons to support this process is poorly understood because population coding is typically studied through simulations in which one sensory feature is encoded without competition. Therefore, to study the effects of feature attention on population-based neural coding, investigations must be extended to include stimuli with both relevant and irrelevant features. We measured noise correlations ( r noise ) within small neural populations in primary auditory cortex while rhesus macaques performed a novel feature-selective attention task. We found that the effect of feature-selective attention on r noise depended not only on the population tuning to the attended feature, but also on the tuning to the distractor feature. To attempt to explain how these observed effects might support enhanced perceptual performance, we propose an extension of a simple and influential model in which shifts in r noise can simultaneously enhance the representation of the attended feature while suppressing the distractor. These findings present a novel mechanism by which attention modulates neural populations to support sensory processing in cluttered environments. SIGNIFICANCE STATEMENT Although feature-selective attention constitutes one of the building blocks of listening in natural environments, its neural bases remain obscure. To address this, we developed a novel auditory feature-selective attention task and measured noise correlations ( r noise ) in rhesus macaque A1 during task performance. Unlike previous studies showing that the effect of attention on r noise depends on population tuning to the attended feature, we show that the effect of attention depends on the tuning to the distractor feature as well. We suggest that these effects represent an efficient process by which sensory cortex simultaneously enhances relevant information and suppresses irrelevant information. Copyright © 2017 the authors 0270-6474/17/375378-15$15.00/0.

Feature-Selective Attention Adaptively Shifts Noise Correlations in Primary Auditory Cortex

PubMed Central

2017-01-01

Sensory environments often contain an overwhelming amount of information, with both relevant and irrelevant information competing for neural resources. Feature attention mediates this competition by selecting the sensory features needed to form a coherent percept. How attention affects the activity of populations of neurons to support this process is poorly understood because population coding is typically studied through simulations in which one sensory feature is encoded without competition. Therefore, to study the effects of feature attention on population-based neural coding, investigations must be extended to include stimuli with both relevant and irrelevant features. We measured noise correlations (rnoise) within small neural populations in primary auditory cortex while rhesus macaques performed a novel feature-selective attention task. We found that the effect of feature-selective attention on rnoise depended not only on the population tuning to the attended feature, but also on the tuning to the distractor feature. To attempt to explain how these observed effects might support enhanced perceptual performance, we propose an extension of a simple and influential model in which shifts in rnoise can simultaneously enhance the representation of the attended feature while suppressing the distractor. These findings present a novel mechanism by which attention modulates neural populations to support sensory processing in cluttered environments. SIGNIFICANCE STATEMENT Although feature-selective attention constitutes one of the building blocks of listening in natural environments, its neural bases remain obscure. To address this, we developed a novel auditory feature-selective attention task and measured noise correlations (rnoise) in rhesus macaque A1 during task performance. Unlike previous studies showing that the effect of attention on rnoise depends on population tuning to the attended feature, we show that the effect of attention depends on the tuning to the distractor feature as well. We suggest that these effects represent an efficient process by which sensory cortex simultaneously enhances relevant information and suppresses irrelevant information. PMID:28432139

Definition and classification of evaluation units for tertiary structure prediction in CASP12 facilitated through semi-automated metrics.

PubMed

Abriata, Luciano A; Kinch, Lisa N; Tamò, Giorgio E; Monastyrskyy, Bohdan; Kryshtafovych, Andriy; Dal Peraro, Matteo

2018-03-01

For assessment purposes, CASP targets are split into evaluation units. We herein present the official definition of CASP12 evaluation units (EUs) and their classification into difficulty categories. Each target can be evaluated as one EU (the whole target) or/and several EUs (separate structural domains or groups of structural domains). The specific scenario for a target split is determined by the domain organization of available templates, the difference in server performance on separate domains versus combination of the domains, and visual inspection. In the end, 71 targets were split into 96 EUs. Classification of the EUs into difficulty categories was done semi-automatically with the assistance of metrics provided by the Prediction Center. These metrics account for sequence and structural similarities of the EUs to potential structural templates from the Protein Data Bank, and for the baseline performance of automated server predictions. The metrics readily separate the 96 EUs into 38 EUs that should be straightforward for template-based modeling (TBM) and 39 that are expected to be hard for homology modeling and are thus left for free modeling (FM). The remaining 19 borderline evaluation units were dubbed FM/TBM, and were inspected case by case. The article also overviews structural and evolutionary features of selected targets relevant to our accompanying article presenting the assessment of FM and FM/TBM predictions, and overviews structural features of the hardest evaluation units from the FM category. We finally suggest improvements for the EU definition and classification procedures. © 2017 Wiley Periodicals, Inc.

An Automated, High-Throughput Method for Interpreting the Tandem Mass Spectra of Glycosaminoglycans

NASA Astrophysics Data System (ADS)

Duan, Jiana; Jonathan Amster, I.

2018-05-01

The biological interactions between glycosaminoglycans (GAGs) and other biomolecules are heavily influenced by structural features of the glycan. The structure of GAGs can be assigned using tandem mass spectrometry (MS2), but analysis of these data, to date, requires manually interpretation, a slow process that presents a bottleneck to the broader deployment of this approach to solving biologically relevant problems. Automated interpretation remains a challenge, as GAG biosynthesis is not template-driven, and therefore, one cannot predict structures from genomic data, as is done with proteins. The lack of a structure database, a consequence of the non-template biosynthesis, requires a de novo approach to interpretation of the mass spectral data. We propose a model for rapid, high-throughput GAG analysis by using an approach in which candidate structures are scored for the likelihood that they would produce the features observed in the mass spectrum. To make this approach tractable, a genetic algorithm is used to greatly reduce the search-space of isomeric structures that are considered. The time required for analysis is significantly reduced compared to an approach in which every possible isomer is considered and scored. The model is coded in a software package using the MATLAB environment. This approach was tested on tandem mass spectrometry data for long-chain, moderately sulfated chondroitin sulfate oligomers that were derived from the proteoglycan bikunin. The bikunin data was previously interpreted manually. Our approach examines glycosidic fragments to localize SO3 modifications to specific residues and yields the same structures reported in literature, only much more quickly.

Exploring the Association Between Emotional Abuse and Childhood Borderline Personality Features: The Moderating Role of Personality Traits

PubMed Central

Gratz, Kim L.; Latzman, Robert D.; Tull, Matthew T.; Reynolds, Elizabeth K.; Lejuez, C. W.

2012-01-01

Most of the extant literature on borderline personality disorder has focused on the course, consequences, and correlates of this disorder among adults. However, little is known about childhood borderline personality (BP) features, or the factors associated with the emergence of BP pathology in childhood. A greater understanding of childhood BP features and associated risk factors has important implications for the development of primary and secondary prevention programs. The goal of the present study was to examine the interrelationships among two BP-relevant traits (affective dysfunction and impulsivity), a BP-relevant environmental stressor (emotional abuse), and BP features in a sample of 225 children aged 11 to 14 years. Results provide support for the role of both trait vulnerabilities and environmental stressors in childhood BP features. Further, findings highlight the moderating role of affective dysfunction in the relationship between emotional abuse and childhood BP features. PMID:21658531

Comparison of Different EHG Feature Selection Methods for the Detection of Preterm Labor

PubMed Central

Alamedine, D.; Khalil, M.; Marque, C.

2013-01-01

Numerous types of linear and nonlinear features have been extracted from the electrohysterogram (EHG) in order to classify labor and pregnancy contractions. As a result, the number of available features is now very large. The goal of this study is to reduce the number of features by selecting only the relevant ones which are useful for solving the classification problem. This paper presents three methods for feature subset selection that can be applied to choose the best subsets for classifying labor and pregnancy contractions: an algorithm using the Jeffrey divergence (JD) distance, a sequential forward selection (SFS) algorithm, and a binary particle swarm optimization (BPSO) algorithm. The two last methods are based on a classifier and were tested with three types of classifiers. These methods have allowed us to identify common features which are relevant for contraction classification. PMID:24454536

Structural damage detection based on stochastic subspace identification and statistical pattern recognition: II. Experimental validation under varying temperature

NASA Astrophysics Data System (ADS)

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

2011-11-01

Although most vibration-based damage detection methods can acquire satisfactory verification on analytical or numerical structures, most of them may encounter problems when applied to real-world structures under varying environments. The damage detection methods that directly extract damage features from the periodically sampled dynamic time history response measurements are desirable but relevant research and field application verification are still lacking. In this second part of a two-part paper, the robustness and performance of the statistics-based damage index using the forward innovation model by stochastic subspace identification of a vibrating structure proposed in the first part have been investigated against two prestressed reinforced concrete (RC) beams tested in the laboratory and a full-scale RC arch bridge tested in the field under varying environments. Experimental verification is focused on temperature effects. It is demonstrated that the proposed statistics-based damage index is insensitive to temperature variations but sensitive to the structural deterioration or state alteration. This makes it possible to detect the structural damage for the real-scale structures experiencing ambient excitations and varying environmental conditions.

Direct laser interference patterning of metallic sleeves for roll-to-roll hot embossing

NASA Astrophysics Data System (ADS)

Lang, Valentin; Rank, Andreas; Lasagni, Andrés. F.

2017-03-01

Surfaces equipped with periodic patterns with feature sizes in the micrometer, submicrometer and nanometer range present outstanding surface properties. Many of these surfaces can be found on different plants and animals. However, there are few methods capable to produce such patterns in a one-step process on relevant technological materials. Direct laser interference patterning (DLIP) provides both high resolution as well as high throughput. Recently, fabrication rates up to 1 m2·min-1 could be achieved. However, resolution was limited to a few micrometers due to typical thermal effects that arise when nanosecond pulsed laser systems are used. Therefore, this study introduces an alternative to ns-DLIP for the fabrication of multi-scaled micrometer and submicrometer structures on nickel surfaces using picosecond pulses (10 ps at a wavelength of 1064 nm). Due to the nature of the interaction process of the metallic surfaces with the ultrashort laser pulses, it was not only possible to directly transfer the shape of the interference pattern intensity distribution to the material (with spatial periods ranging from 1.5 μm to 5.7 μm), but also to selectively obtain laser induce periodic surface structures with feature sizes in the submicrometer and nanometer range. Finally, the structured nickel sleeves are utilized in a roll-to-roll hot embossing unit for structuring of polymer foils. Processing speeds up to 25 m·min-1 are reported.

Methodologies for Verification and Validation of Space Launch System (SLS) Structural Dynamic Models

NASA Technical Reports Server (NTRS)

Coppolino, Robert N.

2018-01-01

Responses to challenges associated with verification and validation (V&V) of Space Launch System (SLS) structural dynamics models are presented in this paper. Four methodologies addressing specific requirements for V&V are discussed. (1) Residual Mode Augmentation (RMA), which has gained acceptance by various principals in the NASA community, defines efficient and accurate FEM modal sensitivity models that are useful in test-analysis correlation and reconciliation and parametric uncertainty studies. (2) Modified Guyan Reduction (MGR) and Harmonic Reduction (HR, introduced in 1976), developed to remedy difficulties encountered with the widely used Classical Guyan Reduction (CGR) method, are presented. MGR and HR are particularly relevant for estimation of "body dominant" target modes of shell-type SLS assemblies that have numerous "body", "breathing" and local component constituents. Realities associated with configuration features and "imperfections" cause "body" and "breathing" mode characteristics to mix resulting in a lack of clarity in the understanding and correlation of FEM- and test-derived modal data. (3) Mode Consolidation (MC) is a newly introduced procedure designed to effectively "de-feature" FEM and experimental modes of detailed structural shell assemblies for unambiguous estimation of "body" dominant target modes. Finally, (4) Experimental Mode Verification (EMV) is a procedure that addresses ambiguities associated with experimental modal analysis of complex structural systems. Specifically, EMV directly separates well-defined modal data from spurious and poorly excited modal data employing newly introduced graphical and coherence metrics.

Estrogen Receptor Folding Modulates cSrc Kinase SH2 Interaction via a Helical Binding Mode.

PubMed

Nieto, Lidia; Tharun, Inga M; Balk, Mark; Wienk, Hans; Boelens, Rolf; Ottmann, Christian; Milroy, Lech-Gustav; Brunsveld, Luc

2015-11-20

The estrogen receptors (ERs) feature, next to their transcriptional role, important nongenomic signaling actions, with emerging clinical relevance. The Src Homology 2 (SH2) domain mediated interaction between cSrc kinase and ER plays a key role in this; however the molecular determinants of this interaction have not been elucidated. Here, we used phosphorylated ER peptide and semisynthetic protein constructs in a combined biochemical and structural study to, for the first time, provide a quantitative and structural characterization of the cSrc SH2-ER interaction. Fluorescence polarization experiments delineated the SH2 binding motif in the ER sequence. Chemical shift perturbation analysis by nuclear magnetic resonance (NMR) together with molecular dynamics (MD) simulations allowed us to put forward a 3D model of the ER-SH2 interaction. The structural basis of this protein-protein interaction has been compared with that of the high affinity SH2 binding sequence GpYEEI. The ER features a different binding mode from that of the "two-pronged plug two-hole socket" model in the so-called specificity determining region. This alternative binding mode is modulated via the folding of ER helix 12, a structural element directly C-terminal of the key phosphorylated tyrosine. The present findings provide novel molecular entries for understanding nongenomic ER signaling and targeting the corresponding disease states.

Multi-channel MRI segmentation of eye structures and tumors using patient-specific features

PubMed Central

Ciller, Carlos; De Zanet, Sandro; Kamnitsas, Konstantinos; Maeder, Philippe; Glocker, Ben; Munier, Francis L.; Rueckert, Daniel; Thiran, Jean-Philippe

2017-01-01

Retinoblastoma and uveal melanoma are fast spreading eye tumors usually diagnosed by using 2D Fundus Image Photography (Fundus) and 2D Ultrasound (US). Diagnosis and treatment planning of such diseases often require additional complementary imaging to confirm the tumor extend via 3D Magnetic Resonance Imaging (MRI). In this context, having automatic segmentations to estimate the size and the distribution of the pathological tissue would be advantageous towards tumor characterization. Until now, the alternative has been the manual delineation of eye structures, a rather time consuming and error-prone task, to be conducted in multiple MRI sequences simultaneously. This situation, and the lack of tools for accurate eye MRI analysis, reduces the interest in MRI beyond the qualitative evaluation of the optic nerve invasion and the confirmation of recurrent malignancies below calcified tumors. In this manuscript, we propose a new framework for the automatic segmentation of eye structures and ocular tumors in multi-sequence MRI. Our key contribution is the introduction of a pathological eye model from which Eye Patient-Specific Features (EPSF) can be computed. These features combine intensity and shape information of pathological tissue while embedded in healthy structures of the eye. We assess our work on a dataset of pathological patient eyes by computing the Dice Similarity Coefficient (DSC) of the sclera, the cornea, the vitreous humor, the lens and the tumor. In addition, we quantitatively show the superior performance of our pathological eye model as compared to the segmentation obtained by using a healthy model (over 4% DSC) and demonstrate the relevance of our EPSF, which improve the final segmentation regardless of the classifier employed. PMID:28350816

Motivation factors for suicidal behavior and their clinical relevance in admitted psychiatric patients.

PubMed

Hayashi, Naoki; Igarashi, Miyabi; Imai, Atsushi; Yoshizawa, Yuka; Asamura, Kaori; Ishikawa, Yoichi; Tokunaga, Taro; Ishimoto, Kayo; Tatebayashi, Yoshitaka; Harima, Hirohiko; Kumagai, Naoki; Ishii, Hidetoki; Okazaki, Yuji

2017-01-01

Suicidal behavior (SB) is a major, worldwide health concern. To date there is limited understanding of the associated motivational aspects which accompany this self-initiated conduct. To develop a method for identifying motivational features associated with SB by studying admitted psychiatric patients, and to examine their clinical relevance. By performing a factor analytic study using data obtained from a patient sample exhibiting high suicidality and a variety of SB methods, Motivations for SB Scale (MSBS) was constructed to measure the features. Data included assessments of DSM-IV psychiatric and personality disorders, suicide intent, depressive symptomatology, overt aggression, recent life events (RLEs) and methods of SB, collated from structured interviews. Association of identified features with clinical variables was examined by correlation analyses and MANCOVA. Factor analyses elicited a 4-factor solution composed of Interpersonal-testing (IT), Interpersonal-change (IC), Self-renunciation (SR) and Self-sustenance (SS). These factors were classified according to two distinctions, namely interpersonal vs. intra-personal directedness, and the level of assumed influence by SB or the relationship to prevailing emotions. Analyses revealed meaningful links between patient features and clinical variables. Interpersonal-motivations (IT and IC) were associated with overt aggression, low suicidality and RLE discord or conflict, while SR was associated with depression, high suicidality and RLE separation or death. Borderline personality disorder showed association with IC and SS. When self-strangulation was set as a reference SB method, self-cutting and overdose-taking were linked to IT and SS, respectively. The factors extracted in this study largely corresponded to factors from previous studies, implying that they may be useful in a wider clinical context. The association of these features with SB-related factors suggests that they constitute an integral part of the process leading to SB. These results provide a base for further research into clinical strategies for patient management and therapy.

Attitude Strength.

PubMed

Howe, Lauren C; Krosnick, Jon A

2017-01-03

Attitude strength has been the focus of a huge volume of research in psychology and related sciences for decades. The insights offered by this literature have tremendous value for understanding attitude functioning and structure and for the effective application of the attitude concept in applied settings. This is the first Annual Review of Psychology article on the topic, and it offers a review of theory and evidence regarding one of the most researched strength-related attitude features: attitude importance. Personal importance is attached to an attitude when the attitude is perceived to be relevant to self-interest, social identification with reference groups or reference individuals, and values. Attaching personal importance to an attitude causes crystallizing of attitudes (via enhanced resistance to change), effortful gathering and processing of relevant information, accumulation of a large store of well-organized relevant information in long-term memory, enhanced attitude extremity and accessibility, enhanced attitude impact on the regulation of interpersonal attraction, energizing of emotional reactions, and enhanced impact of attitudes on behavioral intentions and action. Thus, important attitudes are real and consequential psychological forces, and their study offers opportunities for addressing behavioral change.

Ghost features in Doppler-broadened spectra of rovibrational transitions in trapped HD+ ions

NASA Astrophysics Data System (ADS)

Patra, Sayan; Koelemeij, J. C. J.

2017-02-01

Doppler broadening plays an important role in laser rovibrational spectroscopy of trapped deuterated molecular hydrogen ions (HD+), even at the millikelvin temperatures achieved through sympathetic cooling by laser-cooled beryllium ions. Recently, Biesheuvel et al. (2016) presented a theoretical lineshape model for such transitions which not only considers linestrengths and Doppler broadening, but also the finite sample size and population redistribution by blackbody radiation, which are important in view of the long storage and probe times achievable in ion traps. Here, we employ the rate equation model developed by Biesheuvel et al. to theoretically study the Doppler-broadened hyperfine structure of the (v, L) : (0, 3) → (4, 2) rovibrational transition in HD+ at 1442 nm. We observe prominent yet hitherto unrecognized ghost features in the simulated spectrum, whose positions depend on the Doppler width, transition rates, and saturation levels of the hyperfine components addressed by the laser. We explain the origin and behavior of such features, and we provide a simple quantitative guideline to assess whether ghost features may appear. As such ghost features may be common to saturated Doppler-broadened spectra of rotational and vibrational transitions in trapped ions composed of partly overlapping lines, our work illustrates the necessity to use lineshape models that take into account all the relevant physics.

Left Atrial Anatomy Relevant to Catheter Ablation

PubMed Central

Sánchez-Quintana, Damián; Cabrera, José Angel; Saremi, Farhood

2014-01-01

The rapid development of interventional procedures for the treatment of arrhythmias in humans, especially the use of catheter ablation techniques, has renewed interest in cardiac anatomy. Although the substrates of atrial fibrillation (AF), its initiation and maintenance, remain to be fully elucidated, catheter ablation in the left atrium (LA) has become a common therapeutic option for patients with this arrhythmia. Using ablation catheters, various isolation lines and focal targets are created, the majority of which are based on gross anatomical, electroanatomical, and myoarchitectual patterns of the left atrial wall. Our aim was therefore to review the gross morphological and architectural features of the LA and their relations to extracardiac structures. The latter have also become relevant because extracardiac complications of AF ablation can occur, due to injuries to the phrenic and vagal plexus nerves, adjacent coronary arteries, or the esophageal wall causing devastating consequences. PMID:25057427

Flow-Based Network Analysis of the Caenorhabditis elegans Connectome

PubMed Central

Bacik, Karol A.; Schaub, Michael T.; Billeh, Yazan N.; Barahona, Mauricio

2016-01-01

We exploit flow propagation on the directed neuronal network of the nematode C. elegans to reveal dynamically relevant features of its connectome. We find flow-based groupings of neurons at different levels of granularity, which we relate to functional and anatomical constituents of its nervous system. A systematic in silico evaluation of the full set of single and double neuron ablations is used to identify deletions that induce the most severe disruptions of the multi-resolution flow structure. Such ablations are linked to functionally relevant neurons, and suggest potential candidates for further in vivo investigation. In addition, we use the directional patterns of incoming and outgoing network flows at all scales to identify flow profiles for the neurons in the connectome, without pre-imposing a priori categories. The four flow roles identified are linked to signal propagation motivated by biological input-response scenarios. PMID:27494178

Characteristics of drug demand reduction structures in Britain and Iran

PubMed Central

Narenjiha, Hooman; Noori, Roya; Ghiabi, Maziyar; Khoddami-Vishteh, Hamid-Reza

2016-01-01

Administrative structure of drug demand reduction and the way in which involved organizations interact with each other has been neglected by researchers, policy makers, and administrators at the national level and even in international institutions in this field. Studying such structures in different countries can reveal their attributes and features. In this study, key experts from the addictive behavior department of St George’s University of London and a group of Iranian specialists in the field of drug demand reduction first wrote on a sheet the name of organizations that are in charge of drug demand reduction. Then, via teamwork, they drew the connections between the organizations and compared the two charts to assess the relations between the member organizations. In total, 17 features of efficient structure were obtained as follow: multi-institutional nature, collaborative inter-institutional activities, clear and relevant inter-institutional and intra-institutional job description, the ability to share the experiences, virtual institutions activity, community-based associations activity, mutual relationships, the existence of feedback sys-tems, evaluation, changeability, the ability to collect data rapidly, being rooted in community, flexibility at the local and regional levels, connection with research centers, updated policymaking, empowering the local level, and seeking the maximum benefit and the minimum resources. Recognizing the characteristics of substance related organizations in various countries could help policy makers to improve drug demand reduction structures and to manage the wide-spread use of psychoactive substances more effectively. PMID:27853729

Characteristics of drug demand reduction structures in Britain and Iran.

PubMed

Narenjiha, Hooman; Noori, Roya; Ghiabi, Maziyar; Khoddami-Vishteh, Hamid-Reza

2015-01-01

Administrative structure of drug demand reduction and the way in which involved organizations interact with each other has been neglected by researchers, policy makers, and administrators at the national level and even in international institutions in this field. Studying such structures in different countries can reveal their attributes and features. In this study, key experts from the addictive behavior department of St George's University of London and a group of Iranian specialists in the field of drug demand reduction first wrote on a sheet the name of organizations that are in charge of drug demand reduction. Then, via teamwork, they drew the connections between the organizations and compared the two charts to assess the relations between the member organizations. In total, 17 features of efficient structure were obtained as follow: multi-institutional nature, collaborative inter-institutional activities, clear and relevant inter-institutional and intra-institutional job description, the ability to share the experiences, virtual institutions activity, community-based associations activity, mutual relationships, the existence of feedback sys-tems, evaluation, changeability, the ability to collect data rapidly, being rooted in community, flexibility at the local and regional levels, connection with research centers, updated policymaking, empowering the local level, and seeking the maximum benefit and the minimum resources. Recognizing the characteristics of substance related organizations in various countries could help policy makers to improve drug demand reduction structures and to manage the wide-spread use of psychoactive substances more effectively.

Post-conflict slowing after incongruent stimuli: from general to conflict-specific.

PubMed

Rey-Mermet, Alodie; Meier, Beat

2017-05-01

Encountering a cognitive conflict not only slows current performance, but it can also affect subsequent performance, in particular when the conflict is induced with bivalent stimuli (i.e., stimuli with relevant features for two different tasks) or with incongruent trials (i.e., stimuli with relevant features for two response alternatives). The post-conflict slowing following bivalent stimuli, called "bivalency effect", affects all subsequent stimuli, irrespective of whether the subsequent stimuli share relevant features with the conflict stimuli. To date, it is unknown whether the conflict induced by incongruent stimuli results in a similar post-conflict slowing. To investigate this, we performed six experiments in which participants switched between two tasks. In one task, incongruent stimuli appeared occasionally; in the other task, stimuli shared no feature with the incongruent trials. The results showed an initial performance slowing that affected all tasks after incongruent trials. On further trials, however, the slowing only affected the task sharing features with the conflict stimuli. Therefore, the post-conflict slowing following incongruent stimuli is first general and then becomes conflict-specific across trials. These findings are discussed within current task switching and cognitive control accounts.

The cost of selective attention in category learning: developmental differences between adults and infants.

PubMed

Best, Catherine A; Yim, Hyungwook; Sloutsky, Vladimir M

2013-10-01

Selective attention plays an important role in category learning. However, immaturities of top-down attentional control during infancy coupled with successful category learning suggest that early category learning is achieved without attending selectively. Research presented here examines this possibility by focusing on category learning in infants (6-8months old) and adults. Participants were trained on a novel visual category. Halfway through the experiment, unbeknownst to participants, the to-be-learned category switched to another category, where previously relevant features became irrelevant and previously irrelevant features became relevant. If participants attend selectively to the relevant features of the first category, they should incur a cost of selective attention immediately after the unknown category switch. Results revealed that adults demonstrated a cost, as evidenced by a decrease in accuracy and response time on test trials as well as a decrease in visual attention to newly relevant features. In contrast, infants did not demonstrate a similar cost of selective attention as adults despite evidence of learning both to-be-learned categories. Findings are discussed as supporting multiple systems of category learning and as suggesting that learning mechanisms engaged by adults may be different from those engaged by infants. Copyright © 2013 Elsevier Inc. All rights reserved.

The potential of audiomagnetotellurics in the study of geothermal fields: A case study from the northern segment of the La Candelaria Range, northwestern Argentina

NASA Astrophysics Data System (ADS)

Barcelona, Hernan; Favetto, Alicia; Peri, Veronica Gisel; Pomposiello, Cristina; Ungarelli, Carlo

2013-01-01

Despite its reduced penetration depth, audiomagnetotelluric (AMT) studies can be used to determine a broad range of features related to little studied geothermal fields. This technique requires a stepwise interpretation of results taking into consideration diverse information (e.g. topographic, hydrological, geological and/or structural data) to constrain the characteristics of the study area. In this work, an AMT study was performed at the hot springs in the northern segment of the La Candelaria Range in order to characterize the area at depth. Geometric aspects of the shallow subsurface were determined based on the dimensional and distortion analysis of the impedance tensors. Also, the correlation between structural features and regional strikes allowed us to define two geoelectric domains, useful to determine the controls on fluid circulation. The subsurface resistivity distribution was determined through 1D and 2D models. The patterns of the 1D models were compared with the morpho-structure of the range. Shallow and deep conductive zones were defined and a possible shallow geothermal system scheme proposed. A strong correlation was found between the AMT results and the geological framework of the region, showing the relevance of using AMT in geothermal areas during the early stages of subsurface prospecting.

DISCOVER-AQ

Atmospheric Science Data Center

2017-01-31

... Relevant Documents:  DISCOVER-AQ - Airborne Science Data for Atmospheric Composition DISCOVER-AQ - NASA Earth ... DISCOVER-AQ - Mission Highlight Featured Articles : Articles featuring DISCOVER-AQ data products SCAR-B ...

Remembering complex objects in visual working memory: do capacity limits restrict objects or features?

