Predicting Key Events in the Popularity Evolution of Online Information.

PubMed

Hu, Ying; Hu, Changjun; Fu, Shushen; Fang, Mingzhe; Xu, Wenwen

2017-01-01

The popularity of online information generally experiences a rising and falling evolution. This paper considers the "burst", "peak", and "fade" key events together as a representative summary of popularity evolution. We propose a novel prediction task-predicting when popularity undergoes these key events. It is of great importance to know when these three key events occur, because doing so helps recommendation systems, online marketing, and containment of rumors. However, it is very challenging to solve this new prediction task due to two issues. First, popularity evolution has high variation and can follow various patterns, so how can we identify "burst", "peak", and "fade" in different patterns of popularity evolution? Second, these events usually occur in a very short time, so how can we accurately yet promptly predict them? In this paper we address these two issues. To handle the first one, we use a simple moving average to smooth variation, and then a universal method is presented for different patterns to identify the key events in popularity evolution. To deal with the second one, we extract different types of features that may have an impact on the key events, and then a correlation analysis is conducted in the feature selection step to remove irrelevant and redundant features. The remaining features are used to train a machine learning model. The feature selection step improves prediction accuracy, and in order to emphasize prediction promptness, we design a new evaluation metric which considers both accuracy and promptness to evaluate our prediction task. Experimental and comparative results show the superiority of our prediction solution.

Predicting Key Events in the Popularity Evolution of Online Information

PubMed Central

Fu, Shushen; Fang, Mingzhe; Xu, Wenwen

2017-01-01

The popularity of online information generally experiences a rising and falling evolution. This paper considers the “burst”, “peak”, and “fade” key events together as a representative summary of popularity evolution. We propose a novel prediction task—predicting when popularity undergoes these key events. It is of great importance to know when these three key events occur, because doing so helps recommendation systems, online marketing, and containment of rumors. However, it is very challenging to solve this new prediction task due to two issues. First, popularity evolution has high variation and can follow various patterns, so how can we identify “burst”, “peak”, and “fade” in different patterns of popularity evolution? Second, these events usually occur in a very short time, so how can we accurately yet promptly predict them? In this paper we address these two issues. To handle the first one, we use a simple moving average to smooth variation, and then a universal method is presented for different patterns to identify the key events in popularity evolution. To deal with the second one, we extract different types of features that may have an impact on the key events, and then a correlation analysis is conducted in the feature selection step to remove irrelevant and redundant features. The remaining features are used to train a machine learning model. The feature selection step improves prediction accuracy, and in order to emphasize prediction promptness, we design a new evaluation metric which considers both accuracy and promptness to evaluate our prediction task. Experimental and comparative results show the superiority of our prediction solution. PMID:28046121

Virtual-optical information security system based on public key infrastructure

NASA Astrophysics Data System (ADS)

Peng, Xiang; Zhang, Peng; Cai, Lilong; Niu, Hanben

2005-01-01

A virtual-optical based encryption model with the aid of public key infrastructure (PKI) is presented in this paper. The proposed model employs a hybrid architecture in which our previously published encryption method based on virtual-optics scheme (VOS) can be used to encipher and decipher data while an asymmetric algorithm, for example RSA, is applied for enciphering and deciphering the session key(s). The whole information security model is run under the framework of international standard ITU-T X.509 PKI, which is on basis of public-key cryptography and digital signatures. This PKI-based VOS security approach has additional features like confidentiality, authentication, and integrity for the purpose of data encryption under the environment of network. Numerical experiments prove the effectiveness of the method. The security of proposed model is briefly analyzed by examining some possible attacks from the viewpoint of a cryptanalysis.

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

PubMed

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

2010-06-01

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

Statewide mesoscopic simulation for Wyoming.

DOT National Transportation Integrated Search

2013-10-01

This study developed a mesoscopic simulator which is capable of representing both city-level and statewide roadway : networks. The key feature of such models are the integration of (i) a traffic flow model which is efficient enough to : scale to larg...

Features Extraction of Flotation Froth Images and BP Neural Network Soft-Sensor Model of Concentrate Grade Optimized by Shuffled Cuckoo Searching Algorithm

PubMed Central

Wang, Jie-sheng; Han, Shuang; Shen, Na-na; Li, Shu-xia

2014-01-01

For meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of flotation froth images are extracted, respectively, as the input variables of the proposed soft-sensor model. Then the isometric mapping method is used to reduce the input dimension, the network size, and learning time of BP neural network. Finally, a shuffled cuckoo search algorithm is adopted to optimize the BP neural network soft-sensor model. Simulation results show that the model has better generalization results and prediction accuracy. PMID:25133210

District-Wide Involvement: The Key to Successful School Improvement.

ERIC Educational Resources Information Center

Mundell, Scott; Babich, George

1989-01-01

Describes the self-study process used by the Marana Unified School District to meet accreditation requirements with minimal expense, to emphasize curriculum development, and to improve the school. Considers the key feature of the cyclical review model to be the personal involvement of nearly every faculty member in the 10-school district. (DMM)

Dynamical Model of Drug Accumulation in Bacteria: Sensitivity Analysis and Experimentally Testable Predictions

DOE PAGES

Vesselinova, Neda; Alexandrov, Boian; Wall, Michael E.

2016-11-08

We present a dynamical model of drug accumulation in bacteria. The model captures key features in experimental time courses on ofloxacin accumulation: initial uptake; two-phase response; and long-term acclimation. In combination with experimental data, the model provides estimates of import and export rates in each phase, the time of entry into the second phase, and the decrease of internal drug during acclimation. Global sensitivity analysis, local sensitivity analysis, and Bayesian sensitivity analysis of the model provide information about the robustness of these estimates, and about the relative importance of different parameters in determining the features of the accumulation time coursesmore » in three different bacterial species: Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa. The results lead to experimentally testable predictions of the effects of membrane permeability, drug efflux and trapping (e.g., by DNA binding) on drug accumulation. A key prediction is that a sudden increase in ofloxacin accumulation in both E. coli and S. aureus is accompanied by a decrease in membrane permeability.« less

Dynamical Model of Drug Accumulation in Bacteria: Sensitivity Analysis and Experimentally Testable Predictions

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

Vesselinova, Neda; Alexandrov, Boian; Wall, Michael E.

We present a dynamical model of drug accumulation in bacteria. The model captures key features in experimental time courses on ofloxacin accumulation: initial uptake; two-phase response; and long-term acclimation. In combination with experimental data, the model provides estimates of import and export rates in each phase, the time of entry into the second phase, and the decrease of internal drug during acclimation. Global sensitivity analysis, local sensitivity analysis, and Bayesian sensitivity analysis of the model provide information about the robustness of these estimates, and about the relative importance of different parameters in determining the features of the accumulation time coursesmore » in three different bacterial species: Escherichia coli, Staphylococcus aureus, and Pseudomonas aeruginosa. The results lead to experimentally testable predictions of the effects of membrane permeability, drug efflux and trapping (e.g., by DNA binding) on drug accumulation. A key prediction is that a sudden increase in ofloxacin accumulation in both E. coli and S. aureus is accompanied by a decrease in membrane permeability.« less

Feeding difficulties, a key feature of the Drosophila NDUFS4 mitochondrial disease model

PubMed Central

Foriel, Sarah; Eidhof, Ilse

2018-01-01

ABSTRACT Mitochondrial diseases are associated with a wide variety of clinical symptoms and variable degrees of severity. Patients with such diseases generally have a poor prognosis and often an early fatal disease outcome. With an incidence of 1 in 5000 live births and no curative treatments available, relevant animal models to evaluate new therapeutic regimes for mitochondrial diseases are urgently needed. By knocking down ND-18, the unique Drosophila ortholog of NDUFS4, an accessory subunit of the NADH:ubiquinone oxidoreductase (Complex I), we developed and characterized several dNDUFS4 models that recapitulate key features of mitochondrial disease. Like in humans, the dNDUFS4 KD flies display severe feeding difficulties, an aspect of mitochondrial disorders that has so far been largely ignored in animal models. The impact of this finding, and an approach to overcome it, will be discussed in the context of interpreting disease model characterization and intervention studies. This article has an associated First Person interview with the first author of the paper. PMID:29590638

A Probabilistic Palimpsest Model of Visual Short-term Memory

PubMed Central

Matthey, Loic; Bays, Paul M.; Dayan, Peter

2015-01-01

Working memory plays a key role in cognition, and yet its mechanisms remain much debated. Human performance on memory tasks is severely limited; however, the two major classes of theory explaining the limits leave open questions about key issues such as how multiple simultaneously-represented items can be distinguished. We propose a palimpsest model, with the occurrent activity of a single population of neurons coding for several multi-featured items. Using a probabilistic approach to storage and recall, we show how this model can account for many qualitative aspects of existing experimental data. In our account, the underlying nature of a memory item depends entirely on the characteristics of the population representation, and we provide analytical and numerical insights into critical issues such as multiplicity and binding. We consider representations in which information about individual feature values is partially separate from the information about binding that creates single items out of multiple features. An appropriate balance between these two types of information is required to capture fully the different types of error seen in human experimental data. Our model provides the first principled account of misbinding errors. We also suggest a specific set of stimuli designed to elucidate the representations that subjects actually employ. PMID:25611204

A probabilistic palimpsest model of visual short-term memory.

PubMed

Matthey, Loic; Bays, Paul M; Dayan, Peter

2015-01-01

Working memory plays a key role in cognition, and yet its mechanisms remain much debated. Human performance on memory tasks is severely limited; however, the two major classes of theory explaining the limits leave open questions about key issues such as how multiple simultaneously-represented items can be distinguished. We propose a palimpsest model, with the occurrent activity of a single population of neurons coding for several multi-featured items. Using a probabilistic approach to storage and recall, we show how this model can account for many qualitative aspects of existing experimental data. In our account, the underlying nature of a memory item depends entirely on the characteristics of the population representation, and we provide analytical and numerical insights into critical issues such as multiplicity and binding. We consider representations in which information about individual feature values is partially separate from the information about binding that creates single items out of multiple features. An appropriate balance between these two types of information is required to capture fully the different types of error seen in human experimental data. Our model provides the first principled account of misbinding errors. We also suggest a specific set of stimuli designed to elucidate the representations that subjects actually employ.

A comparative review of the pharmacoeconomic guidelines in South Africa.

PubMed

Carapinha, João L

2017-01-01

To compare the pharmacoeconomic guidelines in South Africa (SA) with other middle- and high-income countries. A comparative review of key features of the pharmacoeconomic guidelines in SA was undertaken using the Comparative Table of Pharmacoeconomic Guidelines developed by the International Society of Pharmacoeconomics and Outcomes Research, and published country-level pharmacoeconomics guidelines. A random sample of guidelines in high- and middle-income countries were analyzed if data on all key features were available. Key features of the pharmacoeconomic guidelines in SA were compared with those in other countries, and divergent features were identified and elaborated. Five upper middle-income countries (Brazil, Colombia, Cuba, Malaysia, and Mexico), one lower middle-income country (Egypt), and six high-income countries (Germany, Ireland, Norway, Portugal, Taiwan, and the Netherlands) were analyzed. The pharmacoeconomic guidelines in SA differ in important areas when compared with other countries. In SA, the study perspective and costs are limited to private health-insurance companies, complex modelling is discouraged and models require pre-approval, equity issues are not explicitly stated, a budget impact analysis is not required, and pharmacoeconomic submissions are voluntary. Future updates to the pharmacoeconomic guidelines in SA may include a societal perspective with limitations, incentivize complex and transparent models, and integrate equity issues. The pharmacoeconomic guidelines could be improved by addressing conflicting objectives with policies on National Health Insurance, incentivize private health insurance companies to disclose reimbursement data, and require the inclusion of a budget impact analysis in all pharmacoeconomic submissions. Further research is also needed on the impact of mandatory pharmacoeconomic submissions in middle-income countries.

Assumptions to the Annual Energy Outlook

EIA Publications

2017-01-01

This report presents the major assumptions of the National Energy Modeling System (NEMS) used to generate the projections in the Annual Energy Outlook, including general features of the model structure, assumptions concerning energy markets, and the key input data and parameters that are the most significant in formulating the model results.

Improved workflow modelling using role activity diagram-based modelling with application to a radiology service case study.

PubMed

Shukla, Nagesh; Keast, John E; Ceglarek, Darek

2014-10-01

The modelling of complex workflows is an important problem-solving technique within healthcare settings. However, currently most of the workflow models use a simplified flow chart of patient flow obtained using on-site observations, group-based debates and brainstorming sessions, together with historic patient data. This paper presents a systematic and semi-automatic methodology for knowledge acquisition with detailed process representation using sequential interviews of people in the key roles involved in the service delivery process. The proposed methodology allows the modelling of roles, interactions, actions, and decisions involved in the service delivery process. This approach is based on protocol generation and analysis techniques such as: (i) initial protocol generation based on qualitative interviews of radiology staff, (ii) extraction of key features of the service delivery process, (iii) discovering the relationships among the key features extracted, and, (iv) a graphical representation of the final structured model of the service delivery process. The methodology is demonstrated through a case study of a magnetic resonance (MR) scanning service-delivery process in the radiology department of a large hospital. A set of guidelines is also presented in this paper to visually analyze the resulting process model for identifying process vulnerabilities. A comparative analysis of different workflow models is also conducted. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Modeling sports highlights using a time-series clustering framework and model interpretation

NASA Astrophysics Data System (ADS)

Radhakrishnan, Regunathan; Otsuka, Isao; Xiong, Ziyou; Divakaran, Ajay

2005-01-01

In our past work on sports highlights extraction, we have shown the utility of detecting audience reaction using an audio classification framework. The audio classes in the framework were chosen based on intuition. In this paper, we present a systematic way of identifying the key audio classes for sports highlights extraction using a time series clustering framework. We treat the low-level audio features as a time series and model the highlight segments as "unusual" events in a background of an "usual" process. The set of audio classes to characterize the sports domain is then identified by analyzing the consistent patterns in each of the clusters output from the time series clustering framework. The distribution of features from the training data so obtained for each of the key audio classes, is parameterized by a Minimum Description Length Gaussian Mixture Model (MDL-GMM). We also interpret the meaning of each of the mixture components of the MDL-GMM for the key audio class (the "highlight" class) that is correlated with highlight moments. Our results show that the "highlight" class is a mixture of audience cheering and commentator's excited speech. Furthermore, we show that the precision-recall performance for highlights extraction based on this "highlight" class is better than that of our previous approach which uses only audience cheering as the key highlight class.

Unravelling Some of the Key Transformations in the Hydrothermal Liquefaction of Lignin.

PubMed

Lui, Matthew Y; Chan, Bun; Yuen, Alexander K L; Masters, Anthony F; Montoya, Alejandro; Maschmeyer, Thomas

2017-05-22

Using both experimental and computational methods, focusing on intermediates and model compounds, some of the main features of the reaction mechanisms that operate during the hydrothermal processing of lignin were elucidated. Key reaction pathways and their connection to different structural features of lignin were proposed. Under neutral conditions, subcritical water was demonstrated to act as a bifunctional acid/base catalyst for the dissection of lignin structures. In a complex web of mutually dependent interactions, guaiacyl units within lignin were shown to significantly affect overall lignin reactivity. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

On the persistence and coherence of subpolar sea surface temperature and salinity anomalies associated with the Atlantic multidecadal variability

NASA Astrophysics Data System (ADS)

Zhang, Rong

2017-08-01

This study identifies key features associated with the Atlantic multidecadal variability (AMV) in both observations and a fully coupled climate model, e.g., decadal persistence of monthly mean subpolar North Atlantic (NA) sea surface temperature (SST) and salinity (SSS) anomalies, and high coherence at low frequency among subpolar NA SST/SSS, upper ocean heat/salt content, and the Atlantic Meridional Overturning Circulation (AMOC) fingerprint. These key AMV features, which can be used to distinguish the AMV mechanism, cannot be explained by the slab ocean model results or the red noise process but are consistent with the ocean dynamics mechanism. This study also shows that at low frequency, the correlation and regression between net surface heat flux and SST anomalies are key indicators of the relative roles of oceanic versus atmospheric forcing in SST anomalies. The oceanic forcing plays a dominant role in the subpolar NA SST anomalies associated with the AMV.

Iris recognition based on key image feature extraction.

PubMed

Ren, X; Tian, Q; Zhang, J; Wu, S; Zeng, Y

2008-01-01

In iris recognition, feature extraction can be influenced by factors such as illumination and contrast, and thus the features extracted may be unreliable, which can cause a high rate of false results in iris pattern recognition. In order to obtain stable features, an algorithm was proposed in this paper to extract key features of a pattern from multiple images. The proposed algorithm built an iris feature template by extracting key features and performed iris identity enrolment. Simulation results showed that the selected key features have high recognition accuracy on the CASIA Iris Set, where both contrast and illumination variance exist.

A modular approach for item response theory modeling with the R package flirt.

PubMed

Jeon, Minjeong; Rijmen, Frank

2016-06-01

The new R package flirt is introduced for flexible item response theory (IRT) modeling of psychological, educational, and behavior assessment data. flirt integrates a generalized linear and nonlinear mixed modeling framework with graphical model theory. The graphical model framework allows for efficient maximum likelihood estimation. The key feature of flirt is its modular approach to facilitate convenient and flexible model specifications. Researchers can construct customized IRT models by simply selecting various modeling modules, such as parametric forms, number of dimensions, item and person covariates, person groups, link functions, etc. In this paper, we describe major features of flirt and provide examples to illustrate how flirt works in practice.

Annotation-Based Learner's Personality Modeling in Distance Learning Context

ERIC Educational Resources Information Center

Omheni, Nizar; Kalboussi, Anis; Mazhoud, Omar; Kacem, Ahmed Hadj

2016-01-01

Researchers in distance education are interested in observing and modeling learners' personality profiles, and adapting their learning experiences accordingly. When learners read and interact with their reading materials, they do unselfconscious activities like annotation which may be key feature of their personalities. Annotation activity…

Techniques for Improved Retrospective Fine-scale Meteorology

EPA Science Inventory

Pleim-Xiu Land-Surface model (PX LSM) was developed for retrospective meteorological simulations to drive chemical transport models. One of the key features of the PX LSM is the indirect soil moisture and temperature nudging. The idea is to provide a three hourly 2-m temperature ...

Combustion system CFD modeling at GE Aircraft Engines

NASA Technical Reports Server (NTRS)

Burrus, D.; Mongia, H.; Tolpadi, Anil K.; Correa, S.; Braaten, M.

1995-01-01

This viewgraph presentation discusses key features of current combustion system CFD modeling capabilities at GE Aircraft Engines provided by the CONCERT code; CONCERT development history; modeling applied for designing engine combustion systems; modeling applied to improve fundamental understanding; CONCERT3D results for current production combustors; CONCERT3D model of NASA/GE E3 combustor; HYBRID CONCERT CFD/Monte-Carlo modeling approach; and future modeling directions.

Combustion system CFD modeling at GE Aircraft Engines

NASA Astrophysics Data System (ADS)

Burrus, D.; Mongia, H.; Tolpadi, Anil K.; Correa, S.; Braaten, M.

1995-03-01

This viewgraph presentation discusses key features of current combustion system CFD modeling capabilities at GE Aircraft Engines provided by the CONCERT code; CONCERT development history; modeling applied for designing engine combustion systems; modeling applied to improve fundamental understanding; CONCERT3D results for current production combustors; CONCERT3D model of NASA/GE E3 combustor; HYBRID CONCERT CFD/Monte-Carlo modeling approach; and future modeling directions.

Toward Best Practice: An Analysis of the Efficacy of Curriculum Models in Gifted Education

ERIC Educational Resources Information Center

VanTassel-Baska, Joyce; Brown, Elissa F.

2007-01-01

This article provides an overview of existing research on 11 curriculum models in the field of gifted education, including the schoolwide enrichment model and the talent search model, and several others that have been used to shape high-level learning experiences for gifted students. The models are critiqued according to the key features they…

Using Analogy and Model to Enhance Conceptual Change in Thai Middle School Students

ERIC Educational Resources Information Center

Wichaidit, Sittichai; Wongyounoi, Somson; Dechsri, Precharn; Chaivisuthangkura, Parin

2011-01-01

This study examined conceptual change of Thai middle school students after learning photosynthesis with analogy and model. The analogy mapped key features from the analog (cooking food) to the target concept (photosynthesis). Modeling photosynthesis activity provided the opportunity for students to understand how plants use sugar to synthesize…

A Formal Model of Capacity Limits in Working Memory

ERIC Educational Resources Information Center

Oberauer, Klaus; Kliegl, Reinhold

2006-01-01

A mathematical model of working-memory capacity limits is proposed on the key assumption of mutual interference between items in working memory. Interference is assumed to arise from overwriting of features shared by these items. The model was fit to time-accuracy data of memory-updating tasks from four experiments using nonlinear mixed effect…

A Scenario-Based Protocol Checker for Public-Key Authentication Scheme

NASA Astrophysics Data System (ADS)

Saito, Takamichi

Security protocol provides communication security for the internet. One of the important features of it is authentication with key exchange. Its correctness is a requirement of the whole of the communication security. In this paper, we introduce three attack models realized as their attack scenarios, and provide an authentication-protocol checker for applying three attack-scenarios based on the models. We also utilize it to check two popular security protocols: Secure SHell (SSH) and Secure Socket Layer/Transport Layer Security (SSL/TLS).

Feature inference with uncertain categorization: Re-assessing Anderson's rational model.

PubMed

Konovalova, Elizaveta; Le Mens, Gaël

2017-09-18

A key function of categories is to help predictions about unobserved features of objects. At the same time, humans are often in situations where the categories of the objects they perceive are uncertain. In an influential paper, Anderson (Psychological Review, 98(3), 409-429, 1991) proposed a rational model for feature inferences with uncertain categorization. A crucial feature of this model is the conditional independence assumption-it assumes that the within category feature correlation is zero. In prior research, this model has been found to provide a poor fit to participants' inferences. This evidence is restricted to task environments inconsistent with the conditional independence assumption. Currently available evidence thus provides little information about how this model would fit participants' inferences in a setting with conditional independence. In four experiments based on a novel paradigm and one experiment based on an existing paradigm, we assess the performance of Anderson's model under conditional independence. We find that this model predicts participants' inferences better than competing models. One model assumes that inferences are based on just the most likely category. The second model is insensitive to categories but sensitive to overall feature correlation. The performance of Anderson's model is evidence that inferences were influenced not only by the more likely category but also by the other candidate category. Our findings suggest that a version of Anderson's model which relaxes the conditional independence assumption will likely perform well in environments characterized by within-category feature correlation.

Standoff Human Identification Using Body Shape

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

Matzner, Shari; Heredia-Langner, Alejandro; Amidan, Brett G.

2015-09-01

The ability to identify individuals is a key component of maintaining safety and security in public spaces and around critical infrastructure. Monitoring an open space is challenging because individuals must be identified and re-identified from a standoff distance nonintrusively, making methods like fingerprinting and even facial recognition impractical. We propose using body shape features as a means for identification from standoff sensing, either complementing other identifiers or as an alternative. An important challenge in monitoring open spaces is reconstructing identifying features when only a partial observation is available, because of the view-angle limitations and occlusion or subject pose changes. Tomore » address this challenge, we investigated the minimum number of features required for a high probability of correct identification, and we developed models for predicting a key body feature—height—from a limited set of observed features. We found that any set of nine randomly selected body measurements was sufficient to correctly identify an individual in a dataset of 4426 subjects. For predicting height, anthropometric measures were investigated for correlation with height. Their correlation coefficients and associated linear models were reported. These results—a sufficient number of features for identification and height prediction from a single feature—contribute to developing systems for standoff identification when views of a subject are limited.« less

The PDS4 Metadata Management System

NASA Astrophysics Data System (ADS)

Raugh, A. C.; Hughes, J. S.

2018-04-01

We present the key features of the Planetary Data System (PDS) PDS4 Information Model as an extendable metadata management system for planetary metadata related to data structure, analysis/interpretation, and provenance.

Summary of the key features of seven biomathematical models of human fatigue and performance.

PubMed

Mallis, Melissa M; Mejdal, Sig; Nguyen, Tammy T; Dinges, David F

2004-03-01

Biomathematical models that quantify the effects of circadian and sleep/wake processes on the regulation of alertness and performance have been developed in an effort to predict the magnitude and timing of fatigue-related responses in a variety of contexts (e.g., transmeridian travel, sustained operations, shift work). This paper summarizes key features of seven biomathematical models reviewed as part of the Fatigue and Performance Modeling Workshop held in Seattle, WA, on June 13-14, 2002. The Workshop was jointly sponsored by the National Aeronautics and Space Administration, U.S. Department of Defense, U.S. Army Medical Research and Materiel Command, Office of Naval Research, Air Force Office of Scientific Research, and U.S. Department of Transportation. An invitation was sent to developers of seven biomathematical models that were commonly cited in scientific literature and/or supported by government funding. On acceptance of the invitation to attend the Workshop, developers were asked to complete a survey of the goals, capabilities, inputs, and outputs of their biomathematical models of alertness and performance. Data from the completed surveys were summarized and juxtaposed to provide a framework for comparing features of the seven models. Survey responses revealed that models varied greatly relative to their reported goals and capabilities. While all modelers reported that circadian factors were key components of their capabilities, they differed markedly with regard to the roles of sleep and work times as input factors for prediction: four of the seven models had work time as their sole input variable(s), while the other three models relied on various aspects of sleep timing for model input. Models also differed relative to outputs: five sought to predict results from laboratory experiments, field, and operational data, while two models were developed without regard to predicting laboratory experimental results. All modelers provided published papers describing their models, with three of the models being proprietary. Although all models appear to have been fundamentally influenced by the two-process model of sleep regulation by Borbély, there is considerable diversity among them in the number and type of input and output variables, and their stated goals and capabilities.

Summary of the key features of seven biomathematical models of human fatigue and performance

NASA Technical Reports Server (NTRS)

Mallis, Melissa M.; Mejdal, Sig; Nguyen, Tammy T.; Dinges, David F.

2004-01-01

BACKGROUND: Biomathematical models that quantify the effects of circadian and sleep/wake processes on the regulation of alertness and performance have been developed in an effort to predict the magnitude and timing of fatigue-related responses in a variety of contexts (e.g., transmeridian travel, sustained operations, shift work). This paper summarizes key features of seven biomathematical models reviewed as part of the Fatigue and Performance Modeling Workshop held in Seattle, WA, on June 13-14, 2002. The Workshop was jointly sponsored by the National Aeronautics and Space Administration, U.S. Department of Defense, U.S. Army Medical Research and Materiel Command, Office of Naval Research, Air Force Office of Scientific Research, and U.S. Department of Transportation. METHODS: An invitation was sent to developers of seven biomathematical models that were commonly cited in scientific literature and/or supported by government funding. On acceptance of the invitation to attend the Workshop, developers were asked to complete a survey of the goals, capabilities, inputs, and outputs of their biomathematical models of alertness and performance. Data from the completed surveys were summarized and juxtaposed to provide a framework for comparing features of the seven models. RESULTS: Survey responses revealed that models varied greatly relative to their reported goals and capabilities. While all modelers reported that circadian factors were key components of their capabilities, they differed markedly with regard to the roles of sleep and work times as input factors for prediction: four of the seven models had work time as their sole input variable(s), while the other three models relied on various aspects of sleep timing for model input. Models also differed relative to outputs: five sought to predict results from laboratory experiments, field, and operational data, while two models were developed without regard to predicting laboratory experimental results. All modelers provided published papers describing their models, with three of the models being proprietary. CONCLUSIONS: Although all models appear to have been fundamentally influenced by the two-process model of sleep regulation by Borbely, there is considerable diversity among them in the number and type of input and output variables, and their stated goals and capabilities.

Community Crowd-Funded Solar Finance

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

Jagerson, Gordon "Ty"

The award supported the demonstration and development of the Village Power Platform, which enables community organizations to more readily develop, finance and operate solar installations on local community organizations. The platform enables partial or complete local ownership of the solar installation. The award specifically supported key features including financial modeling tools, community communications tools, crowdfunding mechanisms, a mobile app, and other critical features.

A Cognitive Perspective in the Treatment of Incarcerated Clients.

ERIC Educational Resources Information Center

Walsh, Thomas C.

1990-01-01

Proposes a cognitive therapy model as a workable approach in treating incarcerated clients. Reviews principal components and techniques of cognitive theory. Uses case vignettes to illustrate application of this approach. Delineates key features of cognitive model which relate to treatment of incarcerated population. (Author/ABL)

Baicalein Reduces Airway Injury in Allergen and IL-13 Induced Airway Inflammation

PubMed Central

Mabalirajan, Ulaganathan; Ahmad, Tanveer; Rehman, Rakhshinda; Leishangthem, Geeta Devi; Dinda, Amit Kumar; Agrawal, Anurag; Ghosh, Balaram; Sharma, Surendra Kumar

2013-01-01

Background Baicalein, a bioflavone present in the dry roots of Scutellaria baicalensis Georgi, is known to reduce eotaxin production in human fibroblasts. However, there are no reports of its anti-asthma activity or its effect on airway injury. Methodology/Principal Findings In a standard experimental asthma model, male Balb/c mice that were sensitized with ovalbumin (OVA), treated with baicalein (10 mg/kg, ip) or a vehicle control, either during (preventive use) or after OVA challenge (therapeutic use). In an alternate model, baicalein was administered to male Balb/c mice which were given either IL-4 or IL-13 intranasally. Features of asthma were determined by estimating airway hyperresponsiveness (AHR), histopathological changes and biochemical assays of key inflammatory molecules. Airway injury was determined with apoptotic assays, transmission electron microscopy and assessing key mitochondrial functions. Baicalein treatment reduced AHR and inflammation in both experimental models. TGF-β1, sub-epithelial fibrosis and goblet cell metaplasia, were also reduced. Furthermore, baicalein treatment significantly reduced 12/15-LOX activity, features of mitochondrial dysfunctions, and apoptosis of bronchial epithelia. Conclusion/Significance Our findings demonstrate that baicalein can attenuate important features of asthma, possibly through the reduction of airway injury and restoration of mitochondrial function. PMID:23646158

A finite element analysis of novel vented dental abutment geometries for cement‐retained crown restorations

PubMed Central

Rodriguez, Lucas C.; Saba, Juliana N.; Meyer, Clark A.; Chung, Kwok‐Hung; Wadhwani, Chandur

2016-01-01

Abstract Recent literature indicates that the long‐term success of dental implants is, in part, attributed to how dental crowns are attached to their associated implants. The commonly utilized method for crown attachment – cementation, has been criticized because of recent links between residual cement and peri‐implant disease. Residual cement extrusion from crown‐abutment margins post‐crown seating is a growing concern. This study aimed at (1) identifying key abutment features, which would improve dental cement flow characteristics, and (2) understanding how these features would impact the mechanical stability of the abutment under functional loads. Computational fluid dynamic modeling was used to evaluate cement flow in novel abutment geometries. These models were then evaluated using 3D‐printed surrogate models. Finite element analysis also provided an understanding of how the mechanical stability of these abutments was altered after key features were incorporated into the geometry. The findings demonstrated that the key features involved in improved venting of the abutment during crown seating were (1) addition of vents, (2) diameter of the vents, (3) location of the vents, (4) addition of a plastic screw insert, and (5) thickness of the abutment wall. This study culminated in a novel design for a vented abutment consisting of 8 vents located radially around the abutment neck‐margin plus a plastic insert to guide the cement during seating and provide retrievability to the abutment system.Venting of the dental abutment has been shown to decrease the risk of undetected residual dental cement post‐cement‐retained crown seating. This article will utilize a finite element analysis approach toward optimizing dental abutment designs for improved dental cement venting. Features investigated include (1) addition of vents, (2) diameter of vents, (3) location of vents, (4) addition of plastic screw insert, and (5) thickness of abutment wall. PMID:29744160

A finite element analysis of novel vented dental abutment geometries for cement-retained crown restorations.

PubMed

Rodriguez, Lucas C; Saba, Juliana N; Meyer, Clark A; Chung, Kwok-Hung; Wadhwani, Chandur; Rodrigues, Danieli C

2016-11-01

Recent literature indicates that the long-term success of dental implants is, in part, attributed to how dental crowns are attached to their associated implants. The commonly utilized method for crown attachment - cementation, has been criticized because of recent links between residual cement and peri-implant disease. Residual cement extrusion from crown-abutment margins post-crown seating is a growing concern. This study aimed at (1) identifying key abutment features, which would improve dental cement flow characteristics, and (2) understanding how these features would impact the mechanical stability of the abutment under functional loads. Computational fluid dynamic modeling was used to evaluate cement flow in novel abutment geometries. These models were then evaluated using 3D-printed surrogate models. Finite element analysis also provided an understanding of how the mechanical stability of these abutments was altered after key features were incorporated into the geometry. The findings demonstrated that the key features involved in improved venting of the abutment during crown seating were (1) addition of vents, (2) diameter of the vents, (3) location of the vents, (4) addition of a plastic screw insert, and (5) thickness of the abutment wall. This study culminated in a novel design for a vented abutment consisting of 8 vents located radially around the abutment neck-margin plus a plastic insert to guide the cement during seating and provide retrievability to the abutment system.Venting of the dental abutment has been shown to decrease the risk of undetected residual dental cement post-cement-retained crown seating. This article will utilize a finite element analysis approach toward optimizing dental abutment designs for improved dental cement venting. Features investigated include (1) addition of vents, (2) diameter of vents, (3) location of vents, (4) addition of plastic screw insert, and (5) thickness of abutment wall.

Single Cell Mathematical Model Successfully Replicates Key Features of GBM: Go-Or-Grow Is Not Necessary.

PubMed

Scribner, Elizabeth; Fathallah-Shaykh, Hassan M

2017-01-01

Glioblastoma (GBM) is a malignant brain tumor that continues to be associated with neurological morbidity and poor survival times. Brain invasion is a fundamental property of malignant glioma cells. The Go-or-Grow (GoG) phenotype proposes that cancer cell motility and proliferation are mutually exclusive. Here, we construct and apply a single glioma cell mathematical model that includes motility and angiogenesis and lacks the GoG phenotype. Simulations replicate key features of GBM including its multilayer structure (i.e.edema, enhancement, and necrosis), its progression patterns associated with bevacizumab treatment, and replicate the survival times of GBM treated or untreated with bevacizumab. These results suggest that the GoG phenotype is not a necessary property for the formation of the multilayer structure, recurrence patterns, and the poor survival times of patients diagnosed with GBM.