PubMed

Hardman, Kyle O; Cowan, Nelson

2015-03-01

Visual working memory stores stimuli from our environment as representations that can be accessed by high-level control processes. This study addresses a longstanding debate in the literature about whether storage limits in visual working memory include a limit to the complexity of discrete items. We examined the issue with a number of change-detection experiments that used complex stimuli that possessed multiple features per stimulus item. We manipulated the number of relevant features of the stimulus objects in order to vary feature load. In all of our experiments, we found that increased feature load led to a reduction in change-detection accuracy. However, we found that feature load alone could not account for the results but that a consideration of the number of relevant objects was also required. This study supports capacity limits for both feature and object storage in visual working memory. PsycINFO Database Record (c) 2015 APA, all rights reserved.

Joint Feature Extraction and Classifier Design for ECG-Based Biometric Recognition.

PubMed

Gutta, Sandeep; Cheng, Qi

2016-03-01

Traditional biometric recognition systems often utilize physiological traits such as fingerprint, face, iris, etc. Recent years have seen a growing interest in electrocardiogram (ECG)-based biometric recognition techniques, especially in the field of clinical medicine. In existing ECG-based biometric recognition methods, feature extraction and classifier design are usually performed separately. In this paper, a multitask learning approach is proposed, in which feature extraction and classifier design are carried out simultaneously. Weights are assigned to the features within the kernel of each task. We decompose the matrix consisting of all the feature weights into sparse and low-rank components. The sparse component determines the features that are relevant to identify each individual, and the low-rank component determines the common feature subspace that is relevant to identify all the subjects. A fast optimization algorithm is developed, which requires only the first-order information. The performance of the proposed approach is demonstrated through experiments using the MIT-BIH Normal Sinus Rhythm database.

A hybrid organic-inorganic perovskite dataset

NASA Astrophysics Data System (ADS)

Kim, Chiho; Huan, Tran Doan; Krishnan, Sridevi; Ramprasad, Rampi

2017-05-01

Hybrid organic-inorganic perovskites (HOIPs) have been attracting a great deal of attention due to their versatility of electronic properties and fabrication methods. We prepare a dataset of 1,346 HOIPs, which features 16 organic cations, 3 group-IV cations and 4 halide anions. Using a combination of an atomic structure search method and density functional theory calculations, the optimized structures, the bandgap, the dielectric constant, and the relative energies of the HOIPs are uniformly prepared and validated by comparing with relevant experimental and/or theoretical data. We make the dataset available at Dryad Digital Repository, NoMaD Repository, and Khazana Repository (http://khazana.uconn.edu/), hoping that it could be useful for future data-mining efforts that can explore possible structure-property relationships and phenomenological models. Progressive extension of the dataset is expected as new organic cations become appropriate within the HOIP framework, and as additional properties are calculated for the new compounds found.

Quantitative structure-activity relationships of selective antagonists of glucagon receptor using QuaSAR descriptors.

PubMed

Manoj Kumar, Palanivelu; Karthikeyan, Chandrabose; Hari Narayana Moorthy, Narayana Subbiah; Trivedi, Piyush

2006-11-01

In the present paper, quantitative structure activity relationship (QSAR) approach was applied to understand the affinity and selectivity of a novel series of triaryl imidazole derivatives towards glucagon receptor. Statistically significant and highly predictive QSARs were derived for glucagon receptor inhibition by triaryl imidazoles using QuaSAR descriptors of molecular operating environment (MOE) employing computer-assisted multiple regression procedure. The generated QSAR models revealed that factors related to hydrophobicity, molecular shape and geometry predominantly influences glucagon receptor binding affinity of the triaryl imidazoles indicating the relevance of shape specific steric interactions between the molecule and the receptor. Further, QSAR models formulated for selective inhibition of glucagon receptor over p38 mitogen activated protein (MAP) kinase of the compounds in the series highlights that the same structural features, which influence the glucagon receptor affinity, also contribute to their selective inhibition.

Molecular clouds and the large-scale structure of the galaxy

NASA Technical Reports Server (NTRS)

Thaddeus, Patrick; Stacy, J. Gregory

1990-01-01

The application of molecular radio astronomy to the study of the large-scale structure of the Galaxy is reviewed and the distribution and characteristic properties of the Galactic population of Giant Molecular Clouds (GMCs), derived primarily from analysis of the Columbia CO survey, and their relation to tracers of Population 1 and major spiral features are described. The properties of the local molecular interstellar gas are summarized. The CO observing programs currently underway with the Center for Astrophysics 1.2 m radio telescope are described, with an emphasis on projects relevant to future comparison with high-energy gamma-ray observations. Several areas are discussed in which high-energy gamma-ray observations by the EGRET (Energetic Gamma-Ray Experiment Telescope) experiment aboard the Gamma Ray Observatory will directly complement radio studies of the Milky Way, with the prospect of significant progress on fundamental issues related to the structure and content of the Galaxy.

Natural products as an inspiration in the diversity-oriented synthesis of bioactive compound libraries

PubMed Central

Cordier, Christopher; Morton, Daniel; Murrison, Sarah; O'Leary-Steele, Catherine

2008-01-01

The purpose of diversity-oriented synthesis is to drive the discovery of small molecules with previously unknown biological functions. Natural products necessarily populate biologically relevant chemical space, since they bind both their biosynthetic enzymes and their target macromolecules. Natural product families are, therefore, libraries of pre-validated, functionally diverse structures in which individual compounds selectively modulate unrelated macromolecular targets. This review describes examples of diversity-oriented syntheses which have, to some extent, been inspired by the structures of natural products. Particular emphasis is placed on innovations that allow the synthesis of compound libraries that, like natural products, are skeletally diverse. Mimicking the broad structural features of natural products may allow the discovery of compounds that modulate the functions of macromolecules for which ligands are not known. The ability of innovations in diversity-oriented synthesis to deliver such compounds is critically assessed. PMID:18663392

VAMPnets for deep learning of molecular kinetics.

PubMed

Mardt, Andreas; Pasquali, Luca; Wu, Hao; Noé, Frank

2018-01-02

There is an increasing demand for computing the relevant structures, equilibria, and long-timescale kinetics of biomolecular processes, such as protein-drug binding, from high-throughput molecular dynamics simulations. Current methods employ transformation of simulated coordinates into structural features, dimension reduction, clustering the dimension-reduced data, and estimation of a Markov state model or related model of the interconversion rates between molecular structures. This handcrafted approach demands a substantial amount of modeling expertise, as poor decisions at any step will lead to large modeling errors. Here we employ the variational approach for Markov processes (VAMP) to develop a deep learning framework for molecular kinetics using neural networks, dubbed VAMPnets. A VAMPnet encodes the entire mapping from molecular coordinates to Markov states, thus combining the whole data processing pipeline in a single end-to-end framework. Our method performs equally or better than state-of-the-art Markov modeling methods and provides easily interpretable few-state kinetic models.

MememxGATE: Unearthing Latent Content Features for Improved Search and Relevancy Ranking Across Scientific Literature

NASA Astrophysics Data System (ADS)

Wilson, B. D.; McGibbney, L. J.; Mattmann, C. A.; Ramirez, P.; Joyce, M.; Whitehall, K. D.

2015-12-01

Quantifying scientific relevancy is of increasing importance to NASA and the research community. Scientific relevancy may be defined by mapping the impacts of a particular NASA mission, instrument, and/or retrieved variables to disciplines such as climate predictions, natural hazards detection and mitigation processes, education, and scientific discoveries. Related to relevancy, is the ability to expose data with similar attributes. This in turn depends upon the ability for us to extract latent, implicit document features from scientific data and resources and make them explicit, accessible and useable for search activities amongst others. This paper presents MemexGATE; a server side application, command line interface and computing environment for running large scale metadata extraction, general architecture text engineering, document classification and indexing tasks over document resources such as social media streams, scientific literature archives, legal documentation, etc. This work builds on existing experiences using MemexGATE (funded, developed and validated through the DARPA Memex Progrjam PI Mattmann) for extracting and leveraging latent content features from document resources within the Materials Research domain. We extend the software functionality capability to the domain of scientific literature with emphasis on the expansion of gazetteer lists, named entity rules, natural language construct labeling (e.g. synonym, antonym, hyponym, etc.) efforts to enable extraction of latent content features from data hosted by wide variety of scientific literature vendors (AGU Meeting Abstract Database, Springer, Wiley Online, Elsevier, etc.) hosting earth science literature. Such literature makes both implicit and explicit references to NASA datasets and relationships between such concepts stored across EOSDIS DAAC's hence we envisage that a significant part of this effort will also include development and understanding of relevancy signals which can ultimately be utilized for improved search and relevancy ranking across scientific literature.

Statistical interpretation of machine learning-based feature importance scores for biomarker discovery.

PubMed

Huynh-Thu, Vân Anh; Saeys, Yvan; Wehenkel, Louis; Geurts, Pierre

2012-07-01

Univariate statistical tests are widely used for biomarker discovery in bioinformatics. These procedures are simple, fast and their output is easily interpretable by biologists but they can only identify variables that provide a significant amount of information in isolation from the other variables. As biological processes are expected to involve complex interactions between variables, univariate methods thus potentially miss some informative biomarkers. Variable relevance scores provided by machine learning techniques, however, are potentially able to highlight multivariate interacting effects, but unlike the p-values returned by univariate tests, these relevance scores are usually not statistically interpretable. This lack of interpretability hampers the determination of a relevance threshold for extracting a feature subset from the rankings and also prevents the wide adoption of these methods by practicians. We evaluated several, existing and novel, procedures that extract relevant features from rankings derived from machine learning approaches. These procedures replace the relevance scores with measures that can be interpreted in a statistical way, such as p-values, false discovery rates, or family wise error rates, for which it is easier to determine a significance level. Experiments were performed on several artificial problems as well as on real microarray datasets. Although the methods differ in terms of computing times and the tradeoff, they achieve in terms of false positives and false negatives, some of them greatly help in the extraction of truly relevant biomarkers and should thus be of great practical interest for biologists and physicians. As a side conclusion, our experiments also clearly highlight that using model performance as a criterion for feature selection is often counter-productive. Python source codes of all tested methods, as well as the MATLAB scripts used for data simulation, can be found in the Supplementary Material.

Illuminating the conceptual structure of the space of moral violations with searchlight representational similarity analysis.

PubMed

Wasserman, E A; Chakroff, A; Saxe, R; Young, L

2017-10-01

Characterizing how representations of moral violations are organized, cognitively and neurally, is central to understanding how people conceive and judge them. Past work has identified brain regions that represent morally relevant features and distinguish moral domains, but has not yet advanced a broader account of where and on what basis neural representations of moral violations are organized. With searchlight representational similarity analysis, we investigate where category membership drives similarity in neural patterns during moral judgment of violations from two key moral domains: Harm and Purity. Representations converge across domains in a network of regions resembling the mentalizing network. However, Harm and Purity violation representations respectively converge in different regions: precuneus (PC) and left inferior frontal gyrus (LIFG). Examining substructure within moral domains, Harm violations converge in PC regardless of subdomain (physical harms, psychological harms), while Purity subdomains (pathogen-related violations, sex-related violations) converge in distinct sets of regions - mirroring a dissociation observed in principal-component analysis of behavioral data. Further, we find initial evidence for representation of morally relevant features within these two domain-encoding regions. The present analyses offer a case study for understanding how organization within the complex conceptual space of moral violations is reflected in the organization of neural patterns across the cortex. Copyright © 2017 Elsevier Inc. All rights reserved.

Stimulus-response correspondence effect as a function of temporal overlap between relevant and irrelevant information processing.

PubMed

Wang, Dong-Yuan Debbie; Richard, F Dan; Ray, Brittany

2016-01-01

The stimulus-response correspondence (SRC) effect refers to advantages in performance when stimulus and response correspond in dimensions or features, even if the common features are irrelevant to the task. Previous research indicated that the SRC effect depends on the temporal course of stimulus information processing. The current study investigated how the temporal overlap between relevant and irrelevant stimulus processing influences the SRC effect. In this experiment, the irrelevant stimulus (a previously associated tone) preceded the relevant stimulus (a coloured rectangle). The irrelevant and relevant stimuli onset asynchrony was varied to manipulate the temporal overlap between the irrelevant and relevant stimuli processing. Results indicated that the SRC effect size varied as a quadratic function of the temporal overlap between the relevant stimulus and irrelevant stimulus. This finding extends previous experimental observations that the SRC effect size varies in an increasing or decreasing function with reaction time. The current study demonstrated a quadratic function between effect size and the temporal overlap.

Natural three-qubit interactions in one-way quantum computing

NASA Astrophysics Data System (ADS)

Tame, M. S.; Paternostro, M.; Kim, M. S.; Vedral, V.

2006-02-01

We address the effects of natural three-qubit interactions on the computational power of one-way quantum computation. A benefit of using more sophisticated entanglement structures is the ability to construct compact and economic simulations of quantum algorithms with limited resources. We show that the features of our study are embodied by suitably prepared optical lattices, where effective three-spin interactions have been theoretically demonstrated. We use this to provide a compact construction for the Toffoli gate. Information flow and two-qubit interactions are also outlined, together with a brief analysis of relevant sources of imperfection.

Second and third order nonlinear optical properties of conjugated molecules and polymers

NASA Technical Reports Server (NTRS)

Perry, Joseph W.; Stiegman, Albert E.; Marder, Seth R.; Coulter, Daniel R.; Beratan, David N.; Brinza, David E.

1988-01-01

Second- and third-order nonlinear optical properties of some newly synthesized organic molecules and polymers are reported. Powder second-harmonic-generation efficiencies of up to 200 times urea have been realized for asymmetric donor-acceptor acetylenes. Third harmonic generation chi(3)s have been determined for a series of small conjugated molecules in solution. THG chi(3)s have also been determined for a series of soluble conjugated copolymers prepared using ring-opening metathesis polymerization. The results are discussed in terms of relevant molecular and/or macroscopic structural features of these conjugated organic materials.

Acanthamoeba polyphaga mimivirus and other giant viruses: an open field to outstanding discoveries

PubMed Central

2014-01-01

In 2003, Acanthamoeba polyphaga mimivirus (APMV) was first described and began to impact researchers around the world, due to its structural and genetic complexity. This virus founded the family Mimiviridae. In recent years, several new giant viruses have been isolated from different environments and specimens. Giant virus research is in its initial phase and information that may arise in the coming years may change current conceptions of life, diversity and evolution. Thus, this review aims to condense the studies conducted so far about the features and peculiarities of APMV, from its discovery to its clinical relevance. PMID:24976356

Discovering Conformational Sub-States Relevant to Protein Function

PubMed Central

Ramanathan, Arvind; Savol, Andrej J.; Langmead, Christopher J.; Agarwal, Pratul K.; Chennubhotla, Chakra S.

2011-01-01

Background Internal motions enable proteins to explore a range of conformations, even in the vicinity of native state. The role of conformational fluctuations in the designated function of a protein is widely debated. Emerging evidence suggests that sub-groups within the range of conformations (or sub-states) contain properties that may be functionally relevant. However, low populations in these sub-states and the transient nature of conformational transitions between these sub-states present significant challenges for their identification and characterization. Methods and Findings To overcome these challenges we have developed a new computational technique, quasi-anharmonic analysis (QAA). QAA utilizes higher-order statistics of protein motions to identify sub-states in the conformational landscape. Further, the focus on anharmonicity allows identification of conformational fluctuations that enable transitions between sub-states. QAA applied to equilibrium simulations of human ubiquitin and T4 lysozyme reveals functionally relevant sub-states and protein motions involved in molecular recognition. In combination with a reaction pathway sampling method, QAA characterizes conformational sub-states associated with cis/trans peptidyl-prolyl isomerization catalyzed by the enzyme cyclophilin A. In these three proteins, QAA allows identification of conformational sub-states, with critical structural and dynamical features relevant to protein function. Conclusions Overall, QAA provides a novel framework to intuitively understand the biophysical basis of conformational diversity and its relevance to protein function. PMID:21297978

Goal-Directed Visual Processing Differentially Impacts Human Ventral and Dorsal Visual Representations

PubMed Central

2017-01-01

Recent studies have challenged the ventral/“what” and dorsal/“where” two-visual-processing-pathway view by showing the existence of “what” and “where” information in both pathways. Is the two-pathway distinction still valid? Here, we examined how goal-directed visual information processing may differentially impact visual representations in these two pathways. Using fMRI and multivariate pattern analysis, in three experiments on human participants (57% females), by manipulating whether color or shape was task-relevant and how they were conjoined, we examined shape-based object category decoding in occipitotemporal and parietal regions. We found that object category representations in all the regions examined were influenced by whether or not object shape was task-relevant. This task effect, however, tended to decrease as task-relevant and irrelevant features were more integrated, reflecting the well-known object-based feature encoding. Interestingly, task relevance played a relatively minor role in driving the representational structures of early visual and ventral object regions. They were driven predominantly by variations in object shapes. In contrast, the effect of task was much greater in dorsal than ventral regions, with object category and task relevance both contributing significantly to the representational structures of the dorsal regions. These results showed that, whereas visual representations in the ventral pathway are more invariant and reflect “what an object is,” those in the dorsal pathway are more adaptive and reflect “what we do with it.” Thus, despite the existence of “what” and “where” information in both visual processing pathways, the two pathways may still differ fundamentally in their roles in visual information representation. SIGNIFICANCE STATEMENT Visual information is thought to be processed in two distinctive pathways: the ventral pathway that processes “what” an object is and the dorsal pathway that processes “where” it is located. This view has been challenged by recent studies revealing the existence of “what” and “where” information in both pathways. Here, we found that goal-directed visual information processing differentially modulates shape-based object category representations in the two pathways. Whereas ventral representations are more invariant to the demand of the task, reflecting what an object is, dorsal representations are more adaptive, reflecting what we do with the object. Thus, despite the existence of “what” and “where” information in both pathways, visual representations may still differ fundamentally in the two pathways. PMID:28821655

Self-assembled structures of Gaussian nematic particles.

PubMed

Nikoubashman, Arash; Likos, Christos N

2010-03-17

We investigate the stable crystalline configurations of a nematic liquid crystal made of soft parallel ellipsoidal particles interacting via a repulsive, anisotropic Gaussian potential. For this purpose, we use genetic algorithms (GA) in order to predict all relevant and possible solid phase candidates into which this fluid can freeze. Subsequently we present and discuss the emerging novel structures and the resulting zero-temperature phase diagram of this system. The latter features a variety of crystalline arrangements, in which the elongated Gaussian particles in general do not align with any one of the high-symmetry crystallographic directions, a compromise arising from the interplay and competition between anisotropic repulsions and crystal ordering. Only at very strong degrees of elongation does a tendency of the Gaussian nematics to align with the longest axis of the elementary unit cell emerge.

Electrospun nanofibers for neural tissue engineering

NASA Astrophysics Data System (ADS)

Xie, Jingwei; MacEwan, Matthew R.; Schwartz, Andrea G.; Xia, Younan

2010-01-01

Biodegradable nanofibers produced by electrospinning represent a new class of promising scaffolds to support nerve regeneration. We begin with a brief discussion on the electrospinning of nanofibers and methods for controlling the structure, porosity, and alignment of the electrospun nanofibers. The methods include control of the nanoscale morphology and microscale alignment of the nanofibers, as well as the fabrication of macroscale, three-dimensional tubular structures. We then highlight recent studies that utilize electrospun nanofibers to manipulate biological processes relevant to nervous tissue regeneration, including stem cell differentiation, guidance of neurite extension, and peripheral nerve injury treatments. The main objective of this feature article is to provide valuable insights into methods for investigating the mechanisms of neurite growth on novel nanofibrous scaffolds and optimization of the nanofiber scaffolds and conduits for repairing peripheral nerve injuries.

Synthetic and structural studies on syringolin A and B reveal critical determinants of selectivity and potency of proteasome inhibition

PubMed Central

Clerc, Jérôme; Groll, Michael; Illich, Damir J.; Bachmann, André S.; Huber, Robert; Schellenberg, Barbara; Dudler, Robert; Kaiser, Markus

2009-01-01

Syrbactins, a family of natural products belonging either to the syringolin or glidobactin class, are highly potent proteasome inhibitors. Although sharing similar structural features, they differ in their macrocyclic lactam core structure and exocyclic side chain. These structural variations critically influence inhibitory potency and proteasome subsite selectivity. Here, we describe the total synthesis of syringolin A and B, which together with enzyme kinetic and structural studies, allowed us to elucidate the structural determinants underlying the proteasomal subsite selectivity and binding affinity of syrbactins. These findings were used successfully in the rational design and synthesis of a syringolin A-based lipophilic derivative, which proved to be the most potent syrbactin-based proteasome inhibitor described so far. With a Ki′ of 8.65 ± 1.13 nM for the chymotryptic activity, this syringolin A derivative displays a 100-fold higher potency than the parent compound syringolin A. In light of the medicinal relevance of proteasome inhibitors as anticancer compounds, the present findings may assist in the rational design and development of syrbactin-based chemotherapeutics. PMID:19359491

Automated renal histopathology: digital extraction and quantification of renal pathology

NASA Astrophysics Data System (ADS)

Sarder, Pinaki; Ginley, Brandon; Tomaszewski, John E.

2016-03-01

The branch of pathology concerned with excess blood serum proteins being excreted in the urine pays particular attention to the glomerulus, a small intertwined bunch of capillaries located at the beginning of the nephron. Normal glomeruli allow moderate amount of blood proteins to be filtered; proteinuric glomeruli allow large amount of blood proteins to be filtered. Diagnosis of proteinuric diseases requires time intensive manual examination of the structural compartments of the glomerulus from renal biopsies. Pathological examination includes cellularity of individual compartments, Bowman's and luminal space segmentation, cellular morphology, glomerular volume, capillary morphology, and more. Long examination times may lead to increased diagnosis time and/or lead to reduced precision of the diagnostic process. Automatic quantification holds strong potential to reduce renal diagnostic time. We have developed a computational pipeline capable of automatically segmenting relevant features from renal biopsies. Our method first segments glomerular compartments from renal biopsies by isolating regions with high nuclear density. Gabor texture segmentation is used to accurately define glomerular boundaries. Bowman's and luminal spaces are segmented using morphological operators. Nuclei structures are segmented using color deconvolution, morphological processing, and bottleneck detection. Average computation time of feature extraction for a typical biopsy, comprising of ~12 glomeruli, is ˜69 s using an Intel(R) Core(TM) i7-4790 CPU, and is ~65X faster than manual processing. Using images from rat renal tissue samples, automatic glomerular structural feature estimation was reproducibly demonstrated for 15 biopsy images, which contained 148 individual glomeruli images. The proposed method holds immense potential to enhance information available while making clinical diagnoses.

Revealing the cell-material interface with nanometer resolution by FIB-SEM

PubMed Central

Santoro, Francesca; Zhao, Wenting; Joubert, Lydia-Marie; Duan, Liting; Schnitker, Jan; van de Burgt, Yoeri; Lou, Hsin-Ya; Liu, Bofei; Salleo, Alberto; Cui, Lifeng; Cui, Yi; Cui, Bianxiao

2018-01-01

The interface between cells and non-biological surfaces regulates cell attachment, chronic tissue responses, and ultimately the success of medical implants or biosensors. Clinical and laboratory studies show that topological features of the surface profoundly influences cellular responses, e.g. titanium surfaces with nano- and microtopographical structures enhance osteoblast attachment and host-implant integration as compare to smooth surface. To understand how cells and tissues respond to different topographical features, it is of critical importance to directly visualize the cell-materials interface at the relevant nanometer length scale. Here, we present a new method for in situ examination of the cell-to-material interface at any desired location, based on focused-ion beam milling and scanning electron microscopy imaging (FIB-SEM) to resolve the cell membrane-to-material interface with 10 nm resolution. By examining how cell membranes interact with topographical features such as nanoscale protrusions or invaginations, we discovered that the cell membrane readily deforms inward and wraps around protruding structures, but hardly deforms outward to contour invaginating structures. This asymmetric membrane response (inward vs. outward deformation) causes the cleft width between the cell membrane and the nanostructure surface to vary for more than an order of magnitude. Our results suggest that surface topology is a crucial consideration for the development of medical implants or biosensors whose performances are strongly influenced by the cell-to-material interface. We anticipate that the method can be used to explore the direct interaction of cells/tissue with medical devices such as metal implants in the future. PMID:28682058

Cellular automata with object-oriented features for parallel molecular network modeling.

PubMed

Zhu, Hao; Wu, Yinghui; Huang, Sui; Sun, Yan; Dhar, Pawan

2005-06-01

Cellular automata are an important modeling paradigm for studying the dynamics of large, parallel systems composed of multiple, interacting components. However, to model biological systems, cellular automata need to be extended beyond the large-scale parallelism and intensive communication in order to capture two fundamental properties characteristic of complex biological systems: hierarchy and heterogeneity. This paper proposes extensions to a cellular automata language, Cellang, to meet this purpose. The extended language, with object-oriented features, can be used to describe the structure and activity of parallel molecular networks within cells. Capabilities of this new programming language include object structure to define molecular programs within a cell, floating-point data type and mathematical functions to perform quantitative computation, message passing capability to describe molecular interactions, as well as new operators, statements, and built-in functions. We discuss relevant programming issues of these features, including the object-oriented description of molecular interactions with molecule encapsulation, message passing, and the description of heterogeneity and anisotropy at the cell and molecule levels. By enabling the integration of modeling at the molecular level with system behavior at cell, tissue, organ, or even organism levels, the program will help improve our understanding of how complex and dynamic biological activities are generated and controlled by parallel functioning of molecular networks. Index Terms-Cellular automata, modeling, molecular network, object-oriented.

The flagellum of Trypanosoma brucei: new tricks from an old dog

PubMed Central

Ralston, Katherine S.; Hill, Kent L.

2010-01-01

African trypanosomes, i.e. Trypanosoma brucei and related sub-species, are devastating human and animal pathogens that cause significant human mortality and limit sustained economic development in sub-Saharan Africa. Trypanosoma brucei is a highly motile protozoan parasite and coordinated motility is central to both disease pathogenesis in the mammalian host and parasite development in the tsetse fly vector. Since motility is critical for parasite development and pathogenesis, understanding unique aspects of the T. brucei flagellum may uncover novel targets for therapeutic intervention in African sleeping sickness. Moreover, studies of conserved features of the T. brucei flagellum are directly relevant to understanding fundamental aspects of flagellum and cilium function in other eukaryotes, making T. brucei an important model system. The T. brucei flagellum contains a canonical 9 + 2 axoneme, together with additional features that are unique to kinetoplastids and a few closely-related organisms. Until recently, much of our knowledge of the structure and function of the trypanosome flagellum was based on analogy and inference from other organisms. There has been an explosion in functional studies in T. brucei in recent years, revealing conserved as well as novel and unexpected structural and functional features of the flagellum. Most notably, the flagellum has been found to be an essential organelle, with critical roles in parasite motility, morphogenesis, cell division and immune evasion. This review highlights recent discoveries on the T. brucei flagellum. PMID:18472102

Emergent explosive synchronization in adaptive complex networks

NASA Astrophysics Data System (ADS)

Avalos-Gaytán, Vanesa; Almendral, Juan A.; Leyva, I.; Battiston, F.; Nicosia, V.; Latora, V.; Boccaletti, S.

2018-04-01

Adaptation plays a fundamental role in shaping the structure of a complex network and improving its functional fitting. Even when increasing the level of synchronization in a biological system is considered as the main driving force for adaptation, there is evidence of negative effects induced by excessive synchronization. This indicates that coherence alone cannot be enough to explain all the structural features observed in many real-world networks. In this work, we propose an adaptive network model where the dynamical evolution of the node states toward synchronization is coupled with an evolution of the link weights based on an anti-Hebbian adaptive rule, which accounts for the presence of inhibitory effects in the system. We found that the emergent networks spontaneously develop the structural conditions to sustain explosive synchronization. Our results can enlighten the shaping mechanisms at the heart of the structural and dynamical organization of some relevant biological systems, namely, brain networks, for which the emergence of explosive synchronization has been observed.

1.45 Å resolution structure of SRPN18 from the malaria vector Anopheles gambiae

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

Meekins, David A.; Zhang, Xin; Battaile, Kevin P.

Serine protease inhibitors (serpins) in insects function within development, wound healing and immunity. The genome of the African malaria vector,Anopheles gambiae, encodes 23 distinct serpin proteins, several of which are implicated in disease-relevant physiological responses.A. gambiaeserpin 18 (SRPN18) was previously categorized as non-inhibitory based on the sequence of its reactive-center loop (RCL), a region responsible for targeting and initiating protease inhibition. The crystal structure ofA. gambiaeSRPN18 was determined to a resolution of 1.45 Å, including nearly the entire RCL in one of the two molecules in the asymmetric unit. The structure reveals that the SRPN18 RCL is extremely short andmore » constricted, a feature associated with noncanonical inhibitors or non-inhibitory serpin superfamily members. Furthermore, the SRPN18 RCL does not contain a suitable protease target site and contains a large number of prolines. The SRPN18 structure therefore reveals a unique RCL architecture among the highly conserved serpin fold.« less

Portrait of an Enzyme, a Complete Structural Analysis of a Multimodular beta-N-Acetylglucosaminidase from Clostridium perfringens

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

Ficko-Blean, E.; Gregg, K; Adams, J

2009-01-01

Common features of the extracellular carbohydrate-active virulence factors involved in host-pathogen interactions are their large sizes and modular complexities. This has made them recalcitrant to structural analysis, and therefore our understanding of the significance of modularity in these important proteins is lagging. Clostridium perfringens is a prevalent human pathogen that harbors a wide array of large, extracellular carbohydrate-active enzymes and is an excellent and relevant model system to approach this problem. Here we describe the complete structure of C. perfringens GH84C (NagJ), a 1001-amino acid multimodular homolog of the C. perfringens ?-toxin, which was determined using a combination of smallmore » angle x-ray scattering and x-ray crystallography. The resulting structure reveals unprecedented insight into how catalysis, carbohydrate-specific adherence, and the formation of molecular complexes with other enzymes via an ultra-tight protein-protein interaction are spatially coordinated in an enzyme involved in a host-pathogen interaction.« less

Function-Oriented Synthesis: How to Design Simplified Analogues of Antibacterial Nucleoside Natural Products?

PubMed

Ichikawa, Satoshi

2016-06-01

It is important to pursue function-oriented synthesis (FOS), a strategy for the design of less structurally complex targets with comparable or superior activity that can be made in a practical manner, because compared to synthetic drugs, many biologically relevant natural products possess large and complex chemical structures that may restrict chemical modifications in a structure-activity relationship study. In this account, we describe recent efforts to simplify complex nucleoside natural products including caprazamycins. Considering the structure-activity relationship study with several truncated analogues, three types of simplified derivatives, namely, oxazolidine, isoxazolidine, and lactam-fused isoxazolidine-containing uridine derivatives, were designed and efficiently synthesized. These simplified derivatives have exhibited promising antibacterial activities. A significant feature of our studies is the rational and drastic simplification of the molecular architecture of caprazamycins. This study provides a novel strategy for the development of a new type of antibacterial agent effective against drug-resistant bacteria. © 2016 The Chemical Society of Japan & Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Tetrahelical structural family adopted by AGCGA-rich regulatory DNA regions

NASA Astrophysics Data System (ADS)

Kocman, Vojč; Plavec, Janez

2017-05-01

Here we describe AGCGA-quadruplexes, an unexpected addition to the well-known tetrahelical families, G-quadruplexes and i-motifs, that have been a focus of intense research due to their potential biological impact in G- and C-rich DNA regions, respectively. High-resolution structures determined by solution-state nuclear magnetic resonance (NMR) spectroscopy demonstrate that AGCGA-quadruplexes comprise four 5'-AGCGA-3' tracts and are stabilized by G-A and G-C base pairs forming GAGA- and GCGC-quartets, respectively. Residues in the core of the structure are connected with edge-type loops. Sequences of alternating 5'-AGCGA-3' and 5'-GGG-3' repeats could be expected to form G-quadruplexes, but are shown herein to form AGCGA-quadruplexes instead. Unique structural features of AGCGA-quadruplexes together with lower sensitivity to cation and pH variation imply their potential biological relevance in regulatory regions of genes responsible for basic cellular processes that are related to neurological disorders, cancer and abnormalities in bone and cartilage development.

Emergent explosive synchronization in adaptive complex networks.

PubMed

Avalos-Gaytán, Vanesa; Almendral, Juan A; Leyva, I; Battiston, F; Nicosia, V; Latora, V; Boccaletti, S

2018-04-01

Adaptation plays a fundamental role in shaping the structure of a complex network and improving its functional fitting. Even when increasing the level of synchronization in a biological system is considered as the main driving force for adaptation, there is evidence of negative effects induced by excessive synchronization. This indicates that coherence alone cannot be enough to explain all the structural features observed in many real-world networks. In this work, we propose an adaptive network model where the dynamical evolution of the node states toward synchronization is coupled with an evolution of the link weights based on an anti-Hebbian adaptive rule, which accounts for the presence of inhibitory effects in the system. We found that the emergent networks spontaneously develop the structural conditions to sustain explosive synchronization. Our results can enlighten the shaping mechanisms at the heart of the structural and dynamical organization of some relevant biological systems, namely, brain networks, for which the emergence of explosive synchronization has been observed.

Dynamical Mechanism of Scaling Behaviors in Multifractal Structure

NASA Astrophysics Data System (ADS)

Kim, Kyungsik; Jung, Jae Won; Kim, Soo Yong

2010-03-01

The pattern of stone distribution in the game of Go (Baduk, Weiqi, or Igo) can be treated in the mathematical and physical languages of multifractals. The concepts of fractals and multifractals have relevance to many fields of science and even arts. A significant and fascinating feature of this approach is that it provides a proper interpretation for the pattern of the two-colored (black and white) stones in terms of the numerical values of the generalized dimension and the scaling exponent. For our case, these statistical quantities can be estimated numerically from the black, white, and mixed stones, assuming the excluded edge effect that the cell form of the Go game has the self-similar structure. The result from the multifractal structure allows us to find a definite and reliable fractal dimension, and it precisely verifies that the fractal dimension becomes larger, as the cell of grids increases. We also find the strength of multifractal structures from the difference in the scaling exponents in the black, white, and mixed stones.

1.45 Å resolution structure of SRPN18 from the malaria vector Anopheles gambiae

PubMed Central

Meekins, David A.; Zhang, Xin; Battaile, Kevin P.; Lovell, Scott; Michel, Kristin

2016-01-01

Serine protease inhibitors (serpins) in insects function within development, wound healing and immunity. The genome of the African malaria vector, Anopheles gambiae, encodes 23 distinct serpin proteins, several of which are implicated in disease-relevant physiological responses. A. gambiae serpin 18 (SRPN18) was previously categorized as non-inhibitory based on the sequence of its reactive-center loop (RCL), a region responsible for targeting and initiating protease inhibition. The crystal structure of A. gambiae SRPN18 was determined to a resolution of 1.45 Å, including nearly the entire RCL in one of the two molecules in the asymmetric unit. The structure reveals that the SRPN18 RCL is extremely short and constricted, a feature associated with noncanonical inhibitors or non-inhibitory serpin superfamily members. Furthermore, the SRPN18 RCL does not contain a suitable protease target site and contains a large number of prolines. The SRPN18 structure therefore reveals a unique RCL architecture among the highly conserved serpin fold. PMID:27917832

Bird sound spectrogram decomposition through Non-Negative Matrix Factorization for the acoustic classification of bird species.

PubMed

Ludeña-Choez, Jimmy; Quispe-Soncco, Raisa; Gallardo-Antolín, Ascensión

2017-01-01

Feature extraction for Acoustic Bird Species Classification (ABSC) tasks has traditionally been based on parametric representations that were specifically developed for speech signals, such as Mel Frequency Cepstral Coefficients (MFCC). However, the discrimination capabilities of these features for ABSC could be enhanced by accounting for the vocal production mechanisms of birds, and, in particular, the spectro-temporal structure of bird sounds. In this paper, a new front-end for ABSC is proposed that incorporates this specific information through the non-negative decomposition of bird sound spectrograms. It consists of the following two different stages: short-time feature extraction and temporal feature integration. In the first stage, which aims at providing a better spectral representation of bird sounds on a frame-by-frame basis, two methods are evaluated. In the first method, cepstral-like features (NMF_CC) are extracted by using a filter bank that is automatically learned by means of the application of Non-Negative Matrix Factorization (NMF) on bird audio spectrograms. In the second method, the features are directly derived from the activation coefficients of the spectrogram decomposition as performed through NMF (H_CC). The second stage summarizes the most relevant information contained in the short-time features by computing several statistical measures over long segments. The experiments show that the use of NMF_CC and H_CC in conjunction with temporal integration significantly improves the performance of a Support Vector Machine (SVM)-based ABSC system with respect to conventional MFCC.

Bird sound spectrogram decomposition through Non-Negative Matrix Factorization for the acoustic classification of bird species

PubMed Central

Quispe-Soncco, Raisa

2017-01-01

Feature extraction for Acoustic Bird Species Classification (ABSC) tasks has traditionally been based on parametric representations that were specifically developed for speech signals, such as Mel Frequency Cepstral Coefficients (MFCC). However, the discrimination capabilities of these features for ABSC could be enhanced by accounting for the vocal production mechanisms of birds, and, in particular, the spectro-temporal structure of bird sounds. In this paper, a new front-end for ABSC is proposed that incorporates this specific information through the non-negative decomposition of bird sound spectrograms. It consists of the following two different stages: short-time feature extraction and temporal feature integration. In the first stage, which aims at providing a better spectral representation of bird sounds on a frame-by-frame basis, two methods are evaluated. In the first method, cepstral-like features (NMF_CC) are extracted by using a filter bank that is automatically learned by means of the application of Non-Negative Matrix Factorization (NMF) on bird audio spectrograms. In the second method, the features are directly derived from the activation coefficients of the spectrogram decomposition as performed through NMF (H_CC). The second stage summarizes the most relevant information contained in the short-time features by computing several statistical measures over long segments. The experiments show that the use of NMF_CC and H_CC in conjunction with temporal integration significantly improves the performance of a Support Vector Machine (SVM)-based ABSC system with respect to conventional MFCC. PMID:28628630

Feature-based memory-driven attentional capture: visual working memory content affects visual attention.

PubMed

Olivers, Christian N L; Meijer, Frank; Theeuwes, Jan

2006-10-01

In 7 experiments, the authors explored whether visual attention (the ability to select relevant visual information) and visual working memory (the ability to retain relevant visual information) share the same content representations. The presence of singleton distractors interfered more strongly with a visual search task when it was accompanied by an additional memory task. Singleton distractors interfered even more when they were identical or related to the object held in memory, but only when it was difficult to verbalize the memory content. Furthermore, this content-specific interaction occurred for features that were relevant to the memory task but not for irrelevant features of the same object or for once-remembered objects that could be forgotten. Finally, memory-related distractors attracted more eye movements but did not result in longer fixations. The results demonstrate memory-driven attentional capture on the basis of content-specific representations. Copyright 2006 APA.

MultiMiTar: a novel multi objective optimization based miRNA-target prediction method.

PubMed

Mitra, Ramkrishna; Bandyopadhyay, Sanghamitra

2011-01-01

Machine learning based miRNA-target prediction algorithms often fail to obtain a balanced prediction accuracy in terms of both sensitivity and specificity due to lack of the gold standard of negative examples, miRNA-targeting site context specific relevant features and efficient feature selection process. Moreover, all the sequence, structure and machine learning based algorithms are unable to distribute the true positive predictions preferentially at the top of the ranked list; hence the algorithms become unreliable to the biologists. In addition, these algorithms fail to obtain considerable combination of precision and recall for the target transcripts that are translationally repressed at protein level. In the proposed article, we introduce an efficient miRNA-target prediction system MultiMiTar, a Support Vector Machine (SVM) based classifier integrated with a multiobjective metaheuristic based feature selection technique. The robust performance of the proposed method is mainly the result of using high quality negative examples and selection of biologically relevant miRNA-targeting site context specific features. The features are selected by using a novel feature selection technique AMOSA-SVM, that integrates the multi objective optimization technique Archived Multi-Objective Simulated Annealing (AMOSA) and SVM. MultiMiTar is found to achieve much higher Matthew's correlation coefficient (MCC) of 0.583 and average class-wise accuracy (ACA) of 0.8 compared to the others target prediction methods for a completely independent test data set. The obtained MCC and ACA values of these algorithms range from -0.269 to 0.155 and 0.321 to 0.582, respectively. Moreover, it shows a more balanced result in terms of precision and sensitivity (recall) for the translationally repressed data set as compared to all the other existing methods. An important aspect is that the true positive predictions are distributed preferentially at the top of the ranked list that makes MultiMiTar reliable for the biologists. MultiMiTar is now available as an online tool at www.isical.ac.in/~bioinfo_miu/multimitar.htm. MultiMiTar software can be downloaded from www.isical.ac.in/~bioinfo_miu/multimitar-download.htm.

Desktop Nanofabrication with Massively Multiplexed Beam Pen Lithography

PubMed Central

Liao, Xing; Brown, Keith A.; Schmucker, Abrin L.; Liu, Guoliang; He, Shu; Shim, Wooyoung; Mirkin, Chad A.

2013-01-01

The development of a lithographic method that can rapidly define nanoscale features across centimeter-scale surfaces has been a long standing goal of the nanotechnology community. If such a ‘desktop nanofab’ could be implemented in a low-cost format, it would bring the possibility of point-of-use nanofabrication for rapidly prototyping diverse functional structures. Here we report the development of a new tool that is capable of writing arbitrary patterns composed of diffraction-unlimited features over square centimeter areas that are in registry with existing patterns and nanostructures. Importantly, this instrument is based on components that are inexpensive compared to the combination of state-of-the-art nanofabrication tools that approach its capabilities. This tool can be used to prototype functional electronic devices in a mask-free fashion in addition to providing a unique platform for performing high throughput nano- to macroscale photochemistry with relevance to biology and medicine. PMID:23868336

Network-induced oscillatory behavior in material flow networks and irregular business cycles

NASA Astrophysics Data System (ADS)

Helbing, Dirk; Lämmer, Stefen; Witt, Ulrich; Brenner, Thomas

2004-11-01

Network theory is rapidly changing our understanding of complex systems, but the relevance of topological features for the dynamic behavior of metabolic networks, food webs, production systems, information networks, or cascade failures of power grids remains to be explored. Based on a simple model of supply networks, we offer an interpretation of instabilities and oscillations observed in biological, ecological, economic, and engineering systems. We find that most supply networks display damped oscillations, even when their units—and linear chains of these units—behave in a nonoscillatory way. Moreover, networks of damped oscillators tend to produce growing oscillations. This surprising behavior offers, for example, a different interpretation of business cycles and of oscillating or pulsating processes. The network structure of material flows itself turns out to be a source of instability, and cyclical variations are an inherent feature of decentralized adjustments.

Single helically folded aromatic oligoamides that mimic the charge surface of double-stranded B-DNA

NASA Astrophysics Data System (ADS)

Ziach, Krzysztof; Chollet, Céline; Parissi, Vincent; Prabhakaran, Panchami; Marchivie, Mathieu; Corvaglia, Valentina; Bose, Partha Pratim; Laxmi-Reddy, Katta; Godde, Frédéric; Schmitter, Jean-Marie; Chaignepain, Stéphane; Pourquier, Philippe; Huc, Ivan

2018-05-01

Numerous essential biomolecular processes require the recognition of DNA surface features by proteins. Molecules mimicking these features could potentially act as decoys and interfere with pharmacologically or therapeutically relevant protein-DNA interactions. Although naturally occurring DNA-mimicking proteins have been described, synthetic tunable molecules that mimic the charge surface of double-stranded DNA are not known. Here, we report the design, synthesis and structural characterization of aromatic oligoamides that fold into single helical conformations and display a double helical array of negatively charged residues in positions that match the phosphate moieties in B-DNA. These molecules were able to inhibit several enzymes possessing non-sequence-selective DNA-binding properties, including topoisomerase 1 and HIV-1 integrase, presumably through specific foldamer-protein interactions, whereas sequence-selective enzymes were not inhibited. Such modular and synthetically accessible DNA mimics provide a versatile platform to design novel inhibitors of protein-DNA interactions.

Dynamic deformation of volcanic ejecta from the Toba caldera: possible relevance to Cretaceous/Tertiary boundary phenomena

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

Carter, N.L.; Officer, C.B.; Chesner, C.A.

1986-05-01

Plagioclase and biotite phenocrysts in ignimbrites erupted from the Toba caldera, Sumatra, show microstructures and textures indicative of shock stress levels higher than 10 GPa. Strong dynamic deformation has resulted in intense kinking in biotite and, with increasing shock intensity, the development of plagioclase of planar features, shock mosaicism, incipient recrystallization, and possible partial melting. Microstructures in quartz indicative of strong shock deformation are rare, however, and many shock lamellae, if formed, may have healed during post-shock residence in the hot ignimbrite; they might be preserved in ash falls. Peak shock stresses from explosive silicic volcanism and other endogenous processesmore » may be high and if so would obviate the need for extraterrestrial impacts to produce all dynamically deformed structures, possibly including shock features observed near the Cretaceous/Tertiary boundary. 38 references, 3 figures.« less

Desktop nanofabrication with massively multiplexed beam pen lithography.

PubMed

Liao, Xing; Brown, Keith A; Schmucker, Abrin L; Liu, Guoliang; He, Shu; Shim, Wooyoung; Mirkin, Chad A

2013-01-01

The development of a lithographic method that can rapidly define nanoscale features across centimetre-scale surfaces has been a long-standing goal for the nanotechnology community. If such a 'desktop nanofab' could be implemented in a low-cost format, it would bring the possibility of point-of-use nanofabrication for rapidly prototyping diverse functional structures. Here we report the development of a new tool that is capable of writing arbitrary patterns composed of diffraction-unlimited features over square centimetre areas that are in registry with existing patterns and nanostructures. Importantly, this instrument is based on components that are inexpensive compared with the combination of state-of-the-art nanofabrication tools that approach its capabilities. This tool can be used to prototype functional electronic devices in a mask-free fashion in addition to providing a unique platform for performing high-throughput nano- to macroscale photochemistry with relevance to biology and medicine.

Controlled Folding, Motional, and Constitutional Dynamic Processes of Polyheterocyclic Molecular Strands.

PubMed

Barboiu, Mihail; Stadler, Adrian-Mihail; Lehn, Jean-Marie

2016-03-18

General design principles have been developed for the control of the structural features of polyheterocyclic strands and their effector-modulated shape changes. Induced defined molecular motions permit designed enforcement of helical as well as linear molecular shapes. The ability of such molecular strands to bind metal cations allows the generation of coiling/uncoiling processes between helically folded and extended linear states. Large molecular motions are produced on coordination of metal ions, which may be made reversible by competition with an ancillary complexing agent and fueled by sequential acid/base neutralization energy. The introduction of hydrazone units into the strands confers upon them constitutional dynamics, whereby interconversion between different strand compositions is achieved through component exchange. These features have relevance for nanomechanical devices. We present a morphological and functional analysis of such systems developed in our laboratories. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Attentional Selection of Feature Conjunctions Is Accomplished by Parallel and Independent Selection of Single Features.

PubMed

Andersen, Søren K; Müller, Matthias M; Hillyard, Steven A

2015-07-08

Experiments that study feature-based attention have often examined situations in which selection is based on a single feature (e.g., the color red). However, in more complex situations relevant stimuli may not be set apart from other stimuli by a single defining property but by a specific combination of features. Here, we examined sustained attentional selection of stimuli defined by conjunctions of color and orientation. Human observers attended to one out of four concurrently presented superimposed fields of randomly moving horizontal or vertical bars of red or blue color to detect brief intervals of coherent motion. Selective stimulus processing in early visual cortex was assessed by recordings of steady-state visual evoked potentials (SSVEPs) elicited by each of the flickering fields of stimuli. We directly contrasted attentional selection of single features and feature conjunctions and found that SSVEP amplitudes on conditions in which selection was based on a single feature only (color or orientation) exactly predicted the magnitude of attentional enhancement of SSVEPs when attending to a conjunction of both features. Furthermore, enhanced SSVEP amplitudes elicited by attended stimuli were accompanied by equivalent reductions of SSVEP amplitudes elicited by unattended stimuli in all cases. We conclude that attentional selection of a feature-conjunction stimulus is accomplished by the parallel and independent facilitation of its constituent feature dimensions in early visual cortex. The ability to perceive the world is limited by the brain's processing capacity. Attention affords adaptive behavior by selectively prioritizing processing of relevant stimuli based on their features (location, color, orientation, etc.). We found that attentional mechanisms for selection of different features belonging to the same object operate independently and in parallel: concurrent attentional selection of two stimulus features is simply the sum of attending to each of those features separately. This result is key to understanding attentional selection in complex (natural) scenes, where relevant stimuli are likely to be defined by a combination of stimulus features. Copyright © 2015 the authors 0270-6474/15/359912-08$15.00/0.

Insights into the Behavior of Potential Structural Failures Originating from Localized High Stress Regions in Configurations Relevant to Solid Rocket Motor Nozzles

NASA Technical Reports Server (NTRS)

McCutcheon, David Matthew

2017-01-01

During the structural certification effort for the Space Launch System solid rocket booster nozzle, it was identified that no consistent method for addressing local negative margins of safety in non-metallic materials had been developed. Relevant areas included bond-line terminations and geometric features in the composite nozzle liners. In order to gain understanding, analog test specimens were designed that very closely mimic the conditions in the actual full scale hardware. Different locations in the nozzle were represented by different analog specimen designs. This paper describes those tests and corresponding results. Finite element analysis results for the tests are presented. Strain gage correlation of the analysis to the test results is addressed. Furthermore, finite fracture mechanics (a coupled stress and energy failure criterion) is utilized to predict the observed crack pop-in loads for the different configurations. The finite fracture mechanics predictions are found to be within a 10% error relative to the average measured pop-in load for each of four configurations. Initiation locations, arrest behaviors, and resistances to further post-arrest crack propagation are also discussed.

A functional model of sensemaking in a neurocognitive architecture.

PubMed

Lebiere, Christian; Pirolli, Peter; Thomson, Robert; Paik, Jaehyon; Rutledge-Taylor, Matthew; Staszewski, James; Anderson, John R

2013-01-01

Sensemaking is the active process of constructing a meaningful representation (i.e., making sense) of some complex aspect of the world. In relation to intelligence analysis, sensemaking is the act of finding and interpreting relevant facts amongst the sea of incoming reports, images, and intelligence. We present a cognitive model of core information-foraging and hypothesis-updating sensemaking processes applied to complex spatial probability estimation and decision-making tasks. While the model was developed in a hybrid symbolic-statistical cognitive architecture, its correspondence to neural frameworks in terms of both structure and mechanisms provided a direct bridge between rational and neural levels of description. Compared against data from two participant groups, the model correctly predicted both the presence and degree of four biases: confirmation, anchoring and adjustment, representativeness, and probability matching. It also favorably predicted human performance in generating probability distributions across categories, assigning resources based on these distributions, and selecting relevant features given a prior probability distribution. This model provides a constrained theoretical framework describing cognitive biases as arising from three interacting factors: the structure of the task environment, the mechanisms and limitations of the cognitive architecture, and the use of strategies to adapt to the dual constraints of cognition and the environment.

Higher order visual input to the mushroom bodies in the bee, Bombus impatiens.

PubMed

Paulk, Angelique C; Gronenberg, Wulfila

2008-11-01

To produce appropriate behaviors based on biologically relevant associations, sensory pathways conveying different modalities are integrated by higher-order central brain structures, such as insect mushroom bodies. To address this function of sensory integration, we characterized the structure and response of optic lobe (OL) neurons projecting to the calyces of the mushroom bodies in bees. Bees are well known for their visual learning and memory capabilities and their brains possess major direct visual input from the optic lobes to the mushroom bodies. To functionally characterize these visual inputs to the mushroom bodies, we recorded intracellularly from neurons in bumblebees (Apidae: Bombus impatiens) and a single neuron in a honeybee (Apidae: Apis mellifera) while presenting color and motion stimuli. All of the mushroom body input neurons were color sensitive while a subset was motion sensitive. Additionally, most of the mushroom body input neurons would respond to the first, but not to subsequent, presentations of repeated stimuli. In general, the medulla or lobula neurons projecting to the calyx signaled specific chromatic, temporal, and motion features of the visual world to the mushroom bodies, which included sensory information required for the biologically relevant associations bees form during foraging tasks.

A Functional Model of Sensemaking in a Neurocognitive Architecture

PubMed Central

Lebiere, Christian; Paik, Jaehyon; Rutledge-Taylor, Matthew; Staszewski, James; Anderson, John R.

2013-01-01

Sensemaking is the active process of constructing a meaningful representation (i.e., making sense) of some complex aspect of the world. In relation to intelligence analysis, sensemaking is the act of finding and interpreting relevant facts amongst the sea of incoming reports, images, and intelligence. We present a cognitive model of core information-foraging and hypothesis-updating sensemaking processes applied to complex spatial probability estimation and decision-making tasks. While the model was developed in a hybrid symbolic-statistical cognitive architecture, its correspondence to neural frameworks in terms of both structure and mechanisms provided a direct bridge between rational and neural levels of description. Compared against data from two participant groups, the model correctly predicted both the presence and degree of four biases: confirmation, anchoring and adjustment, representativeness, and probability matching. It also favorably predicted human performance in generating probability distributions across categories, assigning resources based on these distributions, and selecting relevant features given a prior probability distribution. This model provides a constrained theoretical framework describing cognitive biases as arising from three interacting factors: the structure of the task environment, the mechanisms and limitations of the cognitive architecture, and the use of strategies to adapt to the dual constraints of cognition and the environment. PMID:24302930

Research training in integrative medicine: how can we make teaching and learning in research methods more sustainable and engaging?

PubMed

Witt, Claudia M; Withers, Shelly Rafferty

2013-01-01

The aim of this project was to identify strategies for increasing learner engagement and knowledge retention in clinical research training of complementary and integrative medicine (CIM) practitioners, and to offer a conceptual framework to address clinical research training for CIM practitioners. In a featured large-group discussion (15min presentation and 30min discussion), two questions (strategies that are recommended to overcome these barriers; relevant aspects for a framework for building sustainable knowledge) were put to the audience. The sample consisted of 43 participants at the International Congress of Educators in Complementary and Alternative Medicine, in Washington, DC, in October 2012. The featured discussion was moderated and detailed notes were taken. Notes were synthesized and discussed by both authors until consensus was reached. Based on the results from the featured discussion session and a focused literature search, a framework for building sustainable knowledge and skills in clinical research for CIM practitioners was developed. Participants' responses to the questions of engagement and sustainability included curricular structures, pedagogical strategies for instruction, the use of digital tools to extend the learning experience, the necessity to ground instruction firmly in the medical literature of the field, and the relevance of mentoring. Key considerations for building sustainable knowledge in clinical research for CIM practitioners are as follows: (1) prioritizing clinical research training, (2) issues of curriculum and pedagogy, (3) technology/digital tools, (4) administrative challenges, (5) supporting the formation of communities of practice, and (6) cultural perspectives of CIM practitioners. © 2013 Elsevier Inc. All rights reserved.

Using airborne LiDAR in geoarchaeological contexts: Assessment of an automatic tool for the detection and the morphometric analysis of grazing archaeological structures (French Massif Central).