Registration algorithm of point clouds based on multiscale normal features

NASA Astrophysics Data System (ADS)

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

2015-01-01

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

A School-Based Professional Development Programme for Teachers of Mathematical Modelling in Singapore

ERIC Educational Resources Information Center

Tan, Liang Soon; Ang, Keng Cheng

2016-01-01

A school-based professional development programme (SBPD) aimed at developing secondary school mathematics teachers' competencies to teach mathematical modelling in Singapore is presented and evaluated in this article. The SBPD is characterized by two key features--content elements to develop teachers' knowledge and skills, and transformative…

THE NEW NORRIS HOUSE: A SUSTAINABLE HOME FOR THE 21ST CENTURY

EPA Science Inventory

In 1933 the Tennessee valley Authority constructed a model community, Norris, Tennessee, as part of the Norris Dam construction project. A key feature of this New Deal village was the Norris House, a series of home designs built as models for modern, efficient, and sustainable...

Community LINE Source Model (C-LINE)

EPA Science Inventory

This presentation provides an introduction for the live demo and explains the purpose of C-LINE and its key features. C-LINE is a web-based model designed to inform the community user of local air quality impacts due to mobile-sources in their region of interest using a simplifie...

Resource Room Model for Inclusive Education in China: Practitioners' Conceptualisation and Contextualisation

ERIC Educational Resources Information Center

Poon-McBrayer, Kim Fong

2016-01-01

China launched the "learning in a regular classroom" (LRC) model for inclusive education in the 1980s. In late 1990s, a few major cities of China began to adopt the resource room model as a key feature of the LRC to improve instructional qualities. This exploratory study examined resource teachers' (RTs) attitude towards inclusive…

Examples of Mathematical Modeling

PubMed Central

Johnston, Matthew D.; Edwards, Carina M.; Bodmer, Walter F.; Maini, Philip K.; Chapman, S. Jonathan

2008-01-01

Mathematical modeling is being increasingly recognized within the biomedical sciences as an important tool that can aid the understanding of biological systems. The heavily regulated cell renewal cycle in the colonic crypt provides a good example of how modeling can be used to find out key features of the system kinetics, and help to explain both the breakdown of homeostasis and the initiation of tumorigenesis. We use the cell population model by Johnston et al.5 to illustrate the power of mathematical modeling by considering two key questions about the cell population dynamics in the colonic crypt. We ask: how can a model describe both homeostasis and unregulated growth in tumorigenesis; and to which parameters in the system is the model most sensitive? In order to address these questions, we discuss what type of modeling approach is most appropriate in the crypt. We use the model to argue why tumorigenesis is observed to occur in stages with long lag phases between periods of rapid growth, and we identify the key parameters. PMID:17873520

[A novel method of multi-channel feature extraction combining multivariate autoregression and multiple-linear principal component analysis].

PubMed

Wang, Jinjia; Zhang, Yanna

2015-02-01

Brain-computer interface (BCI) systems identify brain signals through extracting features from them. In view of the limitations of the autoregressive model feature extraction method and the traditional principal component analysis to deal with the multichannel signals, this paper presents a multichannel feature extraction method that multivariate autoregressive (MVAR) model combined with the multiple-linear principal component analysis (MPCA), and used for magnetoencephalography (MEG) signals and electroencephalograph (EEG) signals recognition. Firstly, we calculated the MVAR model coefficient matrix of the MEG/EEG signals using this method, and then reduced the dimensions to a lower one, using MPCA. Finally, we recognized brain signals by Bayes Classifier. The key innovation we introduced in our investigation showed that we extended the traditional single-channel feature extraction method to the case of multi-channel one. We then carried out the experiments using the data groups of IV-III and IV - I. The experimental results proved that the method proposed in this paper was feasible.

OpenCL: A Parallel Programming Standard for Heterogeneous Computing Systems.

PubMed

Stone, John E; Gohara, David; Shi, Guochun

2010-05-01

We provide an overview of the key architectural features of recent microprocessor designs and describe the programming model and abstractions provided by OpenCL, a new parallel programming standard targeting these architectures.

A Model-based Approach to Controlling the ST-5 Constellation Lights-Out Using the GMSEC Message Bus and Simulink

NASA Technical Reports Server (NTRS)

Witt, Kenneth J.; Stanley, Jason; Shendock, Robert; Mandl, Daniel

2005-01-01

Space Technology 5 (ST-5) is a three-satellite constellation, technology validation mission under the New Millennium Program at NASA to be launched in March 2006. One of the key technologies to be validated is a lights-out, model-based operations approach to be used for one week to control the ST-5 constellation with no manual intervention. The ground architecture features the GSFC Mission Services Evolution Center (GMSEC) middleware, which allows easy plugging in of software components and a standardized messaging protocol over a software bus. A predictive modeling tool built on MatLab's Simulink software package makes use of the GMSEC standard messaging protocol to interface to the Advanced Mission Planning System (AMPS) Scenario Scheduler which controls all activities, resource allocation and real-time re-profiling of constellation resources when non-nominal events occur. The key features of this system, which we refer to as the ST-5 Simulink system, are as follows: Original daily plan is checked to make sure that predicted resources needed are available by comparing the plan against the model. As the plan is run in real-time, the system re-profiles future activities in real-time if planned activities do not occur in the predicted timeframe or fashion. Alert messages are sent out on the GMSEC bus by the system if future predicted problems are detected. This will allow the Scenario Scheduler to correct the situation before the problem happens. The predictive model is evolved automatically over time via telemetry updates thus reducing the cost of implementing and maintaining the models by an order of magnitude from previous efforts at GSFC such as the model-based system built for MAP in the mid-1990's. This paper will describe the key features, lessons learned and implications for future missions once this system is successfully validated on-orbit in 2006.

Requirements' Role in Mobilizing and Enabling Design Conversation

NASA Astrophysics Data System (ADS)

Bergman, Mark

Requirements play a critical role in a design conversation of systems and products. Product and system design exists at the crossroads of problems, solutions and requirements. Requirements contextualize problems and solutions, pointing the way to feasible outcomes. These are captured with models and detailed specifications. Still, stakeholders need to be able to understand one-another using shared design representations in order to mobilize bias and transform knowledge towards legitimized, desired results. Many modern modeling languages, including UML, as well as detailed, logic-based specifications are beyond the comprehension of key stakeholders. Hence, they inhibit, rather than promote design conversation. Improved design boundary objects (DBO), especially design requirements boundary objects (DRBO), need to be created and refined to improve the communications between principals. Four key features of design boundary objects that improve and promote design conversation are discussed in detail. A systems analysis and design case study is presented which demonstrates these features in action. It describes how a small team of analysts worked with key stakeholders to mobilize and guide a complex system design discussion towards an unexpected, yet desired outcome within a short time frame.

Core-oscillator model of Caulobacter crescentus

NASA Astrophysics Data System (ADS)

Vandecan, Yves; Biondi, Emanuele; Blossey, Ralf

2016-06-01

The gram-negative bacterium Caulobacter crescentus is a powerful model organism for studies of bacterial cell cycle regulation. Although the major regulators and their connections in Caulobacter have been identified, it still is a challenge to properly understand the dynamics of its circuitry which accounts for both cell cycle progression and arrest. We show that the key decision module in Caulobacter is built from a limit cycle oscillator which controls the DNA replication program. The effect of an induced cell cycle arrest is demonstrated to be a key feature to classify the underlying dynamics.

Research on improving image recognition robustness by combining multiple features with associative memory

NASA Astrophysics Data System (ADS)

Guo, Dongwei; Wang, Zhe

2018-05-01

Convolutional neural networks (CNN) achieve great success in computer vision, it can learn hierarchical representation from raw pixels and has outstanding performance in various image recognition tasks [1]. However, CNN is easy to be fraudulent in terms of it is possible to produce images totally unrecognizable to human eyes that CNNs believe with near certainty are familiar objects. [2]. In this paper, an associative memory model based on multiple features is proposed. Within this model, feature extraction and classification are carried out by CNN, T-SNE and exponential bidirectional associative memory neural network (EBAM). The geometric features extracted from CNN and the digital features extracted from T-SNE are associated by EBAM. Thus we ensure the recognition of robustness by a comprehensive assessment of the two features. In our model, we can get only 8% error rate with fraudulent data. In systems that require a high safety factor or some key areas, strong robustness is extremely important, if we can ensure the image recognition robustness, network security will be greatly improved and the social production efficiency will be extremely enhanced.

The feature-weighted receptive field: an interpretable encoding model for complex feature spaces.

PubMed

St-Yves, Ghislain; Naselaris, Thomas

2017-06-20

We introduce the feature-weighted receptive field (fwRF), an encoding model designed to balance expressiveness, interpretability and scalability. The fwRF is organized around the notion of a feature map-a transformation of visual stimuli into visual features that preserves the topology of visual space (but not necessarily the native resolution of the stimulus). The key assumption of the fwRF model is that activity in each voxel encodes variation in a spatially localized region across multiple feature maps. This region is fixed for all feature maps; however, the contribution of each feature map to voxel activity is weighted. Thus, the model has two separable sets of parameters: "where" parameters that characterize the location and extent of pooling over visual features, and "what" parameters that characterize tuning to visual features. The "where" parameters are analogous to classical receptive fields, while "what" parameters are analogous to classical tuning functions. By treating these as separable parameters, the fwRF model complexity is independent of the resolution of the underlying feature maps. This makes it possible to estimate models with thousands of high-resolution feature maps from relatively small amounts of data. Once a fwRF model has been estimated from data, spatial pooling and feature tuning can be read-off directly with no (or very little) additional post-processing or in-silico experimentation. We describe an optimization algorithm for estimating fwRF models from data acquired during standard visual neuroimaging experiments. We then demonstrate the model's application to two distinct sets of features: Gabor wavelets and features supplied by a deep convolutional neural network. We show that when Gabor feature maps are used, the fwRF model recovers receptive fields and spatial frequency tuning functions consistent with known organizational principles of the visual cortex. We also show that a fwRF model can be used to regress entire deep convolutional networks against brain activity. The ability to use whole networks in a single encoding model yields state-of-the-art prediction accuracy. Our results suggest a wide variety of uses for the feature-weighted receptive field model, from retinotopic mapping with natural scenes, to regressing the activities of whole deep neural networks onto measured brain activity. Copyright © 2017. Published by Elsevier Inc.

OpenCL: A Parallel Programming Standard for Heterogeneous Computing Systems

PubMed Central

Stone, John E.; Gohara, David; Shi, Guochun

2010-01-01

We provide an overview of the key architectural features of recent microprocessor designs and describe the programming model and abstractions provided by OpenCL, a new parallel programming standard targeting these architectures. PMID:21037981

The Process of Change in Higher Education Institutions. AAHE-ERIC/Higher Education Research Report, No. 7, 1982.

ERIC Educational Resources Information Center

Nordvall, Robert C.

Conditions that inhibit change in higher education institutions and various models of the change process are described. Attention is also directed to: organizational character, structural features, planning procedures, key individuals in the change process, and practical advice about change. The major change models for higher education…

UPLOAD: THE NEW NORRIS HOUSE – A SUSTAINABLE HOME FOR THE 21ST CENTURY

EPA Science Inventory

In 1933 the Tennessee Valley Authority constructed a model community, Norris, Tennessee, as part of the Norris Dam construction project. A key feature of this New Deal village was the Norris House, a series of home designs built as models for modern, efficient, and sustain...

Early Admissions at Selective Colleges. NBER Working Paper No. 14844

ERIC Educational Resources Information Center

Avery, Christopher; Levin, Jonathan D.

2009-01-01

Early admissions is widely used by selective colleges and universities. We identify some basic facts about early admissions policies, including the admissions advantage enjoyed by early applicants and patterns in application behavior, and propose a game-theoretic model that matches these facts. The key feature of the model is that colleges want to…

Work-Based Learning and Continuing Professional Development

ERIC Educational Resources Information Center

Sobiechowska, Paula; Maisch, Maire

2007-01-01

Purpose: The purpose of this paper is to provide an evaluation of the key features of a work-based, competency-led curriculum model of continuing professional development for social workers and to present a revised model, which addresses the issues that arise for learners pursuing continuing professional and academic development (CPD) within a…

World-Class Higher Education and the Emerging Chinese Model of the University

ERIC Educational Resources Information Center

Li, Jun

2012-01-01

China's recent quest to develop world-class universities is a significant phenomenon within the worldwide transformation of tertiary education. Taking a cultural approach and drawing on empirical findings, this article investigates the emerging Chinese model of the university, considering its key features and contributions to global communities.…

Systemic Modelling for Relating Labour Market to Vocational Education

ERIC Educational Resources Information Center

Papakitsos, Evangelos C.

2016-01-01

The present study introduces a systemic model that demonstrates a description of the relationship between the labour-market and vocational education from the perspective of systemic theory. Based on the application of the relevant methodology, the two open social systems are identified and analyzed. Their key-features are presented and the points…

A Model for Effective Implementation of Flexible Programme Delivery

ERIC Educational Resources Information Center

Normand, Carey; Littlejohn, Allison; Falconer, Isobel

2008-01-01

The model developed here is the outcome of a project funded by the Quality Assurance Agency Scotland to support implementation of flexible programme delivery (FPD) in post-compulsory education. We highlight key features of FPD, including explicit and implicit assumptions about why flexibility is needed and the perceived barriers and solutions to…

Characterizing and modeling the dynamics of online popularity.

PubMed

Ratkiewicz, Jacob; Fortunato, Santo; Flammini, Alessandro; Menczer, Filippo; Vespignani, Alessandro

2010-10-08

Online popularity has an enormous impact on opinions, culture, policy, and profits. We provide a quantitative, large scale, temporal analysis of the dynamics of online content popularity in two massive model systems: the Wikipedia and an entire country's Web space. We find that the dynamics of popularity are characterized by bursts, displaying characteristic features of critical systems such as fat-tailed distributions of magnitude and interevent time. We propose a minimal model combining the classic preferential popularity increase mechanism with the occurrence of random popularity shifts due to exogenous factors. The model recovers the critical features observed in the empirical analysis of the systems analyzed here, highlighting the key factors needed in the description of popularity dynamics.

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.

A Novel Multi-Class Ensemble Model for Classifying Imbalanced Biomedical Datasets

NASA Astrophysics Data System (ADS)

Bikku, Thulasi; Sambasiva Rao, N., Dr; Rao, Akepogu Ananda, Dr

2017-08-01

This paper mainly focuseson developing aHadoop based framework for feature selection and classification models to classify high dimensionality data in heterogeneous biomedical databases. Wide research has been performing in the fields of Machine learning, Big data and Data mining for identifying patterns. The main challenge is extracting useful features generated from diverse biological systems. The proposed model can be used for predicting diseases in various applications and identifying the features relevant to particular diseases. There is an exponential growth of biomedical repositories such as PubMed and Medline, an accurate predictive model is essential for knowledge discovery in Hadoop environment. Extracting key features from unstructured documents often lead to uncertain results due to outliers and missing values. In this paper, we proposed a two phase map-reduce framework with text preprocessor and classification model. In the first phase, mapper based preprocessing method was designed to eliminate irrelevant features, missing values and outliers from the biomedical data. In the second phase, a Map-Reduce based multi-class ensemble decision tree model was designed and implemented in the preprocessed mapper data to improve the true positive rate and computational time. The experimental results on the complex biomedical datasets show that the performance of our proposed Hadoop based multi-class ensemble model significantly outperforms state-of-the-art baselines.

Secure image retrieval with multiple keys

NASA Astrophysics Data System (ADS)

Liang, Haihua; Zhang, Xinpeng; Wei, Qiuhan; Cheng, Hang

2018-03-01

This article proposes a secure image retrieval scheme under a multiuser scenario. In this scheme, the owner first encrypts and uploads images and their corresponding features to the cloud; then, the user submits the encrypted feature of the query image to the cloud; next, the cloud compares the encrypted features and returns encrypted images with similar content to the user. To find the nearest neighbor in the encrypted features, an encryption with multiple keys is proposed, in which the query feature of each user is encrypted by his/her own key. To improve the key security and space utilization, global optimization and Gaussian distribution are, respectively, employed to generate multiple keys. The experiments show that the proposed encryption can provide effective and secure image retrieval for each user and ensure confidentiality of the query feature of each user.

A practical guide to assessing clinical decision-making skills using the key features approach.

PubMed

Farmer, Elizabeth A; Page, Gordon

2005-12-01

This paper in the series on professional assessment provides a practical guide to writing key features problems (KFPs). Key features problems test clinical decision-making skills in written or computer-based formats. They are based on the concept of critical steps or 'key features' in decision making and represent an advance on the older, less reliable patient management problem (PMP) formats. The practical steps in writing these problems are discussed and illustrated by examples. Steps include assembling problem-writing groups, selecting a suitable clinical scenario or problem and defining its key features, writing the questions, selecting question response formats, preparing scoring keys, reviewing item quality and item banking. The KFP format provides educators with a flexible approach to testing clinical decision-making skills with demonstrated validity and reliability when constructed according to the guidelines provided.

Decoding natural images from evoked brain activities using encoding models with invertible mapping.

PubMed

Li, Chao; Xu, Junhai; Liu, Baolin

2018-05-21

Recent studies have built encoding models in the early visual cortex, and reliable mappings have been made between the low-level visual features of stimuli and brain activities. However, these mappings are irreversible, so that the features cannot be directly decoded. To solve this problem, we designed a sparse framework-based encoding model that predicted brain activities from a complete feature representation. Moreover, according to the distribution and activation rules of neurons in the primary visual cortex (V1), three key transformations were introduced into the basic feature to improve the model performance. In this setting, the mapping was simple enough that it could be inverted using a closed-form formula. Using this mapping, we designed a hybrid identification method based on the support vector machine (SVM), and tested it on a published functional magnetic resonance imaging (fMRI) dataset. The experiments confirmed the rationality of our encoding model, and the identification accuracies for 2 subjects increased from 92% and 72% to 98% and 92% with the chance level only 0.8%. Copyright © 2018 Elsevier Ltd. All rights reserved.

Identifying key radiogenomic associations between DCE-MRI and micro-RNA expressions for breast cancer

NASA Astrophysics Data System (ADS)

Samala, Ravi K.; Chan, Heang-Ping; Hadjiiski, Lubomir; Helvie, Mark A.; Kim, Renaid

2017-03-01

Understanding the key radiogenomic associations for breast cancer between DCE-MRI and micro-RNA expressions is the foundation for the discovery of radiomic features as biomarkers for assessing tumor progression and prognosis. We conducted a study to analyze the radiogenomic associations for breast cancer using the TCGA-TCIA data set. The core idea that tumor etiology is a function of the behavior of miRNAs is used to build the regression models. The associations based on regression are analyzed for three study outcomes: diagnosis, prognosis, and treatment. The diagnosis group consists of miRNAs associated with clinicopathologic features of breast cancer and significant aberration of expression in breast cancer patients. The prognosis group consists of miRNAs which are closely associated with tumor suppression and regulation of cell proliferation and differentiation. The treatment group consists of miRNAs that contribute significantly to the regulation of metastasis thereby having the potential to be part of therapeutic mechanisms. As a first step, important miRNA expressions were identified and their ability to classify the clinical phenotypes based on the study outcomes was evaluated using the area under the ROC curve (AUC) as a figure-of-merit. The key mapping between the selected miRNAs and radiomic features were determined using least absolute shrinkage and selection operator (LASSO) regression analysis within a two-loop leave-one-out cross-validation strategy. These key associations indicated a number of radiomic features from DCE-MRI to be potential biomarkers for the three study outcomes.

Nutrition in primary health care: using a Delphi process to design new interdisciplinary services.

PubMed

Brauer, Paula; Dietrich, Linda; Davidson, Bridget

2006-01-01

A modified Delphi process was used to identify key features of interdisciplinary nutrition services, including provider roles and responsibilities for Ontario Family Health Networks (FHNs), a family physician-based type of primary care. Twenty-three representatives from interested professional organizations, including three FHN demonstration sites, completed a modified Delphi process. Participants reviewed evidence from a systematic literature review, a patient survey, a costing analysis, and key informant interview results before undertaking the Delphi process. Statements describing various options for services were developed at an in-person meeting, which was followed by two rounds of e-mail questionnaires. Teleconference discussions were held between rounds. An interdisciplinary model with differing and complementary roles for health care providers emerged from the process. Additional key features addressing screening for nutrition problems, health promotion and disease prevention, team collaboration, planning and evaluation, administrative support, access to care, and medical directives/delegated acts were identified. Under the proposed model, the registered dietitian is the team member responsible for managing all aspects of nutrition services, from needs assessment to program delivery, as well as for supporting all providers' nutrition services. The proposed interdisciplinary nutrition services model merits evaluation of cost, effectiveness, applicability, and sustainability in team-based primary care service settings.

The Importance of Capturing Topographic Features for Modeling Groundwater Flow and Transport in Mountainous Watersheds

NASA Astrophysics Data System (ADS)

Wang, C.; Gomez-Velez, J. D.; Wilson, J. L.

2017-12-01

Groundwater plays a key role in runoff generation and stream water chemistry from reach to watershed scales. The spatial distribution of ridges and streams can influence the spatial patterns of groundwater recharge and drainage, specially in mountainous terrains where these features are more prominent. However, typical modeling efforts simplify or ignore some of these features due to computational limitations without a systematic investigation of the implications for flow and transport within the watershed. In this study, we investigate the effect of capturing key topographic features on modeled groundwater flow and transport characteristics in a mountainous watershed. We build model scenarios of different topographic complexity levels (TCLs) to capture different levels of representation of streams and ridges in the model. Modeled baseflow and groundwater mean residence time (MRT) are used to quantify the differences among TCLs. Our results show that capturing the streams and ridges has a significant influence on simulated groundwater flow and transport patterns. Topographic complexity controls the proportion of baseflow generated from local, intermediate, and regional flow paths, thus influencing the amount and MRT of basefow flowing into streams of different Horton-Strahler orders. We further simulate the concentration of solute exported into streams from subsurface chemical weathering. The concentration of chemical weathering products in streams is less sensitive to model TCL due to the thermodynamic constraint on the equilibrium concentration of the chemical weathering. We also tested the influence of geology on the effect of TCL. The effect of TCL is consistent under different geological conditions; however, it is enhanced in models with low hydraulic conductivity because more of the flow is forced into shallow and local flow paths. All of these changes can affect our ability to interpret environmental tracer data and predict bio- and geo-chemical evolution of stream water in mountainous watersheds.

Computational Identification of Genomic Features That Influence 3D Chromatin Domain Formation.

PubMed

Mourad, Raphaël; Cuvier, Olivier

2016-05-01

Recent advances in long-range Hi-C contact mapping have revealed the importance of the 3D structure of chromosomes in gene expression. A current challenge is to identify the key molecular drivers of this 3D structure. Several genomic features, such as architectural proteins and functional elements, were shown to be enriched at topological domain borders using classical enrichment tests. Here we propose multiple logistic regression to identify those genomic features that positively or negatively influence domain border establishment or maintenance. The model is flexible, and can account for statistical interactions among multiple genomic features. Using both simulated and real data, we show that our model outperforms enrichment test and non-parametric models, such as random forests, for the identification of genomic features that influence domain borders. Using Drosophila Hi-C data at a very high resolution of 1 kb, our model suggests that, among architectural proteins, BEAF-32 and CP190 are the main positive drivers of 3D domain borders. In humans, our model identifies well-known architectural proteins CTCF and cohesin, as well as ZNF143 and Polycomb group proteins as positive drivers of domain borders. The model also reveals the existence of several negative drivers that counteract the presence of domain borders including P300, RXRA, BCL11A and ELK1.

Computational Identification of Genomic Features That Influence 3D Chromatin Domain Formation

PubMed Central

Mourad, Raphaël; Cuvier, Olivier

2016-01-01

Recent advances in long-range Hi-C contact mapping have revealed the importance of the 3D structure of chromosomes in gene expression. A current challenge is to identify the key molecular drivers of this 3D structure. Several genomic features, such as architectural proteins and functional elements, were shown to be enriched at topological domain borders using classical enrichment tests. Here we propose multiple logistic regression to identify those genomic features that positively or negatively influence domain border establishment or maintenance. The model is flexible, and can account for statistical interactions among multiple genomic features. Using both simulated and real data, we show that our model outperforms enrichment test and non-parametric models, such as random forests, for the identification of genomic features that influence domain borders. Using Drosophila Hi-C data at a very high resolution of 1 kb, our model suggests that, among architectural proteins, BEAF-32 and CP190 are the main positive drivers of 3D domain borders. In humans, our model identifies well-known architectural proteins CTCF and cohesin, as well as ZNF143 and Polycomb group proteins as positive drivers of domain borders. The model also reveals the existence of several negative drivers that counteract the presence of domain borders including P300, RXRA, BCL11A and ELK1. PMID:27203237

Problems of quality and equity in pain management: exploring the role of biomedical culture.

PubMed

Crowley-Matoka, Megan; Saha, Somnath; Dobscha, Steven K; Burgess, Diana J

2009-10-01

To explore how social scientific analyses of the culture of biomedicine may contribute to advancing our understanding of ongoing issues of quality and equity in pain management. Drawing upon the rich body of social scientific literature on the culture of biomedicine, we identify key features of biomedical culture with particular salience for pain management. We then examine how these cultural features of biomedicine may shape key phases of the pain management process in ways that have implications not just for quality, but for equity in pain management as well. We bring together a range of literatures in developing our analysis, including literatures on the culture of biomedicine, pain management and health care disparities. We surveyed the relevant literatures to identify and inter-relate key features of biomedical culture, key phases of the pain management process, and key dimensions of identified problems with suboptimal and inequitable treatment of pain. We identified three key features of biomedical culture with critical implications for pain management: 1) mind-body dualism; 2) a focus on disease vs illness; and 3) a bias toward cure vs care. Each of these cultural features play a role in the key phases of pain management, specifically pain-related communication, assessment and treatment decision-making, in ways that may hinder successful treatment of pain in general -- and of pain patients from disadvantaged groups in particular. Deepening our understanding of the role of biomedical culture in pain management has implications for education, policy and research as part of ongoing efforts to ameliorate problems in both quality and equity in managing pain. In particular, we suggest that building upon the existing the cultural competence movement in medicine to include fostering a deeper understanding of biomedical culture and its impact on physicians may be useful. From a policy perspective, we identify pain management as an area where the need for a shift to a more biopsychosocial model of health care is particularly pressing, and suggest prioritization of inter-disciplinary, multimodal approaches to pain as one key strategy in realizing this shift. Finally, in terms of research, we identify the need for empirical research to assess aspects of biomedical culture that may influence physician's attitudes and behaviors related to pain management, as well as to explore how these cultural values and their effects may vary across different settings within the practice of medicine.

Patient-centred care in general dental practice - a systematic review of the literature

PubMed Central

2014-01-01

Background Delivering improvements in quality is a key objective within most healthcare systems, and a view which has been widely embraced within the NHS in the United Kingdom. Within the NHS, quality is evaluated across three key dimensions: clinical effectiveness, safety and patient experience, with the latter modelled on the Picker Principles of Patient-Centred Care (PCC). Quality improvement is an important feature of the current dental contract reforms in England, with “patient experience” likely to have a central role in the evaluation of quality. An understanding and appreciation of the evidence underpinning PCC within dentistry is highly relevant if we are to use this as a measure of quality in general dental practice. Methods A systematic review of the literature was undertaken to identify the features of PCC relevant to dentistry and ascertain the current research evidence base underpinning its use as a measure of quality within general dental practice. Results Three papers were identified which met the inclusion criteria and demonstrated the use of primary research to provide an understanding of the key features of PCC within dentistry. None of the papers identified were based in general dental practice and none of the three studies sought the views of patients. Some distinct differences were noted between the key features of PCC reported within the dental literature and those developed within the NHS Patient Experience Framework. Conclusions This systematic review reveals a lack of understanding of PCC within dentistry, and in particular general dental practice. There is currently a poor evidence base to support the use of the current patient reported outcome measures as indicators of patient-centredness. Further research is necessary to understand the important features of PCC in dentistry and patients’ views should be central to this research. PMID:24902842

A Bending Willow Tree: A Japanese (Morita Therapy) Model of Human Nature and Client Change.

ERIC Educational Resources Information Center

Ishiyama, F. Ishu

2003-01-01

Japanese Morita therapy is discussed to highlight its culturally and theoretically unique perspectives on human nature and client change. Key features of this theory are: theory of the nervous trait; multiple-dimensional model of causes and treatment of nervous neurosis; theory of mental attachment; reframing anxiety into constructive desires; and…

Measuring the Perceived Quality of an AR-Based Learning Application: A Multidimensional Model

ERIC Educational Resources Information Center

Pribeanu, Costin; Balog, Alexandru; Iordache, Dragos Daniel

2017-01-01

Augmented reality (AR) technologies could enhance learning in several ways. The quality of an AR-based educational platform is a combination of key features that manifests in usability, usefulness, and enjoyment for the learner. In this paper, we present a multidimensional model to measure the quality of an AR-based application as perceived by…

Teacher Peer Excellence Groups (TPEGs): Building Communities of Practice for Instructional Improvement

ERIC Educational Resources Information Center

Cravens, Xiu; Drake, Timothy A.; Goldring, Ellen; Schuermann, Patrick

2017-01-01

Purpose: The purpose of this paper is to study the viability of implementing a protocol-guided model designed to provide structure and focus for teacher collaboration from Shanghai in today's US public schools. The authors examine whether the new model, Teacher Peer Excellence Group (TPEG), fosters the desired key features of productive…

Nuclear thermal propulsion engine system design analysis code development

NASA Astrophysics Data System (ADS)

Pelaccio, Dennis G.; Scheil, Christine M.; Petrosky, Lyman J.; Ivanenok, Joseph F.

1992-01-01

A Nuclear Thermal Propulsion (NTP) Engine System Design Analyis Code has recently been developed to characterize key NTP engine system design features. Such a versatile, standalone NTP system performance and engine design code is required to support ongoing and future engine system and vehicle design efforts associated with proposed Space Exploration Initiative (SEI) missions of interest. Key areas of interest in the engine system modeling effort were the reactor, shielding, and inclusion of an engine multi-redundant propellant pump feed system design option. A solid-core nuclear thermal reactor and internal shielding code model was developed to estimate the reactor's thermal-hydraulic and physical parameters based on a prescribed thermal output which was integrated into a state-of-the-art engine system design model. The reactor code module has the capability to model graphite, composite, or carbide fuels. Key output from the model consists of reactor parameters such as thermal power, pressure drop, thermal profile, and heat generation in cooled structures (reflector, shield, and core supports), as well as the engine system parameters such as weight, dimensions, pressures, temperatures, mass flows, and performance. The model's overall analysis methodology and its key assumptions and capabilities are summarized in this paper.

Remote measurement methods for 3-D modeling purposes using BAE Systems' Software

NASA Astrophysics Data System (ADS)

Walker, Stewart; Pietrzak, Arleta

2015-06-01

Efficient, accurate data collection from imagery is the key to an economical generation of useful geospatial products. Incremental developments of traditional geospatial data collection and the arrival of new image data sources cause new software packages to be created and existing ones to be adjusted to enable such data to be processed. In the past, BAE Systems' digital photogrammetric workstation, SOCET SET®, met fin de siècle expectations in data processing and feature extraction. Its successor, SOCET GXP®, addresses today's photogrammetric requirements and new data sources. SOCET GXP is an advanced workstation for mapping and photogrammetric tasks, with automated functionality for triangulation, Digital Elevation Model (DEM) extraction, orthorectification and mosaicking, feature extraction and creation of 3-D models with texturing. BAE Systems continues to add sensor models to accommodate new image sources, in response to customer demand. New capabilities added in the latest version of SOCET GXP facilitate modeling, visualization and analysis of 3-D features.

Impact and Crashworthiness Characteristics of Venera Type Landers for Future Venus Missions

NASA Technical Reports Server (NTRS)

Schroeder, Kevin; Bayandor, Javid; Samareh, Jamshid

2016-01-01

In this paper an in-depth investigation of the structural design of the Venera 9-14 landers is explored. A complete reverse engineering of the Venera lander was required. The lander was broken down into its fundamental components and analyzed. This provided in-sights into the hidden features of the design. A trade study was performed to find the sensitivity of the lander's overall mass to the variation of several key parameters. For the lander's legs, the location, length, configuration, and number are all parameterized. The size of the impact ring, the radius of the drag plate, and other design features are also parameterized, and all of these features were correlated to the change of mass of the lander. A multi-fidelity design tool used for further investigation of the parameterized lander was developed. As a design was passed down from one level to the next, the fidelity, complexity, accuracy, and run time of the model increased. The low-fidelity model was a highly nonlinear analytical model developed to rapidly predict the mass of each design. The medium and high fidelity models utilized an explicit finite element framework to investigate the performance of various landers upon impact with the surface under a range of landing conditions. This methodology allowed for a large variety of designs to be investigated by the analytical model, which identified designs with the optimum structural mass to payload ratio. As promising designs emerged, investigations in the following higher fidelity models were focused on establishing their reliability and crashworthiness. The developed design tool efficiently modelled and tested the best concepts for any scenario based on critical Venusian mission requirements and constraints. Through this program, the strengths and weaknesses inherent in the Venera-Type landers were thoroughly investigated. Key features identified for the design of robust landers will be used as foundations for the development of the next generation of landers for future exploration missions to Venus.

Approaches to defining reference regimes for river restoration planning

NASA Astrophysics Data System (ADS)

Beechie, T. J.

2014-12-01

Reference conditions or reference regimes can be defined using three general approaches, historical analysis, contemporary reference sites, and theoretical or empirical models. For large features (e.g., floodplain channels and ponds) historical data and maps are generally reliable. For smaller features (e.g., pools and riffles in small tributaries), field data from contemporary reference sites are a reasonable surrogate for historical data. Models are generally used for features that have no historical information or present day reference sites (e.g., beaver pond habitat). Each of these approaches contributes to a watershed-wide understanding of current biophysical conditions relative to potential conditions, which helps create not only a guiding vision for restoration, but also helps quantify and locate the largest or most important restoration opportunities. Common uses of geomorphic and biological reference conditions include identifying key areas for habitat protection or restoration, and informing the choice of restoration targets. Examples of use of each of these three approaches to define reference regimes in western USA illustrate how historical information and current research highlight key restoration opportunities, focus restoration effort in areas that can produce the largest ecological benefit, and contribute to estimating restoration potential and assessing likelihood of achieving restoration goals.