NASA Astrophysics Data System (ADS)

Roussel, Erwan; Toumazet, Jean-Pierre; Florez, Marta; Vautier, Franck; Dousteyssier, Bertrand

2014-05-01

Airborne laser scanning (ALS) of archaeological regions of interest is nowadays a widely used and established method for accurate topographic and microtopographic survey. The penetration of the vegetation cover by the laser beam allows the reconstruction of reliable digital terrain models (DTM) of forested areas where traditional prospection methods are inefficient, time-consuming and non-exhaustive. The ALS technology provides the opportunity to discover new archaeological features hidden by vegetation and provides a comprehensive survey of cultural heritage sites within their environmental context. However, the post-processing of LiDAR points clouds produces a huge quantity of data in which relevant archaeological features are not easily detectable with common visualizing and analysing tools. Undoubtedly, there is an urgent need for automation of structures detection and morphometric extraction techniques, especially for the "archaeological desert" in densely forested areas. This presentation deals with the development of automatic detection procedures applied to archaeological structures located in the French Massif Central, in the western forested part of the Puy-de-Dôme volcano between 950 and 1100 m a.s.l.. These unknown archaeological sites were discovered by the March 2011 ALS mission and display a high density of subcircular depressions with a corridor access. The spatial organization of these depressions vary from isolated to aggregated or aligned features. Functionally, they appear to be former grazing constructions built from the medieval to the modern period. Similar grazing structures are known in other locations of the French Massif Central (Sancy, Artense, Cézallier) where the ground is vegetation-free. In order to develop a reliable process of automatic detection and mapping of these archaeological structures, a learning zone has been delineated within the ALS surveyed area. The grazing features were mapped and typical morphometric attributes were calculated based on 2 methods: (i) The mapping of the archaeological structures by a human operator using common visualisation tools (DTM, multi-direction hillshading & local relief models) within a GIS environment; (ii) The automatic detection and mapping performed by a recognition algorithm based on a user defined geometric pattern of the grazing structures. The efficiency of the automatic tool has been assessed by comparing the number of structures detected and the morphometric attributes calculated by the two methods. Our results indicate that the algorithm is efficient for the detection and the location of grazing structures. Concerning the morphometric results, there is still a discrepancy between automatic and expert calculations, due to both the expert mapping choices and the algorithm calibration.

Measuring the Interestingness of Articles in a Limited User Environment

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

Pon, Raymond K.

Search engines, such as Google, assign scores to news articles based on their relevancy to a query. However, not all relevant articles for the query may be interesting to a user. For example, if the article is old or yields little new information, the article would be uninteresting. Relevancy scores do not take into account what makes an article interesting, which varies from user to user. Although methods such as collaborative filtering have been shown to be effective in recommendation systems, in a limited user environment, there are not enough users that would make collaborative filtering effective. A general framework,more » called iScore, is presented for defining and measuring the 'interestingness' of articles, incorporating user-feedback. iScore addresses various aspects of what makes an article interesting, such as topic relevancy, uniqueness, freshness, source reputation, and writing style. It employs various methods to measure these features and uses a classifier operating on these features to recommend articles. The basic iScore configuration is shown to improve recommendation results by as much as 20%. In addition to the basic iScore features, additional features are presented to address the deficiencies of existing feature extractors, such as one that tracks multiple topics, called MTT, and a version of the Rocchio algorithm that learns its parameters online as it processes documents, called eRocchio. The inclusion of both MTT and eRocchio into iScore is shown to improve iScore recommendation results by as much as 3.1% and 5.6%, respectively. Additionally, in TREC11 Adaptive Filter Task, eRocchio is shown to be 10% better than the best filter in the last run of the task. In addition to these two major topic relevancy measures, other features are also introduced that employ language models, phrases, clustering, and changes in topics to improve recommendation results. These additional features are shown to improve recommendation results by iScore by up to 14%. Due to varying reasons that users hold regarding why an article is interesting, an online feature selection method in naive Bayes is also introduced. Online feature selection can improve recommendation results in iScore by up to 18.9%. In summary, iScore in its best configuration can outperform traditional IR techniques by as much as 50.7%. iScore and its components are evaluated in the news recommendation task using three datasets from Yahoo! News, actual users, and Digg. iScore and its components are also evaluated in the TREC Adaptive Filter task using the Reuters RCV1 corpus.« less

Efficient robust conditional random fields.

PubMed

Song, Dongjin; Liu, Wei; Zhou, Tianyi; Tao, Dacheng; Meyer, David A

2015-10-01

Conditional random fields (CRFs) are a flexible yet powerful probabilistic approach and have shown advantages for popular applications in various areas, including text analysis, bioinformatics, and computer vision. Traditional CRF models, however, are incapable of selecting relevant features as well as suppressing noise from noisy original features. Moreover, conventional optimization methods often converge slowly in solving the training procedure of CRFs, and will degrade significantly for tasks with a large number of samples and features. In this paper, we propose robust CRFs (RCRFs) to simultaneously select relevant features. An optimal gradient method (OGM) is further designed to train RCRFs efficiently. Specifically, the proposed RCRFs employ the l1 norm of the model parameters to regularize the objective used by traditional CRFs, therefore enabling discovery of the relevant unary features and pairwise features of CRFs. In each iteration of OGM, the gradient direction is determined jointly by the current gradient together with the historical gradients, and the Lipschitz constant is leveraged to specify the proper step size. We show that an OGM can tackle the RCRF model training very efficiently, achieving the optimal convergence rate [Formula: see text] (where k is the number of iterations). This convergence rate is theoretically superior to the convergence rate O(1/k) of previous first-order optimization methods. Extensive experiments performed on three practical image segmentation tasks demonstrate the efficacy of OGM in training our proposed RCRFs.

High-order above-threshold photoemission from nanotips controlled with two-color laser fields

NASA Astrophysics Data System (ADS)

Seiffert, Lennart; Paschen, Timo; Hommelhoff, Peter; Fennel, Thomas

2018-07-01

We investigate the process of phase-controlled high-order above-threshold photoemission from metallic nanotips under bichromatic laser fields. Experimental photoelectron spectra resulting from two-color excitation with a moderately intense near-infrared fundamental field (1560 nm) and its weak second harmonic show a strong sensitivity on the relative phase and clear indications for a plateau-like structure that is attributed to elastic backscattering. To explore the relevant control mechanisms, characteristic features, and particular signatures from the near-field inhomogeneity, we performed systematic quantum simulations employing a one-dimensional nanotip model. Besides rich phase-dependent structures in the simulated above-threshold ionization photoelectron spectra we find ponderomotive shifts as well as substantial modifications of the rescattering cutoff as function of the decay length of the near-field. To explore the quantum or classical nature of the observed features and to discriminate the two-color effects stemming from electron propagation and from the ionization rate we compare the quantum results to classical trajectory simulations. We show that signatures from direct electrons as well as the modulations in the plateau region mainly stem from control of the ionization probability, while the modulation in the cutoff region can only be explained by the impact of the two-color field on the electron trajectory. Despite the complexity of the phase-dependent features that render two-color strong-field photoemission from nanotips intriguing for sub-cycle strong-field control, our findings support that the recollision features in the cutoff region provide a robust and reliable method to calibrate the relative two-color phase.

Contextualized Instruction: Teaching Relevant Behaviors in Relevant Contexts.

ERIC Educational Resources Information Center

Reboy, Lisa M.; Semb, George B.

In contextualized instruction, the critical features of a context are considered important for the acquisition and transfer of a skill. Examples of contextualized instruction programs are Functional Context Education (FCE) and Anchored Instruction (AI). FCE involves the teaching of reading and mathematics skills in contexts that are relevant to…

T-ray relevant frequencies for osteosarcoma classification

NASA Astrophysics Data System (ADS)

Withayachumnankul, W.; Ferguson, B.; Rainsford, T.; Findlay, D.; Mickan, S. P.; Abbott, D.

2006-01-01

We investigate the classification of the T-ray response of normal human bone cells and human osteosarcoma cells, grown in culture. Given the magnitude and phase responses within a reliable spectral range as features for input vectors, a trained support vector machine can correctly classify the two cell types to some extent. Performance of the support vector machine is deteriorated by the curse of dimensionality, resulting from the comparatively large number of features in the input vectors. Feature subset selection methods are used to select only an optimal number of relevant features for inputs. As a result, an improvement in generalization performance is attainable, and the selected frequencies can be used for further describing different mechanisms of the cells, responding to T-rays. We demonstrate a consistent classification accuracy of 89.6%, while the only one fifth of the original features are retained in the data set.

Quantifying site-specific physical heterogeneity within an estuarine seascape

USGS Publications Warehouse

Kennedy, Cristina G.; Mather, Martha E.; Smith, Joseph M.

2017-01-01

Quantifying physical heterogeneity is essential for meaningful ecological research and effective resource management. Spatial patterns of multiple, co-occurring physical features are rarely quantified across a seascape because of methodological challenges. Here, we identified approaches that measured total site-specific heterogeneity, an often overlooked aspect of estuarine ecosystems. Specifically, we examined 23 metrics that quantified four types of common physical features: (1) river and creek confluences, (2) bathymetric variation including underwater drop-offs, (3) land features such as islands/sandbars, and (4) major underwater channel networks. Our research at 40 sites throughout Plum Island Estuary (PIE) provided solutions to two problems. The first problem was that individual metrics that measured heterogeneity of a single physical feature showed different regional patterns. We solved this first problem by combining multiple metrics for a single feature using a within-physical feature cluster analysis. With this approach, we identified sites with four different types of confluences and three different types of underwater drop-offs. The second problem was that when multiple physical features co-occurred, new patterns of total site-specific heterogeneity were created across the seascape. This pattern of total heterogeneity has potential ecological relevance to structure-oriented predators. To address this second problem, we identified sites with similar types of total physical heterogeneity using an across-physical feature cluster analysis. Then, we calculated an additive heterogeneity index, which integrated all physical features at a site. Finally, we tested if site-specific additive heterogeneity index values differed for across-physical feature clusters. In PIE, the sites with the highest additive heterogeneity index values were clustered together and corresponded to sites where a fish predator, adult striped bass (Morone saxatilis), aggregated in a related acoustic tracking study. In summary, we have shown general approaches to quantifying site-specific heterogeneity.

Interaction Between Spatial and Feature Attention in Posterior Parietal Cortex

PubMed Central

Ibos, Guilhem; Freedman, David J.

2016-01-01

Summary Lateral intraparietal (LIP) neurons encode a vast array of sensory and cognitive variables. Recently, we proposed that the flexibility of feature representations in LIP reflect the bottom-up integration of sensory signals, modulated by feature-based attention (FBA), from upstream feature-selective cortical neurons. Moreover, LIP activity is also strongly modulated by the position of space-based attention (SBA). However, the mechanisms by which SBA and FBA interact to facilitate the representation of task-relevant spatial and non-spatial features in LIP remain unclear. We recorded from LIP neurons during performance of a task which required monkeys to detect specific conjunctions of color, motion-direction, and stimulus position. Here we show that FBA and SBA potentiate each other’s effect in a manner consistent with attention gating the flow of visual information along the cortical visual pathway. Our results suggest that linear bottom-up integrative mechanisms allow LIP neurons to emphasize task-relevant spatial and non-spatial features. PMID:27499082

Sequence-Based Prediction of RNA-Binding Proteins Using Random Forest with Minimum Redundancy Maximum Relevance Feature Selection.

PubMed

Ma, Xin; Guo, Jing; Sun, Xiao

2015-01-01

The prediction of RNA-binding proteins is one of the most challenging problems in computation biology. Although some studies have investigated this problem, the accuracy of prediction is still not sufficient. In this study, a highly accurate method was developed to predict RNA-binding proteins from amino acid sequences using random forests with the minimum redundancy maximum relevance (mRMR) method, followed by incremental feature selection (IFS). We incorporated features of conjoint triad features and three novel features: binding propensity (BP), nonbinding propensity (NBP), and evolutionary information combined with physicochemical properties (EIPP). The results showed that these novel features have important roles in improving the performance of the predictor. Using the mRMR-IFS method, our predictor achieved the best performance (86.62% accuracy and 0.737 Matthews correlation coefficient). High prediction accuracy and successful prediction performance suggested that our method can be a useful approach to identify RNA-binding proteins from sequence information.

Interaction between Spatial and Feature Attention in Posterior Parietal Cortex.

PubMed

Ibos, Guilhem; Freedman, David J

2016-08-17

Lateral intraparietal (LIP) neurons encode a vast array of sensory and cognitive variables. Recently, we proposed that the flexibility of feature representations in LIP reflect the bottom-up integration of sensory signals, modulated by feature-based attention (FBA), from upstream feature-selective cortical neurons. Moreover, LIP activity is also strongly modulated by the position of space-based attention (SBA). However, the mechanisms by which SBA and FBA interact to facilitate the representation of task-relevant spatial and non-spatial features in LIP remain unclear. We recorded from LIP neurons during performance of a task that required monkeys to detect specific conjunctions of color, motion direction, and stimulus position. Here we show that FBA and SBA potentiate each other's effect in a manner consistent with attention gating the flow of visual information along the cortical visual pathway. Our results suggest that linear bottom-up integrative mechanisms allow LIP neurons to emphasize task-relevant spatial and non-spatial features. Copyright © 2016 Elsevier Inc. All rights reserved.

[Combining speech sample and feature bilateral selection algorithm for classification of Parkinson's disease].

PubMed

Zhang, Xiaoheng; Wang, Lirui; Cao, Yao; Wang, Pin; Zhang, Cheng; Yang, Liuyang; Li, Yongming; Zhang, Yanling; Cheng, Oumei

2018-02-01

Diagnosis of Parkinson's disease (PD) based on speech data has been proved to be an effective way in recent years. However, current researches just care about the feature extraction and classifier design, and do not consider the instance selection. Former research by authors showed that the instance selection can lead to improvement on classification accuracy. However, no attention is paid on the relationship between speech sample and feature until now. Therefore, a new diagnosis algorithm of PD is proposed in this paper by simultaneously selecting speech sample and feature based on relevant feature weighting algorithm and multiple kernel method, so as to find their synergy effects, thereby improving classification accuracy. Experimental results showed that this proposed algorithm obtained apparent improvement on classification accuracy. It can obtain mean classification accuracy of 82.5%, which was 30.5% higher than the relevant algorithm. Besides, the proposed algorithm detected the synergy effects of speech sample and feature, which is valuable for speech marker extraction.

A narrative overview of the current status of MRI of the hip and its relevance for osteoarthritis research - what we know, what has changed and where are we going?

PubMed

Crema, M D; Watts, G J; Guermazi, A; Kim, Y-J; Kijowski, R; Roemer, F W

2017-01-01

To review and discuss the role of magnetic resonance imaging (MRI) in the context of hip osteoarthritis (OA) research. The content of this narrative review, based on an extensive PubMed database research including English literature only, describes the advances in MRI of the hip joint and its potential usefulness in hip OA research, reviews the relevance of different MRI features in regard to symptomatic and structural progression in hip OA, and gives an outlook regarding future use of MRI in hip OA research endeavors. Recent technical advances have helped to overcome many of the past difficulties related to MRI assessment of hip OA. MRI-based morphologic scoring systems allow for detailed assessment of several hip joint tissues and, in combination with the recent advances in MRI, may increase reproducibility and sensitivity to change. Compositional MRI techniques may add to our understanding of disease onset and progression. Knowledge about imaging pitfalls and anatomical variants is crucial to avoid misinterpretation. In comparison to research on knee OA, the associations between MRI features and the incidence and progression of disease as well as with clinical symptoms have been little explored. Anatomic alterations of the hip joint as seen in femoro-acetabular impingement (FAI) seem to play a role in the onset and progression of structural damage. With the technical advances occurring in recent years, MRI may play a major role in investigating the natural history of hip OA and provide an improved method for assessment of the efficacy of new therapeutic approaches. Copyright © 2016 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.

Application of Multilabel Learning Using the Relevant Feature for Each Label in Chronic Gastritis Syndrome Diagnosis

PubMed Central

Liu, Guo-Ping; Yan, Jian-Jun; Wang, Yi-Qin; Fu, Jing-Jing; Xu, Zhao-Xia; Guo, Rui; Qian, Peng

2012-01-01

Background. In Traditional Chinese Medicine (TCM), most of the algorithms are used to solve problems of syndrome diagnosis that only focus on one syndrome, that is, single label learning. However, in clinical practice, patients may simultaneously have more than one syndrome, which has its own symptoms (signs). Methods. We employed a multilabel learning using the relevant feature for each label (REAL) algorithm to construct a syndrome diagnostic model for chronic gastritis (CG) in TCM. REAL combines feature selection methods to select the significant symptoms (signs) of CG. The method was tested on 919 patients using the standard scale. Results. The highest prediction accuracy was achieved when 20 features were selected. The features selected with the information gain were more consistent with the TCM theory. The lowest average accuracy was 54% using multi-label neural networks (BP-MLL), whereas the highest was 82% using REAL for constructing the diagnostic model. For coverage, hamming loss, and ranking loss, the values obtained using the REAL algorithm were the lowest at 0.160, 0.142, and 0.177, respectively. Conclusion. REAL extracts the relevant symptoms (signs) for each syndrome and improves its recognition accuracy. Moreover, the studies will provide a reference for constructing syndrome diagnostic models and guide clinical practice. PMID:22719781

Voting behavior, coalitions and government strength through a complex network analysis.

PubMed

Dal Maso, Carlo; Pompa, Gabriele; Puliga, Michelangelo; Riotta, Gianni; Chessa, Alessandro

2014-01-01

We analyze the network of relations between parliament members according to their voting behavior. In particular, we examine the emergent community structure with respect to political coalitions and government alliances. We rely on tools developed in the Complex Network literature to explore the core of these communities and use their topological features to develop new metrics for party polarization, internal coalition cohesiveness and government strength. As a case study, we focus on the Chamber of Deputies of the Italian Parliament, for which we are able to characterize the heterogeneity of the ruling coalition as well as parties specific contributions to the stability of the government over time. We find sharp contrast in the political debate which surprisingly does not imply a relevant structure based on established parties. We take a closer look to changes in the community structure after parties split up and their effect on the position of single deputies within communities. Finally, we introduce a way to track the stability of the government coalition over time that is able to discern the contribution of each member along with the impact of its possible defection. While our case study relies on the Italian parliament, whose relevance has come into the international spotlight in the present economic downturn, the methods developed here are entirely general and can therefore be applied to a multitude of other scenarios.

Finding regions of interest in pathological images: an attentional model approach

NASA Astrophysics Data System (ADS)

Gómez, Francisco; Villalón, Julio; Gutierrez, Ricardo; Romero, Eduardo

2009-02-01

This paper introduces an automated method for finding diagnostic regions-of-interest (RoIs) in histopathological images. This method is based on the cognitive process of visual selective attention that arises during a pathologist's image examination. Specifically, it emulates the first examination phase, which consists in a coarse search for tissue structures at a "low zoom" to separate the image into relevant regions.1 The pathologist's cognitive performance depends on inherent image visual cues - bottom-up information - and on acquired clinical medicine knowledge - top-down mechanisms -. Our pathologist's visual attention model integrates the latter two components. The selected bottom-up information includes local low level features such as intensity, color, orientation and texture information. Top-down information is related to the anatomical and pathological structures known by the expert. A coarse approximation to these structures is achieved by an oversegmentation algorithm, inspired by psychological grouping theories. The algorithm parameters are learned from an expert pathologist's segmentation. Top-down and bottom-up integration is achieved by calculating a unique index for each of the low level characteristics inside the region. Relevancy is estimated as a simple average of these indexes. Finally, a binary decision rule defines whether or not a region is interesting. The method was evaluated on a set of 49 images using a perceptually-weighted evaluation criterion, finding a quality gain of 3dB when comparing to a classical bottom-up model of attention.

Multivariate pattern analysis reveals subtle brain anomalies relevant to the cognitive phenotype in neurofibromatosis type 1.

PubMed

Duarte, João V; Ribeiro, Maria J; Violante, Inês R; Cunha, Gil; Silva, Eduardo; Castelo-Branco, Miguel

2014-01-01

Neurofibromatosis Type 1 (NF1) is a common genetic condition associated with cognitive dysfunction. However, the pathophysiology of the NF1 cognitive deficits is not well understood. Abnormal brain structure, including increased total brain volume, white matter (WM) and grey matter (GM) abnormalities have been reported in the NF1 brain. These previous studies employed univariate model-driven methods preventing detection of subtle and spatially distributed differences in brain anatomy. Multivariate pattern analysis allows the combination of information from multiple spatial locations yielding a discriminative power beyond that of single voxels. Here we investigated for the first time subtle anomalies in the NF1 brain, using a multivariate data-driven classification approach. We used support vector machines (SVM) to classify whole-brain GM and WM segments of structural T1 -weighted MRI scans from 39 participants with NF1 and 60 non-affected individuals, divided in children/adolescents and adults groups. We also employed voxel-based morphometry (VBM) as a univariate gold standard to study brain structural differences. SVM classifiers correctly classified 94% of cases (sensitivity 92%; specificity 96%) revealing the existence of brain structural anomalies that discriminate NF1 individuals from controls. Accordingly, VBM analysis revealed structural differences in agreement with the SVM weight maps representing the most relevant brain regions for group discrimination. These included the hippocampus, basal ganglia, thalamus, and visual cortex. This multivariate data-driven analysis thus identified subtle anomalies in brain structure in the absence of visible pathology. Our results provide further insight into the neuroanatomical correlates of known features of the cognitive phenotype of NF1. Copyright © 2012 Wiley Periodicals, Inc.

Feature Vector Construction Method for IRIS Recognition

NASA Astrophysics Data System (ADS)

Odinokikh, G.; Fartukov, A.; Korobkin, M.; Yoo, J.

2017-05-01

One of the basic stages of iris recognition pipeline is iris feature vector construction procedure. The procedure represents the extraction of iris texture information relevant to its subsequent comparison. Thorough investigation of feature vectors obtained from iris showed that not all the vector elements are equally relevant. There are two characteristics which determine the vector element utility: fragility and discriminability. Conventional iris feature extraction methods consider the concept of fragility as the feature vector instability without respect to the nature of such instability appearance. This work separates sources of the instability into natural and encodinginduced which helps deeply investigate each source of instability independently. According to the separation concept, a novel approach of iris feature vector construction is proposed. The approach consists of two steps: iris feature extraction using Gabor filtering with optimal parameters and quantization with separated preliminary optimized fragility thresholds. The proposed method has been tested on two different datasets of iris images captured under changing environmental conditions. The testing results show that the proposed method surpasses all the methods considered as a prior art by recognition accuracy on both datasets.

Micromechanical Characterization of Polysilicon Films through On-Chip Tests

PubMed Central

Mirzazadeh, Ramin; Eftekhar Azam, Saeed; Mariani, Stefano

2016-01-01

When the dimensions of polycrystalline structures become comparable to the average grain size, some reliability issues can be reported for the moving parts of inertial microelectromechanical systems (MEMS). Not only the overall behavior of the device turns out to be affected by a large scattering, but also the sensitivity to imperfections gets enhanced. In this work, through on-chip tests, we experimentally investigate the behavior of thin polysilicon samples using standard electrostatic actuation/sensing. The discrepancy between the target and actual responses of each sample has then been exploited to identify: (i) the overall stiffness of the film and, according to standard continuum elasticity, a morphology-based value of its Young’s modulus; (ii) the relevant over-etch induced by the fabrication process. To properly account for the aforementioned stochastic features at the micro-scale, the identification procedure has been based on particle filtering. A simple analytical reduced-order model of the moving structure has been also developed to account for the nonlinearities in the electrical field, up to pull-in. Results are reported for a set of ten film samples of constant slenderness, and the effects of different actuation mechanisms on the identified micromechanical features are thoroughly discussed. PMID:27483268

Micromechanical Characterization of Polysilicon Films through On-Chip Tests.

PubMed

Mirzazadeh, Ramin; Eftekhar Azam, Saeed; Mariani, Stefano

2016-07-28

When the dimensions of polycrystalline structures become comparable to the average grain size, some reliability issues can be reported for the moving parts of inertial microelectromechanical systems (MEMS). Not only the overall behavior of the device turns out to be affected by a large scattering, but also the sensitivity to imperfections gets enhanced. In this work, through on-chip tests, we experimentally investigate the behavior of thin polysilicon samples using standard electrostatic actuation/sensing. The discrepancy between the target and actual responses of each sample has then been exploited to identify: (i) the overall stiffness of the film and, according to standard continuum elasticity, a morphology-based value of its Young's modulus; (ii) the relevant over-etch induced by the fabrication process. To properly account for the aforementioned stochastic features at the micro-scale, the identification procedure has been based on particle filtering. A simple analytical reduced-order model of the moving structure has been also developed to account for the nonlinearities in the electrical field, up to pull-in. Results are reported for a set of ten film samples of constant slenderness, and the effects of different actuation mechanisms on the identified micromechanical features are thoroughly discussed.

Histone Variants and Composition in the Developing Brain: Should MeCP2 Care?

PubMed

Zago, Valentina; Pinar-CabezaDeVaca, Cristina; Vincent, John B; Ausio, Juan

2017-01-01

Specific compositional chromatin features distinguish brain/neuronal chromatin from that of other tissues and are critical to this organ and cell type development and neuroplasticity. These features include a significant turnover of the major constitutive chromosomal proteins, including the (canonical) replication-dependent histones, the replication-independent replacement histone variants, as well as the chromatin associated transcriptional regulator MeCP2 (methyl CpG binding protein 2). Alterations of histones and MeCP2 have already been implicated in many brain disorders. Despite the relevance of histone variants to chromatin structure and function, only recently has some exciting literature started to re-emerge that directly relates them to neuron plasticity and cognition. However, the amount of information available on the functional role of these histones is still very limited. The purpose of this review is to focus attention to this important group of chromatin proteins, which, in the brain, possess overlapping structural and functional roles with the highly abundant presence of MeCP2. There is an imperative need to understand how all these proteins communicate with each other, and future research will hopefully provide us with answers.

Rank Dynamics of Word Usage at Multiple Scales

NASA Astrophysics Data System (ADS)

Morales, José A.; Colman, Ewan; Sánchez, Sergio; Sánchez-Puig, Fernanda; Pineda, Carlos; Iñiguez, Gerardo; Cocho, Germinal; Flores, Jorge; Gershenson, Carlos

2018-05-01

The recent dramatic increase in online data availability has allowed researchers to explore human culture with unprecedented detail, such as the growth and diversification of language. In particular, it provides statistical tools to explore whether word use is similar across languages, and if so, whether these generic features appear at different scales of language structure. Here we use the Google Books N-grams dataset to analyze the temporal evolution of word usage in several languages. We apply measures proposed recently to study rank dynamics, such as the diversity of N-grams in a given rank, the probability that an N-gram changes rank between successive time intervals, the rank entropy, and the rank complexity. Using different methods, results show that there are generic properties for different languages at different scales, such as a core of words necessary to minimally understand a language. We also propose a null model to explore the relevance of linguistic structure across multiple scales, concluding that N-gram statistics cannot be reduced to word statistics. We expect our results to be useful in improving text prediction algorithms, as well as in shedding light on the large-scale features of language use, beyond linguistic and cultural differences across human populations.

Governance versus government: drug consumption rooms in Australia and the UK.

PubMed

Zampini, Giulia Federica

2014-09-01

To evaluate, through a case study, the extent to which elements of governance and elements of government are influential in determining the implementation or non-implementation of a drugs intervention. Comparative analysis of the case of a drug consumption room in the UK (England) and Australia (New South Wales), including 16 semi-structured interviews with key stakeholders and analysis of relevant documents according to characteristic features of governance and government (power decentralisation, power centralisation, independent self-organising policy networks, use of evidence, top-down steering/directing, legislation). Characteristic features of both governance and government are found in the data. Elements of governance are more prominent in New South Wales, Australia than in England, UK, where government prevails. Government is seen as the most important actor at play in the making, or absence, of drug consumption rooms. Both governance and government are useful frameworks in conceptualising the policy process. The governance narrative risks overlooking the importance of traditional government structures. In the case of drug consumption rooms in the UK and Australia, a focus on government is shown to have been crucial in determining whether the intervention was implemented. Copyright © 2014 Elsevier B.V. All rights reserved.

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

PubMed

Biswas, Sujoy Kumar; Milanfar, Peyman

2016-03-01

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

[Medical Rehabilitation as an Attractive Field of Work for Medical Doctors? - A Qualitative Survey].

PubMed

Lederle, Mareike; Kotzjan, Priscilla Simone; Niehues, Christiane; Brüggemann, Silke; Bitzer, Eva-Maria

2017-10-01

In the German Health system there is an increasing competition in the recruitment of specialised staff, especially for rehabilitation centres, which are deemed less attractive. Therefore, this study examines the attractiveness of the field of medical rehabilitation from the point of view of medical professionals. We conducted 16 semi-structured interviews with doctors from 7 rehabilitation centres with different medical specialisations. The interviews were digitized and transcribed. A structured content analysis was carried out using the software MAXQDA 11. 745 codes were identified and assigned to the categories "attractiveness", "unfavourable aspects" and "special features" of rehabilitation. Regarding medical rehabilitation, the interviewees appreciated especially the predictable, flexible working environment with little time pressure. Other than working with rehabilitative patients working as part of an interdisciplinary team was of high importance for the interviewees. Among the special features of rehabilitation in comparison with acute care were the higher relevance of the bio-psycho-social model of health and illness as well as the higher proportion of communication and organisation. Medical rehabilitation in Germany is an attractive field of work for medical doctors. This fact should be considered more with regards to rehabilitation's public image. © Georg Thieme Verlag KG Stuttgart · New York.

Promotion and resignation in employee networks

NASA Astrophysics Data System (ADS)

Yuan, Jia; Zhang, Qian-Ming; Gao, Jian; Zhang, Linyan; Wan, Xue-Song; Yu, Xiao-Jun; Zhou, Tao

2016-02-01

Enterprises have put more and more emphasis on data analysis so as to obtain effective management advices. Managers and researchers are trying to dig out the major factors that lead to employees' promotion and resignation. Most previous analyses are based on questionnaire survey, which usually consists of a small fraction of samples and contains biases caused by psychological defense. In this paper, we successfully collect a data set consisting of all the employees' work-related interactions (action network, AN for short) and online social connections (social network, SN for short) of a company, which inspires us to reveal the correlations between structural features and employees' career development, namely promotion and resignation. Through statistical analysis, we show that the structural features of both AN and SN are correlated and predictive to employees' promotion and resignation, and the AN has higher correlation and predictability. More specifically, the in-degree in AN is the most relevant indicator for promotion, while the k-shell index in AN and in-degree in SN are both very predictive to resignation. Our results provide a novel and actionable understanding of enterprise management and suggest that to enhance the interplays among employees, no matter work-related or social interplays, can be helpful to reduce staffs' turnover risk.

Structural Influence on the Dominance of Virus-Specific CD4 T Cell Epitopes in Zika Virus Infection.

PubMed

Koblischke, Maximilian; Stiasny, Karin; Aberle, Stephan W; Malafa, Stefan; Tschouchnikas, Georgios; Schwaiger, Julia; Kundi, Michael; Heinz, Franz X; Aberle, Judith H

2018-01-01

Zika virus (ZIKV) has recently caused explosive outbreaks in Pacific islands, South- and Central America. Like with other flaviviruses, protective immunity is strongly dependent on potently neutralizing antibodies (Abs) directed against the viral envelope protein E. Such Ab formation is promoted by CD4 T cells through direct interaction with B cells that present epitopes derived from E or other structural proteins of the virus. Here, we examined the extent and epitope dominance of CD4 T cell responses to capsid (C) and envelope proteins in Zika patients. All patients developed ZIKV-specific CD4 T cell responses, with substantial contributions of C and E. In both proteins, immunodominant epitopes clustered at sites that are structurally conserved among flaviviruses but have highly variable sequences, suggesting a strong impact of protein structural features on immunodominant CD4 T cell responses. Our data are particularly relevant for designing flavivirus vaccines and their evaluation in T cell assays and provide insights into the importance of viral protein structure for epitope selection and antigenicity.

Structural Chemistry of Human RNA Methyltransferases.

PubMed

Schapira, Matthieu

2016-03-18

RNA methyltransferases (RNMTs) play important roles in RNA stability, splicing, and epigenetic mechanisms. They constitute a promising target class that is underexplored by the medicinal chemistry community. Information of relevance to drug design can be extracted from the rich structural coverage of human RNMTs. In this work, the structural chemistry of this protein family is analyzed in depth. Unlike most methyltransferases, RNMTs generally feature a substrate-binding site that is largely open on the cofactor-binding pocket, favoring the design of bisubstrate inhibitors. Substrate purine or pyrimidines are often sandwiched between hydrophobic walls that can accommodate planar ring systems. When the substrate base is laying on a shallow surface, a 5' flanking base is sometimes anchored in a druggable cavity. The cofactor-binding site is structurally more diverse than in protein methyltransferases and more druggable in SPOUT than in Rossman-fold enzymes. Finally, conformational plasticity observed both at the substrate and cofactor binding sites may be a challenge for structure-based drug design. The landscape drawn here may inform ongoing efforts toward the discovery of the first human RNMT inhibitors.

An Atlas of Peroxiredoxins Created Using an Active Site Profile-Based Approach to Functionally Relevant Clustering of Proteins.

PubMed

Harper, Angela F; Leuthaeuser, Janelle B; Babbitt, Patricia C; Morris, John H; Ferrin, Thomas E; Poole, Leslie B; Fetrow, Jacquelyn S

2017-02-01

Peroxiredoxins (Prxs or Prdxs) are a large protein superfamily of antioxidant enzymes that rapidly detoxify damaging peroxides and/or affect signal transduction and, thus, have roles in proliferation, differentiation, and apoptosis. Prx superfamily members are widespread across phylogeny and multiple methods have been developed to classify them. Here we present an updated atlas of the Prx superfamily identified using a novel method called MISST (Multi-level Iterative Sequence Searching Technique). MISST is an iterative search process developed to be both agglomerative, to add sequences containing similar functional site features, and divisive, to split groups when functional site features suggest distinct functionally-relevant clusters. Superfamily members need not be identified initially-MISST begins with a minimal representative set of known structures and searches GenBank iteratively. Further, the method's novelty lies in the manner in which isofunctional groups are selected; rather than use a single or shifting threshold to identify clusters, the groups are deemed isofunctional when they pass a self-identification criterion, such that the group identifies itself and nothing else in a search of GenBank. The method was preliminarily validated on the Prxs, as the Prxs presented challenges of both agglomeration and division. For example, previous sequence analysis clustered the Prx functional families Prx1 and Prx6 into one group. Subsequent expert analysis clearly identified Prx6 as a distinct functionally relevant group. The MISST process distinguishes these two closely related, though functionally distinct, families. Through MISST search iterations, over 38,000 Prx sequences were identified, which the method divided into six isofunctional clusters, consistent with previous expert analysis. The results represent the most complete computational functional analysis of proteins comprising the Prx superfamily. The feasibility of this novel method is demonstrated by the Prx superfamily results, laying the foundation for potential functionally relevant clustering of the universe of protein sequences.

An Atlas of Peroxiredoxins Created Using an Active Site Profile-Based Approach to Functionally Relevant Clustering of Proteins

PubMed Central

Babbitt, Patricia C.; Ferrin, Thomas E.

2017-01-01

Peroxiredoxins (Prxs or Prdxs) are a large protein superfamily of antioxidant enzymes that rapidly detoxify damaging peroxides and/or affect signal transduction and, thus, have roles in proliferation, differentiation, and apoptosis. Prx superfamily members are widespread across phylogeny and multiple methods have been developed to classify them. Here we present an updated atlas of the Prx superfamily identified using a novel method called MISST (Multi-level Iterative Sequence Searching Technique). MISST is an iterative search process developed to be both agglomerative, to add sequences containing similar functional site features, and divisive, to split groups when functional site features suggest distinct functionally-relevant clusters. Superfamily members need not be identified initially—MISST begins with a minimal representative set of known structures and searches GenBank iteratively. Further, the method’s novelty lies in the manner in which isofunctional groups are selected; rather than use a single or shifting threshold to identify clusters, the groups are deemed isofunctional when they pass a self-identification criterion, such that the group identifies itself and nothing else in a search of GenBank. The method was preliminarily validated on the Prxs, as the Prxs presented challenges of both agglomeration and division. For example, previous sequence analysis clustered the Prx functional families Prx1 and Prx6 into one group. Subsequent expert analysis clearly identified Prx6 as a distinct functionally relevant group. The MISST process distinguishes these two closely related, though functionally distinct, families. Through MISST search iterations, over 38,000 Prx sequences were identified, which the method divided into six isofunctional clusters, consistent with previous expert analysis. The results represent the most complete computational functional analysis of proteins comprising the Prx superfamily. The feasibility of this novel method is demonstrated by the Prx superfamily results, laying the foundation for potential functionally relevant clustering of the universe of protein sequences. PMID:28187133

Cross-sectional anatomy, computed tomography and magnetic resonance imaging of the head of common dolphin (Delphinus delphis) and striped dolphin (Stenella coeruleoalba).

PubMed

Alonso-Farré, J M; Gonzalo-Orden, M; Barreiro-Vázquez, J D; Barreiro-Lois, A; André, M; Morell, M; Llarena-Reino, M; Monreal-Pawlowsky, T; Degollada, E

2015-02-01

Computed tomography (CT) and low-field magnetic resonance imaging (MRI) were used to scan seven by-caught dolphin cadavers, belonging to two species: four common dolphins (Delphinus delphis) and three striped dolphins (Stenella coeruleoalba). CT and MRI were obtained with the animals in ventral recumbency. After the imaging procedures, six dolphins were frozen at -20°C and sliced in the same position they were examined. Not only CT and MRI scans, but also cross sections of the heads were obtained in three body planes: transverse (slices of 1 cm thickness) in three dolphins, sagittal (5 cm thickness) in two dolphins and dorsal (5 cm thickness) in two dolphins. Relevant anatomical structures were identified and labelled on each cross section, obtaining a comprehensive bi-dimensional topographical anatomy guide of the main features of the common and the striped dolphin head. Furthermore, the anatomical cross sections were compared with their corresponding CT and MRI images, allowing an imaging identification of most of the anatomical features. CT scans produced an excellent definition of the bony and air-filled structures, while MRI allowed us to successfully identify most of the soft tissue structures in the dolphin's head. This paper provides a detailed anatomical description of the head structures of common and striped dolphins and compares anatomical cross sections with CT and MRI scans, becoming a reference guide for the interpretation of imaging studies. © 2014 Blackwell Verlag GmbH.

The intriguing nature of dorsal root ganglion neurons: linking structure with polarity and function.

PubMed

Nascimento, Ana Isabel; Mar, Fernando Milhazes; Sousa, Mónica Mendes

2018-05-02

Dorsal root ganglion (DRG) neurons are the first neurons of the sensory pathway. They are activated by a variety of sensory stimuli that are then transmitted to the central nervous system. An important feature of DRG neurons is their unique morphology where a single process -the stem axon- bifurcates into a peripheral and a central axonal branch, with different functions and cellular properties. Distinctive structural aspects of the two DRG neuron branches may have important implications for their function in health and disease. However, the link between DRG axonal branch structure, polarity and function has been largely neglected in the field, and relevant information is rather scattered across the literature. In particular, ultrastructural differences between the two axonal branches are likely to account for the higher transport and regenerative ability of the peripheral DRG neuron axon when compared to the central one. Nevertheless, the cell intrinsic factors contributing to this central-peripheral asymmetry are still unknown. Here we critically review the factors that may underlie the functional asymmetry between the peripheral and central DRG axonal branches. Also, we discuss the hypothesis that DRG neurons may assemble a structure resembling the axon initial segment that may be responsible, at least in part, for their polarity and electrophysiological features. Ultimately, we suggest that the clarification of the axonal ultrastructure of DRG neurons using state-of-the-art techniques will be crucial to understand the physiology of this peculiar cell type. Copyright © 2018. Published by Elsevier Ltd.

A systematic review of the relationship between subchondral bone features, pain and structural pathology in peripheral joint osteoarthritis.

PubMed

Barr, Andrew J; Campbell, T Mark; Hopkinson, Devan; Kingsbury, Sarah R; Bowes, Mike A; Conaghan, Philip G

2015-08-25

Bone is an integral part of the osteoarthritis (OA) process. We conducted a systematic literature review in order to understand the relationship between non-conventional radiographic imaging of subchondral bone, pain, structural pathology and joint replacement in peripheral joint OA. A search of the Medline, EMBASE and Cochrane library databases was performed for original articles reporting association between non-conventional radiographic imaging-assessed subchondral bone pathologies and joint replacement, pain or structural progression in knee, hip, hand, ankle and foot OA. Each association was qualitatively characterised by a synthesis of the data from each analysis based upon study design, adequacy of covariate adjustment and quality scoring. In total 2456 abstracts were screened and 139 papers were included (70 cross-sectional, 71 longitudinal analyses; 116 knee, 15 hip, six hand, two ankle and involved 113 MRI, eight DXA, four CT, eight scintigraphic and eight 2D shape analyses). BMLs, osteophytes and bone shape were independently associated with structural progression or joint replacement. BMLs and bone shape were independently associated with longitudinal change in pain and incident frequent knee pain respectively. Subchondral bone features have independent associations with structural progression, pain and joint replacement in peripheral OA in the hip and hand but especially in the knee. For peripheral OA sites other than the knee, there are fewer associations and independent associations of bone pathologies with these important OA outcomes which may reflect fewer studies; for example the foot and ankle were poorly studied. Subchondral OA bone appears to be a relevant therapeutic target. PROSPERO registration number: CRD 42013005009.

Research of a real-time overload monitoring and response system of bridges and roads

NASA Astrophysics Data System (ADS)

Yu, Yan; Shi, Yan; Zhao, Xuefeng; Ou, Jinping

2012-04-01

Due to the general overloading of vehicles, premature failure of bridges and roads are more and more obvious. Structural behaviors of engineering structures need real-time monitoring and diagnosis, timely detection of structural damage, evaluation of their safety, and necessary precautions, in order to prevent major accident such as the collapse of bridges and roads. But the existing monitoring system, which is very expensive, does not apply to the low budget structures. Therefore, a potable, low-cost, low-power structural monitoring system, which consists of electric resistance strain gauge, collection and execution unit, graph collection system and analysis software, is designed in this paper. The system can collect the critical data about the force of pavement to take the certain judge algorithm. The alarm will be given and the overburden data will be transmitted to IDC to make the further analysis when the pavement is overburden. At the same time, the plates of overweight vehicles can be collected and sent to the relevant departments. The system has the features of simple structure, easy realization, and low cost, which fills the application gaps in structural health monitoring of low-budget project.

Botulinum neurotoxin structure, engineering, and novel cellular trafficking and targeting.

PubMed

Singh, B R

2006-04-01

Botulinum neurotoxins are multifaceted molecules, which are truly unique not only in their mode of action, but also their utility as a drug carrier either across the gut wall or to the nerve terminals. The molecule is divided in clear functional domains that can operate independently. This feature can be used to employ them as cargo carrier by linking other drugs or vaccines with the binding and translocation domains of BoNT. While the domain structures are largely independent of each other, the dynamic structure of these domains, especially that of the enzymatic domain (L chain), is quite different from the reported crystal structures for several BoNT serotypes and their enzymatic domain. This review discusses the comparative structures of BoNT in crystal and solution for their relevance to the molecular mechanism of BoNT action, especially in view of our recent discovery that the enzymatically active structure of the BoNT exists as a molten-globule and that of the endopeptidase domain as a novel PRIME conformation. Finally, a non-exhaustive discussion has been included to explain the long-lasting biological effects of certain serotypes of BoNT, based on the current knowledge of the structure-function of different serotypes of botulinum neurotoxins.

Dysmorphic features and developmental outcome of 2-year-old children.

PubMed

Seggers, Jorien; Haadsma, Maaike L; Bos, Arend F; Heineman, Maas Jan; Middelburg, Karin J; van den Heuvel, Edwin R; Hadders-Algra, Mijna

2014-11-01

The aim of this study was to assess the associations between dysmorphic features and neurological, mental, psychomotor, and behavioural development in order to improve our understanding of aetiological pathways leading to minor developmental problems. In our cross-sectional study, 272 generally healthy 2-year-olds (143 males, 129 females; median gestational age 39 weeks, [range 30-43wks]), born after a parental history of subfertility either with or without fertility treatment, were examined. Dysmorphic features were classified as abnormalities (clinically relevant or not), minor anomalies, or common variants according to Merks' classification system. Hempel's neurological assessment resulted in a neurological optimality score (NOS) and fluency score. Mental and psychomotor development were assessed with the Dutch version of the Bayley Scales of Infant Development and behavioural development with the Achenbach Child Behaviour Checklist. Of the different types of dysmorphic feature, clinically relevant abnormalities were most strongly associated with a lower NOS (difference -2.53, 95% confidence interval [CI] -4.23 to -0.83) and fluency score (difference -0.62, 95% CI -1.1 to -0.15). The presence of one or more abnormalities (clinically relevant or not) or one or more common variants was significantly associated with a lower NOS, and the presence of three or more minor anomalies was associated with lower fluency scores. Dysmorphic features were not associated with mental, psychomotor, or behavioural development. As dysmorphic features originate during the first trimester of pregnancy, the association between dysmorphic features and minor alterations in neurodevelopment may suggest an early ontogenetic origin of subtle neurological deviations. © 2014 Mac Keith Press.

Gene features selection for three-class disease classification via multiple orthogonal partial least square discriminant analysis and S-plot using microarray data.

PubMed

Yang, Mingxing; Li, Xiumin; Li, Zhibin; Ou, Zhimin; Liu, Ming; Liu, Suhuan; Li, Xuejun; Yang, Shuyu

2013-01-01

DNA microarray analysis is characterized by obtaining a large number of gene variables from a small number of observations. Cluster analysis is widely used to analyze DNA microarray data to make classification and diagnosis of disease. Because there are so many irrelevant and insignificant genes in a dataset, a feature selection approach must be employed in data analysis. The performance of cluster analysis of this high-throughput data depends on whether the feature selection approach chooses the most relevant genes associated with disease classes. Here we proposed a new method using multiple Orthogonal Partial Least Squares-Discriminant Analysis (mOPLS-DA) models and S-plots to select the most relevant genes to conduct three-class disease classification and prediction. We tested our method using Golub's leukemia microarray data. For three classes with subtypes, we proposed hierarchical orthogonal partial least squares-discriminant analysis (OPLS-DA) models and S-plots to select features for two main classes and their subtypes. For three classes in parallel, we employed three OPLS-DA models and S-plots to choose marker genes for each class. The power of feature selection to classify and predict three-class disease was evaluated using cluster analysis. Further, the general performance of our method was tested using four public datasets and compared with those of four other feature selection methods. The results revealed that our method effectively selected the most relevant features for disease classification and prediction, and its performance was better than that of the other methods.

The Lake-Catchment (LakeCat) Dataset for characterizing hydrologically-relevant landscape features for lakes across the conterminous US

EPA Science Inventory

Lake conditions, including their biota, respond to both natural and human-related landscape features. Characterizing these features within the contributing areas (i.e., delineated watersheds) of lakes could improve the analysis and the sustainable use and management of these impo...

Many local pattern texture features: which is better for image-based multilabel human protein subcellular localization classification?

PubMed

Yang, Fan; Xu, Ying-Ying; Shen, Hong-Bin

2014-01-01

Human protein subcellular location prediction can provide critical knowledge for understanding a protein's function. Since significant progress has been made on digital microscopy, automated image-based protein subcellular location classification is urgently needed. In this paper, we aim to investigate more representative image features that can be effectively used for dealing with the multilabel subcellular image samples. We prepared a large multilabel immunohistochemistry (IHC) image benchmark from the Human Protein Atlas database and tested the performance of different local texture features, including completed local binary pattern, local tetra pattern, and the standard local binary pattern feature. According to our experimental results from binary relevance multilabel machine learning models, the completed local binary pattern, and local tetra pattern are more discriminative for describing IHC images when compared to the traditional local binary pattern descriptor. The combination of these two novel local pattern features and the conventional global texture features is also studied. The enhanced performance of final binary relevance classification model trained on the combined feature space demonstrates that different features are complementary to each other and thus capable of improving the accuracy of classification.

Combining local and global limitations of visual search.

PubMed

Põder, Endel

2017-04-01

There are different opinions about the roles of local interactions and central processing capacity in visual search. This study attempts to clarify the problem using a new version of relevant set cueing. A central precue indicates two symmetrical segments (that may contain a target object) within a circular array of objects presented briefly around the fixation point. The number of objects in the relevant segments, and density of objects in the array were varied independently. Three types of search experiments were run: (a) search for a simple visual feature (color, size, and orientation); (b) conjunctions of simple features; and (c) spatial configuration of simple features (rotated Ts). For spatial configuration stimuli, the results were consistent with a fixed global processing capacity and standard crowding zones. For simple features and their conjunctions, the results were different, dependent on the features involved. While color search exhibits virtually no capacity limits or crowding, search for an orientation target was limited by both. Results for conjunctions of features can be partly explained by the results from the respective features. This study shows that visual search is limited by both local interference and global capacity, and the limitations are different for different visual features.

Parallel perceptual enhancement and hierarchic relevance evaluation in an audio-visual conjunction task.

PubMed

Potts, Geoffrey F; Wood, Susan M; Kothmann, Delia; Martin, Laura E

2008-10-21

Attention directs limited-capacity information processing resources to a subset of available perceptual representations. The mechanisms by which attention selects task-relevant representations for preferential processing are not fully known. Triesman and Gelade's [Triesman, A., Gelade, G., 1980. A feature integration theory of attention. Cognit. Psychol. 12, 97-136.] influential attention model posits that simple features are processed preattentively, in parallel, but that attention is required to serially conjoin multiple features into an object representation. Event-related potentials have provided evidence for this model showing parallel processing of perceptual features in the posterior Selection Negativity (SN) and serial, hierarchic processing of feature conjunctions in the Frontal Selection Positivity (FSP). Most prior studies have been done on conjunctions within one sensory modality while many real-world objects have multimodal features. It is not known if the same neural systems of posterior parallel processing of simple features and frontal serial processing of feature conjunctions seen within a sensory modality also operate on conjunctions between modalities. The current study used ERPs and simultaneously presented auditory and visual stimuli in three task conditions: Attend Auditory (auditory feature determines the target, visual features are irrelevant), Attend Visual (visual features relevant, auditory irrelevant), and Attend Conjunction (target defined by the co-occurrence of an auditory and a visual feature). In the Attend Conjunction condition when the auditory but not the visual feature was a target there was an SN over auditory cortex, when the visual but not auditory stimulus was a target there was an SN over visual cortex, and when both auditory and visual stimuli were targets (i.e. conjunction target) there were SNs over both auditory and visual cortex, indicating parallel processing of the simple features within each modality. In contrast, an FSP was present when either the visual only or both auditory and visual features were targets, but not when only the auditory stimulus was a target, indicating that the conjunction target determination was evaluated serially and hierarchically with visual information taking precedence. This indicates that the detection of a target defined by audio-visual conjunction is achieved via the same mechanism as within a single perceptual modality, through separate, parallel processing of the auditory and visual features and serial processing of the feature conjunction elements, rather than by evaluation of a fused multimodal percept.

Beam Flutter and Energy Harvesting in Internal Flow

NASA Astrophysics Data System (ADS)

Tosi, Luis Phillipe; Colonius, Tim; Sherrit, Stewart; Lee, Hyeong Jae

2017-11-01

Aeroelastic flutter, largely studied for causing engineering failures, has more recently been used as a means of extracting energy from the flow. Particularly, flutter of a cantilever or an elastically mounted plate in a converging-diverging flow passage has shown promise as an energy harvesting concept for internal flow applications. The instability onset is observed as a function of throat velocity, internal wall geometry, fluid and structure material properties. To enable these devices, our work explores features of the fluid-structure coupled dynamics as a function of relevant nondimensional parameters. The flutter boundary is examined through stability analysis of a reduced order model, and corroborated with numerical simulations at low Reynolds number. Experiments for an energy harvester design are qualitatively compared to results from analytical and numerical work, suggesting a robust limit cycle ensues due to a subcritical Hopf bifurcation. Bosch Corporation.

Simulation in bronchoscopy: current and future perspectives.

PubMed

Nilsson, Philip Mørkeberg; Naur, Therese Maria Henriette; Clementsen, Paul Frost; Konge, Lars

2017-01-01

To provide an overview of current literature that informs how to approach simulation practice of bronchoscopy and discuss how findings from other simulation research can help inform the use of simulation in bronchoscopy training. We conducted a literature search on simulation training of bronchoscopy and divided relevant studies in three categories: 1) structuring simulation training in bronchoscopy, 2) assessment of competence in bronchoscopy training, and 3) development of cheap alternatives for bronchoscopy simulation. Bronchoscopy simulation is effective, and the training should be structured as distributed practice with mastery learning criteria (ie, training until a certain level of competence is achieved). Dyad practice (training in pairs) is possible and may increase utility of available simulators. Trainee performance should be assessed with assessment tools with established validity. Three-dimensional printing is a promising new technology opening possibilities for developing cheap simulators with innovative features.

Learning physical descriptors for materials science by compressed sensing

NASA Astrophysics Data System (ADS)

Ghiringhelli, Luca M.; Vybiral, Jan; Ahmetcik, Emre; Ouyang, Runhai; Levchenko, Sergey V.; Draxl, Claudia; Scheffler, Matthias

2017-02-01

The availability of big data in materials science offers new routes for analyzing materials properties and functions and achieving scientific understanding. Finding structure in these data that is not directly visible by standard tools and exploitation of the scientific information requires new and dedicated methodology based on approaches from statistical learning, compressed sensing, and other recent methods from applied mathematics, computer science, statistics, signal processing, and information science. In this paper, we explain and demonstrate a compressed-sensing based methodology for feature selection, specifically for discovering physical descriptors, i.e., physical parameters that describe the material and its properties of interest, and associated equations that explicitly and quantitatively describe those relevant properties. As showcase application and proof of concept, we describe how to build a physical model for the quantitative prediction of the crystal structure of binary compound semiconductors.

Pi-Pi contacts are an overlooked protein feature relevant to phase separation

PubMed Central

Vernon, Robert McCoy; Chong, Paul Andrew; Tsang, Brian; Kim, Tae Hun; Bah, Alaji; Farber, Patrick; Lin, Hong

2018-01-01

Protein phase separation is implicated in formation of membraneless organelles, signaling puncta and the nuclear pore. Multivalent interactions of modular binding domains and their target motifs can drive phase separation. However, forces promoting the more common phase separation of intrinsically disordered regions are less understood, with suggested roles for multivalent cation-pi, pi-pi, and charge interactions and the hydrophobic effect. Known phase-separating proteins are enriched in pi-orbital containing residues and thus we analyzed pi-interactions in folded proteins. We found that pi-pi interactions involving non-aromatic groups are widespread, underestimated by force-fields used in structure calculations and correlated with solvation and lack of regular secondary structure, properties associated with disordered regions. We present a phase separation predictive algorithm based on pi interaction frequency, highlighting proteins involved in biomaterials and RNA processing. PMID:29424691

Structure of the SnO2(110 ) -(4 ×1 ) Surface

NASA Astrophysics Data System (ADS)

Merte, Lindsay R.; Jørgensen, Mathias S.; Pussi, Katariina; Gustafson, Johan; Shipilin, Mikhail; Schaefer, Andreas; Zhang, Chu; Rawle, Jonathan; Nicklin, Chris; Thornton, Geoff; Lindsay, Robert; Hammer, Bjørk; Lundgren, Edvin

2017-09-01

Using surface x-ray diffraction (SXRD), quantitative low-energy electron diffraction (LEED), and density-functional theory (DFT) calculations, we have determined the structure of the (4 ×1 ) reconstruction formed by sputtering and annealing of the SnO2(110 ) surface. We find that the reconstruction consists of an ordered arrangement of Sn3O3 clusters bound atop the bulk-terminated SnO2(110 ) surface. The model was found by application of a DFT-based evolutionary algorithm with surface compositions based on SXRD, and shows excellent agreement with LEED and with previously published scanning tunneling microscopy measurements. The model proposed previously consisting of in-plane oxygen vacancies is thus shown to be incorrect, and our result suggests instead that Sn(II) species in interstitial positions are the more relevant features of reduced SnO2(110 ) surfaces.