Guiding gate-etch process development using 3D surface reaction modeling for 7nm and beyond

NASA Astrophysics Data System (ADS)

Dunn, Derren; Sporre, John R.; Deshpande, Vaibhav; Oulmane, Mohamed; Gull, Ronald; Ventzek, Peter; Ranjan, Alok

2017-03-01

Increasingly, advanced process nodes such as 7nm (N7) are fundamentally 3D and require stringent control of critical dimensions over high aspect ratio features. Process integration in these nodes requires a deep understanding of complex physical mechanisms to control critical dimensions from lithography through final etch. Polysilicon gate etch processes are critical steps in several device architectures for advanced nodes that rely on self-aligned patterning approaches to gate definition. These processes are required to meet several key metrics: (a) vertical etch profiles over high aspect ratios; (b) clean gate sidewalls free of etch process residue; (c) minimal erosion of liner oxide films protecting key architectural elements such as fins; and (e) residue free corners at gate interfaces with critical device elements. In this study, we explore how hybrid modeling approaches can be used to model a multi-step finFET polysilicon gate etch process. Initial parts of the patterning process through hardmask assembly are modeled using process emulation. Important aspects of gate definition are then modeled using a particle Monte Carlo (PMC) feature scale model that incorporates surface chemical reactions.1 When necessary, species and energy flux inputs to the PMC model are derived from simulations of the etch chamber. The modeled polysilicon gate etch process consists of several steps including a hard mask breakthrough step (BT), main feature etch steps (ME), and over-etch steps (OE) that control gate profiles at the gate fin interface. An additional constraint on this etch flow is that fin spacer oxides are left intact after final profile tuning steps. A natural optimization required from these processes is to maximize vertical gate profiles while minimizing erosion of fin spacer films.2

A recurrent neural model for proto-object based contour integration and figure-ground segregation.

PubMed

Hu, Brian; Niebur, Ernst

2017-12-01

Visual processing of objects makes use of both feedforward and feedback streams of information. However, the nature of feedback signals is largely unknown, as is the identity of the neuronal populations in lower visual areas that receive them. Here, we develop a recurrent neural model to address these questions in the context of contour integration and figure-ground segregation. A key feature of our model is the use of grouping neurons whose activity represents tentative objects ("proto-objects") based on the integration of local feature information. Grouping neurons receive input from an organized set of local feature neurons, and project modulatory feedback to those same neurons. Additionally, inhibition at both the local feature level and the object representation level biases the interpretation of the visual scene in agreement with principles from Gestalt psychology. Our model explains several sets of neurophysiological results (Zhou et al. Journal of Neuroscience, 20(17), 6594-6611 2000; Qiu et al. Nature Neuroscience, 10(11), 1492-1499 2007; Chen et al. Neuron, 82(3), 682-694 2014), and makes testable predictions about the influence of neuronal feedback and attentional selection on neural responses across different visual areas. Our model also provides a framework for understanding how object-based attention is able to select both objects and the features associated with them.

Data-adaptive harmonic analysis and prediction of sea level change in North Atlantic region

NASA Astrophysics Data System (ADS)

Kondrashov, D. A.; Chekroun, M.

2017-12-01

This study aims to characterize North Atlantic sea level variability across the temporal and spatial scales. We apply recently developed data-adaptive Harmonic Decomposition (DAH) and Multilayer Stuart-Landau Models (MSLM) stochastic modeling techniques [Chekroun and Kondrashov, 2017] to monthly 1993-2017 dataset of Combined TOPEX/Poseidon, Jason-1 and Jason-2/OSTM altimetry fields over North Atlantic region. The key numerical feature of the DAH relies on the eigendecomposition of a matrix constructed from time-lagged spatial cross-correlations. In particular, eigenmodes form an orthogonal set of oscillating data-adaptive harmonic modes (DAHMs) that come in pairs and in exact phase quadrature for a given temporal frequency. Furthermore, the pairs of data-adaptive harmonic coefficients (DAHCs), obtained by projecting the dataset onto associated DAHMs, can be very efficiently modeled by a universal parametric family of simple nonlinear stochastic models - coupled Stuart-Landau oscillators stacked per frequency, and synchronized across different frequencies by the stochastic forcing. Despite the short record of altimetry dataset, developed DAH-MSLM model provides for skillful prediction of key dynamical and statistical features of sea level variability. References M. D. Chekroun and D. Kondrashov, Data-adaptive harmonic spectra and multilayer Stuart-Landau models. HAL preprint, 2017, https://hal.archives-ouvertes.fr/hal-01537797

Hierarchical Feature Extraction With Local Neural Response for Image Recognition.

PubMed

Li, Hong; Wei, Yantao; Li, Luoqing; Chen, C L P

2013-04-01

In this paper, a hierarchical feature extraction method is proposed for image recognition. The key idea of the proposed method is to extract an effective feature, called local neural response (LNR), of the input image with nontrivial discrimination and invariance properties by alternating between local coding and maximum pooling operation. The local coding, which is carried out on the locally linear manifold, can extract the salient feature of image patches and leads to a sparse measure matrix on which maximum pooling is carried out. The maximum pooling operation builds the translation invariance into the model. We also show that other invariant properties, such as rotation and scaling, can be induced by the proposed model. In addition, a template selection algorithm is presented to reduce computational complexity and to improve the discrimination ability of the LNR. Experimental results show that our method is robust to local distortion and clutter compared with state-of-the-art algorithms.

Data-Driven Neural Network Model for Robust Reconstruction of Automobile Casting

NASA Astrophysics Data System (ADS)

Lin, Jinhua; Wang, Yanjie; Li, Xin; Wang, Lu

2017-09-01

In computer vision system, it is a challenging task to robustly reconstruct complex 3D geometries of automobile castings. However, 3D scanning data is usually interfered by noises, the scanning resolution is low, these effects normally lead to incomplete matching and drift phenomenon. In order to solve these problems, a data-driven local geometric learning model is proposed to achieve robust reconstruction of automobile casting. In order to relieve the interference of sensor noise and to be compatible with incomplete scanning data, a 3D convolution neural network is established to match the local geometric features of automobile casting. The proposed neural network combines the geometric feature representation with the correlation metric function to robustly match the local correspondence. We use the truncated distance field(TDF) around the key point to represent the 3D surface of casting geometry, so that the model can be directly embedded into the 3D space to learn the geometric feature representation; Finally, the training labels is automatically generated for depth learning based on the existing RGB-D reconstruction algorithm, which accesses to the same global key matching descriptor. The experimental results show that the matching accuracy of our network is 92.2% for automobile castings, the closed loop rate is about 74.0% when the matching tolerance threshold τ is 0.2. The matching descriptors performed well and retained 81.6% matching accuracy at 95% closed loop. For the sparse geometric castings with initial matching failure, the 3D matching object can be reconstructed robustly by training the key descriptors. Our method performs 3D reconstruction robustly for complex automobile castings.

Beyond Ethnic Tidbits: Toward a Critical and Dialogical Model in Multicultural Social Justice Teacher Preparation

ERIC Educational Resources Information Center

Convertino, Christina

2016-01-01

This praxis article outlines the value of using a critical and dialogical model (CDM) to teach multicultural social justice education to preservice teachers. Based on practitioner research, the article draws on the author's own teaching experiences to highlight how key features of CDM can be used to help pre-service teachers move beyond thinking…

Key Program Features to Enhance the School-to-Career Transition for Youth with Disabilities

ERIC Educational Resources Information Center

Doren, Bonnie; Yan, Min-Chi; Tu, Wei-Mo

2013-01-01

The purpose of the article was to identify key features within research-based school-to-career programs that were linked to positive employment outcomes for youth disabilities. Three key program features were identified and discussed that could be incorporated into the practices and programs of schools and communities to support the employment…

Evaluation of cool season precipitation event characteristics over the Northeast US in a suite of downscaled climate model hindcasts

NASA Astrophysics Data System (ADS)

Loikith, Paul C.; Waliser, Duane E.; Kim, Jinwon; Ferraro, Robert

2017-08-01

Cool season precipitation event characteristics are evaluated across a suite of downscaled climate models over the northeastern US. Downscaled hindcast simulations are produced by dynamically downscaling the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2) using the National Aeronautics and Space Administration (NASA)-Unified Weather Research and Forecasting (WRF) regional climate model (RCM) and the Goddard Earth Observing System Model, Version 5 (GEOS-5) global climate model. NU-WRF RCM simulations are produced at 24, 12, and 4-km horizontal resolutions using a range of spectral nudging schemes while the MERRA2 global downscaled run is provided at 12.5-km. All model runs are evaluated using four metrics designed to capture key features of precipitation events: event frequency, event intensity, even total, and event duration. Overall, the downscaling approaches result in a reasonable representation of many of the key features of precipitation events over the region, however considerable biases exist in the magnitude of each metric. Based on this evaluation there is no clear indication that higher resolution simulations result in more realistic results in general, however many small-scale features such as orographic enhancement of precipitation are only captured at higher resolutions suggesting some added value over coarser resolution. While the differences between simulations produced using nudging and no nudging are small, there is some improvement in model fidelity when nudging is introduced, especially at a cutoff wavelength of 600 km compared to 2000 km. Based on the results of this evaluation, dynamical regional downscaling using NU-WRF results in a more realistic representation of precipitation event climatology than the global downscaling of MERRA2 using GEOS-5.

Mechanisms of perceptual organization provide auto-zoom and auto-localization for attention to objects

PubMed Central

Mihalas, Stefan; Dong, Yi; von der Heydt, Rüdiger; Niebur, Ernst

2011-01-01

Visual attention is often understood as a modulatory field acting at early stages of processing, but the mechanisms that direct and fit the field to the attended object are not known. We show that a purely spatial attention field propagating downward in the neuronal network responsible for perceptual organization will be reshaped, repositioned, and sharpened to match the object's shape and scale. Key features of the model are grouping neurons integrating local features into coherent tentative objects, excitatory feedback to the same local feature neurons that caused grouping neuron activation, and inhibition between incompatible interpretations both at the local feature level and at the object representation level. PMID:21502489

A mouse model of alcoholic liver fibrosis-associated acute kidney injury identifies key molecular pathways

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

Furuya, Shinji; Chappell, Grace A.; Iwata, Yasuhir

Clinical data strongly indicate that acute kidney injury (AKI) is a critical complication in alcoholic hepatitis, an acute-on-chronic form of liver failure in patients with advanced alcoholic fibrosis. Development of targeted therapies for AKI in this setting is hampered by the lack of an animal model. To enable research into molecular drivers and novel therapies for fibrosis- and alcohol-associated AKI, we aimed to combine carbon tetrachloride (CCl{sub 4})-induced fibrosis with chronic intra-gastric alcohol feeding. Male C57BL/6J mice were administered a low dose of CCl{sub 4} (0.2 ml/kg 2 × week/6 weeks) followed by alcohol intragastrically (up to 25 g/kg/day formore » 3 weeks) and with continued CCl{sub 4}. We observed that combined treatment with CCl{sub 4} and alcohol resulted in severe liver injury, more pronounced than using each treatment alone. Importantly, severe kidney injury was evident only in the combined treatment group. This mouse model reproduced distinct pathological features consistent with AKI in human alcoholic hepatitis. Transcriptomic analysis of kidneys revealed profound effects in the combined treatment group, with enrichment for damage-associated pathways, such as apoptosis, inflammation, immune-response and hypoxia. Interestingly, Havcr1 and Lcn2, biomarkers of AKI, were markedly up-regulated. Overall, this study established a novel mouse model of fibrosis- and alcohol-associated AKI and identified key mechanistic pathways. - Highlights: • Acute kidney injury (AKI) is a critical complication in alcoholic hepatitis • We developed a novel mouse model of fibrosis- and alcohol-associated AKI • This model reproduces key molecular and pathological features of human AKI • This animal model can help identify new targeted therapies for alcoholic hepatitis.« less

Generation of a three-dimensional ultrastructural model of human respiratory cilia.

PubMed

Burgoyne, Thomas; Dixon, Mellisa; Luther, Pradeep; Hogg, Claire; Shoemark, Amelia

2012-12-01

The ultrastructures of cilia and flagella are highly similar and well conserved through evolution. Consequently, Chlamydomonas is commonly used as a model organism for the study of human respiratory cilia. Since detailed models of Chlamydomonas axonemes were generated using cryoelectron tomography, disparities among some of the ultrastructural features have become apparent when compared with human cilia. Extrapolating information on human disease from the Chlamydomonas model may lead to discrepancies in translational research. This study aimed to establish the first three-dimensional ultrastructural model of human cilia. Tomograms of transverse sections (n = 6) and longitudinal sections (n = 9) of human nasal respiratory cilia were generated from three healthy volunteers. Key features of the cilium were resolved using subatomic averaging, and were measured. For validation of the method, a model of the well characterized structure of Chlamydomonas reinhardtii was simultaneously generated. Data were combined to create a fully quantified three-dimensional reconstruction of human nasal respiratory cilia. We highlight key differences in the axonemal sheath, microtubular doublets, radial spokes, and dynein arms between the two structures. We show a decreased axial periodicity of the radial spokes, inner dynein arms, and central pair protrusions in the human model. We propose that this first human model will provide a basis for research into the function and structure of human respiratory cilia in health and in disease.

Application of the wavelet transform for speech processing

NASA Technical Reports Server (NTRS)

Maes, Stephane

1994-01-01

Speaker identification and word spotting will shortly play a key role in space applications. An approach based on the wavelet transform is presented that, in the context of the 'modulation model,' enables extraction of speech features which are used as input for the classification process.

Anatomy of an anesthesia information management system.

PubMed

Shah, Nirav J; Tremper, Kevin K; Kheterpal, Sachin

2011-09-01

Anesthesia information management systems (AIMS) have become more prevalent as more sophisticated hardware and software have increased usability and reliability. National mandates and incentives have driven adoption as well. AIMS can be developed in one of several software models (Web based, client/server, or incorporated into a medical device). Irrespective of the development model, the best AIMS have a feature set that allows for comprehensive management of workflow for an anesthesiologist. Key features include preoperative, intraoperative, and postoperative documentation; quality assurance; billing; compliance and operational reporting; patient and operating room tracking; and integration with hospital electronic medical records. Copyright © 2011 Elsevier Inc. All rights reserved.

Selective Laser Treatment on Cold-Sprayed Titanium Coatings: Numerical Modeling and Experimental Analysis

NASA Astrophysics Data System (ADS)

Carlone, Pierpaolo; Astarita, Antonello; Rubino, Felice; Pasquino, Nicola; Aprea, Paolo

2016-12-01

In this paper, a selective laser post-deposition on pure grade II titanium coatings, cold-sprayed on AA2024-T3 sheets, was experimentally and numerically investigated. Morphological features, microstructure, and chemical composition of the treated zone were assessed by means of optical microscopy, scanning electron microscopy, and energy dispersive X-ray spectrometry. Microhardness measurements were also carried out to evaluate the mechanical properties of the coating. A numerical model of the laser treatment was implemented and solved to simulate the process and discuss the experimental outcomes. Obtained results highlighted the key role played by heat input and dimensional features on the effectiveness of the treatment.

A Verification System for Distributed Objects with Asynchronous Method Calls

NASA Astrophysics Data System (ADS)

Ahrendt, Wolfgang; Dylla, Maximilian

We present a verification system for Creol, an object-oriented modeling language for concurrent distributed applications. The system is an instance of KeY, a framework for object-oriented software verification, which has so far been applied foremost to sequential Java. Building on KeY characteristic concepts, like dynamic logic, sequent calculus, explicit substitutions, and the taclet rule language, the system presented in this paper addresses functional correctness of Creol models featuring local cooperative thread parallelism and global communication via asynchronous method calls. The calculus heavily operates on communication histories which describe the interfaces of Creol units. Two example scenarios demonstrate the usage of the system.

Evolutionary crossroads in developmental biology: Cnidaria

PubMed Central

Technau, Ulrich; Steele, Robert E.

2011-01-01

There is growing interest in the use of cnidarians (corals, sea anemones, jellyfish and hydroids) to investigate the evolution of key aspects of animal development, such as the formation of the third germ layer (mesoderm), the nervous system and the generation of bilaterality. The recent sequencing of the Nematostella and Hydra genomes, and the establishment of methods for manipulating gene expression, have inspired new research efforts using cnidarians. Here, we present the main features of cnidarian models and their advantages for research, and summarize key recent findings using these models that have informed our understanding of the evolution of the developmental processes underlying metazoan body plan formation. PMID:21389047

Evolutionary crossroads in developmental biology: Cnidaria.

PubMed

Technau, Ulrich; Steele, Robert E

2011-04-01

There is growing interest in the use of cnidarians (corals, sea anemones, jellyfish and hydroids) to investigate the evolution of key aspects of animal development, such as the formation of the third germ layer (mesoderm), the nervous system and the generation of bilaterality. The recent sequencing of the Nematostella and Hydra genomes, and the establishment of methods for manipulating gene expression, have inspired new research efforts using cnidarians. Here, we present the main features of cnidarian models and their advantages for research, and summarize key recent findings using these models that have informed our understanding of the evolution of the developmental processes underlying metazoan body plan formation.

Molecular modeling and SPRi investigations of interleukin 6 (IL6) protein and DNA aptamers.

PubMed

Rhinehardt, Kristen L; Vance, Stephen A; Mohan, Ram V; Sandros, Marinella; Srinivas, Goundla

2018-06-01

Interleukin 6 (IL6), an inflammatory response protein has major implications in immune-related inflammatory diseases. Identification of aptamers for the IL6 protein aids in diagnostic, therapeutic, and theranostic applications. Three different DNA aptamers and their interactions with IL6 protein were extensively investigated in a phosphate buffed saline (PBS) solution. Molecular-level modeling through molecular dynamics provided insights of structural, conformational changes and specific binding domains of these protein-aptamer complexes. Multiple simulations reveal consistent binding region for all protein-aptamer complexes. Conformational changes coupled with quantitative analysis of center of mass (COM) distance, radius of gyration (R g ), and number of intermolecular hydrogen bonds in each IL6 protein-aptamer complex was used to determine their binding performance strength and obtain molecular configurations with strong binding. A similarity comparison of the molecular configurations with strong binding from molecular-level modeling concurred with Surface Plasmon Resonance imaging (SPRi) for these three aptamer complexes, thus corroborating molecular modeling analysis findings. Insights from the natural progression of IL6 protein-aptamer binding modeled in this work has identified key features such as the orientation and location of the aptamer in the binding event. These key features are not readily feasible from wet lab experiments and impact the efficacy of the aptamers in diagnostic and theranostic applications.

Manipulators with flexible links: A simple model and experiments

NASA Technical Reports Server (NTRS)

Shimoyama, Isao; Oppenheim, Irving J.

1989-01-01

A simple dynamic model proposed for flexible links is briefly reviewed and experimental control results are presented for different flexible systems. A simple dynamic model is useful for rapid prototyping of manipulators and their control systems, for possible application to manipulator design decisions, and for real time computation as might be applied in model based or feedforward control. Such a model is proposed, with the further advantage that clear physical arguments and explanations can be associated with its simplifying features and with its resulting analytical properties. The model is mathematically equivalent to Rayleigh's method. Taking the example of planar bending, the approach originates in its choice of two amplitude variables, typically chosen as the link end rotations referenced to the chord (or the tangent) motion of the link. This particular choice is key in establishing the advantageous features of the model, and it was used to support the series of experiments reported.

A Low-Dimensional Radial Silhouette-Based Feature for Fast Human Action Recognition Fusing Multiple Views.

PubMed

Chaaraoui, Alexandros Andre; Flórez-Revuelta, Francisco

2014-01-01

This paper presents a novel silhouette-based feature for vision-based human action recognition, which relies on the contour of the silhouette and a radial scheme. Its low-dimensionality and ease of extraction result in an outstanding proficiency for real-time scenarios. This feature is used in a learning algorithm that by means of model fusion of multiple camera streams builds a bag of key poses, which serves as a dictionary of known poses and allows converting the training sequences into sequences of key poses. These are used in order to perform action recognition by means of a sequence matching algorithm. Experimentation on three different datasets returns high and stable recognition rates. To the best of our knowledge, this paper presents the highest results so far on the MuHAVi-MAS dataset. Real-time suitability is given, since the method easily performs above video frequency. Therefore, the related requirements that applications as ambient-assisted living services impose are successfully fulfilled.

Web Based Semi-automatic Scientific Validation of Models of the Corona and Inner Heliosphere

NASA Astrophysics Data System (ADS)

MacNeice, P. J.; Chulaki, A.; Taktakishvili, A.; Kuznetsova, M. M.

2013-12-01

Validation is a critical step in preparing models of the corona and inner heliosphere for future roles supporting either or both the scientific research community and the operational space weather forecasting community. Validation of forecasting quality tends to focus on a short list of key features in the model solutions, with an unchanging order of priority. Scientific validation exposes a much larger range of physical processes and features, and as the models evolve to better represent features of interest, the research community tends to shift its focus to other areas which are less well understood and modeled. Given the more comprehensive and dynamic nature of scientific validation, and the limited resources available to the community to pursue this, it is imperative that the community establish a semi-automated process which engages the model developers directly into an ongoing and evolving validation process. In this presentation we describe the ongoing design and develpment of a web based facility to enable this type of validation of models of the corona and inner heliosphere, on the growing list of model results being generated, and on strategies we have been developing to account for model results that incorporate adaptively refined numerical grids.

Issues or Identity? Cognitive Foundations of Voter Choice

PubMed Central

Jenke, Libby; Huettel, Scott A.

2016-01-01

Voter choice is one of the most important problems in political science. The most common models assume that voting is a rational choice based on policy positions (e.g., key issues) and non-policy information (e.g., social identity, personality). Though such models explain macroscopic features of elections, they also reveal important anomalies that have been resistant to explanation. We argue for a new approach that builds upon recent research in cognitive science and neuroscience; specifically, we contend that policy positions and social identities do not combine in merely an additive manner, but compete to determine voter preferences. This model not only explains several key anomalies in voter choice, but also suggests new directions for research in both political science and cognitive science. PMID:27769726

EMDS 3.0: A modeling framework for coping with complexity in environmental assessment and planning.

Treesearch

K.M. Reynolds

2006-01-01

EMDS 3.0 is implemented as an ArcMap® extension and integrates the logic engine of NetWeaver® to perform landscape evaluations, and the decision modeling engine of Criterium DecisionPlus® for evaluating management priorities. Key features of the system's evaluation component include abilities to (1) reason about large, abstract, multifaceted ecosystem management...

Designing and Financing an Integrated Program of College Study: Lessons from the California Academy of Liberal Studies

ERIC Educational Resources Information Center

Goldberger, Susan; Haynes, Leslie

2005-01-01

This document represents the first in a series of design briefs on models for early college high schools. The briefs focus on the academic and organizational design of the college component and tie those key features to a sustainable financing model. By engaging students in up to two years of demanding college-level work while still in high…

Computational Modeling Reveals Key Contributions of KCNQ and hERG Currents to the Malleability of Uterine Action Potentials Underpinning Labor

PubMed Central

Tong, Wing-Chiu; Tribe, Rachel M.; Smith, Roger; Taggart, Michael J.

2014-01-01

The electrical excitability of uterine smooth muscle cells is a key determinant of the contraction of the organ during labor and is manifested by spontaneous, periodic action potentials (APs). Near the end of term, APs vary in shape and size reflecting an ability to change the frequency, duration and amplitude of uterine contractions. A recent mathematical model quantified several ionic features of the electrical excitability in uterine smooth muscle cells. It replicated many of the experimentally recorded uterine AP configurations but its limitations were evident when trying to simulate the long-duration bursting APs characteristic of labor. A computational parameter search suggested that delayed rectifying K+ currents could be a key model component requiring improvement to produce the longer-lasting bursting APs. Of the delayed rectifying K+ currents family it is of interest that KCNQ and hERG channels have been reported to be gestationally regulated in the uterus. These currents exhibit features similar to the broadly defined uterine I K1 of the original mathematical model. We thus formulated new quantitative descriptions for several I KCNQ and I hERG. Incorporation of these currents into the uterine cell model enabled simulations of the long-lasting bursting APs. Moreover, we used this modified model to simulate the effects of different contributions of I KCNQ and I hERG on AP form. Our findings suggest that the alterations in expression of hERG and KCNQ channels can potentially provide a mechanism for fine tuning of AP forms that lends a malleability for changing between plateau-like and long-lasting bursting-type APs as uterine cells prepare for parturition. PMID:25474527

Impacts of Changing Climatic Drivers and Land use features on Future Stormwater Runoff in the Northwest Florida Basin: A Large-Scale Hydrologic Modeling Assessment

NASA Astrophysics Data System (ADS)

Khan, M.; Abdul-Aziz, O. I.

2017-12-01

Potential changes in climatic drivers and land cover features can significantly influence the stormwater budget in the Northwest Florida Basin. We investigated the hydro-climatic and land use sensitivities of stormwater runoff by developing a large-scale process-based rainfall-runoff model for the large basin by using the EPA Storm Water Management Model (SWMM 5.1). Climatic and hydrologic variables, as well as land use/cover features were incorporated into the model to account for the key processes of coastal hydrology and its dynamic interactions with groundwater and sea levels. We calibrated and validated the model by historical daily streamflow observations during 2009-2012 at four major rivers in the basin. Downscaled climatic drivers (precipitation, temperature, solar radiation) projected by twenty GCMs-RCMs under CMIP5, along with the projected future land use/cover features were also incorporated into the model. The basin storm runoff was then simulated for the historical (2000s = 1976-2005) and two future periods (2050s = 2030-2059, and 2080s = 2070-2099). Comparative evaluation of the historical and future scenarios leads to important guidelines for stormwater management in Northwest Florida and similar regions under a changing climate and environment.

Cellular neural network-based hybrid approach toward automatic image registration

NASA Astrophysics Data System (ADS)

Arun, Pattathal VijayaKumar; Katiyar, Sunil Kumar

2013-01-01

Image registration is a key component of various image processing operations that involve the analysis of different image data sets. Automatic image registration domains have witnessed the application of many intelligent methodologies over the past decade; however, inability to properly model object shape as well as contextual information has limited the attainable accuracy. A framework for accurate feature shape modeling and adaptive resampling using advanced techniques such as vector machines, cellular neural network (CNN), scale invariant feature transform (SIFT), coreset, and cellular automata is proposed. CNN has been found to be effective in improving feature matching as well as resampling stages of registration and complexity of the approach has been considerably reduced using coreset optimization. The salient features of this work are cellular neural network approach-based SIFT feature point optimization, adaptive resampling, and intelligent object modelling. Developed methodology has been compared with contemporary methods using different statistical measures. Investigations over various satellite images revealed that considerable success was achieved with the approach. This system has dynamically used spectral and spatial information for representing contextual knowledge using CNN-prolog approach. This methodology is also illustrated to be effective in providing intelligent interpretation and adaptive resampling.

Shock Detector for SURF model

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

Menikoff, Ralph

2016-01-11

SURF and its extension SURFplus are reactive burn models aimed at shock initiation and propagation of detonation waves in high explosives. A distinctive feature of these models is that the burn rate depends on the lead shock pressure. A key part of the models is an algorithm to detect the lead shock. Typically, shock capturing hydro algorithms have small oscillations behind a shock. Here we investigate how well the shock detection algorithm works for a nearly steady propagating detonation wave in one-dimension using the Eulerian xRage code.

A Deep Learning Approach for Fault Diagnosis of Induction Motors in Manufacturing

NASA Astrophysics Data System (ADS)

Shao, Si-Yu; Sun, Wen-Jun; Yan, Ru-Qiang; Wang, Peng; Gao, Robert X.

2017-11-01

Extracting features from original signals is a key procedure for traditional fault diagnosis of induction motors, as it directly influences the performance of fault recognition. However, high quality features need expert knowledge and human intervention. In this paper, a deep learning approach based on deep belief networks (DBN) is developed to learn features from frequency distribution of vibration signals with the purpose of characterizing working status of induction motors. It combines feature extraction procedure with classification task together to achieve automated and intelligent fault diagnosis. The DBN model is built by stacking multiple-units of restricted Boltzmann machine (RBM), and is trained using layer-by-layer pre-training algorithm. Compared with traditional diagnostic approaches where feature extraction is needed, the presented approach has the ability of learning hierarchical representations, which are suitable for fault classification, directly from frequency distribution of the measurement data. The structure of the DBN model is investigated as the scale and depth of the DBN architecture directly affect its classification performance. Experimental study conducted on a machine fault simulator verifies the effectiveness of the deep learning approach for fault diagnosis of induction motors. This research proposes an intelligent diagnosis method for induction motor which utilizes deep learning model to automatically learn features from sensor data and realize working status recognition.

Visual Prediction Error Spreads Across Object Features in Human Visual Cortex

PubMed Central

Summerfield, Christopher; Egner, Tobias

2016-01-01

Visual cognition is thought to rely heavily on contextual expectations. Accordingly, previous studies have revealed distinct neural signatures for expected versus unexpected stimuli in visual cortex. However, it is presently unknown how the brain combines multiple concurrent stimulus expectations such as those we have for different features of a familiar object. To understand how an unexpected object feature affects the simultaneous processing of other expected feature(s), we combined human fMRI with a task that independently manipulated expectations for color and motion features of moving-dot stimuli. Behavioral data and neural signals from visual cortex were then interrogated to adjudicate between three possible ways in which prediction error (surprise) in the processing of one feature might affect the concurrent processing of another, expected feature: (1) feature processing may be independent; (2) surprise might “spread” from the unexpected to the expected feature, rendering the entire object unexpected; or (3) pairing a surprising feature with an expected feature might promote the inference that the two features are not in fact part of the same object. To formalize these rival hypotheses, we implemented them in a simple computational model of multifeature expectations. Across a range of analyses, behavior and visual neural signals consistently supported a model that assumes a mixing of prediction error signals across features: surprise in one object feature spreads to its other feature(s), thus rendering the entire object unexpected. These results reveal neurocomputational principles of multifeature expectations and indicate that objects are the unit of selection for predictive vision. SIGNIFICANCE STATEMENT We address a key question in predictive visual cognition: how does the brain combine multiple concurrent expectations for different features of a single object such as its color and motion trajectory? By combining a behavioral protocol that independently varies expectation of (and attention to) multiple object features with computational modeling and fMRI, we demonstrate that behavior and fMRI activity patterns in visual cortex are best accounted for by a model in which prediction error in one object feature spreads to other object features. These results demonstrate how predictive vision forms object-level expectations out of multiple independent features. PMID:27810936

A Featured-Based Strategy for Stereovision Matching in Sensors with Fish-Eye Lenses for Forest Environments

PubMed Central

Herrera, Pedro Javier; Pajares, Gonzalo; Guijarro, Maria; Ruz, José J.; Cruz, Jesús M.; Montes, Fernando

2009-01-01

This paper describes a novel feature-based stereovision matching process based on a pair of omnidirectional images in forest stands acquired with a stereovision sensor equipped with fish-eye lenses. The stereo analysis problem consists of the following steps: image acquisition, camera modelling, feature extraction, image matching and depth determination. Once the depths of significant points on the trees are obtained, the growing stock volume can be estimated by considering the geometrical camera modelling, which is the final goal. The key steps are feature extraction and image matching. This paper is devoted solely to these two steps. At a first stage a segmentation process extracts the trunks, which are the regions used as features, where each feature is identified through a set of attributes of properties useful for matching. In the second step the features are matched based on the application of the following four well known matching constraints, epipolar, similarity, ordering and uniqueness. The combination of the segmentation and matching processes for this specific kind of sensors make the main contribution of the paper. The method is tested with satisfactory results and compared against the human expert criterion. PMID:22303134

Neural Networks for Segregation of Multiple Objects: Visual Figure-Ground Separation and Auditory Pitch Perception.

NASA Astrophysics Data System (ADS)

Wyse, Lonce

An important component of perceptual object recognition is the segmentation into coherent perceptual units of the "blooming buzzing confusion" that bombards the senses. The work presented herein develops neural network models of some key processes of pre-attentive vision and audition that serve this goal. A neural network model, called an FBF (Feature -Boundary-Feature) network, is proposed for automatic parallel separation of multiple figures from each other and their backgrounds in noisy images. Figure-ground separation is accomplished by iterating operations of a Boundary Contour System (BCS) that generates a boundary segmentation of a scene, and a Feature Contour System (FCS) that compensates for variable illumination and fills-in surface properties using boundary signals. A key new feature is the use of the FBF filling-in process for the figure-ground separation of connected regions, which are subsequently more easily recognized. The new CORT-X 2 model is a feed-forward version of the BCS that is designed to detect, regularize, and complete boundaries in up to 50 percent noise. It also exploits the complementary properties of on-cells and off -cells to generate boundary segmentations and to compensate for boundary gaps during filling-in. In the realm of audition, many sounds are dominated by energy at integer multiples, or "harmonics", of a fundamental frequency. For such sounds (e.g., vowels in speech), the individual frequency components fuse, so that they are perceived as one sound source with a pitch at the fundamental frequency. Pitch is integral to separating auditory sources, as well as to speaker identification and speech understanding. A neural network model of pitch perception called SPINET (SPatial PItch NETwork) is developed and used to simulate a broader range of perceptual data than previous spectral models. The model employs a bank of narrowband filters as a simple model of basilar membrane mechanics, spectral on-center off-surround competitive interactions, and a "harmonic sieve" mechanism whereby the strength of a pitch depends only on spectral regions near harmonics. The model is evaluated using data involving mistuned components, shifted harmonics, complex tones with varying phase relationships, and continuous spectra such as rippled noise and narrow noise bands.

Automation tools for demonstration of goal directed and self-repairing flight control systems

NASA Technical Reports Server (NTRS)

Agarwal, A. K.

1988-01-01

The coupling of expert systems and control design and analysis techniques are documented to provide a realizable self repairing flight control system. Key features of such a flight control system are identified and a limited set of rules for a simple aircraft model are presented.

Computing of Learner's Personality Traits Based on Digital Annotations

ERIC Educational Resources Information Center

Omheni, Nizar; Kalboussi, Anis; Mazhoud, Omar; Kacem, Ahmed Hadj

2017-01-01

Researchers in education are interested in modeling of learner's profile and adapt their learning experiences accordingly. When learners read and interact with their reading materials, they do unconscious practices like annotations which may be, a key feature of their personalities. Annotation activity requires readers to be active, to think…

Recognition of Learner's Personality Traits through Digital Annotations in Distance Learning

ERIC Educational Resources Information Center

Omheni, Nizar; Kalboussi, Anis; Mazhoud, Omar; Kacem, Ahmed Hadj

2017-01-01

Researchers in distance education are interested in observing and modelling of learner's personality profile, and adapting their learning experiences accordingly. When learners read and interact with their reading materials, they do unselfconscious activities like annotation which may be a key feature of their personalities. Annotation activity…

Uncovering the Dark Energy of Aging.

PubMed

Melov, Simon

2016-10-26

A medically relevant understanding of aging requires an appreciation for how time degrades specific, healthy features of individual organisms over the course of their lives. Zach Pincus and colleagues make a key step in this direction, using C. elegans as a model system. Copyright © 2016 Elsevier Inc. All rights reserved.

Talent Development Middle Grades Program. What Works Clearinghouse Intervention Report

ERIC Educational Resources Information Center

What Works Clearinghouse, 2013

2013-01-01

The "Talent Development Middle Grades Program" is a comprehensive reform model that transforms the structure and curriculum of large urban middle schools with the aim of improving student achievement and raising teacher and student expectations. Key features of the "Talent Development Middle Grades Program" include small…

A Collaborative Professional Development Initiative Supporting Early Literacy Coaches

ERIC Educational Resources Information Center

Mraz, Maryann; Kissel, Brian; Algozzine, Bob; Babb, Julie; Foxworth, Kimberly

2011-01-01

Many believe that the key to translating research into successful practice lies in providing teachers with continuous professional development and ongoing coaching support. In this article, we provide an overview of the relevant coaching literature and describe 4 critical features of an evidence-based preschool literacy coaching model: the coach…

Learning Compositional Shape Models of Multiple Distance Metrics by Information Projection.