Improving link prediction in complex networks by adaptively exploiting multiple structural features of networks

NASA Astrophysics Data System (ADS)

Ma, Chuang; Bao, Zhong-Kui; Zhang, Hai-Feng

2017-10-01

So far, many network-structure-based link prediction methods have been proposed. However, these methods only highlight one or two structural features of networks, and then use the methods to predict missing links in different networks. The performances of these existing methods are not always satisfied in all cases since each network has its unique underlying structural features. In this paper, by analyzing different real networks, we find that the structural features of different networks are remarkably different. In particular, even in the same network, their inner structural features are utterly different. Therefore, more structural features should be considered. However, owing to the remarkably different structural features, the contributions of different features are hard to be given in advance. Inspired by these facts, an adaptive fusion model regarding link prediction is proposed to incorporate multiple structural features. In the model, a logistic function combing multiple structural features is defined, then the weight of each feature in the logistic function is adaptively determined by exploiting the known structure information. Last, we use the "learnt" logistic function to predict the connection probabilities of missing links. According to our experimental results, we find that the performance of our adaptive fusion model is better than many similarity indices.

Site‐Selective Disulfide Modification of Proteins: Expanding Diversity beyond the Proteome

PubMed Central

Kuan, Seah Ling; Wang, Tao

2016-01-01

Abstract The synthetic transformation of polypeptides with molecular accuracy holds great promise for providing functional and structural diversity beyond the proteome. Consequently, the last decade has seen an exponential growth of site‐directed chemistry to install additional features into peptides and proteins even inside living cells. The disulfide rebridging strategy has emerged as a powerful tool for site‐selective modifications since most proteins contain disulfide bonds. In this Review, we present the chemical design, advantages and limitations of the disulfide rebridging reagents, while summarizing their relevance for synthetic customization of functional protein bioconjugates, as well as the resultant impact and advancement for biomedical applications. PMID:27778400

Ectodermal Dysplasia: A Clinical Overview for the Dental Practitioner.

PubMed

Halai, Tina; Stevens, Claire

2015-10-01

The term ectodermal dysplasia (ED) is used to describe a group of rare congenital disorders characterized by abnormalities of two or more ectodermal structures such as the skin, hair, nails, teeth and sweat glands. This paper will give an overview of the aetiology of ED and describe the manifestations and dental management of this condition. In particular, the important role of the dental practitioner in the identification and management of patients with ED will be highlighted. CPD/Clinical Relevance: Dental practitioners should be aware of the oral features of ectodermal dysplasia and be able to make timely referrals and provide appropriate continuing care for these patients.

Coarse analysis of collective behaviors: Bifurcation analysis of the optimal velocity model for traffic jam formation

NASA Astrophysics Data System (ADS)

Miura, Yasunari; Sugiyama, Yuki

2017-12-01

We present a general method for analyzing macroscopic collective phenomena observed in many-body systems. For this purpose, we employ diffusion maps, which are one of the dimensionality-reduction techniques, and systematically define a few relevant coarse-grained variables for describing macroscopic phenomena. The time evolution of macroscopic behavior is described as a trajectory in the low-dimensional space constructed by these coarse variables. We apply this method to the analysis of the traffic model, called the optimal velocity model, and reveal a bifurcation structure, which features a transition to the emergence of a moving cluster as a traffic jam.

Novel fiber optic immunosensor instrument

NASA Astrophysics Data System (ADS)

Wang, Zhiyu; Huang, Wenling; Tang, Lei; Zhou, Bo; Li, Yugi; He, Jun

1996-09-01

It has developed and performed a novel fiberoptic immunosensor instrument with operating wavelength 400 - 760 nm and repeatability cv equals 0.27%. The instrument has many excellent features such as simplified operation, faster testing time, higher sensitivity and economic cost. It has completely eliminated recovery period which traditional immunosensor owned due to use separative sensor structure. It can widely apply to test for bacteria, virus, hormone, parasite and cancer protein in clinical examination. The instrument has operated in laboratory and relevant medicine units and successfully tested monoclonal rat-anti-human of 413 cases in clinic and prepared with existing ELISA method, the coincidence probability reached 94 to 100%.

Enjoyment of exercise among people with arthritis: An inductive thematic analysis.

PubMed

Kibblewhite, Julia R; Treharne, Gareth J; Stebbings, Simon; Hegarty, Roisin Sm

2017-09-01

Past research into exercise among people with long-term health conditions has paid surprisingly little attention to the concept of enjoyment. This study explored enjoyment of exercise among people with arthritis. Semi-structured interviews were held with 12 participants aged 20-85 years. The transcripts were analysed using inductive thematic analysis. Four themes were identified: enjoyment of exercise in relation to other people, benefits of exercise in relation to enjoyment, working around barriers to enjoy exercise and finding an enjoyable balance to exercise. These themes highlight the relevance of enjoyment and how it could feature in advice about exercise for people with arthritis.

Flame propagation in two-dimensional solids: Particle-resolved studies with complex plasmas

NASA Astrophysics Data System (ADS)

Yurchenko, S. O.; Yakovlev, E. V.; Couëdel, L.; Kryuchkov, N. P.; Lipaev, A. M.; Naumkin, V. N.; Kislov, A. Yu.; Ovcharov, P. V.; Zaytsev, K. I.; Vorob'ev, E. V.; Morfill, G. E.; Ivlev, A. V.

2017-10-01

Using two-dimensional (2D) complex plasmas as an experimental model system, particle-resolved studies of flame propagation in classical 2D solids are carried out. Combining experiments, theory, and molecular dynamics simulations, we demonstrate that the mode-coupling instability operating in 2D complex plasmas reveals all essential features of combustion, such as an activated heat release, two-zone structure of the self-similar temperature profile ("flame front"), as well as thermal expansion of the medium and temperature saturation behind the front. The presented results are of relevance for various fields ranging from combustion and thermochemistry, to chemical physics and synthesis of materials.

Antimalarial activity of synthetic 1,2,4-trioxanes and cyclic peroxy ketals, a quantum similarity study

NASA Astrophysics Data System (ADS)

Gironés, X.; Gallegos, A.; Carbó-Dorca, R.

2001-12-01

In this work, the antimalarial activity of two series of 20 and 7 synthetic 1,2,4-trioxanes and a set of 20 cyclic peroxy ketals are tested for correlation search by means of Molecular Quantum Similarity Measures (MQSM). QSAR models, dealing with different biological responses (IC90, IC50 and ED90) of the parasite Plasmodium Falciparum, are constructed using MQSM as molecular descriptors and are satisfactorily correlated. The statistical results of the 20 1,2,4-trioxanes are deeply analyzed to elucidate the relevant structural features in the biological activity, revealing the importance of phenyl substitutions.

On the dynamical basis of the classification of normal galaxies

PubMed Central

Haass, J.; Bertin, G.; Lin, C. C.

1982-01-01

Some realistic galaxy models have been found to support discrete unstable spiral modes. Here, through the study of the relevant physical mechanisms and an extensive numerical investigation of the properties of the dominant modes in a wide class of galactic equilibria, we show how spiral structures are excited with different morphological features, depending on the properties of the equilibrium model. We identify the basic dynamical parameters and mechanisms and compare the resulting morphology of spiral modes with the actual classification of galaxies. The present study suggests a dynamical basis for the transition among various types and subclasses of normal and barred spiral galaxies. Images PMID:16593200

Evidence for out-of-equilibrium states in warm dense matter probed by x-ray Thomson scattering.

PubMed

Clérouin, Jean; Robert, Grégory; Arnault, Philippe; Ticknor, Christopher; Kress, Joel D; Collins, Lee A

2015-01-01

A recent and unexpected discrepancy between ab initio simulations and the interpretation of a laser shock experiment on aluminum, probed by x-ray Thomson scattering (XRTS), is addressed. The ion-ion structure factor deduced from the XRTS elastic peak (ion feature) is only compatible with a strongly coupled out-of-equilibrium state. Orbital free molecular dynamics simulations with ions colder than the electrons are employed to interpret the experiment. The relevance of decoupled temperatures for ions and electrons is discussed. The possibility that it mimics a transient, or metastable, out-of-equilibrium state after melting is also suggested.

Optimization techniques applied to spectrum management for communications satellites

NASA Astrophysics Data System (ADS)

Ottey, H. R.; Sullivan, T. M.; Zusman, F. S.

This paper describes user requirements, algorithms and software design features for the application of optimization techniques to the management of the geostationary orbit/spectrum resource. Relevant problems include parameter sensitivity analyses, frequency and orbit position assignment coordination, and orbit position allotment planning. It is shown how integer and nonlinear programming as well as heuristic search techniques can be used to solve these problems. Formalized mathematical objective functions that define the problems are presented. Constraint functions that impart the necessary solution bounds are described. A versatile program structure is outlined, which would allow problems to be solved in stages while varying the problem space, solution resolution, objective function and constraints.

Honing process optimization algorithms

NASA Astrophysics Data System (ADS)

Kadyrov, Ramil R.; Charikov, Pavel N.; Pryanichnikova, Valeria V.

2018-03-01

This article considers the relevance of honing processes for creating high-quality mechanical engineering products. The features of the honing process are revealed and such important concepts as the task for optimization of honing operations, the optimal structure of the honing working cycles, stepped and stepless honing cycles, simulation of processing and its purpose are emphasized. It is noted that the reliability of the mathematical model determines the quality parameters of the honing process control. An algorithm for continuous control of the honing process is proposed. The process model reliably describes the machining of a workpiece in a sufficiently wide area and can be used to operate the CNC machine CC743.

[Anterior dislocation of the popliteus tendon].

PubMed

Martinez Molina, Oscar

2009-01-01

Review the most relevant aspects of the posterolateral corner anatomy of the knee, based on the analysis of papers that throughout the years have made important contributions to the knowledge of these structures. Last et al rejected the idea that the popliteal tendon is an isolated structure, suggesting rather that its variants are closely linked to other anatomical structures. The studies by Tria et al contributed the features of the tendon as it attaches to the lateral condyle, just to mention a couple of examples. This is the case of a 48 year-old female patient with a knee injury caused by an external rotation mechanism. Clinical features included pain, a protruding sensation in the lateral aspect of the knee, and voluntary pseudoblocking resulting from external rotation maneuvers. Knee arthroscopy was performed and dislocation of the popliteal tendon anterior to the lateral condyle was diagnosed, besides a longitudinal tear. The tendon was repositioned, radiofrequency was applied to both the tendon and the popliteal hiatus, and the former was kept in place with a plaster cast worn for 6 weeks. Even though the isolated tear or avulsion of the tendon has already been reported, the dislocation or instability of the popliteal tendon as it relates to the lateral femoral condyle has apparently not been approached yet. As we did in this case, other authors have also confirmed the diagnosis arthroscopically, Naver in 1985, Rose in 1988, and Burstein in 1990.

The development of a core syllabus for the teaching of head and neck anatomy to medical students.

PubMed

Tubbs, R Shane; Sorenson, Edward P; Sharma, Amit; Benninger, Brion; Norton, Neil; Loukas, Marios; Moxham, Bernard J

2014-04-01

The study of human anatomy has traditionally served as a fundamental component in the basic science education of medical students, yet there exists a remarkable lack of firm guidance on essential features that must be included in a gross anatomy course, which would constitute a "Core Syllabus" of absolutely mandatory structures and related clinical pathologies. While universal agreement on the details of a core syllabus is elusive, there is a general consensus that a core syllabus aims to identify the minimum level of knowledge expected of recently qualified medical graduates in order to carry out clinical procedures safely and effectively, while avoiding overloading students with unnecessary facts that have less immediate application to their future careers as clinicians. This paper aims to identify consensus standards of essential features of Head and Neck anatomy via a Delphi Panel consisting of anatomists and clinicians who evaluated syllabus content structures (greater than 1,000) as "essential", "important", "acceptable", or "not required." The goal is to provide guidance for program/course directors who intend to provide the optimal balance between establishing a comprehensive list of clinically relevant essential structures and an overwhelming litany, which would otherwise overburden trainees in their initial years of medical school with superficial rote learning, which potentially dilutes the key and enduring fundamental lessons that prepare students for training in any medical field. Copyright © 2014 Wiley Periodicals, Inc.

Recent results from PHOBOS on particle production at high p T

NASA Astrophysics Data System (ADS)

Alver, B.; Back, B. B.; Baker, M. D.; Ballintijn, M.; Barton, D. S.; Betts, R. R.; Bickley, A. A.; Bindel, R.; Busza, W.; Carroll, A.; Chai, Z.; Chetluru, V.; Decowski, M. P.; García, E.; Gburek, T.; George, N.; Gulbrandsen, K.; Halliwell, C.; Hamblen, J.; Harnarine, I.; Hauer, M.; Henderson, C.; Hofman, D. J.; Hollis, R. S.; Holyński, R.; Holzman, B.; Iordanova, A.; Johnson, E.; Kane, J. L.; Khan, N.; Kulinich, P.; Kuo, C. M.; Li, W.; Lin, W. T.; Loizides, C.; Manly, S.; Mignerey, A. C.; Nouicer, R.; Olszewski, A.; Pak, R.; Reed, C.; Richardson, E.; Roland, C.; Roland, G.; Sagerer, J.; Seals, H.; Sedykh, I.; Smith, C. E.; Stankiewicz, M. A.; Steinberg, P.; Stephans, G. S. F.; Sukhanov, A.; Szostak, A.; Tonjes, M. B.; Trzupek, A.; Vale, C.; van Nieuwenhuizen, G. J.; Vaurynovich, S. S.; Verdier, R.; Veres, G. I.; Walters, P.; Wenger, E.; Willhelm, D.; Wolfs, F. L. H.; Wosiek, B.; Woźniak, K.; Wyngaardt, S.; Wysłouch, B.

2009-06-01

A selection of experimental results from the PHOBOS Collaboration relevant for probing high-energy nuclear collisions with high transverse momentum particles is presented. The inclusive yields of charged particles and comparisons between nuclear and elementary collisions already reveal a large amount of parton energy loss in the hot and dense medium created in heavy ion collisions. Remarkable scaling and factorization features are observed, unifying the data taken at various collision energies, centralities and nuclear sizes. To further analyze the nature of the energy loss, a measurement of pseudorapidity (Δ η) and azimuthal angle (Δ φ) correlations between high transverse momentum charged hadrons ( p T >2.5 GeV/ c) and all associated charged particles is presented at both short-range (small Δ η) and long-range (large Δ η) over a continuous detector acceptance covering -4<Δ η<2. Various near- and away-side features of the correlation structure are discussed as a function of centrality in Au + Au collisions at sqrt{s_{NN}}=200 GeV. The results provide new information about the longitudinal (Δ η) extent of the near-side ‘ridge’ structure, first observed by the STAR Collaboration over a narrower η range. In central Au + Au collisions the ridge structure extends to at least Δ η=4, and its strength completely diminishes as collisions become more peripheral.

Impact of mutations on the allosteric conformational equilibrium

PubMed Central

Weinkam, Patrick; Chen, Yao Chi; Pons, Jaume; Sali, Andrej

2012-01-01

Allostery in a protein involves effector binding at an allosteric site that changes the structure and/or dynamics at a distant, functional site. In addition to the chemical equilibrium of ligand binding, allostery involves a conformational equilibrium between one protein substate that binds the effector and a second substate that less strongly binds the effector. We run molecular dynamics simulations using simple, smooth energy landscapes to sample specific ligand-induced conformational transitions, as defined by the effector-bound and unbound protein structures. These simulations can be performed using our web server: http://salilab.org/allosmod/. We then develop a set of features to analyze the simulations and capture the relevant thermodynamic properties of the allosteric conformational equilibrium. These features are based on molecular mechanics energy functions, stereochemical effects, and structural/dynamic coupling between sites. Using a machine-learning algorithm on a dataset of 10 proteins and 179 mutations, we predict both the magnitude and sign of the allosteric conformational equilibrium shift by the mutation; the impact of a large identifiable fraction of the mutations can be predicted with an average unsigned error of 1 kBT. With similar accuracy, we predict the mutation effects for an 11th protein that was omitted from the initial training and testing of the machine-learning algorithm. We also assess which calculated thermodynamic properties contribute most to the accuracy of the prediction. PMID:23228330

Analysis of respiratory events in obstructive sleep apnea syndrome: Inter-relations and association to simple nocturnal features.

PubMed

Ghandeharioun, H; Rezaeitalab, F; Lotfi, R

2016-01-01

This study carefully evaluates the association of different respiration-related events to each other and to simple nocturnal features in obstructive sleep apnea-hypopnea syndrome (OSAS). The events include apneas, hypopneas, respiratory event-related arousals and snores. We conducted a statistical study on 158 adults who underwent polysomnography between July 2012 and May 2014. To monitor relevance, along with linear statistical strategies like analysis of variance and bootstrapping a correlation coefficient standard error, the non-linear method of mutual information is also applied to illuminate vague results of linear techniques. Based on normalized mutual information weights (NMIW), indices of apnea are 1.3 times more relevant to AHI values than those of hypopnea. NMIW for the number of blood oxygen desaturation below 95% is considerable (0.531). The next relevant feature is "respiratory arousals index" with NMIW of 0.501. Snore indices (0.314), and BMI (0.203) take the next place. Based on NMIW values, snoring events are nearly one-third (29.9%) more dependent to hypopneas than RERAs. 1. The more sever the OSAS is, the more frequently the apneic events happen. 2. The association of snore with hypopnea/RERA revealed which is routinely ignored in regression-based OSAS modeling. 3. The statistical dependencies of oximetry features potentially can lead to home-based screening of OSAS. 4. Poor ESS-AHI relevance in the database under study indicates its disability for the OSA diagnosis compared to oximetry. 5. Based on poor RERA-snore/ESS relevance, detailed history of the symptoms plus polysomnography is suggested for accurate diagnosis of RERAs. Copyright © 2015 Sociedade Portuguesa de Pneumologia. Published by Elsevier España, S.L.U. All rights reserved.

Graphene oxide based contacts as probes of biomedical signals

NASA Astrophysics Data System (ADS)

Hallfors, N. G.; Devarajan, A.; Farhat, I. A. H.; Abdurahman, A.; Liao, K.; Gater, D. L.; Elnaggar, M. I.; Isakovic, A. F.

We have developed a series of graphene oxide (GOx) on polymer contacts and have demonstrated these to be useful for collection of standard biomedically relevant signals, such as electrocardiogram (ECG). The process is wet solution-based and allows for control and tuning of the basic physical parameters of GOx, such as electrical and optical properties, simply by choosing the number of GOx layers. Our GOx characterization measurements show spectral (FTIR, XPS, IR absorbance) features most relevant to such performance, and point towards the likely explanations about the mechanisms for controlling the physical properties relevant for the contact performance. Structural (X-ray topography) and surface characterization (AFM, SEM) indicates to what degree these contacts can be considered homogeneous and therefore provide information on yield and repeatability. We compare the ECG signals recorded by standard commercial probes (Ag/AgCl) and GOx probes, displaying minor differences the solution to which may lead to a whole new way we perform ECG data collection, including wearable electronics and IoT friendly ECG monitoring. We acknowledge support from Mubadala-SRC AC4ES and from SRC 2011-KJ-2190. We thank J. B. Warren and G. L. Carr (BNL) for assistance.

Multi-objective shape optimization of plate structure under stress criteria based on sub-structured mixed FEM and genetic algorithms

NASA Astrophysics Data System (ADS)

Garambois, Pierre; Besset, Sebastien; Jézéquel, Louis

2015-07-01

This paper presents a methodology for the multi-objective (MO) shape optimization of plate structure under stress criteria, based on a mixed Finite Element Model (FEM) enhanced with a sub-structuring method. The optimization is performed with a classical Genetic Algorithm (GA) method based on Pareto-optimal solutions and considers thickness distributions parameters and antagonist objectives among them stress criteria. We implement a displacement-stress Dynamic Mixed FEM (DM-FEM) for plate structure vibrations analysis. Such a model gives a privileged access to the stress within the plate structure compared to primal classical FEM, and features a linear dependence to the thickness parameters. A sub-structuring reduction method is also computed in order to reduce the size of the mixed FEM and split the given structure into smaller ones with their own thickness parameters. Those methods combined enable a fast and stress-wise efficient structure analysis, and improve the performance of the repetitive GA. A few cases of minimizing the mass and the maximum Von Mises stress within a plate structure under a dynamic load put forward the relevance of our method with promising results. It is able to satisfy multiple damage criteria with different thickness distributions, and use a smaller FEM.

Independent and additive repetition priming of motion direction and color in visual search.

PubMed

Kristjánsson, Arni

2009-03-01

Priming of visual search for Gabor patch stimuli, varying in color and local drift direction, was investigated. The task relevance of each feature varied between the different experimental conditions compared. When the target defining dimension was color, a large effect of color repetition was seen as well as a smaller effect of the repetition of motion direction. The opposite priming pattern was seen when motion direction defined the target--the effect of motion direction repetition was this time larger than for color repetition. Finally, when neither was task relevant, and the target defining dimension was the spatial frequency of the Gabor patch, priming was seen for repetition of both color and motion direction, but the effects were smaller than in the previous two conditions. These results show that features do not necessarily have to be task relevant for priming to occur. There is little interaction between priming following repetition of color and motion, these two features show independent and additive priming effects, most likely reflecting that the two features are processed at separate processing sites in the nervous system, consistent with previous findings from neuropsychology & neurophysiology. The implications of the findings for theoretical accounts of priming in visual search are discussed.

Learning to rank-based gene summary extraction.

PubMed

Shang, Yue; Hao, Huihui; Wu, Jiajin; Lin, Hongfei

2014-01-01

In recent years, the biomedical literature has been growing rapidly. These articles provide a large amount of information about proteins, genes and their interactions. Reading such a huge amount of literature is a tedious task for researchers to gain knowledge about a gene. As a result, it is significant for biomedical researchers to have a quick understanding of the query concept by integrating its relevant resources. In the task of gene summary generation, we regard automatic summary as a ranking problem and apply the method of learning to rank to automatically solve this problem. This paper uses three features as a basis for sentence selection: gene ontology relevance, topic relevance and TextRank. From there, we obtain the feature weight vector using the learning to rank algorithm and predict the scores of candidate summary sentences and obtain top sentences to generate the summary. ROUGE (a toolkit for summarization of automatic evaluation) was used to evaluate the summarization result and the experimental results showed that our method outperforms the baseline techniques. According to the experimental result, the combination of three features can improve the performance of summary. The application of learning to rank can facilitate the further expansion of features for measuring the significance of sentences.

The construction of meaning.

PubMed

Kintsch, Walter; Mangalath, Praful

2011-04-01

We argue that word meanings are not stored in a mental lexicon but are generated in the context of working memory from long-term memory traces that record our experience with words. Current statistical models of semantics, such as latent semantic analysis and the Topic model, describe what is stored in long-term memory. The CI-2 model describes how this information is used to construct sentence meanings. This model is a dual-memory model, in that it distinguishes between a gist level and an explicit level. It also incorporates syntactic information about how words are used, derived from dependency grammar. The construction of meaning is conceptualized as feature sampling from the explicit memory traces, with the constraint that the sampling must be contextually relevant both semantically and syntactically. Semantic relevance is achieved by sampling topically relevant features; local syntactic constraints as expressed by dependency relations ensure syntactic relevance. Copyright © 2010 Cognitive Science Society, Inc.

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

PubMed Central

Arruti, Andoni; Cearreta, Idoia; Álvarez, Aitor; Lazkano, Elena; Sierra, Basilio

2014-01-01

Study of emotions in human–computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested. PMID:25279686

Automatic Modulation Classification of Common Communication and Pulse Compression Radar Waveforms using Cyclic Features

DTIC Science & Technology

2013-03-01

intermediate frequency LFM linear frequency modulation MAP maximum a posteriori MATLAB® matrix laboratory ML maximun likelihood OFDM orthogonal frequency...spectrum, frequency hopping, and orthogonal frequency division multiplexing ( OFDM ) modulations. Feature analysis would be a good research thrust to...determine feature relevance and decide if removing any features improves performance. Also, extending the system for simulations using a MIMO receiver or

Slow Feature Analysis on Retinal Waves Leads to V1 Complex Cells

PubMed Central

Dähne, Sven; Wilbert, Niko; Wiskott, Laurenz

2014-01-01

The developing visual system of many mammalian species is partially structured and organized even before the onset of vision. Spontaneous neural activity, which spreads in waves across the retina, has been suggested to play a major role in these prenatal structuring processes. Recently, it has been shown that when employing an efficient coding strategy, such as sparse coding, these retinal activity patterns lead to basis functions that resemble optimal stimuli of simple cells in primary visual cortex (V1). Here we present the results of applying a coding strategy that optimizes for temporal slowness, namely Slow Feature Analysis (SFA), to a biologically plausible model of retinal waves. Previously, SFA has been successfully applied to model parts of the visual system, most notably in reproducing a rich set of complex-cell features by training SFA with quasi-natural image sequences. In the present work, we obtain SFA units that share a number of properties with cortical complex-cells by training on simulated retinal waves. The emergence of two distinct properties of the SFA units (phase invariance and orientation tuning) is thoroughly investigated via control experiments and mathematical analysis of the input-output functions found by SFA. The results support the idea that retinal waves share relevant temporal and spatial properties with natural visual input. Hence, retinal waves seem suitable training stimuli to learn invariances and thereby shape the developing early visual system such that it is best prepared for coding input from the natural world. PMID:24810948

Neural mechanisms of selective attention in the somatosensory system.

PubMed

Gomez-Ramirez, Manuel; Hysaj, Kristjana; Niebur, Ernst

2016-09-01

Selective attention allows organisms to extract behaviorally relevant information while ignoring distracting stimuli that compete for the limited resources of their central nervous systems. Attention is highly flexible, and it can be harnessed to select information based on sensory modality, within-modality feature(s), spatial location, object identity, and/or temporal properties. In this review, we discuss the body of work devoted to understanding mechanisms of selective attention in the somatosensory system. In particular, we describe the effects of attention on tactile behavior and corresponding neural activity in somatosensory cortex. Our focus is on neural mechanisms that select tactile stimuli based on their location on the body (somatotopic-based attention) or their sensory feature (feature-based attention). We highlight parallels between selection mechanisms in touch and other sensory systems and discuss several putative neural coding schemes employed by cortical populations to signal the behavioral relevance of sensory inputs. Specifically, we contrast the advantages and disadvantages of using a gain vs. spike-spike correlation code for representing attended sensory stimuli. We favor a neural network model of tactile attention that is composed of frontal, parietal, and subcortical areas that controls somatosensory cells encoding the relevant stimulus features to enable preferential processing throughout the somatosensory hierarchy. Our review is based on data from noninvasive electrophysiological and imaging data in humans as well as single-unit recordings in nonhuman primates. Copyright © 2016 the American Physiological Society.

Neural mechanisms of selective attention in the somatosensory system

PubMed Central

Hysaj, Kristjana; Niebur, Ernst

2016-01-01

Selective attention allows organisms to extract behaviorally relevant information while ignoring distracting stimuli that compete for the limited resources of their central nervous systems. Attention is highly flexible, and it can be harnessed to select information based on sensory modality, within-modality feature(s), spatial location, object identity, and/or temporal properties. In this review, we discuss the body of work devoted to understanding mechanisms of selective attention in the somatosensory system. In particular, we describe the effects of attention on tactile behavior and corresponding neural activity in somatosensory cortex. Our focus is on neural mechanisms that select tactile stimuli based on their location on the body (somatotopic-based attention) or their sensory feature (feature-based attention). We highlight parallels between selection mechanisms in touch and other sensory systems and discuss several putative neural coding schemes employed by cortical populations to signal the behavioral relevance of sensory inputs. Specifically, we contrast the advantages and disadvantages of using a gain vs. spike-spike correlation code for representing attended sensory stimuli. We favor a neural network model of tactile attention that is composed of frontal, parietal, and subcortical areas that controls somatosensory cells encoding the relevant stimulus features to enable preferential processing throughout the somatosensory hierarchy. Our review is based on data from noninvasive electrophysiological and imaging data in humans as well as single-unit recordings in nonhuman primates. PMID:27334956

Memory for a single object has differently variable precisions for relevant and irrelevant features.

PubMed

Swan, Garrett; Collins, John; Wyble, Brad

2016-01-01

Working memory is a limited resource. To further characterize its limitations, it is vital to understand exactly what is encoded about a visual object beyond the "relevant" features probed in a particular task. We measured the memory quality of a task-irrelevant feature of an attended object by coupling a delayed estimation task with a surprise test. Participants were presented with a single colored arrow and were asked to retrieve just its color for the first half of the experiment before unexpectedly being asked to report its direction. Mixture modeling of the data revealed that participants had highly variable precision on the surprise test, indicating a coarse-grained memory for the irrelevant feature. Following the surprise test, all participants could precisely recall the arrow's direction; however, this improvement in direction memory came at a cost in precision for color memory even though only a single object was being remembered. We attribute these findings to varying levels of attention to different features during memory encoding.

Evaluation of the Relevance of a Web-Based "Ask an Expert" Feature: StratSoy and Soy and Human Health Queries.

ERIC Educational Resources Information Center

Wool, D. L.; Kanfer, A. G.; Michaels, J.; Thompson, S.; Morris, S. A.; Hasler, C. M.

2000-01-01

A study of the "Ask an Expert" feature of StratSoy, a Web-based information system, surveyed 50 users and 48 using it for the first time. Topic areas of interest and web site features desired by respondents were identified. (JOW)

Multiple Mechanisms in the Perception of Face Gender: Effect of Sex-Irrelevant Features

ERIC Educational Resources Information Center

Komori, Masashi; Kawamura, Satoru; Ishihara, Shigekazu

2011-01-01

Effects of sex-relevant and sex-irrelevant facial features on the evaluation of facial gender were investigated. Participants rated masculinity of 48 male facial photographs and femininity of 48 female facial photographs. Eighty feature points were measured on each of the facial photographs. Using a generalized Procrustes analysis, facial shapes…

Unsupervised Feature Learning With Winner-Takes-All Based STDP

PubMed Central

Ferré, Paul; Mamalet, Franck; Thorpe, Simon J.

2018-01-01

We present a novel strategy for unsupervised feature learning in image applications inspired by the Spike-Timing-Dependent-Plasticity (STDP) biological learning rule. We show equivalence between rank order coding Leaky-Integrate-and-Fire neurons and ReLU artificial neurons when applied to non-temporal data. We apply this to images using rank-order coding, which allows us to perform a full network simulation with a single feed-forward pass using GPU hardware. Next we introduce a binary STDP learning rule compatible with training on batches of images. Two mechanisms to stabilize the training are also presented : a Winner-Takes-All (WTA) framework which selects the most relevant patches to learn from along the spatial dimensions, and a simple feature-wise normalization as homeostatic process. This learning process allows us to train multi-layer architectures of convolutional sparse features. We apply our method to extract features from the MNIST, ETH80, CIFAR-10, and STL-10 datasets and show that these features are relevant for classification. We finally compare these results with several other state of the art unsupervised learning methods. PMID:29674961

The pangenome of (Antarctic) Pseudoalteromonas bacteria: evolutionary and functional insights.

PubMed

Bosi, Emanuele; Fondi, Marco; Orlandini, Valerio; Perrin, Elena; Maida, Isabel; de Pascale, Donatella; Tutino, Maria Luisa; Parrilli, Ermenegilda; Lo Giudice, Angelina; Filloux, Alain; Fani, Renato

2017-01-17

Pseudoalteromonas is a genus of ubiquitous marine bacteria used as model organisms to study the biological mechanisms involved in the adaptation to cold conditions. A remarkable feature shared by these bacteria is their ability to produce secondary metabolites with a strong antimicrobial and antitumor activity. Despite their biotechnological relevance, representatives of this genus are still lacking (with few exceptions) an extensive genomic characterization, including features involved in the evolution of secondary metabolites production. Indeed, biotechnological applications would greatly benefit from such analysis. Here, we analyzed the genomes of 38 strains belonging to different Pseudoalteromonas species and isolated from diverse ecological niches, including extreme ones (i.e. Antarctica). These sequences were used to reconstruct the largest Pseudoalteromonas pangenome computed so far, including also the two main groups of Pseudoalteromonas strains (pigmented and not pigmented strains). The downstream analyses were conducted to describe the genomic diversity, both at genus and group levels. This allowed highlighting a remarkable genomic heterogeneity, even for closely related strains. We drafted all the main evolutionary steps that led to the current structure and gene content of Pseudoalteromonas representatives. These, most likely, included an extensive genome reduction and a strong contribution of Horizontal Gene Transfer (HGT), which affected biotechnologically relevant gene sets and occurred in a strain-specific fashion. Furthermore, this study also identified the genomic determinants related to some of the most interesting features of the Pseudoalteromonas representatives, such as the production of secondary metabolites, the adaptation to cold temperatures and the resistance to abiotic compounds. This study poses the bases for a comprehensive understanding of the evolutionary trajectories followed in time by this peculiar bacterial genus and for a focused exploitation of their biotechnological potential.

Unmixing-Based Denoising as a Pre-Processing Step for Coral Reef Analysis

NASA Astrophysics Data System (ADS)

Cerra, D.; Traganos, D.; Gege, P.; Reinartz, P.

2017-05-01

Coral reefs, among the world's most biodiverse and productive submerged habitats, have faced several mass bleaching events due to climate change during the past 35 years. In the course of this century, global warming and ocean acidification are expected to cause corals to become increasingly rare on reef systems. This will result in a sharp decrease in the biodiversity of reef communities and carbonate reef structures. Coral reefs may be mapped, characterized and monitored through remote sensing. Hyperspectral images in particular excel in being used in coral monitoring, being characterized by very rich spectral information, which results in a strong discrimination power to characterize a target of interest, and separate healthy corals from bleached ones. Being submerged habitats, coral reef systems are difficult to analyse in airborne or satellite images, as relevant information is conveyed in bands in the blue range which exhibit lower signal-to-noise ratio (SNR) with respect to other spectral ranges; furthermore, water is absorbing most of the incident solar radiation, further decreasing the SNR. Derivative features, which are important in coral analysis, result greatly affected by the resulting noise present in relevant spectral bands, justifying the need of new denoising techniques able to keep local spatial and spectral features. In this paper, Unmixing-based Denoising (UBD) is used to enable analysis of a hyperspectral image acquired over a coral reef system in the Red Sea based on derivative features. UBD reconstructs pixelwise a dataset with reduced noise effects, by forcing each spectrum to a linear combination of other reference spectra, exploiting the high dimensionality of hyperspectral datasets. Results show clear enhancements with respect to traditional denoising methods based on spatial and spectral smoothing, facilitating the coral detection task.

Methodologies for semiquantitative evaluation of hip osteoarthritis by magnetic resonance imaging: approaches based on the whole organ and focused on active lesions.

PubMed

Jaremko, Jacob L; Lambert, Robert G W; Zubler, Veronika; Weber, Ulrich; Loeuille, Damien; Roemer, Frank W; Cibere, Jolanda; Pianta, Marcus; Gracey, David; Conaghan, Philip; Ostergaard, Mikkel; Maksymowych, Walter P

2014-02-01

As a wider variety of therapeutic options for osteoarthritis (OA) becomes available, there is an increasing need to objectively evaluate disease severity on magnetic resonance imaging (MRI). This is more technically challenging at the hip than at the knee, and as a result, few systematic scoring systems exist. The OMERACT (Outcome Measures in Rheumatology) filter of truth, discrimination, and feasibility can be used to validate image-based scoring systems. Our objective was (1) to review the imaging features relevant to the assessment of severity and progression of hip OA; and (2) to review currently used methods to grade these features in existing hip OA scoring systems. A systematic literature review was conducted. MEDLINE keyword search was performed for features of arthropathy (such as hip + bone marrow edema or lesion, synovitis, cyst, effusion, cartilage, etc.) and scoring system (hip + OA + MRI + score or grade), with a secondary manual search for additional references in the retrieved publications. Findings relevant to the severity of hip OA include imaging markers associated with inflammation (bone marrow lesion, synovitis, effusion), structural damage (cartilage loss, osteophytes, subchondral cysts, labral tears), and predisposing geometric factors (hip dysplasia, femoral-acetabular impingement). Two approaches to the semiquantitative assessment of hip OA are represented by Hip OA MRI Scoring System (HOAMS), a comprehensive whole organ assessment of nearly all findings, and the Hip Inflammation MRI Scoring System (HIMRISS), which selectively scores only active lesions (bone marrow lesion, synovitis/effusion). Validation is presently confined to limited assessment of reliability. Two methods for semiquantitative assessment of hip OA on MRI have been described and validation according to the OMERACT Filter is limited to evaluation of reliability.

Ultraviolet spectral reflectance of carbonaceous materials

NASA Astrophysics Data System (ADS)

Applin, Daniel M.; Izawa, Matthew R. M.; Cloutis, Edward A.; Gillis-Davis, Jeffrey J.; Pitman, Karly M.; Roush, Ted L.; Hendrix, Amanda R.; Lucey, Paul G.

2018-06-01

A number of planetary spacecraft missions have carried instruments with sensors covering the ultraviolet (UV) wavelength range. However, there exists a general lack of relevant UV reflectance laboratory data to compare against these planetary surface remote sensing observations in order to make confident material identifications. To address this need, we have systematically analyzed reflectance spectra of carbonaceous materials in the 200-500 nm spectral range, and found spectral-compositional-structural relationships that suggest this wavelength region could distinguish between otherwise difficult-to-identify carbon phases. In particular (and by analogy with the infrared spectral region), large changes over short wavelength intervals in the refractive indices associated with the trigonal sp2π-π* transition of carbon can lead to Fresnel peaks and Christiansen-like features in reflectance. Previous studies extending to shorter wavelengths also show that anomalous dispersion caused by the σ-σ* transition associated with both the trigonal sp2 and tetrahedral sp3 sites causes these features below λ = 200 nm. The peak wavelength positions and shapes of π-π* and σ-σ* features contain information on sp3/sp2, structure, crystallinity, and powder grain size. A brief comparison with existing observational data indicates that the carbon fraction of the surface of Mercury is likely amorphous and submicroscopic, as is that on the surface of the martian satellites Phobos and Deimos, and possibly comet 67P/Churyumov-Gerasimenko, while further coordinated observations and laboratory experiments should refine these feature assignments and compositional hypotheses. The new laboratory diffuse reflectance data reported here provide an important new resource for interpreting UV reflectance measurements from planetary surfaces throughout the solar system, and confirm that the UV can be rich in important spectral information.

The application of 3D Zernike moments for the description of "model-free" molecular structure, functional motion, and structural reliability.

PubMed

Grandison, Scott; Roberts, Carl; Morris, Richard J

2009-03-01

Protein structures are not static entities consisting of equally well-determined atomic coordinates. Proteins undergo continuous motion, and as catalytic machines, these movements can be of high relevance for understanding function. In addition to this strong biological motivation for considering shape changes is the necessity to correctly capture different levels of detail and error in protein structures. Some parts of a structural model are often poorly defined, and the atomic displacement parameters provide an excellent means to characterize the confidence in an atom's spatial coordinates. A mathematical framework for studying these shape changes, and handling positional variance is therefore of high importance. We present an approach for capturing various protein structure properties in a concise mathematical framework that allows us to compare features in a highly efficient manner. We demonstrate how three-dimensional Zernike moments can be employed to describe functions, not only on the surface of a protein but throughout the entire molecule. A number of proof-of-principle examples are given which demonstrate how this approach may be used in practice for the representation of movement and uncertainty.

[Pharmacology of the antifungals used in the treatment of aspergillosis].

PubMed

Azanza, José Ramón; Sádaba, Belén; Gómez-Guíu, Almudena

2014-01-01

The treatment of invasive aspergillosis requires the use of drugs that characteristically have complex pharmacokinetic properties, the knowledge of which is essential to achieve maximum efficacy with minimal risk to the patient. The lipid-based amphotericin B formulations vary significantly in their pharmacokinetic behaviour, with very high plasma concentrations of the liposomal form, probably related to the presence of cholesterol in their structure. Azoles have a variable absorption profile, particularly in the case of itraconazole and posaconazole, with the latter very dependent on multiple factors. This may also lead to variations in voriconazole, which requires considering the possibility of monitoring plasma concentrations. The aim of this article is to review some of the most relevant aspects of the pharmacology of the antifungals used in the prophylaxis and treatment of the Aspergillus infection. For this reason, it includes the most relevant features of some of the azoles normally prescribed in this infection (itraconazole, posaconazole and voriconazole) and the amphotericin B formulations. Copyright © 2014. Published by Elsevier Espana.

Controlling Plasma Stability of Hydroxamic Acids: A MedChem Toolbox.

PubMed

Hermant, Paul; Bosc, Damien; Piveteau, Catherine; Gealageas, Ronan; Lam, BaoVy; Ronco, Cyril; Roignant, Matthieu; Tolojanahary, Hasina; Jean, Ludovic; Renard, Pierre-Yves; Lemdani, Mohamed; Bourotte, Marilyne; Herledan, Adrien; Bedart, Corentin; Biela, Alexandre; Leroux, Florence; Deprez, Benoit; Deprez-Poulain, Rebecca

2017-11-09

Hydroxamic acids are outstanding zinc chelating groups that can be used to design potent and selective metalloenzyme inhibitors in various therapeutic areas. Some hydroxamic acids display a high plasma clearance resulting in poor in vivo activity, though they may be very potent compounds in vitro. We designed a 57-member library of hydroxamic acids to explore the structure-plasma stability relationships in these series and to identify which enzyme(s) and which pharmacophores are critical for plasma stability. Arylesterases and carboxylesterases were identified as the main metabolic enzymes for hydroxamic acids. Finally, we suggest structural features to be introduced or removed to improve stability. This work thus provides the first medicinal chemistry toolbox (experimental procedures and structural guidance) to assess and control the plasma stability of hydroxamic acids and realize their full potential as in vivo pharmacological probes and therapeutic agents. This study is particularly relevant to preclinical development as it allows obtaining compounds equally stable in human and rodent models.

Relationship of Interplanetary Shock Micro and Macro Characteristics: A Wind Study

NASA Technical Reports Server (NTRS)

Szabo, Adam; Koval, A

2008-01-01

The non-linear least squared MHD fitting technique of Szabo 11 9941 has been recently further refined to provide realistic confidence regions for interplanetary shock normal directions and speeds. Analyzing Wind observed interplanetary shocks from 1995 to 200 1, macro characteristics such as shock strength, Theta Bn and Mach numbers can be compared to the details of shock micro or kinetic structures. The now commonly available very high time resolution (1 1 or 22 vectors/sec) Wind magnetic field data allows the precise characterization of shock kinetic structures, such as the size of the foot, ramp, overshoot and the duration of damped oscillations on either side of the shock. Detailed comparison of the shock micro and macro characteristics will be given. This enables the elucidation of shock kinetic features, relevant for particle energization processes, for observations where high time resolution data is not available. Moreover, establishing a quantitative relationship between the shock micro and macro structures will improve the confidence level of shock fitting techniques during disturbed solar wind conditions.

Machine Detection of Enhanced Electromechanical Energy Conversion in PbZr 0.2Ti 0.8O 3 Thin Films

DOE PAGES

Agar, Joshua C.; Cao, Ye; Naul, Brett; ...

2018-05-28

Many energy conversion, sensing, and microelectronic applications based on ferroic materials are determined by the domain structure evolution under applied stimuli. New hyperspectral, multidimensional spectroscopic techniques now probe dynamic responses at relevant length and time scales to provide an understanding of how these nanoscale domain structures impact macroscopic properties. Such approaches, however, remain limited in use because of the difficulties that exist in extracting and visualizing scientific insights from these complex datasets. Using multidimensional band-excitation scanning probe spectroscopy and adapting tools from both computer vision and machine learning, an automated workflow is developed to featurize, detect, and classify signatures ofmore » ferroelectric/ferroelastic switching processes in complex ferroelectric domain structures. This approach enables the identification and nanoscale visualization of varied modes of response and a pathway to statistically meaningful quantification of the differences between those modes. Lastly, among other things, the importance of domain geometry is spatially visualized for enhancing nanoscale electromechanical energy conversion.« less

Biochemical and functional characterization of an albumin protein belonging to the hemopexin superfamily from Lens culinaris seeds.

PubMed

Scarafoni, Alessio; Gualtieri, Elisa; Barbiroli, Alberto; Carpen, Aristodemo; Negri, Armando; Duranti, Marcello

2011-09-14

The present paper reports the purification and biochemical characterization of an albumin identified in mature lentil seeds with high sequence similarity to pea PA2. These proteins are found in many edible seeds and are considered potentially detrimental for human health due to the potential allergenicity and lectin-like activity. Thus, the description of their possible presence in food and the assessment of the molecular properties are relevant. The M(r), pI, and N-terminal sequence of this protein have been determined. The work included the study of (i) the binding properties to hemine to assess the presence of hemopexin structural domains and (ii) the binding properties of the protein to thiamin. In addition, the structural changes induced by heating have been evaluated by means of spectroscopic techniques. Denaturation temperature has also been determined. The present work provides new insights about the structural molecular features and the ligand-binding properties and dynamics of this kind of seed albumin.

Further explorations of cosmogonic shadow effects in the Saturnian rings

NASA Technical Reports Server (NTRS)

Alfven, H.; Axnaes, I.; Brenning, N.; Lindqvist, P. A.

1985-01-01

The mass distribution in the Saturnian ring system is compared with predictions from the cosmogonic theory of Alfven and Arrhenius (1975) in which matter in the rings was once a magnetized plasma, with gravitation balanced by centrifugal force and by the magnetic field. As the plasma is neutralized, the magnetic force disappears and the matter can be shown to fall in to a distance 2/3 of the original. This supports the cosmogonic shadow effect, also demonstrated for the astroidal belt and in the large scale structure of the Saturnian ring system. The relevance of the comogonic shadow effect for parts of the finer structures of the Saturnian ring system is investigated. It is shown that many structures of the present ring system can be understood as shadows and antishadows of cosmogonic origin. These appear in the form of double rings centered around a position a factor 0.64 (slightly 2/3) closer to Saturn than the causing feature.

Machine Detection of Enhanced Electromechanical Energy Conversion in PbZr 0.2Ti 0.8O 3 Thin Films

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

Agar, Joshua C.; Cao, Ye; Naul, Brett

Many energy conversion, sensing, and microelectronic applications based on ferroic materials are determined by the domain structure evolution under applied stimuli. New hyperspectral, multidimensional spectroscopic techniques now probe dynamic responses at relevant length and time scales to provide an understanding of how these nanoscale domain structures impact macroscopic properties. Such approaches, however, remain limited in use because of the difficulties that exist in extracting and visualizing scientific insights from these complex datasets. Using multidimensional band-excitation scanning probe spectroscopy and adapting tools from both computer vision and machine learning, an automated workflow is developed to featurize, detect, and classify signatures ofmore » ferroelectric/ferroelastic switching processes in complex ferroelectric domain structures. This approach enables the identification and nanoscale visualization of varied modes of response and a pathway to statistically meaningful quantification of the differences between those modes. Lastly, among other things, the importance of domain geometry is spatially visualized for enhancing nanoscale electromechanical energy conversion.« less

Explaining TeV cosmic-ray anisotropies with non-diffusive cosmic-ray propagation

DOE PAGES

Harding, James Patrick; Fryer, Chris Lee; Mendel, Susan Marie

2016-05-11

Constraining the behavior of cosmic ray data observed at Earth requires a precise understanding of how the cosmic rays propagate in the interstellar medium. The interstellar medium is not homogeneous; although turbulent magnetic fields dominate over large scales, small coherent regions of magnetic field exist on scales relevant to particle propagation in the nearby Galaxy. Guided propagation through a coherent field is significantly different from random particle diffusion and could be the explanation of spatial anisotropies in the observed cosmic rays. We present a Monte Carlo code to propagate cosmic particle through realistic magnetic field structures. We discuss the detailsmore » of the model as well as some preliminary studies which indicate that coherent magnetic structures are important effects in local cosmic-ray propagation, increasing the flux of cosmic rays by over two orders of magnitude at anisotropic locations on the sky. Furthermore, the features induced by coherent magnetic structure could be the cause of the observed TeV cosmic-ray anisotropy.« less

In crystallo optical spectroscopy (icOS) as a complementary tool on the macromolecular crystallography beamlines of the ESRF

PubMed Central

von Stetten, David; Giraud, Thierry; Carpentier, Philippe; Sever, Franc; Terrien, Maxime; Dobias, Fabien; Juers, Douglas H.; Flot, David; Mueller-Dieckmann, Christoph; Leonard, Gordon A.; de Sanctis, Daniele; Royant, Antoine

2015-01-01

The analysis of structural data obtained by X-ray crystallo­graphy benefits from information obtained from complementary techniques, especially as applied to the crystals themselves. As a consequence, optical spectroscopies in structural biology have become instrumental in assessing the relevance and context of many crystallographic results. Since the year 2000, it has been possible to record such data adjacent to, or directly on, the Structural Biology Group beamlines of the ESRF. A core laboratory featuring various spectrometers, named the Cryobench, is now in its third version and houses portable devices that can be directly mounted on beamlines. This paper reports the current status of the Cryobench, which is now located on the MAD beamline ID29 and is thus called the ID29S-Cryobench (where S stands for ‘spectroscopy’). It also reviews the diverse experiments that can be performed at the Cryobench, highlighting the various scientific questions that can be addressed. PMID:25615856

EXPLAINING TEV COSMIC-RAY ANISOTROPIES WITH NON-DIFFUSIVE COSMIC-RAY PROPAGATION

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

Harding, J. Patrick; Fryer, Chris L.; Mendel, Susan, E-mail: jpharding@lanl.gov, E-mail: fryer@lanl.gov, E-mail: smendel@lanl.gov

2016-05-10

Constraining the behavior of cosmic ray data observed at Earth requires a precise understanding of how the cosmic rays propagate in the interstellar medium. The interstellar medium is not homogeneous; although turbulent magnetic fields dominate over large scales, small coherent regions of magnetic field exist on scales relevant to particle propagation in the nearby Galaxy. Guided propagation through a coherent field is significantly different from random particle diffusion and could be the explanation of spatial anisotropies in the observed cosmic rays. We present a Monte Carlo code to propagate cosmic particle through realistic magnetic field structures. We discuss the detailsmore » of the model as well as some preliminary studies which indicate that coherent magnetic structures are important effects in local cosmic-ray propagation, increasing the flux of cosmic rays by over two orders of magnitude at anisotropic locations on the sky. The features induced by coherent magnetic structure could be the cause of the observed TeV cosmic-ray anisotropy.« less

Up, Down, and All Around: Scale-Dependent Spatial Variation in Rocky-Shore Communities of Fildes Peninsula, King George Island, Antarctica

PubMed Central

Valdivia, Nelson; Díaz, María J.; Holtheuer, Jorge; Garrido, Ignacio; Huovinen, Pirjo; Gómez, Iván

2014-01-01

Understanding the variation of biodiversity along environmental gradients and multiple spatial scales is relevant for theoretical and management purposes. Hereby, we analysed the spatial variability in diversity and structure of intertidal and subtidal macrobenthic Antarctic communities along vertical environmental stress gradients and across multiple horizontal spatial scales. Since biotic interactions and local topographic features are likely major factors for coastal assemblages, we tested the hypothesis that fine-scale processes influence the effects of the vertical environmental stress gradients on the macrobenthic diversity and structure. We used nested sampling designs in the intertidal and subtidal habitats, including horizontal spatial scales ranging from few centimetres to 1000s of metres along the rocky shore of Fildes Peninsula, King George Island. In both intertidal and subtidal habitats, univariate and multivariate analyses showed a marked vertical zonation in taxon richness and community structure. These patterns depended on the horizontal spatial scale of observation, as all analyses showed a significant interaction between height (or depth) and the finer spatial scale analysed. Variance and pseudo-variance components supported our prediction for taxon richness, community structure, and the abundance of dominant species such as the filamentous green alga Urospora penicilliformis (intertidal), the herbivore Nacella concinna (intertidal), the large kelp-like Himantothallus grandifolius (subtidal), and the red crustose red alga Lithothamnion spp. (subtidal). We suggest that in coastal ecosystems strongly governed by physical factors, fine-scale processes (e.g. biotic interactions and refugia availability) are still relevant for the structuring and maintenance of the local communities. The spatial patterns found in this study serve as a necessary benchmark to understand the dynamics and adaptation of natural assemblages in response to observed and predicted environmental changes in Antarctica. PMID:24956114

High-Performance First-Principles Molecular Dynamics for Predictive Theory and Modeling

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

Gygi, Francois; Galli, Giulia; Schwegler, Eric

This project focused on developing high-performance software tools for First-Principles Molecular Dynamics (FPMD) simulations, and applying them in investigations of materials relevant to energy conversion processes. FPMD is an atomistic simulation method that combines a quantum-mechanical description of electronic structure with the statistical description provided by molecular dynamics (MD) simulations. This reliance on fundamental principles allows FPMD simulations to provide a consistent description of structural, dynamical and electronic properties of a material. This is particularly useful in systems for which reliable empirical models are lacking. FPMD simulations are increasingly used as a predictive tool for applications such as batteries, solarmore » energy conversion, light-emitting devices, electro-chemical energy conversion devices and other materials. During the course of the project, several new features were developed and added to the open-source Qbox FPMD code. The code was further optimized for scalable operation of large-scale, Leadership-Class DOE computers. When combined with Many-Body Perturbation Theory (MBPT) calculations, this infrastructure was used to investigate structural and electronic properties of liquid water, ice, aqueous solutions, nanoparticles and solid-liquid interfaces. Computing both ionic trajectories and electronic structure in a consistent manner enabled the simulation of several spectroscopic properties, such as Raman spectra, infrared spectra, and sum-frequency generation spectra. The accuracy of the approximations used allowed for direct comparisons of results with experimental data such as optical spectra, X-ray and neutron diffraction spectra. The software infrastructure developed in this project, as applied to various investigations of solids, liquids and interfaces, demonstrates that FPMD simulations can provide a detailed, atomic-scale picture of structural, vibrational and electronic properties of complex systems relevant to energy conversion devices.« less

Nanoscale Confinement Controls the Crystallization of Calcium Phosphate: Relevance to Bone Formation

PubMed Central

Cantaert, Bram; Beniash, Elia; Meldrum, Fiona C.

2015-01-01

A key feature of biomineralization processes is that they take place within confined volumes, in which the local environment can have significant effects on mineral formation. Herein, we investigate the influence of confinement on the formation mechanism and structure of calcium phosphate (CaP). This is of particular relevance to the formation of dentine and bone, structures of which are based on highly mineralized collagen fibrils. CaP was precipitated within 25–300 nm diameter, cylindrical pores of track etched and anodised alumina membranes under physiological conditions, in which this system enables systematic study of the effects of the pore size in the absence of a structural match between the matrix and the growing crystals. Our results show that the main products were polycrystalline hydroxapatite (HAP) rods, together with some single crystal octacalcium phosphate (OCP) rods. Notably, we demonstrate that these were generated though an intermediate amorphous calcium phosphate (ACP) phase, and that ACP is significantly stabilised in confinement. This effect may have significance to the mineralization of bone, which can occur through a transient ACP phase. We also show that orientation of the HAP comparable, or even superior to that seen in bone can be achieved through confinement effects alone. Although this simple experimental system cannot be considered, a direct mimic of the in vivo formation of ultrathin HAP platelets within collagen fibrils, our results show that the effects of physical confinement should not be neglected when considering the mechanisms of formation of structures, such as bones and teeth. PMID:24115275

Nanoscale confinement controls the crystallization of calcium phosphate: relevance to bone formation.