PubMed

Luo, Ping; Lin, Liang; Liu, Xiaobai

2016-07-01

This paper presents a novel compositional contour-based shape model by incorporating multiple distance metrics to account for varying shape distortions or deformations. Our approach contains two key steps: 1) contour feature generation and 2) generative model pursuit. For each category, we first densely sample an ensemble of local prototype contour segments from a few positive shape examples and describe each segment using three different types of distance metrics. These metrics are diverse and complementary with each other to capture various shape deformations. We regard the parameterized contour segment plus an additive residual ϵ as a basic subspace, namely, ϵ -ball, in the sense that it represents local shape variance under the certain distance metric. Using these ϵ -balls as features, we then propose a generative learning algorithm to pursue the compositional shape model, which greedily selects the most representative features under the information projection principle. In experiments, we evaluate our model on several public challenging data sets, and demonstrate that the integration of multiple shape distance metrics is capable of dealing various shape deformations, articulations, and background clutter, hence boosting system performance.

Interprofessional teamwork and team interventions in chronic care: A systematic review.

PubMed

Körner, Mirjam; Bütof, Sarah; Müller, Christian; Zimmermann, Linda; Becker, Sonja; Bengel, Jürgen

2016-01-01

To identify key features of teamwork and interventions for enhancing interprofessional teamwork (IPT) in chronic care and to develop a framework for further research, we conducted a systematic literature review of IPT in chronic care for the years 2002-2014. Database searches yielded 3217 abstracts, 21 of which fulfilled inclusion criteria. We identified two more studies on the topic by scanning the reference lists of included articles, which resulted in a final total of 23 included studies. The key features identified in the articles (e.g., team member characteristics, common task, communication, cooperation, coordination, responsibility, participation, staff satisfaction, patient satisfaction, and efficiency) were structured in line with the input-process-output model, and evaluated interventions, such as tools, workshops, and changes in team structure, were added to the model. The most frequently evaluated team interventions were complex intervention programs. All but one of the 14 evaluation studies resulted in enhancement of teamwork and/or staff-related, patient-related, and organization-related outcome criteria. To date, there is no consensus about the main features of IPT and the most effective team interventions in chronic care. However, the findings may be used to standardize the implementation and evaluation of IPT and team interventions in practice and for further research.

Mice lacking cyclin-dependent kinase-like 5 manifest autistic and ADHD-like behaviors.

PubMed

Jhang, Cian-Ling; Huang, Tzyy-Nan; Hsueh, Yi-Ping; Liao, Wenlin

2017-10-15

Neurodevelopmental disorders frequently share common clinical features and appear high rate of comorbidity, such as those present in patients with attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorders (ASD). While characterizing behavioral phenotypes in the mouse model of cyclin-dependent kinase-like 5 (CDKL5) disorder, a neurodevelopmental disorder caused by mutations in the X-linked gene encoding CDKL5, we found that these mice manifested behavioral phenotypes mimicking multiple key features of ASD, such as impaired social interaction and communication, as well as increased stereotypic digging behaviors. These mice also displayed hyper-locomotion, increased aggressiveness and impulsivity, plus deficits in motor and associative learning, resembling primary symptoms of ADHD. Through brain region-specific biochemical analysis, we uncovered that loss of CDKL5 disrupts dopamine synthesis and the expression of social communication-related key genes, such as forkhead-box P2 and mu-opioid receptor, in the corticostriatal circuit. Together, our findings support that CDKL5 plays a role in the comorbid features of autism and ADHD, and mice lacking CDKL5 may serve as an animal model to study the molecular and circuit mechanisms underlying autism-ADHD comorbidity. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

A generalized reaction diffusion model for spatial structure formed by motile cells.

PubMed

Ochoa, F L

1984-01-01

A non-linear stability analysis using a multi-scale perturbation procedure is carried out on a model of a generalized reaction diffusion mechanism which involves only a single equation but which nevertheless exhibits bifurcation to non-uniform states. The patterns generated by this model by variation in a parameter related to the scalar dimensions of domain of definition, indicate its capacity to represent certain key morphogenetic features of multicellular systems formed by motile cells.

Incorporating physically-based microstructures in materials modeling: Bridging phase field and crystal plasticity frameworks

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

Lim, Hojun; Abdeljawad, Fadi; Owen, Steven J.

Here, the mechanical properties of materials systems are highly influenced by various features at the microstructural level. The ability to capture these heterogeneities and incorporate them into continuum-scale frameworks of the deformation behavior is considered a key step in the development of complex non-local models of failure. In this study, we present a modeling framework that incorporates physically-based realizations of polycrystalline aggregates from a phase field (PF) model into a crystal plasticity finite element (CP-FE) framework. Simulated annealing via the PF model yields ensembles of materials microstructures with various grain sizes and shapes. With the aid of a novel FEmore » meshing technique, FE discretizations of these microstructures are generated, where several key features, such as conformity to interfaces, and triple junction angles, are preserved. The discretizations are then used in the CP-FE framework to simulate the mechanical response of polycrystalline α-iron. It is shown that the conformal discretization across interfaces reduces artificial stress localization commonly observed in non-conformal FE discretizations. The work presented herein is a first step towards incorporating physically-based microstructures in lieu of the overly simplified representations that are commonly used. In broader terms, the proposed framework provides future avenues to explore bridging models of materials processes, e.g. additive manufacturing and microstructure evolution of multi-phase multi-component systems, into continuum-scale frameworks of the mechanical properties.« less

Incorporating physically-based microstructures in materials modeling: Bridging phase field and crystal plasticity frameworks

DOE PAGES

Lim, Hojun; Abdeljawad, Fadi; Owen, Steven J.; ...

2016-04-25

Here, the mechanical properties of materials systems are highly influenced by various features at the microstructural level. The ability to capture these heterogeneities and incorporate them into continuum-scale frameworks of the deformation behavior is considered a key step in the development of complex non-local models of failure. In this study, we present a modeling framework that incorporates physically-based realizations of polycrystalline aggregates from a phase field (PF) model into a crystal plasticity finite element (CP-FE) framework. Simulated annealing via the PF model yields ensembles of materials microstructures with various grain sizes and shapes. With the aid of a novel FEmore » meshing technique, FE discretizations of these microstructures are generated, where several key features, such as conformity to interfaces, and triple junction angles, are preserved. The discretizations are then used in the CP-FE framework to simulate the mechanical response of polycrystalline α-iron. It is shown that the conformal discretization across interfaces reduces artificial stress localization commonly observed in non-conformal FE discretizations. The work presented herein is a first step towards incorporating physically-based microstructures in lieu of the overly simplified representations that are commonly used. In broader terms, the proposed framework provides future avenues to explore bridging models of materials processes, e.g. additive manufacturing and microstructure evolution of multi-phase multi-component systems, into continuum-scale frameworks of the mechanical properties.« less

Adaptive Failure Compensation for Aircraft Tracking Control Using Engine Differential Based Model

NASA Technical Reports Server (NTRS)

Liu, Yu; Tang, Xidong; Tao, Gang; Joshi, Suresh M.

2006-01-01

An aircraft model that incorporates independently adjustable engine throttles and ailerons is employed to develop an adaptive control scheme in the presence of actuator failures. This model captures the key features of aircraft flight dynamics when in the engine differential mode. Based on this model an adaptive feedback control scheme for asymptotic state tracking is developed and applied to a transport aircraft model in the presence of two types of failures during operation, rudder failure and aileron failure. Simulation results are presented to demonstrate the adaptive failure compensation scheme.

Prediction of interface residue based on the features of residue interaction network.

PubMed

Jiao, Xiong; Ranganathan, Shoba

2017-11-07

Protein-protein interaction plays a crucial role in the cellular biological processes. Interface prediction can improve our understanding of the molecular mechanisms of the related processes and functions. In this work, we propose a classification method to recognize the interface residue based on the features of a weighted residue interaction network. The random forest algorithm is used for the prediction and 16 network parameters and the B-factor are acting as the element of the input feature vector. Compared with other similar work, the method is feasible and effective. The relative importance of these features also be analyzed to identify the key feature for the prediction. Some biological meaning of the important feature is explained. The results of this work can be used for the related work about the structure-function relationship analysis via a residue interaction network model. Copyright © 2017 Elsevier Ltd. All rights reserved.

Configurational coupled cluster approach with applications to magnetic model systems

NASA Astrophysics Data System (ADS)

Wu, Siyuan; Nooijen, Marcel

2018-05-01

A general exponential, coupled cluster like, approach is discussed to extract an effective Hamiltonian in configurational space, as a sum of 1-body, 2-body up to n-body operators. The simplest two-body approach is illustrated by calculations on simple magnetic model systems. A key feature of the approach is that equations up to a certain rank do not depend on higher body cluster operators.

Revisiting the case for genetically engineered mouse models in human myelodysplastic syndrome research.

PubMed

Zhou, Ting; Kinney, Marsha C; Scott, Linda M; Zinkel, Sandra S; Rebel, Vivienne I

2015-08-27

Much-needed attention has been given of late to diseases specifically associated with an expanding elderly population. Myelodysplastic syndrome (MDS), a hematopoietic stem cell-based blood disease, is one of these. The lack of clear understanding of the molecular mechanisms underlying the pathogenesis of this disease has hampered the development of efficacious therapies, especially in the presence of comorbidities. Mouse models could potentially provide new insights into this disease, although primary human MDS cells grow poorly in xenografted mice. This makes genetically engineered murine models a more attractive proposition, although this approach is not without complications. In particular, it is unclear if or how myelodysplasia (abnormal blood cell morphology), a key MDS feature in humans, presents in murine cells. Here, we evaluate the histopathologic features of wild-type mice and 23 mouse models with verified myelodysplasia. We find that certain features indicative of myelodysplasia in humans, such as Howell-Jolly bodies and low neutrophilic granularity, are commonplace in healthy mice, whereas other features are similarly abnormal in humans and mice. Quantitative hematopoietic parameters, such as blood cell counts, are required to distinguish between MDS and related diseases. We provide data that mouse models of MDS can be genetically engineered and faithfully recapitulate human disease. © 2015 by The American Society of Hematology.

A feature selection approach towards progressive vector transmission over the Internet

NASA Astrophysics Data System (ADS)

Miao, Ru; Song, Jia; Feng, Min

2017-09-01

WebGIS has been applied for visualizing and sharing geospatial information popularly over the Internet. In order to improve the efficiency of the client applications, the web-based progressive vector transmission approach is proposed. Important features should be selected and transferred firstly, and the methods for measuring the importance of features should be further considered in the progressive transmission. However, studies on progressive transmission for large-volume vector data have mostly focused on map generalization in the field of cartography, but rarely discussed on the selection of geographic features quantitatively. This paper applies information theory for measuring the feature importance of vector maps. A measurement model for the amount of information of vector features is defined based upon the amount of information for dealing with feature selection issues. The measurement model involves geometry factor, spatial distribution factor and thematic attribute factor. Moreover, a real-time transport protocol (RTP)-based progressive transmission method is then presented to improve the transmission of vector data. To clearly demonstrate the essential methodology and key techniques, a prototype for web-based progressive vector transmission is presented, and an experiment of progressive selection and transmission for vector features is conducted. The experimental results indicate that our approach clearly improves the performance and end-user experience of delivering and manipulating large vector data over the Internet.

Multiple receptor conformation docking, dock pose clustering and 3D QSAR studies on human poly(ADP-ribose) polymerase-1 (PARP-1) inhibitors.

PubMed

Fatima, Sabiha; Jatavath, Mohan Babu; Bathini, Raju; Sivan, Sree Kanth; Manga, Vijjulatha

2014-10-01

Poly(ADP-ribose) polymerase-1 (PARP-1) functions as a DNA damage sensor and signaling molecule. It plays a vital role in the repair of DNA strand breaks induced by radiation and chemotherapeutic drugs; inhibitors of this enzyme have the potential to improve cancer chemotherapy or radiotherapy. Three-dimensional quantitative structure activity relationship (3D QSAR) models were developed using comparative molecular field analysis, comparative molecular similarity indices analysis and docking studies. A set of 88 molecules were docked into the active site of six X-ray crystal structures of poly(ADP-ribose)polymerase-1 (PARP-1), by a procedure called multiple receptor conformation docking (MRCD), in order to improve the 3D QSAR models through the analysis of binding conformations. The docked poses were clustered to obtain the best receptor binding conformation. These dock poses from clustering were used for 3D QSAR analysis. Based on MRCD and QSAR information, some key features have been identified that explain the observed variance in the activity. Two receptor-based QSAR models were generated; these models showed good internal and external statistical reliability that is evident from the [Formula: see text], [Formula: see text] and [Formula: see text]. The identified key features enabled us to design new PARP-1 inhibitors.

Micro-Tom Tomato as an Alternative Plant Model System: Mutant Collection and Efficient Transformation.

PubMed

Shikata, Masahito; Ezura, Hiroshi

2016-01-01

Tomato is a model plant for fruit development, a unique feature that classical model plants such as Arabidopsis and rice do not have. The tomato genome was sequenced in 2012 and tomato is becoming very popular as an alternative system for plant research. Among many varieties of tomato, Micro-Tom has been recognized as a model cultivar for tomato research because it shares some key advantages with Arabidopsis including its small size, short life cycle, and capacity to grow under fluorescent lights at a high density. Mutants and transgenic plants are essential materials for functional genomics research, and therefore, the availability of mutant resources and methods for genetic transformation are key tools to facilitate tomato research. Here, we introduce the Micro-Tom mutant database "TOMATOMA" and an efficient transformation protocol for Micro-Tom.

Deep Multimodal Distance Metric Learning Using Click Constraints for Image Ranking.

PubMed

Yu, Jun; Yang, Xiaokang; Gao, Fei; Tao, Dacheng

2017-12-01

How do we retrieve images accurately? Also, how do we rank a group of images precisely and efficiently for specific queries? These problems are critical for researchers and engineers to generate a novel image searching engine. First, it is important to obtain an appropriate description that effectively represent the images. In this paper, multimodal features are considered for describing images. The images unique properties are reflected by visual features, which are correlated to each other. However, semantic gaps always exist between images visual features and semantics. Therefore, we utilize click feature to reduce the semantic gap. The second key issue is learning an appropriate distance metric to combine these multimodal features. This paper develops a novel deep multimodal distance metric learning (Deep-MDML) method. A structured ranking model is adopted to utilize both visual and click features in distance metric learning (DML). Specifically, images and their related ranking results are first collected to form the training set. Multimodal features, including click and visual features, are collected with these images. Next, a group of autoencoders is applied to obtain initially a distance metric in different visual spaces, and an MDML method is used to assign optimal weights for different modalities. Next, we conduct alternating optimization to train the ranking model, which is used for the ranking of new queries with click features. Compared with existing image ranking methods, the proposed method adopts a new ranking model to use multimodal features, including click features and visual features in DML. We operated experiments to analyze the proposed Deep-MDML in two benchmark data sets, and the results validate the effects of the method.

Machine learning methods enable predictive modeling of antibody feature:function relationships in RV144 vaccinees.

PubMed

Choi, Ickwon; Chung, Amy W; Suscovich, Todd J; Rerks-Ngarm, Supachai; Pitisuttithum, Punnee; Nitayaphan, Sorachai; Kaewkungwal, Jaranit; O'Connell, Robert J; Francis, Donald; Robb, Merlin L; Michael, Nelson L; Kim, Jerome H; Alter, Galit; Ackerman, Margaret E; Bailey-Kellogg, Chris

2015-04-01

The adaptive immune response to vaccination or infection can lead to the production of specific antibodies to neutralize the pathogen or recruit innate immune effector cells for help. The non-neutralizing role of antibodies in stimulating effector cell responses may have been a key mechanism of the protection observed in the RV144 HIV vaccine trial. In an extensive investigation of a rich set of data collected from RV144 vaccine recipients, we here employ machine learning methods to identify and model associations between antibody features (IgG subclass and antigen specificity) and effector function activities (antibody dependent cellular phagocytosis, cellular cytotoxicity, and cytokine release). We demonstrate via cross-validation that classification and regression approaches can effectively use the antibody features to robustly predict qualitative and quantitative functional outcomes. This integration of antibody feature and function data within a machine learning framework provides a new, objective approach to discovering and assessing multivariate immune correlates.

Machine Learning Methods Enable Predictive Modeling of Antibody Feature:Function Relationships in RV144 Vaccinees

PubMed Central

Choi, Ickwon; Chung, Amy W.; Suscovich, Todd J.; Rerks-Ngarm, Supachai; Pitisuttithum, Punnee; Nitayaphan, Sorachai; Kaewkungwal, Jaranit; O'Connell, Robert J.; Francis, Donald; Robb, Merlin L.; Michael, Nelson L.; Kim, Jerome H.; Alter, Galit; Ackerman, Margaret E.; Bailey-Kellogg, Chris

2015-01-01

The adaptive immune response to vaccination or infection can lead to the production of specific antibodies to neutralize the pathogen or recruit innate immune effector cells for help. The non-neutralizing role of antibodies in stimulating effector cell responses may have been a key mechanism of the protection observed in the RV144 HIV vaccine trial. In an extensive investigation of a rich set of data collected from RV144 vaccine recipients, we here employ machine learning methods to identify and model associations between antibody features (IgG subclass and antigen specificity) and effector function activities (antibody dependent cellular phagocytosis, cellular cytotoxicity, and cytokine release). We demonstrate via cross-validation that classification and regression approaches can effectively use the antibody features to robustly predict qualitative and quantitative functional outcomes. This integration of antibody feature and function data within a machine learning framework provides a new, objective approach to discovering and assessing multivariate immune correlates. PMID:25874406

Using the Properties of Broad Absorption Line Quasars to Illuminate Quasar Structure

NASA Astrophysics Data System (ADS)

Yong, Suk Yee; King, Anthea L.; Webster, Rachel L.; Bate, Nicholas F.; O'Dowd, Matthew J.; Labrie, Kathleen

2018-06-01

A key to understanding quasar unification paradigms is the emission properties of broad absorption line quasars (BALQs). The fact that only a small fraction of quasar spectra exhibit deep absorption troughs blueward of the broad permitted emission lines provides a crucial clue to the structure of quasar emitting regions. To learn whether it is possible to discriminate between the BALQ and non-BALQ populations given the observed spectral properties of a quasar, we employ two approaches: one based on statistical methods and the other supervised machine learning classification, applied to quasar samples from the Sloan Digital Sky Survey. The features explored include continuum and emission line properties, in particular the absolute magnitude, redshift, spectral index, line width, asymmetry, strength, and relative velocity offsets of high-ionisation C IV λ1549 and low-ionisation Mg II λ2798 lines. We consider a complete population of quasars, and assume that the statistical distributions of properties represent all angles where the quasar is viewed without obscuration. The distributions of the BALQ and non-BALQ sample properties show few significant differences. None of the observed continuum and emission line features are capable of differentiating between the two samples. Most published narrow disk-wind models are inconsistent with these observations, and an alternative disk-wind model is proposed. The key feature of the proposed model is a disk-wind filling a wide opening angle with multiple radial streams of dense clumps.

Issues or Identity? Cognitive Foundations of Voter Choice.

PubMed

Jenke, Libby; Huettel, Scott A

2016-11-01

Voter choice is one of the most important problems in political science. The most common models assume that voting is a rational choice based on policy positions (e.g., key issues) and nonpolicy information (e.g., social identity, personality). Though such models explain macroscopic features of elections, they also reveal important anomalies that have been resistant to explanation. We argue for a new approach that builds upon recent research in cognitive science and neuroscience; specifically, we contend that policy positions and social identities do not combine in merely an additive manner, but compete to determine voter preferences. This model not only explains several key anomalies in voter choice, but also suggests new directions for research in both political science and cognitive science. Copyright © 2016 Elsevier Ltd. All rights reserved.

Recovery and Resilience After a Nuclear Power Plant Disaster: A Medical Decision model for Managing an Effective, Timely, and Balanced Response

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

Coleman, C. Norman; Blumenthal, Daniel J.

2013-05-01

Based on experiences in Tokyo responding to the Fukushima Daiichi nuclear power plant crisis, a real-time, medical decision model is presented by which to make key health-related decisions given the central role of health and medical issues in such disasters. Focus is on response and recovery activities that are safe, timely, effective, and well-organized. This approach empowers on-site decision makers to make interim decisions without undue delay using readily available and high-level scientific, medical, communication, and policy expertise. Key features of this approach include ongoing assessment, consultation, information, and adaption to the changing conditions. This medical decision model presented ismore » compatible with the existing US National Response Framework structure.« less

Key features of an EU health information system: a concept mapping study.

PubMed

Rosenkötter, Nicole; Achterberg, Peter W; van Bon-Martens, Marja J H; Michelsen, Kai; van Oers, Hans A M; Brand, Helmut

2016-02-01

Despite the acknowledged value of an EU health information system (EU-HISys) and the many achievements in this field, the landscape is still heavily fragmented and incomplete. Through a systematic analysis of the opinions and valuations of public health stakeholders, this study aims to conceptualize key features of an EU-HISys. Public health professionals and policymakers were invited to participate in a concept mapping procedure. First, participants (N = 34) formulated statements that reflected their vision of an EU-HISys. Second, participants (N = 28) rated the relative importance of each statement and grouped conceptually similar ones. Principal Component and cluster analyses were used to condense these results to EU-HISys key features in a concept map. The number of key features and the labelling of the concept map were determined by expert consensus. The concept map contains 10 key features that summarize 93 statements. The map consists of a horizontal axis that represents the relevance of an 'organizational strategy', which deals with the 'efforts' to design and develop an EU-HISys and the 'achievements' gained by a functioning EU-HISys. The vertical axis represents the 'professional orientation' of the EU-HISys, ranging from the 'scientific' through to the 'policy' perspective. The top ranking statement expressed the need to establish a system that is permanent and sustainable. The top ranking key feature focuses on data and information quality. This study provides insights into key features of an EU-HISys. The results can be used to guide future planning and to support the development of a health information system for Europe. © The Author 2015. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.

Defining competency-based evaluation objectives in family medicine

PubMed Central

Lawrence, Kathrine; Allen, Tim; Brailovsky, Carlos; Crichton, Tom; Bethune, Cheri; Donoff, Michel; Laughlin, Tom; Wetmore, Stephen; Carpentier, Marie-Pierre; Visser, Shaun

2011-01-01

Abstract Objective To develop key features for priority topics previously identified by the College of Family Physicians of Canada that, together with skill dimensions and phases of the clinical encounter, broadly describe competence in family medicine. Design Modified nominal group methodology, which was used to develop key features for each priority topic through an iterative process. Setting The College of Family Physicians of Canada. Participants An expert group of 7 family physicians and 1 educational consultant, all of whom had experience in assessing competence in family medicine. Group members represented the Canadian family medicine context with respect to region, sex, language, community type, and experience. Methods The group used a modified Delphi process to derive a detailed operational definition of competence, using multiple iterations until consensus was achieved for the items under discussion. The group met 3 to 4 times a year from 2000 to 2007. Main findings The group analyzed 99 topics and generated 773 key features. There were 2 to 20 (average 7.8) key features per topic; 63% of the key features focused on the diagnostic phase of the clinical encounter. Conclusion This project expands previous descriptions of the process of generating key features for assessment, and removes this process from the context of written examinations. A key-features analysis of topics focuses on higher-order cognitive processes of clinical competence. The project did not define all the skill dimensions of competence to the same degree, but it clearly identified those requiring further definition. This work generates part of a discipline-specific, competency-based definition of family medicine for assessment purposes. It limits the domain for assessment purposes, which is an advantage for the teaching and assessment of learners. A validation study on the content of this work would ensure that it truly reflects competence in family medicine. PMID:21998245

A sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image

NASA Astrophysics Data System (ADS)

Li, Jing; Xie, Weixin; Pei, Jihong

2018-03-01

Sea-land segmentation is one of the key technologies of sea target detection in remote sensing images. At present, the existing algorithms have the problems of low accuracy, low universality and poor automatic performance. This paper puts forward a sea-land segmentation algorithm based on multi-feature fusion for a large-field remote sensing image removing island. Firstly, the coastline data is extracted and all of land area is labeled by using the geographic information in large-field remote sensing image. Secondly, three features (local entropy, local texture and local gradient mean) is extracted in the sea-land border area, and the three features combine a 3D feature vector. And then the MultiGaussian model is adopted to describe 3D feature vectors of sea background in the edge of the coastline. Based on this multi-gaussian sea background model, the sea pixels and land pixels near coastline are classified more precise. Finally, the coarse segmentation result and the fine segmentation result are fused to obtain the accurate sea-land segmentation. Comparing and analyzing the experimental results by subjective vision, it shows that the proposed method has high segmentation accuracy, wide applicability and strong anti-disturbance ability.

Dialectical Features of Students' Argumentation: A Critical Review of Argumentation Studies in Science Education

NASA Astrophysics Data System (ADS)

Nielsen, Jan Alexis

2013-02-01

This paper explores the challenges of using the Toulmin model to analyze students' dialogical argumentation. The paper presents a theoretical exposition of what is involved in an empirical study of real dialogic argumentation. Dialogic argumentation embodies dialectical features — i.e. the features that are operative when students collaboratively manage disagreement by providing arguments and engaging critically with the arguments provided by others. The paper argues that while dialectical features cannot readily be understood from a Toulminian perspective, it appears that an investigation of them is a prerequisite for conducting Toulminian analysis. This claim is substantiated by a detailed review of five of the ten most significant papers on students' argumentation in science education. This leads to the surprising notion that empirical studies in the argumentation strand — even those studies that have employed non-dialectical frameworks such as the Toulmin model — have implicitly struggled to come to terms with the dialectical features of students' discourse. The paper finally explores how some scholars have worked to attend directly to these dialectical features; and it presents five key issues that need to be addressed in a continued scholarly discussion.

A Prestressed Cable Network Model of the Adherent Cell Cytoskeleton

PubMed Central

Coughlin, Mark F.; Stamenović, Dimitrije

2003-01-01

A prestressed cable network is used to model the deformability of the adherent cell actin cytoskeleton. The overall and microstructural model geometries and cable mechanical properties were assigned values based on observations from living cells and mechanical measurements on isolated actin filaments, respectively. The models were deformed to mimic cell poking (CP), magnetic twisting cytometry (MTC) and magnetic bead microrheometry (MBM) measurements on living adherent cells. The models qualitatively and quantitatively captured the fibroblast cell response to the deformation imposed by CP while exhibiting only some qualitative features of the cell response to MTC and MBM. The model for CP revealed that the tensed peripheral actin filaments provide the key resistance to indentation. The actin filament tension that provides mechanical integrity to the network was estimated at ∼158 pN, and the nonlinear mechanical response during CP originates from filament kinematics. The MTC and MBM simulations revealed that the model is incomplete, however, these simulations show cable tension as a key determinant of the model response. PMID:12547813

A prestressed cable network model of the adherent cell cytoskeleton.

PubMed

Coughlin, Mark F; Stamenović, Dimitrije

2003-02-01

A prestressed cable network is used to model the deformability of the adherent cell actin cytoskeleton. The overall and microstructural model geometries and cable mechanical properties were assigned values based on observations from living cells and mechanical measurements on isolated actin filaments, respectively. The models were deformed to mimic cell poking (CP), magnetic twisting cytometry (MTC) and magnetic bead microrheometry (MBM) measurements on living adherent cells. The models qualitatively and quantitatively captured the fibroblast cell response to the deformation imposed by CP while exhibiting only some qualitative features of the cell response to MTC and MBM. The model for CP revealed that the tensed peripheral actin filaments provide the key resistance to indentation. The actin filament tension that provides mechanical integrity to the network was estimated at approximately 158 pN, and the nonlinear mechanical response during CP originates from filament kinematics. The MTC and MBM simulations revealed that the model is incomplete, however, these simulations show cable tension as a key determinant of the model response.

Classification and Feature Selection Algorithms for Modeling Ice Storm Climatology

NASA Astrophysics Data System (ADS)

Swaminathan, R.; Sridharan, M.; Hayhoe, K.; Dobbie, G.

2015-12-01

Ice storms account for billions of dollars of winter storm loss across the continental US and Canada. In the future, increasing concentration of human populations in areas vulnerable to ice storms such as the northeastern US will only exacerbate the impacts of these extreme events on infrastructure and society. Quantifying the potential impacts of global climate change on ice storm prevalence and frequency is challenging, as ice storm climatology is driven by complex and incompletely defined atmospheric processes, processes that are in turn influenced by a changing climate. This makes the underlying atmospheric and computational modeling of ice storm climatology a formidable task. We propose a novel computational framework that uses sophisticated stochastic classification and feature selection algorithms to model ice storm climatology and quantify storm occurrences from both reanalysis and global climate model outputs. The framework is based on an objective identification of ice storm events by key variables derived from vertical profiles of temperature, humidity and geopotential height. Historical ice storm records are used to identify days with synoptic-scale upper air and surface conditions associated with ice storms. Evaluation using NARR reanalysis and historical ice storm records corresponding to the northeastern US demonstrates that an objective computational model with standard performance measures, with a relatively high degree of accuracy, identify ice storm events based on upper-air circulation patterns and provide insights into the relationships between key climate variables associated with ice storms.

Predicting Second Language Writing Proficiency: The Roles of Cohesion and Linguistic Sophistication

ERIC Educational Resources Information Center

Crossley, Scott A.; McNamara, Danielle S.

2012-01-01

This study addresses research gaps in predicting second language (L2) writing proficiency using linguistic features. Key to this analysis is the inclusion of linguistic measures at the surface, textbase and situation model level that assess text cohesion and linguistic sophistication. The results of this study demonstrate that five variables…

Access to a Schoolwide Thinking Curriculum: Leadership Challenges and Solutions.

ERIC Educational Resources Information Center

Morocco, Catherine Cobb; Walker, Andrea; Lewis, Leslie R.

2003-01-01

This article discusses how an urban middle school designed to reflect a Schools for Thought model has demonstrated that urban schools can achieve excellent results on statewide testing for all students, including those with disabilities. Key school features are highlighted, including the use of "cross-talk" to stimulate discussion and student…

The relationship between two fast/slow analysis techniques for bursting oscillations

PubMed Central

Teka, Wondimu; Tabak, Joël; Bertram, Richard

2012-01-01

Bursting oscillations in excitable systems reflect multi-timescale dynamics. These oscillations have often been studied in mathematical models by splitting the equations into fast and slow subsystems. Typically, one treats the slow variables as parameters of the fast subsystem and studies the bifurcation structure of this subsystem. This has key features such as a z-curve (stationary branch) and a Hopf bifurcation that gives rise to a branch of periodic spiking solutions. In models of bursting in pituitary cells, we have recently used a different approach that focuses on the dynamics of the slow subsystem. Characteristic features of this approach are folded node singularities and a critical manifold. In this article, we investigate the relationships between the key structures of the two analysis techniques. We find that the z-curve and Hopf bifurcation of the two-fast/one-slow decomposition are closely related to the voltage nullcline and folded node singularity of the one-fast/two-slow decomposition, respectively. They become identical in the double singular limit in which voltage is infinitely fast and calcium is infinitely slow. PMID:23278052

Some key features in the evolution of self psychology and psychoanalysis.

PubMed

Fosshage, James L

2009-04-01

Psychoanalysis, as every science and its application, has continued to evolve over the past century, especially accelerating over the last 30 years. Self psychology has played a constitutive role in that evolution and has continued to change itself. These movements have been supported and augmented by a wide range of emergent research and theory, especially that of cognitive psychology, infant and attachment research, rapid eye movement and dream research, psychotherapy research, and neuroscience. I present schematically some of what I consider to be the key features of the evolution of self psychology and their interconnection with that of psychoanalysis at large, including the revolutionary paradigm changes, the new epistemology, listening/experiencing perspectives, from narcissism to the development of the self, the new organization model of transference, the new organization model of dreams, and the implicit and explicit dimensions of analytic work. I conclude with a focus on the radical ongoing extension of the analyst's participation in the analytic relationship, using, as an example, the co-creation of analytic love, and providing several brief clinical illustrations. The leading edge question guiding my discussion is "How does analytic change occur?"

11-Step Total Synthesis of (−)-Maoecrystal V

PubMed Central

2016-01-01

An expedient, practical, and enantioselective route to the highly congested ent-kaurane diterpene maoecrystal V is presented. This route, which has been several years in the making, is loosely modeled after a key pinacol shift in the proposed biosynthesis. Only 11 steps, many of which are strategic in that they build key skeletal bonds and incorporate critical functionalities, are required to access (−)-maoecrystal V. Several unique and unexpected maneuvers are featured in this potentially scalable pathway. Reevaluation of the biological activity calls into question the initial exuberance surrounding this natural product. PMID:27457680

A mixed model framework for teratology studies.

PubMed

Braeken, Johan; Tuerlinckx, Francis

2009-10-01

A mixed model framework is presented to model the characteristic multivariate binary anomaly data as provided in some teratology studies. The key features of the model are the incorporation of covariate effects, a flexible random effects distribution by means of a finite mixture, and the application of copula functions to better account for the relation structure of the anomalies. The framework is motivated by data of the Boston Anticonvulsant Teratogenesis study and offers an integrated approach to investigate substantive questions, concerning general and anomaly-specific exposure effects of covariates, interrelations between anomalies, and objective diagnostic measurement.

Spectral method for a kinetic swarming model

DOE PAGES

Gamba, Irene M.; Haack, Jeffrey R.; Motsch, Sebastien

2015-04-28

Here we present the first numerical method for a kinetic description of the Vicsek swarming model. The kinetic model poses a unique challenge, as there is a distribution dependent collision invariant to satisfy when computing the interaction term. We use a spectral representation linked with a discrete constrained optimization to compute these interactions. To test the numerical scheme we investigate the kinetic model at different scales and compare the solution with the microscopic and macroscopic descriptions of the Vicsek model. Lastly, we observe that the kinetic model captures key features such as vortex formation and traveling waves.

Feature learning and change feature classification based on deep learning for ternary change detection in SAR images

NASA Astrophysics Data System (ADS)

Gong, Maoguo; Yang, Hailun; Zhang, Puzhao

2017-07-01

Ternary change detection aims to detect changes and group the changes into positive change and negative change. It is of great significance in the joint interpretation of spatial-temporal synthetic aperture radar images. In this study, sparse autoencoder, convolutional neural networks (CNN) and unsupervised clustering are combined to solve ternary change detection problem without any supervison. Firstly, sparse autoencoder is used to transform log-ratio difference image into a suitable feature space for extracting key changes and suppressing outliers and noise. And then the learned features are clustered into three classes, which are taken as the pseudo labels for training a CNN model as change feature classifier. The reliable training samples for CNN are selected from the feature maps learned by sparse autoencoder with certain selection rules. Having training samples and the corresponding pseudo labels, the CNN model can be trained by using back propagation with stochastic gradient descent. During its training procedure, CNN is driven to learn the concept of change, and more powerful model is established to distinguish different types of changes. Unlike the traditional methods, the proposed framework integrates the merits of sparse autoencoder and CNN to learn more robust difference representations and the concept of change for ternary change detection. Experimental results on real datasets validate the effectiveness and superiority of the proposed framework.