PubMed

Cantaert, Bram; Beniash, Elia; Meldrum, Fiona C

2013-10-25

A key feature of biomineralization processes is that they take place within confined volumes, in which the local environment can have significant effects on mineral formation. Herein, we investigate the influence of confinement on the formation mechanism and structure of calcium phosphate (CaP). This is of particular relevance to the formation of dentine and bone, structures of which are based on highly mineralized collagen fibrils. CaP was precipitated within 25-300 nm diameter, cylindrical pores of track etched and anodised alumina membranes under physiological conditions, in which this system enables systematic study of the effects of the pore size in the absence of a structural match between the matrix and the growing crystals. Our results show that the main products were polycrystalline hydroxapatite (HAP) rods, together with some single crystal octacalcium phosphate (OCP) rods. Notably, we demonstrate that these were generated though an intermediate amorphous calcium phosphate (ACP) phase, and that ACP is significantly stabilised in confinement. This effect may have significance to the mineralization of bone, which can occur through a transient ACP phase. We also show that orientation of the HAP comparable, or even superior to that seen in bone can be achieved through confinement effects alone. Although this simple experimental system cannot be considered, a direct mimic of the in vivo formation of ultrathin HAP platelets within collagen fibrils, our results show that the effects of physical confinement should not be neglected when considering the mechanisms of formation of structures, such as bones and teeth. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The Role of the Auditory Brainstem in Processing Musically Relevant Pitch

PubMed Central

Bidelman, Gavin M.

2013-01-01

Neuroimaging work has shed light on the cerebral architecture involved in processing the melodic and harmonic aspects of music. Here, recent evidence is reviewed illustrating that subcortical auditory structures contribute to the early formation and processing of musically relevant pitch. Electrophysiological recordings from the human brainstem and population responses from the auditory nerve reveal that nascent features of tonal music (e.g., consonance/dissonance, pitch salience, harmonic sonority) are evident at early, subcortical levels of the auditory pathway. The salience and harmonicity of brainstem activity is strongly correlated with listeners’ perceptual preferences and perceived consonance for the tonal relationships of music. Moreover, the hierarchical ordering of pitch intervals/chords described by the Western music practice and their perceptual consonance is well-predicted by the salience with which pitch combinations are encoded in subcortical auditory structures. While the neural correlates of consonance can be tuned and exaggerated with musical training, they persist even in the absence of musicianship or long-term enculturation. As such, it is posited that the structural foundations of musical pitch might result from innate processing performed by the central auditory system. A neurobiological predisposition for consonant, pleasant sounding pitch relationships may be one reason why these pitch combinations have been favored by composers and listeners for centuries. It is suggested that important perceptual dimensions of music emerge well before the auditory signal reaches cerebral cortex and prior to attentional engagement. While cortical mechanisms are no doubt critical to the perception, production, and enjoyment of music, the contribution of subcortical structures implicates a more integrated, hierarchically organized network underlying music processing within the brain. PMID:23717294

Proposed health state awareness of helicopter blades using an artificial neural network strategy

NASA Astrophysics Data System (ADS)

Lee, Andrew; Habtour, Ed; Gadsden, S. A.

2016-05-01

Structural health prognostics and diagnosis strategies can be classified as either model or signal-based. Artificial neural network strategies are popular signal-based techniques. This paper proposes the use of helicopter blades in order to study the sensitivity of an artificial neural network to structural fatigue. The experimental setup consists of a scale aluminum helicopter blade exposed to transverse vibratory excitation at the hub using single axis electrodynamic shaker. The intent of this study is to optimize an algorithm for processing high-dimensional data while retaining important information content in an effort to select input features and weights, as well as health parameters, for training a neural network. Data from accelerometers and piezoelectric transducers is collected from a known system designated as healthy. Structural damage will be introduced to different blades, which they will be designated as unhealthy. A variety of different tests will be performed to track the evolution and severity of the damage. A number of damage detection and diagnosis strategies will be implemented. A preliminary experiment was performed on aluminum cantilever beams providing a simpler model for implementation and proof of concept. Future work will look at utilizing the detection information as part of a hierarchical control system in order to mitigate structural damage and fatigue. The proposed approach may eliminate massive data storage on board of an aircraft through retaining relevant information only. The control system can then employ the relevant information to intelligently reconfigure adaptive maneuvers to avoid harmful regimes, thus, extending the life of the aircraft.

Structures of Pseudomonas aeruginosa β-ketoacyl-(acyl-carrier-protein) synthase II (FabF) and a C164Q mutant provide templates for antibacterial drug discovery and identify a buried potassium ion and a ligand-binding site that is an artefact of the crystal form

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

Baum, Bernhard; Lecker, Laura S. M.; Zoltner, Martin

Three crystal structures of recombinant P. aeruginosa FabF are reported: the apoenzyme, an active-site mutant and a complex with a fragment of a natural product inhibitor. The characterization provides reagents and new information to support antibacterial drug discovery. Bacterial infections remain a serious health concern, in particular causing life-threatening infections of hospitalized and immunocompromised patients. The situation is exacerbated by the rise in antibacterial drug resistance, and new treatments are urgently sought. In this endeavour, accurate structures of molecular targets can support early-stage drug discovery. Here, crystal structures, in three distinct forms, of recombinant Pseudomonas aeruginosa β-ketoacyl-(acyl-carrier-protein) synthase II (FabF)more » are presented. This enzyme, which is involved in fatty-acid biosynthesis, has been validated by genetic and chemical means as an antibiotic target in Gram-positive bacteria and represents a potential target in Gram-negative bacteria. The structures of apo FabF, of a C164Q mutant in which the binding site is altered to resemble the substrate-bound state and of a complex with 3-(benzoylamino)-2-hydroxybenzoic acid are reported. This compound mimics aspects of a known natural product inhibitor, platensimycin, and surprisingly was observed binding outside the active site, interacting with a symmetry-related molecule. An unusual feature is a completely buried potassium-binding site that was identified in all three structures. Comparisons suggest that this may represent a conserved structural feature of FabF relevant to fold stability. The new structures provide templates for structure-based ligand design and, together with the protocols and reagents, may underpin a target-based drug-discovery project for urgently needed antibacterials.« less

No-reference image quality assessment based on natural scene statistics and gradient magnitude similarity

NASA Astrophysics Data System (ADS)

Jia, Huizhen; Sun, Quansen; Ji, Zexuan; Wang, Tonghan; Chen, Qiang

2014-11-01

The goal of no-reference/blind image quality assessment (NR-IQA) is to devise a perceptual model that can accurately predict the quality of a distorted image as human opinions, in which feature extraction is an important issue. However, the features used in the state-of-the-art "general purpose" NR-IQA algorithms are usually natural scene statistics (NSS) based or are perceptually relevant; therefore, the performance of these models is limited. To further improve the performance of NR-IQA, we propose a general purpose NR-IQA algorithm which combines NSS-based features with perceptually relevant features. The new method extracts features in both the spatial and gradient domains. In the spatial domain, we extract the point-wise statistics for single pixel values which are characterized by a generalized Gaussian distribution model to form the underlying features. In the gradient domain, statistical features based on neighboring gradient magnitude similarity are extracted. Then a mapping is learned to predict quality scores using a support vector regression. The experimental results on the benchmark image databases demonstrate that the proposed algorithm correlates highly with human judgments of quality and leads to significant performance improvements over state-of-the-art methods.

Deep learning based classification of breast tumors with shear-wave elastography.

PubMed

Zhang, Qi; Xiao, Yang; Dai, Wei; Suo, Jingfeng; Wang, Congzhi; Shi, Jun; Zheng, Hairong

2016-12-01

This study aims to build a deep learning (DL) architecture for automated extraction of learned-from-data image features from the shear-wave elastography (SWE), and to evaluate the DL architecture in differentiation between benign and malignant breast tumors. We construct a two-layer DL architecture for SWE feature extraction, comprised of the point-wise gated Boltzmann machine (PGBM) and the restricted Boltzmann machine (RBM). The PGBM contains task-relevant and task-irrelevant hidden units, and the task-relevant units are connected to the RBM. Experimental evaluation was performed with five-fold cross validation on a set of 227 SWE images, 135 of benign tumors and 92 of malignant tumors, from 121 patients. The features learned with our DL architecture were compared with the statistical features quantifying image intensity and texture. Results showed that the DL features achieved better classification performance with an accuracy of 93.4%, a sensitivity of 88.6%, a specificity of 97.1%, and an area under the receiver operating characteristic curve of 0.947. The DL-based method integrates feature learning with feature selection on SWE. It may be potentially used in clinical computer-aided diagnosis of breast cancer. Copyright © 2016 Elsevier B.V. All rights reserved.

Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers

NASA Astrophysics Data System (ADS)

Weinmann, Martin; Jutzi, Boris; Hinz, Stefan; Mallet, Clément

2015-07-01

3D scene analysis in terms of automatically assigning 3D points a respective semantic label has become a topic of great importance in photogrammetry, remote sensing, computer vision and robotics. In this paper, we address the issue of how to increase the distinctiveness of geometric features and select the most relevant ones among these for 3D scene analysis. We present a new, fully automated and versatile framework composed of four components: (i) neighborhood selection, (ii) feature extraction, (iii) feature selection and (iv) classification. For each component, we consider a variety of approaches which allow applicability in terms of simplicity, efficiency and reproducibility, so that end-users can easily apply the different components and do not require expert knowledge in the respective domains. In a detailed evaluation involving 7 neighborhood definitions, 21 geometric features, 7 approaches for feature selection, 10 classifiers and 2 benchmark datasets, we demonstrate that the selection of optimal neighborhoods for individual 3D points significantly improves the results of 3D scene analysis. Additionally, we show that the selection of adequate feature subsets may even further increase the quality of the derived results while significantly reducing both processing time and memory consumption.

Anxiety or agitation in mood disorder with mixed features: A review with a focus on validity as a dimensional criterion.

PubMed

Shim, In Hee; Bae, Dong Sik; Bahk, Won-Myong

2016-08-01

The diagnostic validity of mixed features, excluding anxiety or psychomotor agitation in mood disorders, has not yet been fully examined. PubMed and relevant English-language literature (regardless of year) were searched. Keywords were mixed or mixed state or mixed features or mixed episode and anxious or anxiety or agitation and bipolar disorder or depressive disorder or mood disorder or affective disorder. Most studies on anxiety or psychomotor agitation have included a significant correlation relevant to the "with mixed features" specifier, although it is common in both poles of mood episodes regardless of the predominant polarity. There is some confusion between the characteristic of classical mixed states and the definition of the mixed features specifier with the newly added anxious distress specifier in DSM-5, specifically, whether to include anxiety and agitation as significant characteristics. This change is of concern because a large proportion of patients with mixed features are now unspecified, and this may influence treatment planning and prognosis. The findings of our review suggest that anxiety and psychomotor agitation can be core symptoms in mood episodes with mixed features and important clinical clues for prediction of treatment effects and disease course.

Identification of Chinese medicine syndromes in persistent insomnia associated with major depressive disorder: a latent tree analysis.

PubMed

Yeung, Wing-Fai; Chung, Ka-Fai; Zhang, Nevin Lian-Wen; Zhang, Shi Ping; Yung, Kam-Ping; Chen, Pei-Xian; Ho, Yan-Yee

2016-01-01

Chinese medicine (CM) syndrome (zheng) differentiation is based on the co-occurrence of CM manifestation profiles, such as signs and symptoms, and pulse and tongue features. Insomnia is a symptom that frequently occurs in major depressive disorder despite adequate antidepressant treatment. This study aims to identify co-occurrence patterns in participants with persistent insomnia and major depressive disorder from clinical feature data using latent tree analysis, and to compare the latent variables with relevant CM syndromes. One hundred and forty-two participants with persistent insomnia and a history of major depressive disorder completed a standardized checklist (the Chinese Medicine Insomnia Symptom Checklist) specially developed for CM syndrome classification of insomnia. The checklist covers symptoms and signs, including tongue and pulse features. The clinical features assessed by the checklist were analyzed using Lantern software. CM practitioners with relevant experience compared the clinical feature variables under each latent variable with reference to relevant CM syndromes, based on a previous review of CM syndromes. The symptom data were analyzed to build the latent tree model and the model with the highest Bayes information criterion score was regarded as the best model. This model contained 18 latent variables, each of which divided participants into two clusters. Six clusters represented more than 50 % of the sample. The clinical feature co-occurrence patterns of these six clusters were interpreted as the CM syndromes Liver qi stagnation transforming into fire, Liver fire flaming upward, Stomach disharmony, Hyperactivity of fire due to yin deficiency, Heart-kidney noninteraction, and Qi deficiency of the heart and gallbladder. The clinical feature variables that contributed significant cumulative information coverage (at least 95 %) were identified. Latent tree model analysis on a sample of depressed participants with insomnia revealed 13 clinical feature co-occurrence patterns, four mutual-exclusion patterns, and one pattern with a single clinical feature variable.

[Criteria of quality of structure in rehabilitation units with inpatient treatment].

PubMed

Klein, K; Farin, E; Jäckel, W H; Blatt, O; Schliehe, F

2004-04-01

The structure of a rehabilitation unit is an important feature of the quality of care. Adequate and qualitatively good structures provide the basis for appropriate therapy offers and treatment and eventually, a better health for rehabilitants. The quality of structures is generally recorded without any evaluation of the aspects in particular. The definition of standards is the basis for such an evaluation. The project presented is aimed at the definition of relevant structural standards for rehab units with inpatient treatment for musculoskeletal, cardiac, neurological, gastroenterological, oncological, pneumological and dermatological diseases. Here, the distinction between basal criteria which have to be fulfilled by every rehab unit with inpatient treatment and criteria important for a well-aimed assignment of patients with specific needs ("assignment criteria") should be made. Apart from the documentation of structural attributes, the structural quality of a rehab unit can be described individually as well as in comparison with other units. Relevant structural criteria were defined in expert meetings by means of a modified Delphi-technique with five inquiries. Overall, 199 "basal criteria" and "assignment criteria" were defined. All criteria can be assigned to the two domains general structural characteristics (general characteristics and equipment of rooms; medical/technical equipment; therapy, education, care; staff) and process-related structures (conceptual frames; internal quality management; internal communication and personnel development). The structural standards are applicable to units for musculoskeletal, cardiac, neurological, oncological, gastroenterological, dermatological and pneumological rehabilitation financed by the two main providers of rehabilitation, the statutory pension insurance scheme and the statutory health insurance scheme for all other five indications. The definition of structural standards agreed by experts in a formal consensus process, provides comprehensive and concrete requirements for German rehab units with inpatient medical rehabilitation. If the two main providers of rehabilitation both use the standards this can be regarded as a hallmark on the path to a unitary programme for quality management. The results enable units to analyse their weak points not just on an individual basis but allow also for a comparison between units, along with contributing to optimizing the structural quality of rehab units.

The relationship between visual working memory and attention: retention of precise colour information in the absence of effects on perceptual selection.

PubMed

Hollingworth, Andrew; Hwang, Seongmin

2013-10-19

We examined the conditions under which a feature value in visual working memory (VWM) recruits visual attention to matching stimuli. Previous work has suggested that VWM supports two qualitatively different states of representation: an active state that interacts with perceptual selection and a passive (or accessory) state that does not. An alternative hypothesis is that VWM supports a single form of representation, with the precision of feature memory controlling whether or not the representation interacts with perceptual selection. The results of three experiments supported the dual-state hypothesis. We established conditions under which participants retained a relatively precise representation of a parcticular colour. If the colour was immediately task relevant, it reliably recruited attention to matching stimuli. However, if the colour was not immediately task relevant, it failed to interact with perceptual selection. Feature maintenance in VWM is not necessarily equivalent with feature-based attentional selection.

Learning feature representations with a cost-relevant sparse autoencoder.

PubMed

Längkvist, Martin; Loutfi, Amy

2015-02-01

There is an increasing interest in the machine learning community to automatically learn feature representations directly from the (unlabeled) data instead of using hand-designed features. The autoencoder is one method that can be used for this purpose. However, for data sets with a high degree of noise, a large amount of the representational capacity in the autoencoder is used to minimize the reconstruction error for these noisy inputs. This paper proposes a method that improves the feature learning process by focusing on the task relevant information in the data. This selective attention is achieved by weighting the reconstruction error and reducing the influence of noisy inputs during the learning process. The proposed model is trained on a number of publicly available image data sets and the test error rate is compared to a standard sparse autoencoder and other methods, such as the denoising autoencoder and contractive autoencoder.

Weighted Distance Functions Improve Analysis of High-Dimensional Data: Application to Molecular Dynamics Simulations.

PubMed

Blöchliger, Nicolas; Caflisch, Amedeo; Vitalis, Andreas

2015-11-10

Data mining techniques depend strongly on how the data are represented and how distance between samples is measured. High-dimensional data often contain a large number of irrelevant dimensions (features) for a given query. These features act as noise and obfuscate relevant information. Unsupervised approaches to mine such data require distance measures that can account for feature relevance. Molecular dynamics simulations produce high-dimensional data sets describing molecules observed in time. Here, we propose to globally or locally weight simulation features based on effective rates. This emphasizes, in a data-driven manner, slow degrees of freedom that often report on the metastable states sampled by the molecular system. We couple this idea to several unsupervised learning protocols. Our approach unmasks slow side chain dynamics within the native state of a miniprotein and reveals additional metastable conformations of a protein. The approach can be combined with most algorithms for clustering or dimensionality reduction.

Application of machine learning techniques to analyse the effects of physical exercise in ventricular fibrillation.

PubMed

Caravaca, Juan; Soria-Olivas, Emilio; Bataller, Manuel; Serrano, Antonio J; Such-Miquel, Luis; Vila-Francés, Joan; Guerrero, Juan F

2014-02-01

This work presents the application of machine learning techniques to analyse the influence of physical exercise in the physiological properties of the heart, during ventricular fibrillation. To this end, different kinds of classifiers (linear and neural models) are used to classify between trained and sedentary rabbit hearts. The use of those classifiers in combination with a wrapper feature selection algorithm allows to extract knowledge about the most relevant features in the problem. The obtained results show that neural models outperform linear classifiers (better performance indices and a better dimensionality reduction). The most relevant features to describe the benefits of physical exercise are those related to myocardial heterogeneity, mean activation rate and activation complexity. © 2013 Published by Elsevier Ltd.

Smokers' and drinkers' choice of smartphone applications and expectations of engagement: a think aloud and interview study.

PubMed

Perski, Olga; Blandford, Ann; Ubhi, Harveen Kaur; West, Robert; Michie, Susan

2017-02-28

Public health organisations such as the National Health Service in the United Kingdom and the National Institutes of Health in the United States provide access to online libraries of publicly endorsed smartphone applications (apps); however, there is little evidence that users rely on this guidance. Rather, one of the most common methods of finding new apps is to search an online store. As hundreds of smoking cessation and alcohol-related apps are currently available on the market, smokers and drinkers must actively choose which app to download prior to engaging with it. The influences on this choice are yet to be identified. This study aimed to investigate 1) design features that shape users' choice of smoking cessation or alcohol reduction apps, and 2) design features judged to be important for engagement. Adult smokers (n = 10) and drinkers (n = 10) interested in using an app to quit/cut down were asked to search an online store to identify and explore a smoking cessation or alcohol reduction app of their choice whilst thinking aloud. Semi-structured interview techniques were used to allow participants to elaborate on their statements. An interpretivist theoretical framework informed the analysis. Verbal reports were audio recorded, transcribed verbatim and analysed using inductive thematic analysis. Participants chose apps based on their immediate look and feel, quality as judged by others' ratings and brand recognition ('social proof'), and titles judged to be realistic and relevant. Monitoring and feedback, goal setting, rewards and prompts were identified as important for engagement, fostering motivation and autonomy. Tailoring of content, a non-judgmental communication style, privacy and accuracy were viewed as important for engagement, fostering a sense of personal relevance and trust. Sharing progress on social media and the use of craving management techniques in social settings were judged not to be engaging because of concerns about others' negative reactions. Choice of a smoking cessation or alcohol reduction app may be influenced by its immediate look and feel, 'social proof' and titles that appear realistic. Design features that enhance motivation, autonomy, personal relevance and credibility may be important for engagement.

Organizing Books and Authors by Multilayer SOM.

PubMed

Zhang, Haijun; Chow, Tommy W S; Wu, Q M Jonathan

2016-12-01

This paper introduces a new framework for the organization of electronic books (e-books) and their corresponding authors using a multilayer self-organizing map (MLSOM). An author is modeled by a rich tree-structured representation, and an MLSOM-based system is used as an efficient solution to the organizational problem of structured data. The tree-structured representation formulates author features in a hierarchy of author biography, books, pages, and paragraphs. To efficiently tackle the tree-structured representation, we used an MLSOM algorithm that serves as a clustering technique to handle e-books and their corresponding authors. A book and author recommender system is then implemented using the proposed framework. The effectiveness of our approach was examined in a large-scale data set containing 3868 authors along with the 10500 e-books that they wrote. We also provided visualization results of MLSOM for revealing the relevance patterns hidden from presented author clusters. The experimental results corroborate that the proposed method outperforms other content-based models (e.g., rate adapting poisson, latent Dirichlet allocation, probabilistic latent semantic indexing, and so on) and offers a promising solution to book recommendation, author recommendation, and visualization.

Modeling Protein Expression and Protein Signaling Pathways

PubMed Central

Telesca, Donatello; Müller, Peter; Kornblau, Steven M.; Suchard, Marc A.; Ji, Yuan

2015-01-01

High-throughput functional proteomic technologies provide a way to quantify the expression of proteins of interest. Statistical inference centers on identifying the activation state of proteins and their patterns of molecular interaction formalized as dependence structure. Inference on dependence structure is particularly important when proteins are selected because they are part of a common molecular pathway. In that case, inference on dependence structure reveals properties of the underlying pathway. We propose a probability model that represents molecular interactions at the level of hidden binary latent variables that can be interpreted as indicators for active versus inactive states of the proteins. The proposed approach exploits available expert knowledge about the target pathway to define an informative prior on the hidden conditional dependence structure. An important feature of this prior is that it provides an instrument to explicitly anchor the model space to a set of interactions of interest, favoring a local search approach to model determination. We apply our model to reverse-phase protein array data from a study on acute myeloid leukemia. Our inference identifies relevant subpathways in relation to the unfolding of the biological process under study. PMID:26246646

The P600 in Implicit Artificial Grammar Learning.

PubMed

Silva, Susana; Folia, Vasiliki; Hagoort, Peter; Petersson, Karl Magnus

2017-01-01

The suitability of the artificial grammar learning (AGL) paradigm to capture relevant aspects of the acquisition of linguistic structures has been empirically tested in a number of EEG studies. Some have shown a syntax-related P600 component, but it has not been ruled out that the AGL P600 effect is a response to surface features (e.g., subsequence familiarity) rather than the underlying syntax structure. Therefore, in this study, we controlled for the surface characteristics of the test sequences (associative chunk strength) and recorded the EEG before (baseline preference classification) and after (preference and grammaticality classification) exposure to a grammar. After exposure, a typical, centroparietal P600 effect was elicited by grammatical violations and not by unfamiliar subsequences, suggesting that the AGL P600 effect signals a response to structural irregularities. Moreover, preference and grammaticality classification showed a qualitatively similar ERP profile, strengthening the idea that the implicit structural mere-exposure paradigm in combination with preference classification is a suitable alternative to the traditional grammaticality classification test. Copyright © 2016 Cognitive Science Society, Inc.

Bowl Inversion and Electronic Switching of Buckybowls on Gold.

PubMed

Fujii, Shintaro; Ziatdinov, Maxim; Higashibayashi, Shuhei; Sakurai, Hidehiro; Kiguchi, Manabu

2016-09-21

Bowl-shaped π-conjugated compounds, or buckybowls, are a novel class of sp(2)-hybridized nanocarbon materials. In contrast to tubular carbon nanotubes and ball-shaped fullerenes, the buckybowls feature structural flexibility. Bowl-to-bowl structural inversion is one of the unique properties of the buckybowls in solutions. Bowl inversion on a surface modifies the metal-molecule interactions through bistable switching between bowl-up and bowl-down states on the surface, which makes surface-adsorbed buckybowls a relevant model system for elucidation of the mechano-electronic properties of nanocarbon materials. Here, we report a combination of scanning tunneling microscopy (STM) measurements and ab initio atomistic simulations to identify the adlayer structure of the sumanene buckybowl on Au(111) and reveal its unique bowl inversion behavior. We demonstrate that the bowl inversion can be induced by approaching the STM tip toward the molecule. By tuning the local metal-molecule interaction using the STM tip, the sumanene buckybowl exhibits structural bistability with a switching rate that is two orders of magnitude faster than that of the stochastic inversion process.

Uncommon and/or bizarre features of dementia: Part III.

PubMed

Cipriani, Gabriele; Nuti, Angelo; Danti, Sabrina; Picchi, Lucia; Di Fiorino, Mario

2018-06-01

Clinical neurologists have long recognized that dementia can present as atypical or variant syndromes/symptoms. This study aimed at describing uncommon or bizarre symptoms/syndromes observed in patients suffering from dementia. Medline and Google scholar searches were conducted for relevant articles, chapters, and books published before 2018. Search terms used included compulsion, dementia, extracampine hallucination, disordered gambling, humour, and obsession. Publications found through this indexed search were reviewed for further relevant references. The uncommon/bizarre feature of dementia was described as case reports and there were no systematic investigations.

Multitasking: Effects of processing multiple auditory feature patterns

PubMed Central

Miller, Tova; Chen, Sufen; Lee, Wei Wei; Sussman, Elyse S.

2016-01-01

ERPs and behavioral responses were measured to assess how task-irrelevant sounds interact with task processing demands and affect the ability to monitor and track multiple sound events. Participants listened to four-tone sequential frequency patterns, and responded to frequency pattern deviants (reversals of the pattern). Irrelevant tone feature patterns (duration and intensity) and respective pattern deviants were presented together with frequency patterns and frequency pattern deviants in separate conditions. Responses to task-relevant and task-irrelevant feature pattern deviants were used to test processing demands for irrelevant sound input. Behavioral performance was significantly better when there were no distracting feature patterns. Errors primarily occurred in response to the to-be-ignored feature pattern deviants. Task-irrelevant elicitation of ERP components was consistent with the error analysis, indicating a level of processing for the irrelevant features. Task-relevant elicitation of ERP components was consistent with behavioral performance, demonstrating a “cost” of performance when there were two feature patterns presented simultaneously. These results provide evidence that the brain tracked the irrelevant duration and intensity feature patterns, affecting behavioral performance. Overall, our results demonstrate that irrelevant informational streams are processed at a cost, which may be considered a type of multitasking that is an ongoing, automatic processing of taskirrelevant sensory events. PMID:25939456

Longitudinal MRI assessment: the identification of relevant features in the development of Posterior Fossa Syndrome in children

NASA Astrophysics Data System (ADS)

Spiteri, M.; Lewis, E.; Windridge, D.; Avula, S.

2015-03-01

Up to 25% of children who undergo brain tumour resection surgery in the posterior fossa develop posterior fossa syndrome (PFS). This syndrome is characterised by mutism and disturbance in speech. Our hypothesis is that there is a correlation between PFS and the occurrence of hypertrophic olivary degeneration (HOD) in lobes within the posterior fossa, known as the inferior olivary nuclei (ION). HOD is exhibited as an increase in size and intensity of the ION on an MR image. Intra-operative MRI (IoMRI) is used during surgical procedures at the Alder Hey Children's Hospital, Liver- pool, England, in the treatment of Posterior Fossa tumours and allows visualisation of the brain during surgery. The final MR scan on the IoMRI allows early assessment of the ION immediately after the surgical procedure. The longitudinal MRI data of 28 patients was analysed in a collaborative study with Alder Hey Children's Hospital, in order to identify the most relevant imaging features that relate to the development of PFS, specifically related to HOD. A semi-automated segmentation process was carried out to delineate the ION on each MRI. Feature selection techniques were used to identify the most relevant features amongst the MRI data, demographics and clinical data provided by the hospital. A support vector machine (SVM) was used to analyse the discriminative ability of the selected features. The results indicate the presence of HOD as the most efficient feature that correlates with the development of PFS, followed by the change in intensity and size of the ION and whether HOD occurred bilaterally or unilaterally.