Hum-mPLoc 3.0: prediction enhancement of human protein subcellular localization through modeling the hidden correlations of gene ontology and functional domain features.

PubMed

Zhou, Hang; Yang, Yang; Shen, Hong-Bin

2017-03-15

Protein subcellular localization prediction has been an important research topic in computational biology over the last decade. Various automatic methods have been proposed to predict locations for large scale protein datasets, where statistical machine learning algorithms are widely used for model construction. A key step in these predictors is encoding the amino acid sequences into feature vectors. Many studies have shown that features extracted from biological domains, such as gene ontology and functional domains, can be very useful for improving the prediction accuracy. However, domain knowledge usually results in redundant features and high-dimensional feature spaces, which may degenerate the performance of machine learning models. In this paper, we propose a new amino acid sequence-based human protein subcellular location prediction approach Hum-mPLoc 3.0, which covers 12 human subcellular localizations. The sequences are represented by multi-view complementary features, i.e. context vocabulary annotation-based gene ontology (GO) terms, peptide-based functional domains, and residue-based statistical features. To systematically reflect the structural hierarchy of the domain knowledge bases, we propose a novel feature representation protocol denoted as HCM (Hidden Correlation Modeling), which will create more compact and discriminative feature vectors by modeling the hidden correlations between annotation terms. Experimental results on four benchmark datasets show that HCM improves prediction accuracy by 5-11% and F 1 by 8-19% compared with conventional GO-based methods. A large-scale application of Hum-mPLoc 3.0 on the whole human proteome reveals proteins co-localization preferences in the cell. www.csbio.sjtu.edu.cn/bioinf/Hum-mPLoc3/. hbshen@sjtu.edu.cn. Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

A methodology for global-sensitivity analysis of time-dependent outputs in systems biology modelling.

PubMed

Sumner, T; Shephard, E; Bogle, I D L

2012-09-07

One of the main challenges in the development of mathematical and computational models of biological systems is the precise estimation of parameter values. Understanding the effects of uncertainties in parameter values on model behaviour is crucial to the successful use of these models. Global sensitivity analysis (SA) can be used to quantify the variability in model predictions resulting from the uncertainty in multiple parameters and to shed light on the biological mechanisms driving system behaviour. We present a new methodology for global SA in systems biology which is computationally efficient and can be used to identify the key parameters and their interactions which drive the dynamic behaviour of a complex biological model. The approach combines functional principal component analysis with established global SA techniques. The methodology is applied to a model of the insulin signalling pathway, defects of which are a major cause of type 2 diabetes and a number of key features of the system are identified.

Haptic exploration of fingertip-sized geometric features using a multimodal tactile sensor

NASA Astrophysics Data System (ADS)

Ponce Wong, Ruben D.; Hellman, Randall B.; Santos, Veronica J.

2014-06-01

Haptic perception remains a grand challenge for artificial hands. Dexterous manipulators could be enhanced by "haptic intelligence" that enables identification of objects and their features via touch alone. Haptic perception of local shape would be useful when vision is obstructed or when proprioceptive feedback is inadequate, as observed in this study. In this work, a robot hand outfitted with a deformable, bladder-type, multimodal tactile sensor was used to replay four human-inspired haptic "exploratory procedures" on fingertip-sized geometric features. The geometric features varied by type (bump, pit), curvature (planar, conical, spherical), and footprint dimension (1.25 - 20 mm). Tactile signals generated by active fingertip motions were used to extract key parameters for use as inputs to supervised learning models. A support vector classifier estimated order of curvature while support vector regression models estimated footprint dimension once curvature had been estimated. A distal-proximal stroke (along the long axis of the finger) enabled estimation of order of curvature with an accuracy of 97%. Best-performing, curvature-specific, support vector regression models yielded R2 values of at least 0.95. While a radial-ulnar stroke (along the short axis of the finger) was most helpful for estimating feature type and size for planar features, a rolling motion was most helpful for conical and spherical features. The ability to haptically perceive local shape could be used to advance robot autonomy and provide haptic feedback to human teleoperators of devices ranging from bomb defusal robots to neuroprostheses.

A grain boundary damage model for delamination

NASA Astrophysics Data System (ADS)

Messner, M. C.; Beaudoin, A. J.; Dodds, R. H.

2015-07-01

Intergranular failure in metallic materials represents a multiscale damage mechanism: some feature of the material microstructure triggers the separation of grain boundaries on the microscale, but the intergranular fractures develop into long cracks on the macroscale. This work develops a multiscale model of grain boundary damage for modeling intergranular delamination—a failure of one particular family of grain boundaries sharing a common normal direction. The key feature of the model is a physically-consistent and mesh independent, multiscale scheme that homogenizes damage at many grain boundaries on the microscale into a single damage parameter on the macroscale to characterize material failure across a plane. The specific application of the damage framework developed here considers delamination failure in modern Al-Li alloys. However, the framework may be readily applied to other metals or composites and to other non-delamination interface geometries—for example, multiple populations of material interfaces with different geometric characteristics.

Use of fuzzy sets in modeling of GIS objects

NASA Astrophysics Data System (ADS)

Mironova, Yu N.

2018-05-01

The paper discusses modeling and methods of data visualization in geographic information systems. Information processing in Geoinformatics is based on the use of models. Therefore, geoinformation modeling is a key in the chain of GEODATA processing. When solving problems, using geographic information systems often requires submission of the approximate or insufficient reliable information about the map features in the GIS database. Heterogeneous data of different origin and accuracy have some degree of uncertainty. In addition, not all information is accurate: already during the initial measurements, poorly defined terms and attributes (e.g., "soil, well-drained") are used. Therefore, there are necessary methods for working with uncertain requirements, classes, boundaries. The author proposes using spatial information fuzzy sets. In terms of a characteristic function, a fuzzy set is a natural generalization of ordinary sets, when one rejects the binary nature of this feature and assumes that it can take any value in the interval.

Modelling students' knowledge organisation: Genealogical conceptual networks

NASA Astrophysics Data System (ADS)

Koponen, Ismo T.; Nousiainen, Maija

2018-04-01

Learning scientific knowledge is largely based on understanding what are its key concepts and how they are related. The relational structure of concepts also affects how concepts are introduced in teaching scientific knowledge. We model here how students organise their knowledge when they represent their understanding of how physics concepts are related. The model is based on assumptions that students use simple basic linking-motifs in introducing new concepts and mostly relate them to concepts that were introduced a few steps earlier, i.e. following a genealogical ordering. The resulting genealogical networks have relatively high local clustering coefficients of nodes but otherwise resemble networks obtained with an identical degree distribution of nodes but with random linking between them (i.e. the configuration-model). However, a few key nodes having a special structural role emerge and these nodes have a higher than average communicability betweenness centralities. These features agree with the empirically found properties of students' concept networks.

A double hit model for the distribution of time to AIDS onset

NASA Astrophysics Data System (ADS)

Chillale, Nagaraja Rao

2013-09-01

Incubation time is a key epidemiologic descriptor of an infectious disease. In the case of HIV infection this is a random variable and is probably the longest one. The probability distribution of incubation time is the major determinant of the relation between the incidences of HIV infection and its manifestation to Aids. This is also one of the key factors used for accurate estimation of AIDS incidence in a region. The present article i) briefly reviews the work done, points out uncertainties in estimation of AIDS onset time and stresses the need for its precise estimation, ii) highlights some of the modelling features of onset distribution including immune failure mechanism, and iii) proposes a 'Double Hit' model for the distribution of time to AIDS onset in the cases of (a) independent and (b) dependent time variables of the two markers and examined the applicability of a few standard probability models.

Optical Flow Estimation for Flame Detection in Videos

PubMed Central

Mueller, Martin; Karasev, Peter; Kolesov, Ivan; Tannenbaum, Allen

2014-01-01

Computational vision-based flame detection has drawn significant attention in the past decade with camera surveillance systems becoming ubiquitous. Whereas many discriminating features, such as color, shape, texture, etc., have been employed in the literature, this paper proposes a set of motion features based on motion estimators. The key idea consists of exploiting the difference between the turbulent, fast, fire motion, and the structured, rigid motion of other objects. Since classical optical flow methods do not model the characteristics of fire motion (e.g., non-smoothness of motion, non-constancy of intensity), two optical flow methods are specifically designed for the fire detection task: optimal mass transport models fire with dynamic texture, while a data-driven optical flow scheme models saturated flames. Then, characteristic features related to the flow magnitudes and directions are computed from the flow fields to discriminate between fire and non-fire motion. The proposed features are tested on a large video database to demonstrate their practical usefulness. Moreover, a novel evaluation method is proposed by fire simulations that allow for a controlled environment to analyze parameter influences, such as flame saturation, spatial resolution, frame rate, and random noise. PMID:23613042

Chronic cerebral hypoperfusion: a key mechanism leading to vascular cognitive impairment and dementia. Closing the translational gap between rodent models and human vascular cognitive impairment and dementia.

PubMed

Duncombe, Jessica; Kitamura, Akihiro; Hase, Yoshiki; Ihara, Masafumi; Kalaria, Raj N; Horsburgh, Karen

2017-10-01

Increasing evidence suggests that vascular risk factors contribute to neurodegeneration, cognitive impairment and dementia. While there is considerable overlap between features of vascular cognitive impairment and dementia (VCID) and Alzheimer's disease (AD), it appears that cerebral hypoperfusion is the common underlying pathophysiological mechanism which is a major contributor to cognitive decline and degenerative processes leading to dementia. Sustained cerebral hypoperfusion is suggested to be the cause of white matter attenuation, a key feature common to both AD and dementia associated with cerebral small vessel disease (SVD). White matter changes increase the risk for stroke, dementia and disability. A major gap has been the lack of mechanistic insights into the evolution and progress of VCID. However, this gap is closing with the recent refinement of rodent models which replicate chronic cerebral hypoperfusion. In this review, we discuss the relevance and advantages of these models in elucidating the pathogenesis of VCID and explore the interplay between hypoperfusion and the deposition of amyloid β (Aβ) protein, as it relates to AD. We use examples of our recent investigations to illustrate the utility of the model in preclinical testing of candidate drugs and lifestyle factors. We propose that the use of such models is necessary for tackling the urgently needed translational gap from preclinical models to clinical treatments. © 2017 The Author(s). Published by Portland Press Limited on behalf of the Biochemical Society.

Intracranial glioblastoma models in preclinical neuro-oncology: neuropathological characterization and tumor progression

PubMed Central

Candolfi, Marianela; Curtin, James F.; Stephen Nichols, W.; Muhammad, AKM. G.; King, Gwendalyn D.; Elizabeth Pluhar, G.; McNiel, Elizabeth A.; Ohlfest, John R.; Freese, Andrew B.; Moore, Peter F.; Lerner, Jonathan; Lowenstein, Pedro R.

2008-01-01

Although rodent glioblastoma (GBM) models have been used for over 30 years, the extent to which they recapitulate the characteristics encountered in human GBMs remains controversial. We studied the histopathological features of dog GBM and human xenograft GBM models in immune-deficient mice (U251 and U87 GBM in nude Balb/c), and syngeneic GBMs in immune-competent rodents (GL26 cells in C57BL/6 mice, CNS-1 cells in Lewis rats). All GBMs studied exhibited neovascularization, pleomorphism, vimentin immunoreactivity, and infiltration of T-cells and macrophages. All the tumors showed necrosis and hemorrhages, except the U87 human xenograft, in which the most salient feature was its profuse neovascularization. The tumors differed in the expression of astrocytic intermediate filaments: human and dog GBMs, as well as U251 xenografts expressed glial fibrillary acidic protein (GFAP) and vimentin, while the U87 xenograft and the syngeneic rodent GBMs were GFAP− and vimentin+. Also, only dog GBMs exhibited endothelial proliferation, a key feature that was absent in the murine models. In all spontaneous and implanted GBMs we found histopathological features compatible with tumor invasion into the non-neoplastic brain parenchyma. Our data indicate that murine models of GBM appear to recapitulate several of the human GBM histopathological features and, considering their reproducibility and availability, they constitute a valuable in vivo system for preclinical studies. Importantly, our results indicate that dog GBM emerges as an attractive animal model for testing novel therapies in a spontaneous tumor in the context of a larger brain. PMID:17874037

Evolution and regulation of complex life cycles: a brown algal perspective.

PubMed

Cock, J Mark; Godfroy, Olivier; Macaisne, Nicolas; Peters, Akira F; Coelho, Susana M

2014-02-01

The life cycle of an organism is one of its fundamental features, influencing many aspects of its biology. The brown algae exhibit a diverse range of life cycles indicating that transitions between life cycle types may have been key adaptive events in the evolution of this group. Life cycle mutants, identified in the model organism Ectocarpus, are providing information about how life cycle progression is regulated at the molecular level in brown algae. We explore some of the implications of the phenotypes of the life cycle mutants described to date and draw comparisons with recent insights into life cycle regulation in the green lineage. Given the importance of coordinating growth and development with life cycle progression, we suggest that the co-option of ancient life cycle regulators to control key developmental events may be a common feature in diverse groups of multicellular eukaryotes. Copyright © 2013 Elsevier Ltd. All rights reserved.

The Paraná-Etendeka Continental Flood Basalt Province: A historical perspective of current knowledge and future research trends

NASA Astrophysics Data System (ADS)

Cañón-Tapia, Edgardo

2018-04-01

The development of ideas concerning Continental Flood Basalt Provinces is not new, and many studies were completed on specific provinces before the advent of plate tectonics. The Paraná-Etendeka Province is not an exception, and actually is an example of a province that has been thoroughly studied for > 100 years. In this work, I present a brief summary of various aspects of this province from a rather general point of view, including many references of difficult access to a reader not versed on the Portuguese language. Key features include the presence of alkaline volcanism along the edges of the main basin, before and after a markedly tholeiitic event, the uneven spatial distribution of eruptive products relative to the location of continental rupture, the apparent lack of a pattern of temporal activity across the whole province and the close relationship between the structure of the underlying sedimentary basin and the distribution of volcanic rocks. By bringing together information relevant to all of those key features, an evolutionary model emphasizing the role played by the changing local structure is outlined. This model is an example of how key observations (many of which were overlooked for > 50 years) provide the required impetus for the completion of future research that has the potential to substantially change the form in which this province has been visualized for at least the past 30 years.

Modeling repetitive, non‐globular proteins

PubMed Central

Basu, Koli; Campbell, Robert L.; Guo, Shuaiqi; Sun, Tianjun

2016-01-01

Abstract While ab initio modeling of protein structures is not routine, certain types of proteins are more straightforward to model than others. Proteins with short repetitive sequences typically exhibit repetitive structures. These repetitive sequences can be more amenable to modeling if some information is known about the predominant secondary structure or other key features of the protein sequence. We have successfully built models of a number of repetitive structures with novel folds using knowledge of the consensus sequence within the sequence repeat and an understanding of the likely secondary structures that these may adopt. Our methods for achieving this success are reviewed here. PMID:26914323

Intelligence by design in an entropic power grid

NASA Astrophysics Data System (ADS)

Negrete-Pincetic, Matias Alejandro

In this work, the term Entropic Grid is coined to describe a power grid with increased levels of uncertainty and dynamics. These new features will require the reconsideration of well-established paradigms in the way of planning and operating the grid and its associated markets. New tools and models able to handle uncertainty and dynamics will form the required scaffolding to properly capture the behavior of the physical system, along with the value of new technologies and policies. The leverage of this knowledge will facilitate the design of new architectures to organize power and energy systems and their associated markets. This work presents several results, tools and models with the goal of contributing to that design objective. A central idea of this thesis is that the definition of products is critical in electricity markets. When markets are constructed with appropriate product definitions in mind, the interference between the physical and the market/financial systems seen in today's markets can be reduced. A key element of evaluating market designs is understanding the impact that salient features of an entropic grid---uncertainty, dynamics, constraints---can have on the electricity markets. Dynamic electricity market models tailored to capture such features are developed in this work. Using a multi-settlement dynamic electricity market, the impact of volatility is investigated. The results show the need to implement policies and technologies able to cope with the volatility of renewable sources. Similarly, using a dynamic electricity market model in which ramping costs are considered, the impacts of those costs on electricity markets are investigated. The key conclusion is that those additional ramping costs, in average terms, are not reflected in electricity prices. These results reveal several difficulties with today's real-time markets. Elements of an alternative architecture to organize these markets are also discussed.

Laser vibrometry exploitation for vehicle identification

NASA Astrophysics Data System (ADS)

Nolan, Adam; Lingg, Andrew; Goley, Steve; Sigmund, Kevin; Kangas, Scott

2014-06-01

Vibration signatures sensed from distant vehicles using laser vibrometry systems provide valuable information that may be used to help identify key vehicle features such as engine type, engine speed, and number of cylinders. Through the use of physics models of the vibration phenomenology, features are chosen to support classification algorithms. Various individual exploitation algorithms were developed using these models to classify vibration signatures into engine type (piston vs. turbine), engine configuration (Inline 4 vs. Inline 6 vs. V6 vs. V8 vs. V12) and vehicle type. The results of these algorithms will be presented for an 8 class problem. Finally, the benefits of using a factor graph representation to link these independent algorithms together will be presented which constructs a classification hierarchy for the vibration exploitation problem.

Machine Learning: A Crucial Tool for Sensor Design

PubMed Central

Zhao, Weixiang; Bhushan, Abhinav; Santamaria, Anthony D.; Simon, Melinda G.; Davis, Cristina E.

2009-01-01

Sensors have been widely used for disease diagnosis, environmental quality monitoring, food quality control, industrial process analysis and control, and other related fields. As a key tool for sensor data analysis, machine learning is becoming a core part of novel sensor design. Dividing a complete machine learning process into three steps: data pre-treatment, feature extraction and dimension reduction, and system modeling, this paper provides a review of the methods that are widely used for each step. For each method, the principles and the key issues that affect modeling results are discussed. After reviewing the potential problems in machine learning processes, this paper gives a summary of current algorithms in this field and provides some feasible directions for future studies. PMID:20191110

A Detailed Data-Driven Network Model of Prefrontal Cortex Reproduces Key Features of In Vivo Activity

PubMed Central

Hass, Joachim; Hertäg, Loreen; Durstewitz, Daniel

2016-01-01

The prefrontal cortex is centrally involved in a wide range of cognitive functions and their impairment in psychiatric disorders. Yet, the computational principles that govern the dynamics of prefrontal neural networks, and link their physiological, biochemical and anatomical properties to cognitive functions, are not well understood. Computational models can help to bridge the gap between these different levels of description, provided they are sufficiently constrained by experimental data and capable of predicting key properties of the intact cortex. Here, we present a detailed network model of the prefrontal cortex, based on a simple computationally efficient single neuron model (simpAdEx), with all parameters derived from in vitro electrophysiological and anatomical data. Without additional tuning, this model could be shown to quantitatively reproduce a wide range of measures from in vivo electrophysiological recordings, to a degree where simulated and experimentally observed activities were statistically indistinguishable. These measures include spike train statistics, membrane potential fluctuations, local field potentials, and the transmission of transient stimulus information across layers. We further demonstrate that model predictions are robust against moderate changes in key parameters, and that synaptic heterogeneity is a crucial ingredient to the quantitative reproduction of in vivo-like electrophysiological behavior. Thus, we have produced a physiologically highly valid, in a quantitative sense, yet computationally efficient PFC network model, which helped to identify key properties underlying spike time dynamics as observed in vivo, and can be harvested for in-depth investigation of the links between physiology and cognition. PMID:27203563

Predicting human olfactory perception from chemical features of odor molecules.

PubMed

Keller, Andreas; Gerkin, Richard C; Guan, Yuanfang; Dhurandhar, Amit; Turu, Gabor; Szalai, Bence; Mainland, Joel D; Ihara, Yusuke; Yu, Chung Wen; Wolfinger, Russ; Vens, Celine; Schietgat, Leander; De Grave, Kurt; Norel, Raquel; Stolovitzky, Gustavo; Cecchi, Guillermo A; Vosshall, Leslie B; Meyer, Pablo

2017-02-24

It is still not possible to predict whether a given molecule will have a perceived odor or what olfactory percept it will produce. We therefore organized the crowd-sourced DREAM Olfaction Prediction Challenge. Using a large olfactory psychophysical data set, teams developed machine-learning algorithms to predict sensory attributes of molecules based on their chemoinformatic features. The resulting models accurately predicted odor intensity and pleasantness and also successfully predicted 8 among 19 rated semantic descriptors ("garlic," "fish," "sweet," "fruit," "burnt," "spices," "flower," and "sour"). Regularized linear models performed nearly as well as random forest-based ones, with a predictive accuracy that closely approaches a key theoretical limit. These models help to predict the perceptual qualities of virtually any molecule with high accuracy and also reverse-engineer the smell of a molecule. Copyright © 2017, American Association for the Advancement of Science.

Operational Details of the Five Domains Model and Its Key Applications to the Assessment and Management of Animal Welfare

PubMed Central

Mellor, David J.

2017-01-01

Simple Summary The Five Domains Model is a focusing device to facilitate systematic, structured, comprehensive and coherent assessment of animal welfare; it is not a definition of animal welfare, nor is it intended to be an accurate representation of body structure and function. The purpose of each of the five domains is to draw attention to areas that are relevant to both animal welfare assessment and management. This paper begins by briefly describing the major features of the Model and the operational interactions between the five domains, and then it details seven interacting applications of the Model. These underlie its utility and increasing application to welfare assessment and management in diverse animal use sectors. Abstract In accord with contemporary animal welfare science understanding, the Five Domains Model has a significant focus on subjective experiences, known as affects, which collectively contribute to an animal’s overall welfare state. Operationally, the focus of the Model is on the presence or absence of various internal physical/functional states and external circumstances that give rise to welfare-relevant negative and/or positive mental experiences, i.e., affects. The internal states and external circumstances of animals are evaluated systematically by referring to each of the first four domains of the Model, designated “Nutrition”, “Environment”, “Health” and “Behaviour”. Then affects, considered carefully and cautiously to be generated by factors in these domains, are accumulated into the fifth domain, designated “Mental State”. The scientific foundations of this operational procedure, published in detail elsewhere, are described briefly here, and then seven key ways the Model may be applied to the assessment and management of animal welfare are considered. These applications have the following beneficial objectives—they (1) specify key general foci for animal welfare management; (2) highlight the foundations of specific welfare management objectives; (3) identify previously unrecognised features of poor and good welfare; (4) enable monitoring of responses to specific welfare-focused remedial interventions and/or maintenance activities; (5) facilitate qualitative grading of particular features of welfare compromise and/or enhancement; (6) enable both prospective and retrospective animal welfare assessments to be conducted; and, (7) provide adjunct information to support consideration of quality of life evaluations in the context of end-of-life decisions. However, also noted is the importance of not overstating what utilisation of the Model can achieve. PMID:28792485

Pathos in Criticism: Edwin Black's Communism-as-Cancer Metaphor

ERIC Educational Resources Information Center

Condit, Celeste M.

2013-01-01

Edwin Black's essay on "The Second Persona," introduced to rhetorical critics a rationale and model for a type of ideological criticism. Because it ignored the role of pathos in both the rhetoric Black purported to critique and in the construction of his own audience, Black's essay mis-described key features of Robert Welch's "Blue Book", which…

Teacher Professional Development as Knowledge Building: A Popperian Analysis

ERIC Educational Resources Information Center

Chitpin, Stephanie; Evers, Colin W.

2005-01-01

This paper offers an analysis of how six experienced teachers, and two in particular, used portfolios to aid and chart steps in their own professional development. The key finding of the study was that the pattern of growth of professional knowledge conformed strikingly to the central features of the model proposed by the philosopher of science,…

Assessing exposure of human and ecological values to wildfire in Sardinia, Italy

Treesearch

Michele Salis; Alan A. Ager; Bachisio Arca; Mark A. Finney; Valentina Bacciu; Pierpaolo Duce; Donatella Spano

2012-01-01

We used simulation modelling to analyze spatial variation in wildfire exposure relative to key social and economic features on the island of Sardinia, Italy. Sardinia contains a high density of urban interfaces, recreational values and highly valued agricultural areas that are increasingly being threatened by severe wildfires. Historical fire data and wildfire...

Thirty Years of Evolution in Instructional Technology, as Reflected in a Textbook

ERIC Educational Resources Information Center

Smaldino, Sharon E.; Lowther, Deborah L.; Russell, James D.

2011-01-01

This article describes how a textbook has traced 30 years of evolution in instructional technology. One of the book's key continuing features is the ASSURE Model. To connect technology to learning, the Classroom Link was developed. As standards were formulated for teachers and students, they were included in the textbook. Other evolutionary…

Engineering Design for Engineering Design: Benefits, Models, and Examples from Practice

ERIC Educational Resources Information Center

Turner, Ken L., Jr.; Kirby, Melissa; Bober, Sue

2016-01-01

Engineering design, a framework for studying and solving societal problems, is a key component of STEM education. It is also the area of greatest challenge within the Next Generation Science Standards, NGSS. Many teachers feel underprepared to teach or create activities that feature engineering design, and integrating a lesson plan of core content…

Adaptive Role Playing Games: An Immersive Approach for Problem Based Learning

ERIC Educational Resources Information Center

Sancho, Pilar; Moreno-Ger, Pablo; Fuentes-Fernandez, Ruben; Fernandez-Manjon, Baltasar

2009-01-01

In this paper we present a general framework, called NUCLEO, for the application of socio-constructive educational approaches in higher education. The underlying pedagogical approach relies on an adaptation model in order to improve group dynamics, as this has been identified as one of the key features in the success of collaborative learning…

Remote health monitoring: predicting outcome success based on contextual features for cardiovascular disease.

PubMed

Alshurafa, Nabil; Eastwood, Jo-Ann; Pourhomayoun, Mohammad; Liu, Jason J; Sarrafzadeh, Majid

2014-01-01

Current studies have produced a plethora of remote health monitoring (RHM) systems designed to enhance the care of patients with chronic diseases. Many RHM systems are designed to improve patient risk factors for cardiovascular disease, including physiological parameters such as body mass index (BMI) and waist circumference, and lipid profiles such as low density lipoprotein (LDL) and high density lipoprotein (HDL). There are several patient characteristics that could be determining factors for a patient's RHM outcome success, but these characteristics have been largely unidentified. In this paper, we analyze results from an RHM system deployed in a six month Women's Heart Health study of 90 patients, and apply advanced feature selection and machine learning algorithms to identify patients' key baseline contextual features and build effective prediction models that help determine RHM outcome success. We introduce Wanda-CVD, a smartphone-based RHM system designed to help participants with cardiovascular disease risk factors by motivating participants through wireless coaching using feedback and prompts as social support. We analyze key contextual features that secure positive patient outcomes in both physiological parameters and lipid profiles. Results from the Women's Heart Health study show that health threat of heart disease, quality of life, family history, stress factors, social support, and anxiety at baseline all help predict patient RHM outcome success.

Possible Detection of an Emission Cyclotron Resonance Scattering Feature from the Accretion-Powered Pulsar 4U 1626-67

NASA Technical Reports Server (NTRS)

Iwakiri, W. B.; Terada, Y.; Tashiro, M. S.; Mihara, T.; Angelini, L.; Yamada, S.; Enoto, T.; Makishima, K.; Nakajima, M.; Yoshida, A.

2012-01-01

We present analysis of 4U 1626-67, a 7.7 s pulsar in a low-mass X-ray binary system, observed with the hard X-ray detector of the Japanese X-ray satellite Suzaku in 2006 March for a net exposure of 88 ks. The source was detected at an average 10-60 keY flux of approx 4 x 10-10 erg / sq cm/ s. The phase-averaged spectrum is reproduced well by combining a negative and positive power-law times exponential cutoff (NPEX) model modified at approx 37 keY by a cyclotron resonance scattering feature (CRSF). The phase-resolved analysis shows that the spectra at the bright phases are well fit by the NPEX with CRSF model. On the other hand. the spectrum in the dim phase lacks the NPEX high-energy cutoff component, and the CRSF can be reproduced by either an emission or an absorption profile. When fitting the dim phase spectrum with the NPEX plus Gaussian model. we find that the feature is better described in terms of an emission rather than an absorption profile. The statistical significance of this result, evaluated by means of an F test, is between 2.91 x 10(exp -3) and 1.53 x 10(exp -5), taking into account the systematic errors in the background evaluation of HXD-PIN. We find that the emission profile is more feasible than the absorption one for comparing the physical parameters in other phases. Therefore, we have possibly detected an emission line at the cyclotron resonance energy in the dim phase.

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

PubMed

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

2011-09-01

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

Model-based assist feature insertion for sub-40nm memory device

NASA Astrophysics Data System (ADS)

Suh, Sungsoo; Lee, Suk-joo; Choi, Seong-woon; Lee, Sung-Woo; Park, Chan-hoon

2009-04-01

Many issues need to be resolved for a production-worthy model based assist feature insertion flow for single and double exposure patterning process to extend low k1 process at 193 nm immersion technology. Model based assist feature insertion is not trivial to implement either for single and double exposure patterning compared to rule based methods. As shown in Fig. 1, pixel based mask inversion technology in itself has difficulties in mask writing and inspection although it presents as one of key technology to extend single exposure for contact layer. Thus far, inversion technology is tried as a cooptimization of target mask to simultaneously generate optimized main and sub-resolution assists features for a desired process window. Alternatively, its technology can also be used to optimize for a target feature after an assist feature types are inserted in order to simplify the mask complexity. Simplification of inversion mask is one of major issue with applying inversion technology to device development even if a smaller mask feature can be fabricated since the mask writing time is also a major factor. As shown in Figure 2, mask writing time may be a limiting factor in determining whether or not an inversion solution is viable. It can be reasoned that increased number of shot counts relates to increase in margin for inversion methodology. On the other hand, there is a limit on how complex a mask can be in order to be production worthy. There is also source and mask co-optimization which influences the final mask patterns and assist feature sizes and positions for a given target. In this study, we will discuss assist feature insertion methods for sub 40-nm technology.

Derivation of Ground Surface and Vegetation in a Coastal Florida Wetland with Airborne Laser Technology

USGS Publications Warehouse

Raabe, Ellen A.; Harris, Melanie S.; Shrestha, Ramesh L.; Carter, William E.

2008-01-01

The geomorphology and vegetation of marsh-dominated coastal lowlands were mapped from airborne laser data points collected on the Gulf Coast of Florida near Cedar Key. Surface models were developed using low- and high-point filters to separate ground-surface and vegetation-canopy intercepts. In a non-automated process, the landscape was partitioned into functional landscape units to manage the modeling of key landscape features in discrete processing steps. The final digital ground surface-elevation model offers a faithful representation of topographic relief beneath canopies of tidal marsh and coastal forest. Bare-earth models approximate field-surveyed heights by + 0.17 m in the open marsh and + 0.22 m under thick marsh or forest canopy. The laser-derived digital surface models effectively delineate surface features of relatively inaccessible coastal habitats with a geographic coverage and vertical detail previously unavailable. Coastal topographic details include tidal-creek tributaries, levees, modest topographic undulations in the intertidal zone, karst features, silviculture, and relict sand dunes under coastal-forest canopy. A combination of laser-derived ground-surface and canopy-height models and intensity values provided additional mapping capabilities to differentiate between tidal-marsh zones and forest types such as mesic flatwood, hydric hammock, and oak scrub. Additional derived products include fine-scale shoreline and topographic profiles. The derived products demonstrate the capability to identify areas of concern to resource managers and unique components of the coastal system from laser altimetry. Because the very nature of a wetland system presents difficulties for access and data collection, airborne coverage from remote sensors has become an accepted alternative for monitoring wetland regions. Data acquisition with airborne laser represents a viable option for mapping coastal topography and for evaluating habitats and coastal change on marsh-dominated coasts. Such datasets can be instrumental in effective coastal-resource management.

Automating the generation of finite element dynamical cores with Firedrake

NASA Astrophysics Data System (ADS)

Ham, David; Mitchell, Lawrence; Homolya, Miklós; Luporini, Fabio; Gibson, Thomas; Kelly, Paul; Cotter, Colin; Lange, Michael; Kramer, Stephan; Shipton, Jemma; Yamazaki, Hiroe; Paganini, Alberto; Kärnä, Tuomas

2017-04-01

The development of a dynamical core is an increasingly complex software engineering undertaking. As the equations become more complete, the discretisations more sophisticated and the hardware acquires ever more fine-grained parallelism and deeper memory hierarchies, the problem of building, testing and modifying dynamical cores becomes increasingly complex. Here we present Firedrake, a code generation system for the finite element method with specialist features designed to support the creation of geoscientific models. Using Firedrake, the dynamical core developer writes the partial differential equations in weak form in a high level mathematical notation. Appropriate function spaces are chosen and time stepping loops written at the same high level. When the programme is run, Firedrake generates high performance C code for the resulting numerics which are executed in parallel. Models in Firedrake typically take a tiny fraction of the lines of code required by traditional hand-coding techniques. They support more sophisticated numerics than are easily achieved by hand, and the resulting code is frequently higher performance. Critically, debugging, modifying and extending a model written in Firedrake is vastly easier than by traditional methods due to the small, highly mathematical code base. Firedrake supports a wide range of key features for dynamical core creation: A vast range of discretisations, including both continuous and discontinuous spaces and mimetic (C-grid-like) elements which optimally represent force balances in geophysical flows. High aspect ratio layered meshes suitable for ocean and atmosphere domains. Curved elements for high accuracy representations of the sphere. Support for non-finite element operators, such as parametrisations. Access to PETSc, a world-leading library of programmable linear and nonlinear solvers. High performance adjoint models generated automatically by symbolically reasoning about the forward model. This poster will present the key features of the Firedrake system, as well as those of Gusto, an atmospheric dynamical core, and Thetis, a coastal ocean model, both of which are written in Firedrake.

Modelling deep water habitats to develop a spatially explicit, fine scale understanding of the distribution of the western rock lobster, Panulirus cygnus.

PubMed

Hovey, Renae K; Van Niel, Kimberly P; Bellchambers, Lynda M; Pember, Matthew B

2012-01-01

The western rock lobster, Panulirus cygnus, is endemic to Western Australia and supports substantial commercial and recreational fisheries. Due to and its wide distribution and the commercial and recreational importance of the species a key component of managing western rock lobster is understanding the ecological processes and interactions that may influence lobster abundance and distribution. Using terrain analyses and distribution models of substrate and benthic biota, we assess the physical drivers that influence the distribution of lobsters at a key fishery site. Using data collected from hydroacoustic and towed video surveys, 20 variables (including geophysical, substrate and biota variables) were developed to predict the distributions of substrate type (three classes of reef, rhodoliths and sand) and dominant biota (kelp, sessile invertebrates and macroalgae) within a 40 km(2) area about 30 km off the west Australian coast. Lobster presence/absence data were collected within this area using georeferenced pots. These datasets were used to develop a classification tree model for predicting the distribution of the western rock lobster. Interestingly, kelp and reef were not selected as predictors. Instead, the model selected geophysical and geomorphic scalar variables, which emphasise a mix of terrain within limited distances. The model of lobster presence had an adjusted D(2) of 64 and an 80% correct classification. Species distribution models indicate that juxtaposition in fine scale terrain is most important to the western rock lobster. While key features like kelp and reef may be important to lobster distribution at a broad scale, it is the fine scale features in terrain that are likely to define its ecological niche. Determining the most appropriate landscape configuration and scale will be essential to refining niche habitats and will aid in selecting appropriate sites for protecting critical lobster habitats.

Modelling Deep Water Habitats to Develop a Spatially Explicit, Fine Scale Understanding of the Distribution of the Western Rock Lobster, Panulirus cygnus

PubMed Central

Hovey, Renae K.; Van Niel, Kimberly P.; Bellchambers, Lynda M.; Pember, Matthew B.

2012-01-01

Background The western rock lobster, Panulirus cygnus, is endemic to Western Australia and supports substantial commercial and recreational fisheries. Due to and its wide distribution and the commercial and recreational importance of the species a key component of managing western rock lobster is understanding the ecological processes and interactions that may influence lobster abundance and distribution. Using terrain analyses and distribution models of substrate and benthic biota, we assess the physical drivers that influence the distribution of lobsters at a key fishery site. Methods and Findings Using data collected from hydroacoustic and towed video surveys, 20 variables (including geophysical, substrate and biota variables) were developed to predict the distributions of substrate type (three classes of reef, rhodoliths and sand) and dominant biota (kelp, sessile invertebrates and macroalgae) within a 40 km2 area about 30 km off the west Australian coast. Lobster presence/absence data were collected within this area using georeferenced pots. These datasets were used to develop a classification tree model for predicting the distribution of the western rock lobster. Interestingly, kelp and reef were not selected as predictors. Instead, the model selected geophysical and geomorphic scalar variables, which emphasise a mix of terrain within limited distances. The model of lobster presence had an adjusted D2 of 64 and an 80% correct classification. Conclusions Species distribution models indicate that juxtaposition in fine scale terrain is most important to the western rock lobster. While key features like kelp and reef may be important to lobster distribution at a broad scale, it is the fine scale features in terrain that are likely to define its ecological niche. Determining the most appropriate landscape configuration and scale will be essential to refining niche habitats and will aid in selecting appropriate sites for protecting critical lobster habitats. PMID:22506021

Intelligent Fault Diagnosis of HVCB with Feature Space Optimization-Based Random Forest

PubMed Central

Ma, Suliang; Wu, Jianwen; Wang, Yuhao; Jia, Bowen; Jiang, Yuan

2018-01-01

Mechanical faults of high-voltage circuit breakers (HVCBs) always happen over long-term operation, so extracting the fault features and identifying the fault type have become a key issue for ensuring the security and reliability of power supply. Based on wavelet packet decomposition technology and random forest algorithm, an effective identification system was developed in this paper. First, compared with the incomplete description of Shannon entropy, the wavelet packet time-frequency energy rate (WTFER) was adopted as the input vector for the classifier model in the feature selection procedure. Then, a random forest classifier was used to diagnose the HVCB fault, assess the importance of the feature variable and optimize the feature space. Finally, the approach was verified based on actual HVCB vibration signals by considering six typical fault classes. The comparative experiment results show that the classification accuracy of the proposed method with the origin feature space reached 93.33% and reached up to 95.56% with optimized input feature vector of classifier. This indicates that feature optimization procedure is successful, and the proposed diagnosis algorithm has higher efficiency and robustness than traditional methods. PMID:29659548

Research on Radar Micro-Doppler Feature Parameter Estimation of Propeller Aircraft

NASA Astrophysics Data System (ADS)

He, Zhihua; Tao, Feixiang; Duan, Jia; Luo, Jingsheng

2018-01-01

The micro-motion modulation effect of the rotated propellers to radar echo can be a steady feature for aircraft target recognition. Thus, micro-Doppler feature parameter estimation is a key to accurate target recognition. In this paper, the radar echo of rotated propellers is modelled and simulated. Based on which, the distribution characteristics of the micro-motion modulation energy in time, frequency and time-frequency domain are analyzed. The micro-motion modulation energy produced by the scattering points of rotating propellers is accumulated using the Inverse-Radon (I-Radon) transform, which can be used to accomplish the estimation of micro-modulation parameter. Finally, it is proved that the proposed parameter estimation method is effective with measured data. The micro-motion parameters of aircraft can be used as the features of radar target recognition.

Fast Localization in Large-Scale Environments Using Supervised Indexing of Binary Features.

PubMed

Youji Feng; Lixin Fan; Yihong Wu

2016-01-01

The essence of image-based localization lies in matching 2D key points in the query image and 3D points in the database. State-of-the-art methods mostly employ sophisticated key point detectors and feature descriptors, e.g., Difference of Gaussian (DoG) and Scale Invariant Feature Transform (SIFT), to ensure robust matching. While a high registration rate is attained, the registration speed is impeded by the expensive key point detection and the descriptor extraction. In this paper, we propose to use efficient key point detectors along with binary feature descriptors, since the extraction of such binary features is extremely fast. The naive usage of binary features, however, does not lend itself to significant speedup of localization, since existing indexing approaches, such as hierarchical clustering trees and locality sensitive hashing, are not efficient enough in indexing binary features and matching binary features turns out to be much slower than matching SIFT features. To overcome this, we propose a much more efficient indexing approach for approximate nearest neighbor search of binary features. This approach resorts to randomized trees that are constructed in a supervised training process by exploiting the label information derived from that multiple features correspond to a common 3D point. In the tree construction process, node tests are selected in a way such that trees have uniform leaf sizes and low error rates, which are two desired properties for efficient approximate nearest neighbor search. To further improve the search efficiency, a probabilistic priority search strategy is adopted. Apart from the label information, this strategy also uses non-binary pixel intensity differences available in descriptor extraction. By using the proposed indexing approach, matching binary features is no longer much slower but slightly faster than matching SIFT features. Consequently, the overall localization speed is significantly improved due to the much faster key point detection and descriptor extraction. It is empirically demonstrated that the localization speed is improved by an order of magnitude as compared with state-of-the-art methods, while comparable registration rate and localization accuracy are still maintained.

Filter size definition in anisotropic subgrid models for large eddy simulation on irregular grids

NASA Astrophysics Data System (ADS)

Abbà, Antonella; Campaniello, Dario; Nini, Michele

2017-06-01

The definition of the characteristic filter size to be used for subgrid scales models in large eddy simulation using irregular grids is still an unclosed problem. We investigate some different approaches to the definition of the filter length for anisotropic subgrid scale models and we propose a tensorial formulation based on the inertial ellipsoid of the grid element. The results demonstrate an improvement in the prediction of several key features of the flow when the anisotropicity of the grid is explicitly taken into account with the tensorial filter size.

Biased ART: a neural architecture that shifts attention toward previously disregarded features following an incorrect prediction.

PubMed

Carpenter, Gail A; Gaddam, Sai Chaitanya

2010-04-01

Memories in Adaptive Resonance Theory (ART) networks are based on matched patterns that focus attention on those portions of bottom-up inputs that match active top-down expectations. While this learning strategy has proved successful for both brain models and applications, computational examples show that attention to early critical features may later distort memory representations during online fast learning. For supervised learning, biased ARTMAP (bARTMAP) solves the problem of over-emphasis on early critical features by directing attention away from previously attended features after the system makes a predictive error. Small-scale, hand-computed analog and binary examples illustrate key model dynamics. Two-dimensional simulation examples demonstrate the evolution of bARTMAP memories as they are learned online. Benchmark simulations show that featural biasing also improves performance on large-scale examples. One example, which predicts movie genres and is based, in part, on the Netflix Prize database, was developed for this project. Both first principles and consistent performance improvements on all simulation studies suggest that featural biasing should be incorporated by default in all ARTMAP systems. Benchmark datasets and bARTMAP code are available from the CNS Technology Lab Website: http://techlab.bu.edu/bART/. Copyright 2009 Elsevier Ltd. All rights reserved.

Configuration interaction of hydropathic waves enables ubiquitin functionality

NASA Astrophysics Data System (ADS)

Allan, Douglas C.; Phillips, J. C.

2018-02-01

Ubiquitin, discovered less than 50 years ago, tags thousands of diseased proteins for destruction. It is small (only 76 amino acids), and is found unchanged in mammals, birds, fish and even worms. Key features of its functionality are identified here using critical point thermodynamic scaling theory. These include Fano interference between first- and second-order elements of correlated long-range globular surface shape transitions. Comparison with its closest relative, 76 amino acid Nedd8, shows that the latter lacks these features. A cracked elastic network model is proposed for the common target shared by many diseased proteins.

Spatiotemporal chaos of fractional order logistic equation in nonlinear coupled lattices

NASA Astrophysics Data System (ADS)

Zhang, Ying-Qian; Wang, Xing-Yuan; Liu, Li-Yan; He, Yi; Liu, Jia

2017-11-01

We investigate a new spatiotemporal dynamics with fractional order differential logistic map and spatial nonlinear coupling. The spatial nonlinear coupling features such as the higher percentage of lattices in chaotic behaviors for most of parameters and none periodic windows in bifurcation diagrams are held, which are more suitable for encryptions than the former adjacent coupled map lattices. Besides, the proposed model has new features such as the wider parameter range and wider range of state amplitude for ergodicity, which contributes a wider range of key space when applied in encryptions. The simulations and theoretical analyses are developed in this paper.

Do event horizons exist?

NASA Astrophysics Data System (ADS)

Baccetti, Valentina; Mann, Robert B.; Terno, Daniel R.

Event horizons are the defining feature of classical black holes. They are the key ingredient of the information loss paradox which, as paradoxes in quantum foundations, is built on a combination of predictions of quantum theory and counterfactual classical features: neither horizon formation nor its crossing by a test body can be detected by a distant observer. Furthermore, horizons are unnecessary for the production of Hawking-like radiation. We demonstrate that when this radiation is taken into account, it can prevent horizon crossing/formation in a large class of models. We conjecture that horizon avoidance is a general feature of collapse. The nonexistence of event horizons dispels the paradox, but opens up important questions about thermodynamic properties of the resulting objects and correlations between different degrees of freedom.

Defining the conserved internal architecture of a protein kinase.

PubMed

Kornev, Alexandr P; Taylor, Susan S

2010-03-01

Protein kinases constitute a large protein family of important regulators in all eukaryotic cells. All of the protein kinases have a similar bilobal fold, and their key structural features have been well studied. However, the recent discovery of non-contiguous hydrophobic ensembles inside the protein kinase core shed new light on the internal organization of these molecules. Two hydrophobic "spines" traverse both lobes of the protein kinase molecule, providing a firm but flexible connection between its key elements. The spine model introduces a useful framework for analysis of intramolecular communications, molecular dynamics, and drug design. Published by Elsevier B.V.

Learning Oriented Region-based Convolutional Neural Networks for Building Detection in Satellite Remote Sensing Images

NASA Astrophysics Data System (ADS)

Chen, C.; Gong, W.; Hu, Y.; Chen, Y.; Ding, Y.

2017-05-01

The automated building detection in aerial images is a fundamental problem encountered in aerial and satellite images analysis. Recently, thanks to the advances in feature descriptions, Region-based CNN model (R-CNN) for object detection is receiving an increasing attention. Despite the excellent performance in object detection, it is problematic to directly leverage the features of R-CNN model for building detection in single aerial image. As we know, the single aerial image is in vertical view and the buildings possess significant directional feature. However, in R-CNN model, direction of the building is ignored and the detection results are represented by horizontal rectangles. For this reason, the detection results with horizontal rectangle cannot describe the building precisely. To address this problem, in this paper, we proposed a novel model with a key feature related to orientation, namely, Oriented R-CNN (OR-CNN). Our contributions are mainly in the following two aspects: 1) Introducing a new oriented layer network for detecting the rotation angle of building on the basis of the successful VGG-net R-CNN model; 2) the oriented rectangle is proposed to leverage the powerful R-CNN for remote-sensing building detection. In experiments, we establish a complete and bran-new data set for training our oriented R-CNN model and comprehensively evaluate the proposed method on a publicly available building detection data set. We demonstrate State-of-the-art results compared with the previous baseline methods.

A distance constrained synaptic plasticity model of C. elegans neuronal network

NASA Astrophysics Data System (ADS)

Badhwar, Rahul; Bagler, Ganesh

2017-03-01

Brain research has been driven by enquiry for principles of brain structure organization and its control mechanisms. The neuronal wiring map of C. elegans, the only complete connectome available till date, presents an incredible opportunity to learn basic governing principles that drive structure and function of its neuronal architecture. Despite its apparently simple nervous system, C. elegans is known to possess complex functions. The nervous system forms an important underlying framework which specifies phenotypic features associated to sensation, movement, conditioning and memory. In this study, with the help of graph theoretical models, we investigated the C. elegans neuronal network to identify network features that are critical for its control. The 'driver neurons' are associated with important biological functions such as reproduction, signalling processes and anatomical structural development. We created 1D and 2D network models of C. elegans neuronal system to probe the role of features that confer controllability and small world nature. The simple 1D ring model is critically poised for the number of feed forward motifs, neuronal clustering and characteristic path-length in response to synaptic rewiring, indicating optimal rewiring. Using empirically observed distance constraint in the neuronal network as a guiding principle, we created a distance constrained synaptic plasticity model that simultaneously explains small world nature, saturation of feed forward motifs as well as observed number of driver neurons. The distance constrained model suggests optimum long distance synaptic connections as a key feature specifying control of the network.

A 4-Week Model of House Dust Mite (HDM) Induced Allergic Airways Inflammation with Airway Remodeling.

PubMed

Woo, L N; Guo, W Y; Wang, X; Young, A; Salehi, S; Hin, A; Zhang, Y; Scott, J A; Chow, C W

2018-05-02

Animal models of allergic airways inflammation are useful tools in studying the pathogenesis of asthma and potential therapeutic interventions. The different allergic airways inflammation models available to date employ varying doses, frequency, duration and types of allergen, which lead to the development of different features of asthma; showing varying degrees of airways inflammation and hyper-responsiveness (AHR) and airways remodeling. Models that also exhibit airway remodeling, a key feature of asthma, in addition to AHR and airway inflammation typically require 5-12 weeks to develop. In this report, we describe a 4-week mouse model of house dust mite (HDM)-induced allergic airways inflammation, and compare the phenotypic features of two different doses of HDM exposures (10 µg and 25 µg) for 5 days/week with a well-characterized 8-week chronic HDM model. We found that 4 weeks of intranasal HDM (25 µg in 35 µl saline; 5 days/week) resulted in AHR, airway inflammation and airway remodeling that were comparable to the 8-week model. We conclude that this new 4-week HDM model is another useful tool in studies of human asthma that offers advantages of shorter duration for development and decreased costs when compared to other models that require longer durations of exposure (5-12 weeks) to develop.

RIPPLE - A new model for incompressible flows with free surfaces

NASA Technical Reports Server (NTRS)

Kothe, D. B.; Mjolsness, R. C.

1991-01-01

A new free surface flow model, RIPPLE, is summarized. RIPPLE obtains finite difference solutions for incompressible flow problems having strong surface tension forces at free surfaces of arbitrarily complex topology. The key innovation is the continuum surface force model which represents surface tension as a (strongly) localized volume force. Other features include a higher-order momentum advection model, a volume-of-fluid free surface treatment, and an efficient two-step projection solution method. RIPPLE's unique capabilities are illustrated with two example problems: low-gravity jet-induced tank flow, and the collision and coalescence of two cylindrical rods.

Supersymmetric model for dark matter and baryogenesis motivated by the recent CDMS result.

PubMed

Allahverdi, Rouzbeh; Dutta, Bhaskar; Mohapatra, Rabindra N; Sinha, Kuver

2013-08-02

We discuss a supersymmetric model for cogenesis of dark and baryonic matter where the dark matter (DM) has mass in the 8-10 GeV range as indicated by several direct detection searches, including most recently the CDMS experiment with the desired cross section. The DM candidate is a real scalar field. Two key distinguishing features of the model are the following: (i) in contrast with the conventional weakly interacting massive particle dark matter scenarios where thermal freeze-out is responsible for the observed relic density, our model uses nonthermal production of dark matter after reheating of the Universe caused by moduli decay at temperatures below the QCD phase transition, a feature which alleviates the relic overabundance problem caused by small annihilation cross section of light DM particles and (ii) baryogenesis occurs also at similar low temperatures from the decay of TeV scale mediator particles arising from moduli decay. A possible test of this model is the existence of colored particles with TeV masses accessible at the LHC.

Control of automated behavior: insights from the discrete sequence production task

PubMed Central

Abrahamse, Elger L.; Ruitenberg, Marit F. L.; de Kleine, Elian; Verwey, Willem B.

2013-01-01

Work with the discrete sequence production (DSP) task has provided a substantial literature on discrete sequencing skill over the last decades. The purpose of the current article is to provide a comprehensive overview of this literature and of the theoretical progress that it has prompted. We start with a description of the DSP task and the phenomena that are typically observed with it. Then we propose a cognitive model, the dual processor model (DPM), which explains performance of (skilled) discrete key-press sequences. Key features of this model are the distinction between a cognitive processor and a motor system (i.e., motor buffer and motor processor), the interplay between these two processing systems, and the possibility to execute familiar sequences in two different execution modes. We further discuss how this model relates to several related sequence skill research paradigms and models, and we outline outstanding questions for future research throughout the paper. We conclude by sketching a tentative neural implementation of the DPM. PMID:23515430

Automatic updating and 3D modeling of airport information from high resolution images using GIS and LIDAR data

NASA Astrophysics Data System (ADS)

Lv, Zheng; Sui, Haigang; Zhang, Xilin; Huang, Xianfeng

2007-11-01

As one of the most important geo-spatial objects and military establishment, airport is always a key target in fields of transportation and military affairs. Therefore, automatic recognition and extraction of airport from remote sensing images is very important and urgent for updating of civil aviation and military application. In this paper, a new multi-source data fusion approach on automatic airport information extraction, updating and 3D modeling is addressed. Corresponding key technologies including feature extraction of airport information based on a modified Ostu algorithm, automatic change detection based on new parallel lines-based buffer detection algorithm, 3D modeling based on gradual elimination of non-building points algorithm, 3D change detecting between old airport model and LIDAR data, typical CAD models imported and so on are discussed in detail. At last, based on these technologies, we develop a prototype system and the results show our method can achieve good effects.

A fast image matching algorithm based on key points

NASA Astrophysics Data System (ADS)

Wang, Huilin; Wang, Ying; An, Ru; Yan, Peng

2014-05-01

Image matching is a very important technique in image processing. It has been widely used for object recognition and tracking, image retrieval, three-dimensional vision, change detection, aircraft position estimation, and multi-image registration. Based on the requirements of matching algorithm for craft navigation, such as speed, accuracy and adaptability, a fast key point image matching method is investigated and developed. The main research tasks includes: (1) Developing an improved celerity key point detection approach using self-adapting threshold of Features from Accelerated Segment Test (FAST). A method of calculating self-adapting threshold was introduced for images with different contrast. Hessian matrix was adopted to eliminate insecure edge points in order to obtain key points with higher stability. This approach in detecting key points has characteristics of small amount of computation, high positioning accuracy and strong anti-noise ability; (2) PCA-SIFT is utilized to describe key point. 128 dimensional vector are formed based on the SIFT method for the key points extracted. A low dimensional feature space was established by eigenvectors of all the key points, and each eigenvector was projected onto the feature space to form a low dimensional eigenvector. These key points were re-described by dimension-reduced eigenvectors. After reducing the dimension by the PCA, the descriptor was reduced to 20 dimensions from the original 128. This method can reduce dimensions of searching approximately near neighbors thereby increasing overall speed; (3) Distance ratio between the nearest neighbour and second nearest neighbour searching is regarded as the measurement criterion for initial matching points from which the original point pairs matched are obtained. Based on the analysis of the common methods (e.g. RANSAC (random sample consensus) and Hough transform cluster) used for elimination false matching point pairs, a heuristic local geometric restriction strategy is adopted to discard false matched point pairs further; and (4) Affine transformation model is introduced to correct coordinate difference between real-time image and reference image. This resulted in the matching of the two images. SPOT5 Remote sensing images captured at different date and airborne images captured with different flight attitude were used to test the performance of the method from matching accuracy, operation time and ability to overcome rotation. Results show the effectiveness of the approach.

Progress in Validation of Wind-US for Ramjet/Scramjet Combustion

NASA Technical Reports Server (NTRS)

Engblom, William A.; Frate, Franco C.; Nelson, Chris C.

2005-01-01

Validation of the Wind-US flow solver against two sets of experimental data involving high-speed combustion is attempted. First, the well-known Burrows- Kurkov supersonic hydrogen-air combustion test case is simulated, and the sensitively of ignition location and combustion performance to key parameters is explored. Second, a numerical model is developed for simulation of an X-43B candidate, full-scale, JP-7-fueled, internal flowpath operating in ramjet mode. Numerical results using an ethylene-air chemical kinetics model are directly compared against previously existing pressure-distribution data along the entire flowpath, obtained in direct-connect testing conducted at NASA Langley Research Center. Comparison to derived quantities such as burn efficiency and thermal throat location are also made. Reasonable to excellent agreement with experimental data is demonstrated for key parameters in both simulation efforts. Additional Wind-US feature needed to improve simulation efforts are described herein, including maintaining stagnation conditions at inflow boundaries for multi-species flow. An open issue regarding the sensitivity of isolator unstart to key model parameters is briefly discussed.

An Extended Normalization Model of Attention Accounts for Feature-Based Attentional Enhancement of Both Response and Coherence Gain

PubMed Central

Krishna, B. Suresh; Treue, Stefan

2016-01-01

Paying attention to a sensory feature improves its perception and impairs that of others. Recent work has shown that a Normalization Model of Attention (NMoA) can account for a wide range of physiological findings and the influence of different attentional manipulations on visual performance. A key prediction of the NMoA is that attention to a visual feature like an orientation or a motion direction will increase the response of neurons preferring the attended feature (response gain) rather than increase the sensory input strength of the attended stimulus (input gain). This effect of feature-based attention on neuronal responses should translate to similar patterns of improvement in behavioral performance, with psychometric functions showing response gain rather than input gain when attention is directed to the task-relevant feature. In contrast, we report here that when human subjects are cued to attend to one of two motion directions in a transparent motion display, attentional effects manifest as a combination of input and response gain. Further, the impact on input gain is greater when attention is directed towards a narrow range of motion directions than when it is directed towards a broad range. These results are captured by an extended NMoA, which either includes a stimulus-independent attentional contribution to normalization or utilizes direction-tuned normalization. The proposed extensions are consistent with the feature-similarity gain model of attention and the attentional modulation in extrastriate area MT, where neuronal responses are enhanced and suppressed by attention to preferred and non-preferred motion directions respectively. PMID:27977679

FaceTOON: a unified platform for feature-based cartoon expression generation

NASA Astrophysics Data System (ADS)

Zaharia, Titus; Marre, Olivier; Prêteux, Françoise; Monjaux, Perrine

2008-02-01

This paper presents the FaceTOON system, a semi-automatic platform dedicated to the creation of verbal and emotional facial expressions, within the applicative framework of 2D cartoon production. The proposed FaceTOON platform makes it possible to rapidly create 3D facial animations with a minimum amount of user interaction. In contrast with existing commercial 3D modeling softwares, which usually require from the users advanced 3D graphics skills and competences, the FaceTOON system is based exclusively on 2D interaction mechanisms, the 3D modeling stage being completely transparent for the user. The system takes as input a neutral 3D face model, free of any facial feature, and a set of 2D drawings, representing the desired facial features. A 2D/3D virtual mapping procedure makes it possible to obtain a ready-for-animation model which can be directly manipulated and deformed for generating expressions. The platform includes a complete set of dedicated tools for 2D/3D interactive deformation, pose management, key-frame interpolation and MPEG-4 compliant animation and rendering. The proposed FaceTOON system is currently considered for industrial evaluation and commercialization by the Quadraxis company.

Modeling the interdependent network based on two-mode networks

NASA Astrophysics Data System (ADS)

An, Feng; Gao, Xiangyun; Guan, Jianhe; Huang, Shupei; Liu, Qian

2017-10-01

Among heterogeneous networks, there exist obviously and closely interdependent linkages. Unlike existing research primarily focus on the theoretical research of physical interdependent network model. We propose a two-layer interdependent network model based on two-mode networks to explore the interdependent features in the reality. Specifically, we construct a two-layer interdependent loan network and develop several dependent features indices. The model is verified to enable us to capture the loan dependent features of listed companies based on loan behaviors and shared shareholders. Taking Chinese debit and credit market as case study, the main conclusions are: (1) only few listed companies shoulder the main capital transmission (20% listed companies occupy almost 70% dependent degree). (2) The control of these key listed companies will be more effective of avoiding the spreading of financial risks. (3) Identifying the companies with high betweenness centrality and controlling them could be helpful to monitor the financial risk spreading. (4) The capital transmission channel among Chinese financial listed companies and Chinese non-financial listed companies are relatively strong. However, under greater pressure of demand of capital transmission (70% edges failed), the transmission channel, which constructed by debit and credit behavior, will eventually collapse.

Exploring an Activist Approach of Working with Boys from Socially Vulnerable Backgrounds in a Sport Context

ERIC Educational Resources Information Center

Luguetti, Carla; Oliver, Kimberly L.; Kirk, David; Dantas, Luiz

2017-01-01

This study explores an activist approach for co-creating a prototype pedagogical model of sport for working with boys from socially vulnerable backgrounds. This paper addresses the key features that emerged when we identified what facilitated and hindered the boys' engagement in sport. This study was an activist research project that was conducted…

Motivation, Satisfaction, and Morale in Army Careers: A Review of Theory and Measurement

DTIC Science & Technology

1976-12-01

subjective goali on performance. Their model of "task motivation" has the following key features (Locke, Cartledge, & Knerr, 1968, p. 135): I. The... pulling himself up in the world and should work hard with the hope of being promoted to a higher level job. "* A man should choose the Job which pays the

Learning about Social Learning in MOOCs: From Statistical Analysis to Generative Model

ERIC Educational Resources Information Center

Brinton, Christopher G.; Chiang, Mung; Jain, Shaili; Lam, Henry; Liu, Zhenming; Wong, Felix Ming Fai

2014-01-01

We study user behavior in the courses offered by a major massive online open course (MOOC) provider during the summer of 2013. Since social learning is a key element of scalable education on MOOC and is done via online discussion forums, our main focus is on understanding forum activities. Two salient features of these activities drive our…

"Structures beneath the Skin": How School Leaders Use Their Power and Authority To Create Institutional Opportunities for Developing Positive Interethnic Communities.

ERIC Educational Resources Information Center

Norte, Edmundo

1999-01-01

Explores key features of processes school leaders employ to create positive interethnic school communities, identifying five elements for effective intervention and applying an analytical model to each to provide a schema for framing elements of central importance. Addresses how school leaders use their power and authority and how they determine…

Person Response Functions and the Definition of Units in the Social Sciences

ERIC Educational Resources Information Center

Engelhard, George, Jr.; Perkins, Aminah F.

2011-01-01

Humphry (this issue) has written a thought-provoking piece on the interpretation of item discrimination parameters as scale units in item response theory. One of the key features of his work is the description of an item response theory (IRT) model that he calls the logistic measurement function that combines aspects of two traditions in IRT that…

Summary of Research 1998, Department of Mechanical Engineering.

DTIC Science & Technology

1999-08-01

thermoacoustic behavior in strong zero-mean oscillatory flows with potential application to the design of heat exchangers in thermoacoustic engines...important feature in the thermal characterization of microtubes , which are to be used in microheat exchangers . DoD KEY TECHNOLOGY AREA: Modeling and...Simulation KEYWORDS: Laminar Duct Flows, Convection and Conduction Heat Transfer, Axial Conduction, Micro- heat Exchang - ers DEVELOPMENT AND CALIBRATION

Bidirectional RNN for Medical Event Detection in Electronic Health Records.

PubMed

Jagannatha, Abhyuday N; Yu, Hong

2016-06-01

Sequence labeling for extraction of medical events and their attributes from unstructured text in Electronic Health Record (EHR) notes is a key step towards semantic understanding of EHRs. It has important applications in health informatics including pharmacovigilance and drug surveillance. The state of the art supervised machine learning models in this domain are based on Conditional Random Fields (CRFs) with features calculated from fixed context windows. In this application, we explored recurrent neural network frameworks and show that they significantly out-performed the CRF models.

pytc: Open-Source Python Software for Global Analyses of Isothermal Titration Calorimetry Data.

PubMed

Duvvuri, Hiranmayi; Wheeler, Lucas C; Harms, Michael J

2018-05-08

Here we describe pytc, an open-source Python package for global fits of thermodynamic models to multiple isothermal titration calorimetry experiments. Key features include simplicity, the ability to implement new thermodynamic models, a robust maximum likelihood fitter, a fast Bayesian Markov-Chain Monte Carlo sampler, rigorous implementation, extensive documentation, and full cross-platform compatibility. pytc fitting can be done using an application program interface or via a graphical user interface. It is available for download at https://github.com/harmslab/pytc .

Market dynamics and stock price volatility

NASA Astrophysics Data System (ADS)

Li, H.; Rosser, J. B., Jr.

2004-06-01

This paper presents a possible explanation for some of the empirical properties of asset returns within a heterogeneous-agents framework. The model turns out, even if we assume the input fundamental value follows an simple Gaussian distribution lacking both fat tails and volatility dependence, these features can show up in the time series of asset returns. In this model, the profit comparison and switching between heterogeneous play key roles, which build a connection between endogenous market and the emergence of stylized facts.

Key Features of High-Quality Policies and Guidelines to Support Social and Emotional Learning: Recommendations and Examples for the Collaborating States Initiative (CSI)

ERIC Educational Resources Information Center

Dusenbury, Linda; Yoder, Nick

2017-01-01

The current document serves two purposes. First, it provides an overview of six key features of a high-quality, comprehensive package of policies and guidance to support student social and emotional learning (SEL). These features are based on Collaborative for Academic Social, and Emotional Learning's (CASEL's) review of the research literature on…

Tachyon cosmology with non-vanishing minimum potential: a unified model

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

Li, Huiquan, E-mail: hqli@ustc.edu.cn

2012-07-01

We investigate the tachyon condensation process in the effective theory with non-vanishing minimum potential and its implications to cosmology. It is shown that the tachyon condensation on an unstable three-brane described by this modified tachyon field theory leads to lower-dimensional branes (defects) forming within a stable three-brane. Thus, in the cosmological background, we can get well-behaved tachyon matter after tachyon inflation, (partially) avoiding difficulties encountered in the original tachyon cosmological models. This feature also implies that the tachyon inflated and reheated universe is followed by a Chaplygin gas dark matter and dark energy universe. Hence, such an unstable three-brane behavesmore » quite like our universe, reproducing the key features of the whole evolutionary history of the universe and providing a unified description of inflaton, dark matter and dark energy in a very simple single-scalar field model.« less

Predicting the constitutive behavior of semi-solids via a direct finite element simulation: application to AA5182

NASA Astrophysics Data System (ADS)

Phillion, A. B.; Cockcroft, S. L.; Lee, P. D.

2009-07-01

The methodology of direct finite element (FE) simulation was used to predict the semi-solid constitutive behavior of an industrially important aluminum-magnesium alloy, AA5182. Model microstructures were generated that detail key features of the as-cast semi-solid: equiaxed-globular grains of random size and shape, interconnected liquid films, and pores at the triple-junctions. Based on the results of over fifty different simulations, a model-based constitutive relationship which includes the effects of the key microstructure features—fraction solid, grain size and fraction porosity—was derived using regression analysis. This novel constitutive equation was then validated via comparison with both the FE simulations and experimental stress/strain data. Such an equation can now be used to incorporate the effects of microstructure on the bulk semi-solid flow stress within a macro- scale process model.

Design of the HELICS High-Performance Transmission-Distribution-Communication-Market Co-Simulation Framework

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

Palmintier, Bryan S; Krishnamurthy, Dheepak; Top, Philip

This paper describes the design rationale for a new cyber-physical-energy co-simulation framework for electric power systems. This new framework will support very large-scale (100,000+ federates) co-simulations with off-the-shelf power-systems, communication, and end-use models. Other key features include cross-platform operating system support, integration of both event-driven (e.g. packetized communication) and time-series (e.g. power flow) simulation, and the ability to co-iterate among federates to ensure model convergence at each time step. After describing requirements, we begin by evaluating existing co-simulation frameworks, including HLA and FMI, and conclude that none provide the required features. Then we describe the design for the new layeredmore » co-simulation architecture.« less

Design of the HELICS High-Performance Transmission-Distribution-Communication-Market Co-Simulation Framework: Preprint

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

Palmintier, Bryan S; Krishnamurthy, Dheepak; Top, Philip

This paper describes the design rationale for a new cyber-physical-energy co-simulation framework for electric power systems. This new framework will support very large-scale (100,000+ federates) co-simulations with off-the-shelf power-systems, communication, and end-use models. Other key features include cross-platform operating system support, integration of both event-driven (e.g. packetized communication) and time-series (e.g. power flow) simulation, and the ability to co-iterate among federates to ensure model convergence at each time step. After describing requirements, we begin by evaluating existing co-simulation frameworks, including HLA and FMI, and conclude that none provide the required features. Then we describe the design for the new layeredmore » co-simulation architecture.« less

Epidemic modeling in complex realities.

PubMed

Colizza, Vittoria; Barthélemy, Marc; Barrat, Alain; Vespignani, Alessandro

2007-04-01

In our global world, the increasing complexity of social relations and transport infrastructures are key factors in the spread of epidemics. In recent years, the increasing availability of computer power has enabled both to obtain reliable data allowing one to quantify the complexity of the networks on which epidemics may propagate and to envision computational tools able to tackle the analysis of such propagation phenomena. These advances have put in evidence the limits of homogeneous assumptions and simple spatial diffusion approaches, and stimulated the inclusion of complex features and heterogeneities relevant in the description of epidemic diffusion. In this paper, we review recent progresses that integrate complex systems and networks analysis with epidemic modelling and focus on the impact of the various complex features of real systems on the dynamics of epidemic spreading.

Binding of Thioflavin T and Related Probes to Polymorphic Models of Amyloid-β Fibrils.

PubMed

Peccati, Francesca; Pantaleone, Stefano; Riffet, Vanessa; Solans-Monfort, Xavier; Contreras-García, Julia; Guallar, Victor; Sodupe, Mariona

2017-09-28

Alzheimer's disease is a challenge of the utmost importance for contemporary society. An early diagnosis is essential for the development of treatments and for establishing a network of support for the patient. In this light, the deposition in the brain of amyloid-β fibrillar aggregates, which is a distinctive feature of Alzheimer, is key for an early detection of this disease. In this work we propose an atomistic study of the interaction of amyloid tracers with recently published polymorphic models of amyloid-β 1-40 and 1-42 fibrils, highlighting the relationship between marker architectures and binding affinity. This work uncovers the importance of quaternary structure, and in particular of junctions between amyloid-β protofilaments, as the key areas for marker binding.

Cost-effectiveness of Rotavirus vaccination in Vietnam

PubMed Central

Kim, Sun-Young; Goldie, Sue J; Salomon, Joshua A

2009-01-01

Background Rotavirus is the most common cause of severe diarrhea leading to hospitalization or disease-specific death among young children. New rotavirus vaccines have recently been approved. Some previous studies have provided broad qualitative insights into the health and economic consequences of introducing the vaccines into low-income countries, representing several features of rotavirus infection, such as varying degrees of severity and age-dependency of clinical manifestation, in their model-based analyses. We extend this work to reflect additional features of rotavirus (e.g., the possibility of reinfection and varying degrees of partial immunity conferred by natural infection), and assess the influence of the features on the cost-effectiveness of rotavirus vaccination. Methods We developed a Markov model that reflects key features of rotavirus infection, using the most recent data available. We applied the model to the 2004 Vietnamese birth cohort and re-evaluated the cost-effectiveness (2004 US dollars per disability-adjusted life year [DALY]) of rotavirus vaccination (Rotarix®) compared to no vaccination, from both societal and health care system perspectives. We conducted univariate sensitivity analyses and also performed a probabilistic sensitivity analysis, based on Monte Carlo simulations drawing parameter values from the distributions assigned to key uncertain parameters. Results Rotavirus vaccination would not completely protect young children against rotavirus infection due to the partial nature of vaccine immunity, but would effectively reduce severe cases of rotavirus gastroenteritis (outpatient visits, hospitalizations, or deaths) by about 67% over the first 5 years of life. Under base-case assumptions (94% coverage and $5 per dose), the incremental cost per DALY averted from vaccination compared to no vaccination would be $540 from the societal perspective and $550 from the health care system perspective. Conclusion Introducing rotavirus vaccines would be a cost-effective public health intervention in Vietnam. However, given the uncertainty about vaccine efficacy and potential changes in rotavirus epidemiology in local settings, further clinical research and re-evaluation of rotavirus vaccination programs may be necessary as new information emerges. PMID:19159483

Quantifying relationships between abundances of cold-water coral Lophelia pertusa and terrain features: A case study on the Norwegian margin

NASA Astrophysics Data System (ADS)

Tong, Ruiju; Purser, Autun; Guinan, Janine; Unnithan, Vikram; Yu, Jinsongdi; Zhang, Chengcheng

2016-03-01

An understanding of how terrain features influence abundance of a particular species greatly aids in the development of accurate predictive habitat suitability models. In this study, we investigated the observed seafloor coverage of cold-water coral Lophelia pertusa in relation to seabed topography at the Sotbakken and Røst Reefs on the Norwegian margin. The primary terrain features at the study sites are a SW-NE stretching mound at Sotbakken Reef and SW-NE running ridges at Røst Reef, located at depths of ~300-400 m and ~250-320 m respectively. Ship-borne multibeam bathymetry data, JAGO dive video data and JAGO positioning data were used in this study. Terrain variables were calculated at scales of 30 m, 90 m and 170 m based on the bathymetry data. Additionally, we investigated the relationships between the terrain variables at multiple scales using the Unweighted Pair Group Method. The observed L. pertusa coverage at both reefs was found to be significantly correlated with most investigated terrain variables, with correlations increasing in strength with increase in analysis scale, suggesting that large scale terrain features likely play an important role in influencing L. pertusa distribution. Small scale terrain variations appear less important in determining the suitability of a region of seafloor for L. pertusa colonization. We conclude that bathymetric position index and curvature, as well as seabed aspect, most strongly correlate with coral coverage, indicating that local topographic highs, with an orientation into inflowing bottom currents, are most suitable for L. pertusa habitation. These results indicate that developing habitat suitability models for L. pertusa will benefit from inclusion of particular key terrain variables (e.g. aspect, plan curvature, mean curvature and slope) and that these should ideally be computed at multiple spatial scales with a greater gap in scales than we used in this study, to maximize the inclusion of the key variables in the model whilst minimizing redundancy.

Analyzing the Adaptive Mesh Refinement (AMR) Characteristics of a High-Order 2D Cubed-Sphere Shallow-Water Model

DOE PAGES

Ferguson, Jared O.; Jablonowski, Christiane; Johansen, Hans; ...

2016-11-09

Adaptive mesh refinement (AMR) is a technique that has been featured only sporadically in atmospheric science literature. This study aims to demonstrate the utility of AMR for simulating atmospheric flows. Several test cases are implemented in a 2D shallow-water model on the sphere using the Chombo-AMR dynamical core. This high-order finite-volume model implements adaptive refinement in both space and time on a cubed-sphere grid using a mapped-multiblock mesh technique. The tests consist of the passive advection of a tracer around moving vortices, a steady-state geostrophic flow, an unsteady solid-body rotation, a gravity wave impinging on a mountain, and the interactionmore » of binary vortices. Both static and dynamic refinements are analyzed to determine the strengths and weaknesses of AMR in both complex flows with small-scale features and large-scale smooth flows. The different test cases required different AMR criteria, such as vorticity or height-gradient based thresholds, in order to achieve the best accuracy for cost. The simulations show that the model can accurately resolve key local features without requiring global high-resolution grids. The adaptive grids are able to track features of interest reliably without inducing noise or visible distortions at the coarse–fine interfaces. Finally and furthermore, the AMR grids keep any degradations of the large-scale smooth flows to a minimum.« less

Analyzing the Adaptive Mesh Refinement (AMR) Characteristics of a High-Order 2D Cubed-Sphere Shallow-Water Model

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

Ferguson, Jared O.; Jablonowski, Christiane; Johansen, Hans

Adaptive mesh refinement (AMR) is a technique that has been featured only sporadically in atmospheric science literature. This study aims to demonstrate the utility of AMR for simulating atmospheric flows. Several test cases are implemented in a 2D shallow-water model on the sphere using the Chombo-AMR dynamical core. This high-order finite-volume model implements adaptive refinement in both space and time on a cubed-sphere grid using a mapped-multiblock mesh technique. The tests consist of the passive advection of a tracer around moving vortices, a steady-state geostrophic flow, an unsteady solid-body rotation, a gravity wave impinging on a mountain, and the interactionmore » of binary vortices. Both static and dynamic refinements are analyzed to determine the strengths and weaknesses of AMR in both complex flows with small-scale features and large-scale smooth flows. The different test cases required different AMR criteria, such as vorticity or height-gradient based thresholds, in order to achieve the best accuracy for cost. The simulations show that the model can accurately resolve key local features without requiring global high-resolution grids. The adaptive grids are able to track features of interest reliably without inducing noise or visible distortions at the coarse–fine interfaces. Finally and furthermore, the AMR grids keep any degradations of the large-scale smooth flows to a minimum.« less

Physical activity classification with dynamic discriminative methods.

PubMed

Ray, Evan L; Sasaki, Jeffer E; Freedson, Patty S; Staudenmayer, John

2018-06-19

A person's physical activity has important health implications, so it is important to be able to measure aspects of physical activity objectively. One approach to doing that is to use data from an accelerometer to classify physical activity according to activity type (e.g., lying down, sitting, standing, or walking) or intensity (e.g., sedentary, light, moderate, or vigorous). This can be formulated as a labeled classification problem, where the model relates a feature vector summarizing the accelerometer signal in a window of time to the activity type or intensity in that window. These data exhibit two key characteristics: (1) the activity classes in different time windows are not independent, and (2) the accelerometer features have moderately high dimension and follow complex distributions. Through a simulation study and applications to three datasets, we demonstrate that a model's classification performance is related to how it addresses these aspects of the data. Dynamic methods that account for temporal dependence achieve better performance than static methods that do not. Generative methods that explicitly model the distribution of the accelerometer signal features do not perform as well as methods that take a discriminative approach to establishing the relationship between the accelerometer signal and the activity class. Specifically, Conditional Random Fields consistently have better performance than commonly employed methods that ignore temporal dependence or attempt to model the accelerometer features. © 2018, The International Biometric Society.

3D scanning and printing skeletal tissues for anatomy education.

PubMed

Thomas, Daniel B; Hiscox, Jessica D; Dixon, Blair J; Potgieter, Johan

2016-09-01

Detailed anatomical models can be produced with consumer-level 3D scanning and printing systems. 3D replication techniques are significant advances for anatomical education as they allow practitioners to more easily introduce diverse or numerous specimens into classrooms. Here we present a methodology for producing anatomical models in-house, with the chondrocranium cartilage from a spiny dogfish (Squalus acanthias) and the skeleton of a cane toad (Rhinella marina) as case studies. 3D digital replicas were produced using two consumer-level scanners and specimens were 3D-printed with selective laser sintering. The fidelity of the two case study models was determined with respect to key anatomical features. Larger-scale features of the dogfish chondrocranium and frog skeleton were all well-resolved and distinct in the 3D digital models, and many finer-scale features were also well-resolved, but some more subtle features were absent from the digital models (e.g. endolymphatic foramina in chondrocranium). All characters identified in the digital chondrocranium could be identified in the subsequent 3D print; however, three characters in the 3D-printed frog skeleton could not be clearly delimited (palatines, parasphenoid and pubis). Characters that were absent in the digital models or 3D prints had low-relief in the original scanned specimen and represent a minor loss of fidelity. Our method description and case studies show that minimal equipment and training is needed to produce durable skeletal specimens. These technologies support the tailored production of models for specific classes or research aims. © 2016 Anatomical Society.

The effect of coherent stirring on the advection–condensation of water vapour

PubMed Central

Vanneste, Jacques

2017-01-01

Atmospheric water vapour is an essential ingredient of weather and climate. The key features of its distribution can be represented by kinematic models which treat it as a passive scalar advected by a prescribed flow and reacting through condensation. Condensation acts as a sink that maintains specific humidity below a prescribed, space-dependent saturation value. To investigate how the interplay between large-scale advection, small-scale turbulence and condensation controls moisture distribution, we develop simple kinematic models which combine a single circulating flow with a Brownian-motion representation of turbulence. We first study the drying mechanism of a water-vapour anomaly released inside a vortex at an initial time. Next, we consider a cellular flow with a moisture source at a boundary. The statistically steady state attained shows features reminiscent of the Hadley cell such as boundary layers, a region of intense precipitation and a relative humidity minimum. Explicit results provide a detailed characterization of these features in the limit of strong flow. PMID:28690417

The effect of coherent stirring on the advection-condensation of water vapour

NASA Astrophysics Data System (ADS)

Tsang, Yue-Kin; Vanneste, Jacques

2017-06-01

Atmospheric water vapour is an essential ingredient of weather and climate. The key features of its distribution can be represented by kinematic models which treat it as a passive scalar advected by a prescribed flow and reacting through condensation. Condensation acts as a sink that maintains specific humidity below a prescribed, space-dependent saturation value. To investigate how the interplay between large-scale advection, small-scale turbulence and condensation controls moisture distribution, we develop simple kinematic models which combine a single circulating flow with a Brownian-motion representation of turbulence. We first study the drying mechanism of a water-vapour anomaly released inside a vortex at an initial time. Next, we consider a cellular flow with a moisture source at a boundary. The statistically steady state attained shows features reminiscent of the Hadley cell such as boundary layers, a region of intense precipitation and a relative humidity minimum. Explicit results provide a detailed characterization of these features in the limit of strong flow.

The effect of coherent stirring on the advection-condensation of water vapour.

PubMed

Tsang, Yue-Kin; Vanneste, Jacques

2017-06-01

Atmospheric water vapour is an essential ingredient of weather and climate. The key features of its distribution can be represented by kinematic models which treat it as a passive scalar advected by a prescribed flow and reacting through condensation. Condensation acts as a sink that maintains specific humidity below a prescribed, space-dependent saturation value. To investigate how the interplay between large-scale advection, small-scale turbulence and condensation controls moisture distribution, we develop simple kinematic models which combine a single circulating flow with a Brownian-motion representation of turbulence. We first study the drying mechanism of a water-vapour anomaly released inside a vortex at an initial time. Next, we consider a cellular flow with a moisture source at a boundary. The statistically steady state attained shows features reminiscent of the Hadley cell such as boundary layers, a region of intense precipitation and a relative humidity minimum. Explicit results provide a detailed characterization of these features in the limit of strong flow.

Mouse model of pulmonary cavitary tuberculosis and expression of matrix metalloproteinase-9.

PubMed

Ordonez, Alvaro A; Tasneen, Rokeya; Pokkali, Supriya; Xu, Ziyue; Converse, Paul J; Klunk, Mariah H; Mollura, Daniel J; Nuermberger, Eric L; Jain, Sanjay K

2016-07-01

Cavitation is a key pathological feature of human tuberculosis (TB), and is a well-recognized risk factor for transmission of infection, relapse after treatment and the emergence of drug resistance. Despite intense interest in the mechanisms underlying cavitation and its negative impact on treatment outcomes, there has been limited study of this phenomenon, owing in large part to the limitations of existing animal models. Although cavitation does not occur in conventional mouse strains after infection with Mycobacterium tuberculosis, cavitary lung lesions have occasionally been observed in C3HeB/FeJ mice. However, to date, there has been no demonstration that cavitation can be produced consistently enough to support C3HeB/FeJ mice as a new and useful model of cavitary TB. We utilized serial computed tomography (CT) imaging to detect pulmonary cavitation in C3HeB/FeJ mice after aerosol infection with M. tuberculosis Post-mortem analyses were performed to characterize lung lesions and to localize matrix metalloproteinases (MMPs) previously implicated in cavitary TB in situ A total of 47-61% of infected mice developed cavities during primary disease or relapse after non-curative treatments. Key pathological features of human TB, including simultaneous presence of multiple pathologies, were noted in lung tissues. Optical imaging demonstrated increased MMP activity in TB lesions and MMP-9 was significantly expressed in cavitary lesions. Tissue MMP-9 activity could be abrogated by specific inhibitors. In situ, three-dimensional analyses of cavitary lesions demonstrated that 22.06% of CD11b+ signal colocalized with MMP-9. C3HeB/FeJ mice represent a reliable, economical and tractable model of cavitary TB, with key similarities to human TB. This model should provide an excellent tool to better understand the pathogenesis of cavitation and its effects on TB treatments. © 2016. Published by The Company of Biologists Ltd.

Mouse model of pulmonary cavitary tuberculosis and expression of matrix metalloproteinase-9

PubMed Central

Ordonez, Alvaro A.; Tasneen, Rokeya; Pokkali, Supriya; Xu, Ziyue; Converse, Paul J.; Klunk, Mariah H.; Mollura, Daniel J.; Nuermberger, Eric L.

2016-01-01

ABSTRACT Cavitation is a key pathological feature of human tuberculosis (TB), and is a well-recognized risk factor for transmission of infection, relapse after treatment and the emergence of drug resistance. Despite intense interest in the mechanisms underlying cavitation and its negative impact on treatment outcomes, there has been limited study of this phenomenon, owing in large part to the limitations of existing animal models. Although cavitation does not occur in conventional mouse strains after infection with Mycobacterium tuberculosis, cavitary lung lesions have occasionally been observed in C3HeB/FeJ mice. However, to date, there has been no demonstration that cavitation can be produced consistently enough to support C3HeB/FeJ mice as a new and useful model of cavitary TB. We utilized serial computed tomography (CT) imaging to detect pulmonary cavitation in C3HeB/FeJ mice after aerosol infection with M. tuberculosis. Post-mortem analyses were performed to characterize lung lesions and to localize matrix metalloproteinases (MMPs) previously implicated in cavitary TB in situ. A total of 47-61% of infected mice developed cavities during primary disease or relapse after non-curative treatments. Key pathological features of human TB, including simultaneous presence of multiple pathologies, were noted in lung tissues. Optical imaging demonstrated increased MMP activity in TB lesions and MMP-9 was significantly expressed in cavitary lesions. Tissue MMP-9 activity could be abrogated by specific inhibitors. In situ, three-dimensional analyses of cavitary lesions demonstrated that 22.06% of CD11b+ signal colocalized with MMP-9. C3HeB/FeJ mice represent a reliable, economical and tractable model of cavitary TB, with key similarities to human TB. This model should provide an excellent tool to better understand the pathogenesis of cavitation and its effects on TB treatments. PMID:27482816

Investigating the Role of Mesoscale Processes and Ice Dynamics in Carbon and Iron Fluxes in a Changing Amundsen Sea (INSPIRE)

NASA Astrophysics Data System (ADS)

Mu, L.; Yager, P. L.; St-Laurent, P.; Dinniman, M.; Oliver, H.; Stammerjohn, S. E.; Sherrell, R. M.; Hofmann, E. E.

2016-02-01

The Amundsen Sea, in the remote S. Pacific sector of the Southern Ocean, is one of the least studied Antarctic continental shelf regions. It shares key processes with other W. Antarctic shelf regions, such as formation of a recurring polynya, important ice shelf-ocean linkages, and high biological production, but has unique characteristics as well. The Amundsen Sea Polynya (ASP), features 1) large intrusions of modified Circumpolar Deep Water (mCDW) onto the continental shelf, 2) the fastest melting ice sheets in Antarctica, 3) the most productive coastal polynya and a large atmospheric CO2 sink, and 4) very rapid declines in seasonal sea ice. Here we report on a new effort for this region that unites independent, state-of-the-art modeling and field data synthesis efforts to address important unanswered questions about carbon fluxes, iron supply, and climate sensitivity in this key region of the coastal Antarctic. Following on the heels of a highly successful oceanographic field program, the Amundsen Sea Polynya International Research Expedition (ASPIRE; which sampled the ASP with high spatial resolution during the onset of the enormous phytoplankton bloom of 2011), the INSPIRE project is a collaboration between ASPIRE senior scientists and an experienced team of physical and biogeochemical modelers who can use ASPIRE field data to both validate and extend the capabilities of an existing Regional Ocean Modeling System (ROMS) for the Amundsen Sea. This new effort will add biology and biogeochemistry (including features potentially unique to the ASP region) to an existing physical model, allowing us to address key questions about bloom mechanisms and climate sensitivity that could not be answered by field campaigns or modeling alone. This project is expected to generate new insights and hypotheses that will ultimately guide sampling strategies of future field efforts investigating how present and future climate change impacts this important region of the world.

Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection

NASA Astrophysics Data System (ADS)

Wang, Haibo; Cruz-Roa, Angel; Basavanhally, Ajay; Gilmore, Hannah; Shih, Natalie; Feldman, Mike; Tomaszewski, John; Gonzalez, Fabio; Madabhushi, Anant

2014-03-01

Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. A key component of BCa grade is mitotic count, which involves quantifying the number of cells in the process of dividing (i.e. undergoing mitosis) at a specific point in time. Currently mitosis counting is done manually by a pathologist looking at multiple high power fields on a glass slide under a microscope, an extremely laborious and time consuming process. The development of computerized systems for automated detection of mitotic nuclei, while highly desirable, is confounded by the highly variable shape and appearance of mitoses. Existing methods use either handcrafted features that capture certain morphological, statistical or textural attributes of mitoses or features learned with convolutional neural networks (CNN). While handcrafted features are inspired by the domain and the particular application, the data-driven CNN models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. On the other hand, CNN is computationally more complex and needs a large number of labeled training instances. Since handcrafted features attempt to model domain pertinent attributes and CNN approaches are largely unsupervised feature generation methods, there is an appeal to attempting to combine these two distinct classes of feature generation strategies to create an integrated set of attributes that can potentially outperform either class of feature extraction strategies individually. In this paper, we present a cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features (morphology, color and texture features). By employing a light CNN model, the proposed approach is far less demanding computationally, and the cascaded strategy of combining handcrafted features and CNN-derived features enables the possibility of maximizing performance by leveraging the disconnected feature sets. Evaluation on the public ICPR12 mitosis dataset that has 226 mitoses annotated on 35 High Power Fields (HPF, x400 magnification) by several pathologists and 15 testing HPFs yielded an F-measure of 0.7345. Apart from this being the second best performance ever recorded for this MITOS dataset, our approach is faster and requires fewer computing resources compared to extant methods, making this feasible for clinical use.

Efficient quantitative assessment of facial paralysis using iris segmentation and active contour-based key points detection with hybrid classifier.

PubMed

Barbosa, Jocelyn; Lee, Kyubum; Lee, Sunwon; Lodhi, Bilal; Cho, Jae-Gu; Seo, Woo-Keun; Kang, Jaewoo

2016-03-12

Facial palsy or paralysis (FP) is a symptom that loses voluntary muscles movement in one side of the human face, which could be very devastating in the part of the patients. Traditional methods are solely dependent to clinician's judgment and therefore time consuming and subjective in nature. Hence, a quantitative assessment system becomes apparently invaluable for physicians to begin the rehabilitation process; and to produce a reliable and robust method is challenging and still underway. We introduce a novel approach for a quantitative assessment of facial paralysis that tackles classification problem for FP type and degree of severity. Specifically, a novel method of quantitative assessment is presented: an algorithm that extracts the human iris and detects facial landmarks; and a hybrid approach combining the rule-based and machine learning algorithm to analyze and prognosticate facial paralysis using the captured images. A method combining the optimized Daugman's algorithm and Localized Active Contour (LAC) model is proposed to efficiently extract the iris and facial landmark or key points. To improve the performance of LAC, appropriate parameters of initial evolving curve for facial features' segmentation are automatically selected. The symmetry score is measured by the ratio between features extracted from the two sides of the face. Hybrid classifiers (i.e. rule-based with regularized logistic regression) were employed for discriminating healthy and unhealthy subjects, FP type classification, and for facial paralysis grading based on House-Brackmann (H-B) scale. Quantitative analysis was performed to evaluate the performance of the proposed approach. Experiments show that the proposed method demonstrates its efficiency. Facial movement feature extraction on facial images based on iris segmentation and LAC-based key point detection along with a hybrid classifier provides a more efficient way of addressing classification problem on facial palsy type and degree of severity. Combining iris segmentation and key point-based method has several merits that are essential for our real application. Aside from the facial key points, iris segmentation provides significant contribution as it describes the changes of the iris exposure while performing some facial expressions. It reveals the significant difference between the healthy side and the severe palsy side when raising eyebrows with both eyes directed upward, and can model the typical changes in the iris region.

How well do CMIP5 models simulate the low-level jet in western Colombia?

NASA Astrophysics Data System (ADS)

Sierra, Juan P.; Arias, Paola A.; Vieira, Sara C.; Agudelo, Jhoana

2017-11-01

The Choco jet is an important atmospheric feature of Colombian and northern South America hydro-climatology. This work assesses the ability of 26 coupled and 11 uncoupled (AMIP) global climate models (GCMs) included in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) archive to simulate the climatological basic features (annual cycle, spatial distribution and vertical structure) of this jet. Using factor and cluster analysis, we objectively classify models in Best, Worst, and Intermediate groups. Despite the coarse resolution of the GCMs, this study demonstrates that nearly all models can represent the existence of the Choco low-level jet. AMIP and Best models present a more realistic simulation of jet. Worst models exhibit biases such as an anomalous southward location of the Choco jet during the whole year and a shallower jet. The model skill to represent this jet comes from their ability to reproduce some of its main causes, such as the temperature and pressure differences between particular regions in the eastern Pacific and western Colombian lands, which are non-local features. Conversely, Worst models considerably underestimate temperature and pressure differences between these key regions. We identify a close relationship between the location of the Choco jet and the Inter-tropical Convergence Zone (ITCZ), and CMIP5 models are able to represent such relationship. Errors in Worst models are related with bias in the location of the ITCZ over the eastern tropical Pacific Ocean, as well as the representation of the topography and the horizontal resolution.

Agent-Based Models of Strategies for the Emergence and Evolution of Grammatical Agreement

PubMed Central

Beuls, Katrien; Steels, Luc

2013-01-01

Grammatical agreement means that features associated with one linguistic unit (for example number or gender) become associated with another unit and then possibly overtly expressed, typically with morphological markers. It is one of the key mechanisms used in many languages to show that certain linguistic units within an utterance grammatically depend on each other. Agreement systems are puzzling because they can be highly complex in terms of what features they use and how they are expressed. Moreover, agreement systems have undergone considerable change in the historical evolution of languages. This article presents language game models with populations of agents in order to find out for what reasons and by what cultural processes and cognitive strategies agreement systems arise. It demonstrates that agreement systems are motivated by the need to minimize combinatorial search and semantic ambiguity, and it shows, for the first time, that once a population of agents adopts a strategy to invent, acquire and coordinate meaningful markers through social learning, linguistic self-organization leads to the spontaneous emergence and cultural transmission of an agreement system. The article also demonstrates how attested grammaticalization phenomena, such as phonetic reduction and conventionalized use of agreement markers, happens as a side effect of additional economizing principles, in particular minimization of articulatory effort and reduction of the marker inventory. More generally, the article illustrates a novel approach for studying how key features of human languages might emerge. PMID:23527055

Computational Properties of the Hippocampus Increase the Efficiency of Goal-Directed Foraging through Hierarchical Reinforcement Learning

PubMed Central

Chalmers, Eric; Luczak, Artur; Gruber, Aaron J.

2016-01-01

The mammalian brain is thought to use a version of Model-based Reinforcement Learning (MBRL) to guide “goal-directed” behavior, wherein animals consider goals and make plans to acquire desired outcomes. However, conventional MBRL algorithms do not fully explain animals' ability to rapidly adapt to environmental changes, or learn multiple complex tasks. They also require extensive computation, suggesting that goal-directed behavior is cognitively expensive. We propose here that key features of processing in the hippocampus support a flexible MBRL mechanism for spatial navigation that is computationally efficient and can adapt quickly to change. We investigate this idea by implementing a computational MBRL framework that incorporates features inspired by computational properties of the hippocampus: a hierarchical representation of space, “forward sweeps” through future spatial trajectories, and context-driven remapping of place cells. We find that a hierarchical abstraction of space greatly reduces the computational load (mental effort) required for adaptation to changing environmental conditions, and allows efficient scaling to large problems. It also allows abstract knowledge gained at high levels to guide adaptation to new obstacles. Moreover, a context-driven remapping mechanism allows learning and memory of multiple tasks. Simulating dorsal or ventral hippocampal lesions in our computational framework qualitatively reproduces behavioral deficits observed in rodents with analogous lesions. The framework may thus embody key features of how the brain organizes model-based RL to efficiently solve navigation and other difficult tasks. PMID:28018203

Mouse model of necrotic tuberculosis granulomas develops hypoxic lesions.

PubMed

Harper, Jamie; Skerry, Ciaran; Davis, Stephanie L; Tasneen, Rokeya; Weir, Mariah; Kramnik, Igor; Bishai, William R; Pomper, Martin G; Nuermberger, Eric L; Jain, Sanjay K

2012-02-15

Preclinical evaluation of tuberculosis drugs is generally limited to mice. However, necrosis and hypoxia, key features of human tuberculosis lesions, are lacking in conventional mouse strains. We used C3HeB/FeJ mice, which develop necrotic lesions in response to Mycobacterium tuberculosis infection. Positron emission tomography in live infected animals, postmortem pimonidazole immunohistochemistry, and bacterial gene expression analyses were used to assess whether tuberculosis lesions in C3HeB/FeJ are hypoxic. Efficacy of combination drug treatment, including PA-824, active against M. tuberculosis under hypoxic conditions, was also evaluated. Tuberculosis lesions in C3HeB/FeJ (but not BALB/c) were found to be hypoxic and associated with up-regulation of known hypoxia-associated bacterial genes (P < .001). Contrary to sustained activity reported elsewhere in BALB/c mice, moxifloxacin and pyrazinamide (MZ) combination was not bactericidal beyond 3 weeks in C3HeB/FeJ. Although PA-824 added significant activity, the novel combination of PA-824 and MZ was less effective than the standard first-line regimen in C3HeB/FeJ. We demonstrate that tuberculosis lesions in C3HeB/FeJ are hypoxic. Activities of some key tuberculosis drug regimens in development are represented differently in C3HeB/FeJ versus BALB/c mice. Because C3HeB/FeJ display key features of human tuberculosis, this strain warrants evaluation as a more pathologically relevant model for preclinical studies.

A proto-architecture for innate directionally selective visual maps.

PubMed

Adams, Samantha V; Harris, Chris M

2014-01-01

Self-organizing artificial neural networks are a popular tool for studying visual system development, in particular the cortical feature maps present in real systems that represent properties such as ocular dominance (OD), orientation-selectivity (OR) and direction selectivity (DS). They are also potentially useful in artificial systems, for example robotics, where the ability to extract and learn features from the environment in an unsupervised way is important. In this computational study we explore a DS map that is already latent in a simple artificial network. This latent selectivity arises purely from the cortical architecture without any explicit coding for DS and prior to any self-organising process facilitated by spontaneous activity or training. We find DS maps with local patchy regions that exhibit features similar to maps derived experimentally and from previous modeling studies. We explore the consequences of changes to the afferent and lateral connectivity to establish the key features of this proto-architecture that support DS.

Cone beam computed tomography of plastinated hearts for instruction of radiological anatomy.

PubMed

Chang, Chih-Wei; Atkinson, Gregory; Gandhi, Niket; Farrell, Michael L; Labrash, Steven; Smith, Alice B; Norton, Neil S; Matsui, Takashi; Lozanoff, Scott

2016-09-01

Radiological anatomy education is an important aspect of the medical curriculum. The purpose of this study was to establish and demonstrate the use of plastinated anatomical specimens, specifically human hearts, for use in radiological anatomy education. Four human hearts were processed with routine plastination procedures at room temperature. Specimens were subjected to cone beam computed tomography and a graphics program (ER3D) was applied to generate 3D cardiac models. A comparison was conducted between plastinated hearts and their corresponding computer models based on a list of morphological cardiac features commonly studied in the gross anatomy laboratory. Results showed significant correspondence between plastinations and CBCT-generated 3D models (98 %; p < .01) for external structures and 100 % for internal cardiac features, while 85 % correspondence was achieved between plastinations and 2D CBCT slices. Complete correspondence (100 %) was achieved between key observations on the plastinations and internal radiological findings typically required of medical student. All pathologic features seen on the plastinated hearts were also visualized internally with the CBCT-generated models and 2D slices. These results suggest that CBCT-derived slices and models can be successfully generated from plastinated material and provide accurate representations for radiological anatomy education.

A Machine Learning Approach to Discover Rules for Expressive Performance Actions in Jazz Guitar Music.

PubMed

Giraldo, Sergio I; Ramirez, Rafael

2016-01-01

Expert musicians introduce expression in their performances by manipulating sound properties such as timing, energy, pitch, and timbre. Here, we present a data driven computational approach to induce expressive performance rule models for note duration, onset, energy, and ornamentation transformations in jazz guitar music. We extract high-level features from a set of 16 commercial audio recordings (and corresponding music scores) of jazz guitarist Grant Green in order to characterize the expression in the pieces. We apply machine learning techniques to the resulting features to learn expressive performance rule models. We (1) quantitatively evaluate the accuracy of the induced models, (2) analyse the relative importance of the considered musical features, (3) discuss some of the learnt expressive performance rules in the context of previous work, and (4) assess their generailty. The accuracies of the induced predictive models is significantly above base-line levels indicating that the audio performances and the musical features extracted contain sufficient information to automatically learn informative expressive performance patterns. Feature analysis shows that the most important musical features for predicting expressive transformations are note duration, pitch, metrical strength, phrase position, Narmour structure, and tempo and key of the piece. Similarities and differences between the induced expressive rules and the rules reported in the literature were found. Differences may be due to the fact that most previously studied performance data has consisted of classical music recordings. Finally, the rules' performer specificity/generality is assessed by applying the induced rules to performances of the same pieces performed by two other professional jazz guitar players. Results show a consistency in the ornamentation patterns between Grant Green and the other two musicians, which may be interpreted as a good indicator for generality of the ornamentation rules.

A Machine Learning Approach to Discover Rules for Expressive Performance Actions in Jazz Guitar Music

PubMed Central

Giraldo, Sergio I.; Ramirez, Rafael

2016-01-01

Expert musicians introduce expression in their performances by manipulating sound properties such as timing, energy, pitch, and timbre. Here, we present a data driven computational approach to induce expressive performance rule models for note duration, onset, energy, and ornamentation transformations in jazz guitar music. We extract high-level features from a set of 16 commercial audio recordings (and corresponding music scores) of jazz guitarist Grant Green in order to characterize the expression in the pieces. We apply machine learning techniques to the resulting features to learn expressive performance rule models. We (1) quantitatively evaluate the accuracy of the induced models, (2) analyse the relative importance of the considered musical features, (3) discuss some of the learnt expressive performance rules in the context of previous work, and (4) assess their generailty. The accuracies of the induced predictive models is significantly above base-line levels indicating that the audio performances and the musical features extracted contain sufficient information to automatically learn informative expressive performance patterns. Feature analysis shows that the most important musical features for predicting expressive transformations are note duration, pitch, metrical strength, phrase position, Narmour structure, and tempo and key of the piece. Similarities and differences between the induced expressive rules and the rules reported in the literature were found. Differences may be due to the fact that most previously studied performance data has consisted of classical music recordings. Finally, the rules' performer specificity/generality is assessed by applying the induced rules to performances of the same pieces performed by two other professional jazz guitar players. Results show a consistency in the ornamentation patterns between Grant Green and the other two musicians, which may be interpreted as a good indicator for generality of the ornamentation rules. PMID:28066290

Missouri Program Highlights How Standards Make a Difference

ERIC Educational Resources Information Center

Killion, Joellen

2017-01-01

Professional development designed to integrate key features of research-based professional learning has positive and significant effects on teacher practice and student achievement in mathematics when implemented in schools that meet specified technology-readiness criteria. Key features of research-based professional learning include intensive…

Features of asthma which provide meaningful insights for understanding the disease heterogeneity.

PubMed

Deliu, M; Yavuz, T S; Sperrin, M; Belgrave, D; Sahiner, U M; Sackesen, C; Kalayci, O; Custovic, A

2018-01-01

Data-driven methods such as hierarchical clustering (HC) and principal component analysis (PCA) have been used to identify asthma subtypes, with inconsistent results. To develop a framework for the discovery of stable and clinically meaningful asthma subtypes. We performed HC in a rich data set from 613 asthmatic children, using 45 clinical variables (Model 1), and after PCA dimensionality reduction (Model 2). Clinical experts then identified a set of asthma features/domains which informed clusters in the two analyses. In Model 3, we reclustered the data using these features to ascertain whether this improved the discovery process. Cluster stability was poor in Models 1 and 2. Clinical experts highlighted four asthma features/domains which differentiated the clusters in two models: age of onset, allergic sensitization, severity, and recent exacerbations. In Model 3 (HC using these four features), cluster stability improved substantially. The cluster assignment changed, providing more clinically interpretable results. In a 5-cluster model, we labelled the clusters as: "Difficult asthma" (n = 132); "Early-onset mild atopic" (n = 210); "Early-onset mild non-atopic: (n = 153); "Late-onset" (n = 105); and "Exacerbation-prone asthma" (n = 13). Multinomial regression demonstrated that lung function was significantly diminished among children with "Difficult asthma"; blood eosinophilia was a significant feature of "Difficult," "Early-onset mild atopic," and "Late-onset asthma." Children with moderate-to-severe asthma were present in each cluster. An integrative approach of blending the data with clinical expert domain knowledge identified four features, which may be informative for ascertaining asthma endotypes. These findings suggest that variables which are key determinants of asthma presence, severity, or control may not be the most informative for determining asthma subtypes. Our results indicate that exacerbation-prone asthma may be a separate asthma endotype and that severe asthma is not a single entity, but an extreme end of the spectrum of several different asthma endotypes. © 2017 The Authors. Clinical & Experimental Allergy published by John Wiley & Sons Ltd.

Analysis of the Source Physics Experiment SPE4 Prime Using State-Of Parallel Numerical Tools.

NASA Astrophysics Data System (ADS)

Vorobiev, O.; Ezzedine, S. M.; Antoun, T.; Glenn, L.

2015-12-01

This work describes a methodology used for large scale modeling of wave propagation from underground chemical explosions conducted at the Nevada National Security Site (NNSS) fractured granitic rock. We show that the discrete natures of rock masses as well as the spatial variability of the fabric of rock properties are very important to understand ground motions induced by underground explosions. In order to build a credible conceptual model of the subsurface we integrated the geological, geomechanical and geophysical characterizations conducted during recent test at the NNSS as well as historical data from the characterization during the underground nuclear test conducted at the NNSS. Because detailed site characterization is limited, expensive and, in some instances, impossible we have numerically investigated the effects of the characterization gaps on the overall response of the system. We performed several computational studies to identify the key important geologic features specific to fractured media mainly the joints characterized at the NNSS. We have also explored common key features to both geological environments such as saturation and topography and assess which characteristics affect the most the ground motion in the near-field and in the far-field. Stochastic representation of these features based on the field characterizations has been implemented into LLNL's Geodyn-L hydrocode. Simulations were used to guide site characterization efforts in order to provide the essential data to the modeling community. We validate our computational results by comparing the measured and computed ground motion at various ranges for the recently executed SPE4 prime experiment. We have also conducted a comparative study between SPE4 prime and previous experiments SPE1 and SPE3 to assess similarities and differences and draw conclusions on designing SPE5.

Integrative Data Analysis of Multi-Platform Cancer Data with a Multimodal Deep Learning Approach.

PubMed

Liang, Muxuan; Li, Zhizhong; Chen, Ting; Zeng, Jianyang

2015-01-01

Identification of cancer subtypes plays an important role in revealing useful insights into disease pathogenesis and advancing personalized therapy. The recent development of high-throughput sequencing technologies has enabled the rapid collection of multi-platform genomic data (e.g., gene expression, miRNA expression, and DNA methylation) for the same set of tumor samples. Although numerous integrative clustering approaches have been developed to analyze cancer data, few of them are particularly designed to exploit both deep intrinsic statistical properties of each input modality and complex cross-modality correlations among multi-platform input data. In this paper, we propose a new machine learning model, called multimodal deep belief network (DBN), to cluster cancer patients from multi-platform observation data. In our integrative clustering framework, relationships among inherent features of each single modality are first encoded into multiple layers of hidden variables, and then a joint latent model is employed to fuse common features derived from multiple input modalities. A practical learning algorithm, called contrastive divergence (CD), is applied to infer the parameters of our multimodal DBN model in an unsupervised manner. Tests on two available cancer datasets show that our integrative data analysis approach can effectively extract a unified representation of latent features to capture both intra- and cross-modality correlations, and identify meaningful disease subtypes from multi-platform cancer data. In addition, our approach can identify key genes and miRNAs that may play distinct roles in the pathogenesis of different cancer subtypes. Among those key miRNAs, we found that the expression level of miR-29a is highly correlated with survival time in ovarian cancer patients. These results indicate that our multimodal DBN based data analysis approach may have practical applications in cancer pathogenesis studies and provide useful guidelines for personalized cancer therapy.

Age structure is critical to the population dynamics and survival of honeybee colonies.

PubMed

Betti, M I; Wahl, L M; Zamir, M

2016-11-01

Age structure is an important feature of the division of labour within honeybee colonies, but its effects on colony dynamics have rarely been explored. We present a model of a honeybee colony that incorporates this key feature, and use this model to explore the effects of both winter and disease on the fate of the colony. The model offers a novel explanation for the frequently observed phenomenon of 'spring dwindle', which emerges as a natural consequence of the age-structured dynamics. Furthermore, the results indicate that a model taking age structure into account markedly affects the predicted timing and severity of disease within a bee colony. The timing of the onset of disease with respect to the changing seasons may also have a substantial impact on the fate of a honeybee colony. Finally, simulations predict that an infection may persist in a honeybee colony over several years, with effects that compound over time. Thus, the ultimate collapse of the colony may be the result of events several years past.

Chinese Sentence Classification Based on Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Gu, Chengwei; Wu, Ming; Zhang, Chuang

2017-10-01

Sentence classification is one of the significant issues in Natural Language Processing (NLP). Feature extraction is often regarded as the key point for natural language processing. Traditional ways based on machine learning can not take high level features into consideration, such as Naive Bayesian Model. The neural network for sentence classification can make use of contextual information to achieve greater results in sentence classification tasks. In this paper, we focus on classifying Chinese sentences. And the most important is that we post a novel architecture of Convolutional Neural Network (CNN) to apply on Chinese sentence classification. In particular, most of the previous methods often use softmax classifier for prediction, we embed a linear support vector machine to substitute softmax in the deep neural network model, minimizing a margin-based loss to get a better result. And we use tanh as an activation function, instead of ReLU. The CNN model improve the result of Chinese sentence classification tasks. Experimental results on the Chinese news title database validate the effectiveness of our model.

Improved GSO Optimized ESN Soft-Sensor Model of Flotation Process Based on Multisource Heterogeneous Information Fusion

PubMed Central

Wang, Jie-sheng; Han, Shuang; Shen, Na-na

2014-01-01

For predicting the key technology indicators (concentrate grade and tailings recovery rate) of flotation process, an echo state network (ESN) based fusion soft-sensor model optimized by the improved glowworm swarm optimization (GSO) algorithm is proposed. Firstly, the color feature (saturation and brightness) and texture features (angular second moment, sum entropy, inertia moment, etc.) based on grey-level co-occurrence matrix (GLCM) are adopted to describe the visual characteristics of the flotation froth image. Then the kernel principal component analysis (KPCA) method is used to reduce the dimensionality of the high-dimensional input vector composed by the flotation froth image characteristics and process datum and extracts the nonlinear principal components in order to reduce the ESN dimension and network complex. The ESN soft-sensor model of flotation process is optimized by the GSO algorithm with congestion factor. Simulation results show that the model has better generalization and prediction accuracy to meet the online soft-sensor requirements of the real-time control in the flotation process. PMID:24982935

Ischemic stroke lesion segmentation in multi-spectral MR images with support vector machine classifiers

NASA Astrophysics Data System (ADS)

Maier, Oskar; Wilms, Matthias; von der Gablentz, Janina; Krämer, Ulrike; Handels, Heinz

2014-03-01

Automatic segmentation of ischemic stroke lesions in magnetic resonance (MR) images is important in clinical practice and for neuroscientific trials. The key problem is to detect largely inhomogeneous regions of varying sizes, shapes and locations. We present a stroke lesion segmentation method based on local features extracted from multi-spectral MR data that are selected to model a human observer's discrimination criteria. A support vector machine classifier is trained on expert-segmented examples and then used to classify formerly unseen images. Leave-one-out cross validation on eight datasets with lesions of varying appearances is performed, showing our method to compare favourably with other published approaches in terms of accuracy and robustness. Furthermore, we compare a number of feature selectors and closely examine each feature's and MR sequence's contribution.

Double-hit mouse model of cigarette smoke priming for acute lung injury.

PubMed

Sakhatskyy, Pavlo; Wang, Zhengke; Borgas, Diana; Lomas-Neira, Joanne; Chen, Yaping; Ayala, Alfred; Rounds, Sharon; Lu, Qing

2017-01-01

Epidemiological studies indicate that cigarette smoking (CS) increases the risk and severity of acute lung injury (ALI)/acute respiratory distress syndrome (ARDS). The mechanism is not understood, at least in part because of lack of animal models that reproduce the key features of the CS priming process. In this study, using two strains of mice, we characterized a double-hit mouse model of ALI induced by CS priming of injury caused by lipopolysaccharide (LPS). C57BL/6 and AKR mice were preexposed to CS briefly (3 h) or subacutely (3 wk) before intratracheal instillation of LPS and ALI was assessed 18 h after LPS administration by measuring lung static compliance, lung edema, vascular permeability, inflammation, and alveolar apoptosis. We found that as little as 3 h of exposure to CS enhanced LPS-induced ALI in both strains of mice. Similar exacerbating effects were observed after 3 wk of preexposure to CS. However, there was a strain difference in susceptibility to CS priming for ALI, with a greater effect in AKR mice. The key features we observed suggest that 3 wk of CS preexposure of AKR mice is a reproducible, clinically relevant animal model that is useful for studying mechanisms and treatment of CS priming for a second-hit-induced ALI. Our data also support the concept that increased susceptibility to ALI/ARDS is an important adverse health consequence of CS exposure that needs to be taken into consideration when treating critically ill individuals.

Double-hit mouse model of cigarette smoke priming for acute lung injury

PubMed Central

Sakhatskyy, Pavlo; Wang, Zhengke; Borgas, Diana; Lomas-Neira, Joanne; Chen, Yaping; Ayala, Alfred; Rounds, Sharon

2016-01-01

Epidemiological studies indicate that cigarette smoking (CS) increases the risk and severity of acute lung injury (ALI)/acute respiratory distress syndrome (ARDS). The mechanism is not understood, at least in part because of lack of animal models that reproduce the key features of the CS priming process. In this study, using two strains of mice, we characterized a double-hit mouse model of ALI induced by CS priming of injury caused by lipopolysaccharide (LPS). C57BL/6 and AKR mice were preexposed to CS briefly (3 h) or subacutely (3 wk) before intratracheal instillation of LPS and ALI was assessed 18 h after LPS administration by measuring lung static compliance, lung edema, vascular permeability, inflammation, and alveolar apoptosis. We found that as little as 3 h of exposure to CS enhanced LPS-induced ALI in both strains of mice. Similar exacerbating effects were observed after 3 wk of preexposure to CS. However, there was a strain difference in susceptibility to CS priming for ALI, with a greater effect in AKR mice. The key features we observed suggest that 3 wk of CS preexposure of AKR mice is a reproducible, clinically relevant animal model that is useful for studying mechanisms and treatment of CS priming for a second-hit-induced ALI. Our data also support the concept that increased susceptibility to ALI/ARDS is an important adverse health consequence of CS exposure that needs to be taken into consideration when treating critically ill individuals. PMID:27864287

Reconstructing Demography and Social Behavior During the Neolithic Expansion from Genomic Diversity Across Island Southeast Asia.

PubMed

Vallée, François; Luciani, Aurélien; Cox, Murray P

2016-12-01

Archaeology, linguistics, and increasingly genetics are clarifying how populations moved from mainland Asia, through Island Southeast Asia, and out into the Pacific during the farming revolution. Yet key features of this process remain poorly understood, particularly how social behaviors intersected with demographic drivers to create the patterns of genomic diversity observed across Island Southeast Asia today. Such questions are ripe for computer modeling. Here, we construct an agent-based model to simulate human mobility across Island Southeast Asia from the Neolithic period to the present, with a special focus on interactions between individuals with Asian, Papuan, and mixed Asian-Papuan ancestry. Incorporating key features of the region, including its complex geography (islands and sea), demographic drivers (fecundity and migration), and social behaviors (marriage preferences), the model simultaneously tracks a full suite of genomic markers (autosomes, X chromosome, mitochondrial DNA, and Y chromosome). Using Bayesian inference, model parameters were determined that produce simulations that closely resemble the admixture profiles of 2299 individuals from 84 populations across Island Southeast Asia. The results highlight that greater propensity to migrate and elevated birth rates are related drivers behind the expansion of individuals with Asian ancestry relative to individuals with Papuan ancestry, that offspring preferentially resulted from marriages between Asian women and Papuan men, and that in contrast to current thinking, individuals with Asian ancestry were likely distributed across large parts of western Island Southeast Asia before the Neolithic expansion. Copyright © 2016 Vallée et al.

Reconstructing Demography and Social Behavior During the Neolithic Expansion from Genomic Diversity Across Island Southeast Asia

PubMed Central

Vallée, François; Luciani, Aurélien; Cox, Murray P.

2016-01-01

Archaeology, linguistics, and increasingly genetics are clarifying how populations moved from mainland Asia, through Island Southeast Asia, and out into the Pacific during the farming revolution. Yet key features of this process remain poorly understood, particularly how social behaviors intersected with demographic drivers to create the patterns of genomic diversity observed across Island Southeast Asia today. Such questions are ripe for computer modeling. Here, we construct an agent-based model to simulate human mobility across Island Southeast Asia from the Neolithic period to the present, with a special focus on interactions between individuals with Asian, Papuan, and mixed Asian–Papuan ancestry. Incorporating key features of the region, including its complex geography (islands and sea), demographic drivers (fecundity and migration), and social behaviors (marriage preferences), the model simultaneously tracks a full suite of genomic markers (autosomes, X chromosome, mitochondrial DNA, and Y chromosome). Using Bayesian inference, model parameters were determined that produce simulations that closely resemble the admixture profiles of 2299 individuals from 84 populations across Island Southeast Asia. The results highlight that greater propensity to migrate and elevated birth rates are related drivers behind the expansion of individuals with Asian ancestry relative to individuals with Papuan ancestry, that offspring preferentially resulted from marriages between Asian women and Papuan men, and that in contrast to current thinking, individuals with Asian ancestry were likely distributed across large parts of western Island Southeast Asia before the Neolithic expansion. PMID:27683274

Neural Systems with Numerically Matched Input-Output Statistic: Isotonic Bivariate Statistical Modeling

PubMed Central

Fiori, Simone

2007-01-01

Bivariate statistical modeling from incomplete data is a useful statistical tool that allows to discover the model underlying two data sets when the data in the two sets do not correspond in size nor in ordering. Such situation may occur when the sizes of the two data sets do not match (i.e., there are “holes” in the data) or when the data sets have been acquired independently. Also, statistical modeling is useful when the amount of available data is enough to show relevant statistical features of the phenomenon underlying the data. We propose to tackle the problem of statistical modeling via a neural (nonlinear) system that is able to match its input-output statistic to the statistic of the available data sets. A key point of the new implementation proposed here is that it is based on look-up-table (LUT) neural systems, which guarantee a computationally advantageous way of implementing neural systems. A number of numerical experiments, performed on both synthetic and real-world data sets, illustrate the features of the proposed modeling procedure. PMID:18566641

Topical video object discovery from key frames by modeling word co-occurrence prior.

PubMed

Zhao, Gangqiang; Yuan, Junsong; Hua, Gang; Yang, Jiong

2015-12-01

A topical video object refers to an object, that is, frequently highlighted in a video. It could be, e.g., the product logo and the leading actor/actress in a TV commercial. We propose a topic model that incorporates a word co-occurrence prior for efficient discovery of topical video objects from a set of key frames. Previous work using topic models, such as latent Dirichelet allocation (LDA), for video object discovery often takes a bag-of-visual-words representation, which ignored important co-occurrence information among the local features. We show that such data driven co-occurrence information from bottom-up can conveniently be incorporated in LDA with a Gaussian Markov prior, which combines top-down probabilistic topic modeling with bottom-up priors in a unified model. Our experiments on challenging videos demonstrate that the proposed approach can discover different types of topical objects despite variations in scale, view-point, color and lighting changes, or even partial occlusions. The efficacy of the co-occurrence prior is clearly demonstrated when compared with topic models without such priors.

Dynamic Socialized Gaussian Process Models for Human Behavior Prediction in a Health Social Network

PubMed Central

Shen, Yelong; Phan, NhatHai; Xiao, Xiao; Jin, Ruoming; Sun, Junfeng; Piniewski, Brigitte; Kil, David; Dou, Dejing

2016-01-01

Modeling and predicting human behaviors, such as the level and intensity of physical activity, is a key to preventing the cascade of obesity and helping spread healthy behaviors in a social network. In our conference paper, we have developed a social influence model, named Socialized Gaussian Process (SGP), for socialized human behavior modeling. Instead of explicitly modeling social influence as individuals' behaviors influenced by their friends' previous behaviors, SGP models the dynamic social correlation as the result of social influence. The SGP model naturally incorporates personal behavior factor and social correlation factor (i.e., the homophily principle: Friends tend to perform similar behaviors) into a unified model. And it models the social influence factor (i.e., an individual's behavior can be affected by his/her friends) implicitly in dynamic social correlation schemes. The detailed experimental evaluation has shown the SGP model achieves better prediction accuracy compared with most of baseline methods. However, a Socialized Random Forest model may perform better at the beginning compared with the SGP model. One of the main reasons is the dynamic social correlation function is purely based on the users' sequential behaviors without considering other physical activity-related features. To address this issue, we further propose a novel “multi-feature SGP model” (mfSGP) which improves the SGP model by using multiple physical activity-related features in the dynamic social correlation learning. Extensive experimental results illustrate that the mfSGP model clearly outperforms all other models in terms of prediction accuracy and running time. PMID:27746515

What are the Starting Points? Evaluating Base-Year Assumptions in the Asian Modeling Exercise

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

Chaturvedi, Vaibhav; Waldhoff, Stephanie; Clarke, Leon E.

2012-12-01

A common feature of model inter-comparison efforts is that the base year numbers for important parameters such as population and GDP can differ substantially across models. This paper explores the sources and implications of this variation in Asian countries across the models participating in the Asian Modeling Exercise (AME). Because the models do not all have a common base year, each team was required to provide data for 2005 for comparison purposes. This paper compares the year 2005 information for different models, noting the degree of variation in important parameters, including population, GDP, primary energy, electricity, and CO2 emissions. Itmore » then explores the difference in these key parameters across different sources of base-year information. The analysis confirms that the sources provide different values for many key parameters. This variation across data sources and additional reasons why models might provide different base-year numbers, including differences in regional definitions, differences in model base year, and differences in GDP transformation methodologies, are then discussed in the context of the AME scenarios. Finally, the paper explores the implications of base-year variation on long-term model results.« less

Development of a Stochastically-driven, Forward Predictive Performance Model for PEMFCs

NASA Astrophysics Data System (ADS)

Harvey, David Benjamin Paul

A one-dimensional multi-scale coupled, transient, and mechanistic performance model for a PEMFC membrane electrode assembly has been developed. The model explicitly includes each of the 5 layers within a membrane electrode assembly and solves for the transport of charge, heat, mass, species, dissolved water, and liquid water. Key features of the model include the use of a multi-step implementation of the HOR reaction on the anode, agglomerate catalyst sub-models for both the anode and cathode catalyst layers, a unique approach that links the composition of the catalyst layer to key properties within the agglomerate model and the implementation of a stochastic input-based approach for component material properties. The model employs a new methodology for validation using statistically varying input parameters and statistically-based experimental performance data; this model represents the first stochastic input driven unit cell performance model. The stochastic input driven performance model was used to identify optimal ionomer content within the cathode catalyst layer, demonstrate the role of material variation in potential low performing MEA materials, provide explanation for the performance of low-Pt loaded MEAs, and investigate the validity of transient-sweep experimental diagnostic methods.

A compilation and analysis of helicopter handling qualities data. Volume 2: Data analysis

NASA Technical Reports Server (NTRS)

Heffley, R. K.

1979-01-01

A compilation and an analysis of helicopter handling qualities data are presented. Multiloop manual control methods are used to analyze the descriptive data, stability derivatives, and transfer functions for a six degrees of freedom, quasi static model. A compensatory loop structure is applied to coupled longitudinal, lateral and directional equations in such a way that key handling qualities features are examined directly.

Modelling the Preferences of Students for Alternative Assignment Designs Using the Discrete Choice Experiment Methodology

ERIC Educational Resources Information Center

Kennelly, Brendan; Flannery, Darragh; Considine, John; Doherty, Edel; Hynes, Stephen

2014-01-01

This paper outlines how a discrete choice experiment (DCE) can be used to learn more about how students are willing to trade off various features of assignments such as the nature and timing of feedback and the method used to submit assignments. A DCE identifies plausible levels of the key attributes of a good or service and then presents the…

PDBsum new things.

PubMed

Laskowski, Roman A

2009-01-01

PDBsum (http://www.ebi.ac.uk/pdbsum) provides summary information about each experimentally determined structural model in the Protein Data Bank (PDB). Here we describe some of its most recent features, including figures from the structure's key reference, citation data, Pfam domain diagrams, topology diagrams and protein-protein interactions. Furthermore, it now accepts users' own PDB format files and generates a private set of analyses for each uploaded structure.

Modeling Expert Opinion: Likelihoods under Incomplete Probabilistic Specification

DTIC Science & Technology

1992-12-09

regarding points per game for participants in the 1991 NBA championship basketball series. 2 1. Introduction Expert opinion is often sought with regard to...for the participants in the 1991 NBA championship basketball series. We present a synthesis of this opinion. The key features of our approach are...applied to opinion collected regarding points per game for participants in the 1991 NBA championship basketball series.

XFEM-based modeling of successive resections for preoperative image updating

NASA Astrophysics Data System (ADS)

Vigneron, Lara M.; Robe, Pierre A.; Warfield, Simon K.; Verly, Jacques G.

2006-03-01

We present a new method for modeling organ deformations due to successive resections. We use a biomechanical model of the organ, compute its volume-displacement solution based on the eXtended Finite Element Method (XFEM). The key feature of XFEM is that material discontinuities induced by every new resection can be handled without remeshing or mesh adaptation, as would be required by the conventional Finite Element Method (FEM). We focus on the application of preoperative image updating for image-guided surgery. Proof-of-concept demonstrations are shown for synthetic and real data in the context of neurosurgery.

Naturally-Occurring Canine Invasive Urothelial Carcinoma: A Model for Emerging Therapies

PubMed Central

Sommer, Breann C.; Dhawan, Deepika; Ratliff, Timothy L.; Knapp, Deborah W.

2018-01-01

The development of targeted therapies and the resurgence of immunotherapy offer enormous potential to dramatically improve the outlook for patients with invasive urothelial carcinoma (InvUC). Optimization of these therapies, however, is crucial as only a minority of patients achieve dramatic remission, and toxicities are common. With the complexities of the therapies, and the growing list of possible drug combinations to test, highly relevant animal models are needed to assess and select the most promising approaches to carry forward into human trials. The animal model(s) should possess key features that dictate success or failure of cancer drugs in humans including tumor heterogeneity, genetic-epigenetic crosstalk, immune cell responsiveness, invasive and metastatic behavior, and molecular subtypes (e.g., luminal, basal). While it may not be possible to create these collective features in experimental models, these features are present in naturally-occurring InvUC in pet dogs. Naturally occurring canine InvUC closely mimics muscle-invasive bladder cancer in humans in regards to cellular and molecular features, molecular subtypes, biological behavior (sites and frequency of metastasis), and response to therapy. Clinical treatment trials in pet dogs with InvUC are considered a win-win scenario; the individual dog benefits from effective treatment, the results are expected to help other dogs, and the findings are expected to translate to better treatment outcomes in humans. This review will provide an overview of canine InvUC, the similarities to the human condition, and the potential for dogs with InvUC to serve as a model to predict the outcomes of targeted therapy and immunotherapy in humans. PMID:29732386

Naturally-Occurring Canine Invasive Urothelial Carcinoma: A Model for Emerging Therapies.

PubMed

Sommer, Breann C; Dhawan, Deepika; Ratliff, Timothy L; Knapp, Deborah W

2018-04-26

The development of targeted therapies and the resurgence of immunotherapy offer enormous potential to dramatically improve the outlook for patients with invasive urothelial carcinoma (InvUC). Optimization of these therapies, however, is crucial as only a minority of patients achieve dramatic remission, and toxicities are common. With the complexities of the therapies, and the growing list of possible drug combinations to test, highly relevant animal models are needed to assess and select the most promising approaches to carry forward into human trials. The animal model(s) should possess key features that dictate success or failure of cancer drugs in humans including tumor heterogeneity, genetic-epigenetic crosstalk, immune cell responsiveness, invasive and metastatic behavior, and molecular subtypes (e.g., luminal, basal). While it may not be possible to create these collective features in experimental models, these features are present in naturally-occurring InvUC in pet dogs. Naturally occurring canine InvUC closely mimics muscle-invasive bladder cancer in humans in regards to cellular and molecular features, molecular subtypes, biological behavior (sites and frequency of metastasis), and response to therapy. Clinical treatment trials in pet dogs with InvUC are considered a win-win scenario; the individual dog benefits from effective treatment, the results are expected to help other dogs, and the findings are expected to translate to better treatment outcomes in humans. This review will provide an overview of canine InvUC, the similarities to the human condition, and the potential for dogs with InvUC to serve as a model to predict the outcomes of targeted therapy and immunotherapy in humans.

Study for Updated Gout Classification Criteria (SUGAR): identification of features to classify gout

PubMed Central

Taylor, William J.; Fransen, Jaap; Jansen, Tim L.; Dalbeth, Nicola; Schumacher, H. Ralph; Brown, Melanie; Louthrenoo, Worawit; Vazquez-Mellado, Janitzia; Eliseev, Maxim; McCarthy, Geraldine; Stamp, Lisa K.; Perez-Ruiz, Fernando; Sivera, Francisca; Ea, Hang-Korng; Gerritsen, Martijn; Scire, Carlo; Cavagna, Lorenzo; Lin, Chingtsai; Chou, Yin-Yi; Tausche, Anne-Kathrin; Vargas-Santos, Ana Beatriz; Janssen, Matthijs; Chen, Jiunn-Horng; Slot, Ole; Cimmino, Marco A.; Uhlig, Till; Neogi, Tuhina

2015-01-01

Objective To determine which clinical, laboratory and imaging features most accurately distinguished gout from non-gout. Methods A cross-sectional study of consecutive rheumatology clinic patients with at least one swollen joint or subcutaneous tophus. Gout was defined by synovial fluid or tophus aspirate microscopy by certified examiners in all patients. The sample was randomly divided into a model development (2/3) and test sample (1/3). Univariate and multivariate association between clinical features and MSU-defined gout was determined using logistic regression modelling. Shrinkage of regression weights was performed to prevent over-fitting of the final model. Latent class analysis was conducted to identify patterns of joint involvement. Results In total, 983 patients were included. Gout was present in 509 (52%). In the development sample (n=653), these features were selected for the final model (multivariate OR) joint erythema (2.13), difficulty walking (7.34), time to maximal pain < 24 hours (1.32), resolution by 2 weeks (3.58), tophus (7.29), MTP1 ever involved (2.30), location of currently tender joints: Other foot/ankle (2.28), MTP1 (2.82), serum urate level > 6 mg/dl (0.36 mmol/l) (3.35), ultrasound double contour sign (7.23), Xray erosion or cyst (2.49). The final model performed adequately in the test set with no evidence of misfit, high discrimination and predictive ability. MTP1 involvement was the most common joint pattern (39.4%) in gout cases. Conclusion Ten key discriminating features have been identified for further evaluation for new gout classification criteria. Ultrasound findings and degree of uricemia add discriminating value, and will significantly contribute to more accurate classification criteria. PMID:25777045

Statistical mechanics of unsupervised feature learning in a restricted Boltzmann machine with binary synapses

NASA Astrophysics Data System (ADS)

Huang, Haiping

2017-05-01

Revealing hidden features in unlabeled data is called unsupervised feature learning, which plays an important role in pretraining a deep neural network. Here we provide a statistical mechanics analysis of the unsupervised learning in a restricted Boltzmann machine with binary synapses. A message passing equation to infer the hidden feature is derived, and furthermore, variants of this equation are analyzed. A statistical analysis by replica theory describes the thermodynamic properties of the model. Our analysis confirms an entropy crisis preceding the non-convergence of the message passing equation, suggesting a discontinuous phase transition as a key characteristic of the restricted Boltzmann machine. Continuous phase transition is also confirmed depending on the embedded feature strength in the data. The mean-field result under the replica symmetric assumption agrees with that obtained by running message passing algorithms on single instances of finite sizes. Interestingly, in an approximate Hopfield model, the entropy crisis is absent, and a continuous phase transition is observed instead. We also develop an iterative equation to infer the hyper-parameter (temperature) hidden in the data, which in physics corresponds to iteratively imposing Nishimori condition. Our study provides insights towards understanding the thermodynamic properties of the restricted Boltzmann machine learning, and moreover important theoretical basis to build simplified deep networks.

Classification of SD-OCT volumes for DME detection: an anomaly detection approach

NASA Astrophysics Data System (ADS)

Sankar, S.; Sidibé, D.; Cheung, Y.; Wong, T. Y.; Lamoureux, E.; Milea, D.; Meriaudeau, F.

2016-03-01

Diabetic Macular Edema (DME) is the leading cause of blindness amongst diabetic patients worldwide. It is characterized by accumulation of water molecules in the macula leading to swelling. Early detection of the disease helps prevent further loss of vision. Naturally, automated detection of DME from Optical Coherence Tomography (OCT) volumes plays a key role. To this end, a pipeline for detecting DME diseases in OCT volumes is proposed in this paper. The method is based on anomaly detection using Gaussian Mixture Model (GMM). It starts with pre-processing the B-scans by resizing, flattening, filtering and extracting features from them. Both intensity and Local Binary Pattern (LBP) features are considered. The dimensionality of the extracted features is reduced using PCA. As the last stage, a GMM is fitted with features from normal volumes. During testing, features extracted from the test volume are evaluated with the fitted model for anomaly and classification is made based on the number of B-scans detected as outliers. The proposed method is tested on two OCT datasets achieving a sensitivity and a specificity of 80% and 93% on the first dataset, and 100% and 80% on the second one. Moreover, experiments show that the proposed method achieves better classification performances than other recently published works.

Evolutionary optimization of radial basis function classifiers for data mining applications.

PubMed

Buchtala, Oliver; Klimek, Manuel; Sick, Bernhard

2005-10-01

In many data mining applications that address classification problems, feature and model selection are considered as key tasks. That is, appropriate input features of the classifier must be selected from a given (and often large) set of possible features and structure parameters of the classifier must be adapted with respect to these features and a given data set. This paper describes an evolutionary algorithm (EA) that performs feature and model selection simultaneously for radial basis function (RBF) classifiers. In order to reduce the optimization effort, various techniques are integrated that accelerate and improve the EA significantly: hybrid training of RBF networks, lazy evaluation, consideration of soft constraints by means of penalty terms, and temperature-based adaptive control of the EA. The feasibility and the benefits of the approach are demonstrated by means of four data mining problems: intrusion detection in computer networks, biometric signature verification, customer acquisition with direct marketing methods, and optimization of chemical production processes. It is shown that, compared to earlier EA-based RBF optimization techniques, the runtime is reduced by up to 99% while error rates are lowered by up to 86%, depending on the application. The algorithm is independent of specific applications so that many ideas and solutions can be transferred to other classifier paradigms.

Cell-type-specific modelling of intracellular calcium signalling: a urothelial cell model.

PubMed

Appleby, Peter A; Shabir, Saqib; Southgate, Jennifer; Walker, Dawn

2013-09-06

Calcium signalling plays a central role in regulating a wide variety of cell processes. A number of calcium signalling models exist in the literature that are capable of reproducing a variety of experimentally observed calcium transients. These models have been used to examine in more detail the mechanisms underlying calcium transients, but very rarely has a model been directly linked to a particular cell type and experimentally verified. It is important to show that this can be achieved within the general theoretical framework adopted by these models. Here, we develop a framework designed specifically for modelling cytosolic calcium transients in urothelial cells. Where possible, we draw upon existing calcium signalling models, integrating descriptions of components known to be important in this cell type from a number of studies in the literature. We then add descriptions of several additional pathways that play a specific role in urothelial cell signalling, including an explicit ionic influx term and an active pumping mechanism that drives the cytosolic calcium concentration to a target equilibrium. The resulting one-pool model of endoplasmic reticulum (ER)-dependent calcium signalling relates the cytosolic, extracellular and ER calcium concentrations and can generate a wide range of calcium transients, including spikes, bursts, oscillations and sustained elevations in the cytosolic calcium concentration. Using single-variate robustness and multivariate sensitivity analyses, we quantify how varying each of the parameters of the model leads to changes in key features of the calcium transient, such as initial peak amplitude and the frequency of bursting or spiking, and in the transitions between bursting- and plateau-dominated modes. We also show that, novel to our urothelial cell model, the ionic and purinergic P2Y pathways make distinct contributions to the calcium transient. We then validate the model using human bladder epithelial cells grown in monolayer cell culture and show that the model robustly captures the key features of the experimental data in a way that is not possible using more generic calcium models from the literature.

Extracting Tree Height from Repeat-Pass PolInSAR Data : Experiments with JPL and ESA Airborne Systems

NASA Technical Reports Server (NTRS)

Lavalle, Marco; Ahmed, Razi; Neumann, Maxim; Hensley, Scott

2013-01-01

In this paper we present our latest developments and experiments with the random-motion-over-ground (RMoG) model used to extract canopy height and other important forest parameters from repeat-pass polarimetricinterferometric SAR (Pol-InSAR) data. More specifically, we summarize the key features of the RMoG model in contrast with the random-volume-over-ground (RVoG) model, describe in detail a possible inversion scheme for the RMoG model and illustrate the results of the RMoG inversion using airborne data collected by the Jet Propulsion Laboratory (JPL) and the European Space Agency (ESA).

Space Shuttle propulsion performance reconstruction from flight data

NASA Technical Reports Server (NTRS)

Rogers, Robert M.

1989-01-01

The aplication of extended Kalman filtering to estimating Space Shuttle Solid Rocket Booster (SRB) performance, specific impulse, from flight data in a post-flight processing computer program. The flight data used includes inertial platform acceleration, SRB head pressure, and ground based radar tracking data. The key feature in this application is the model used for the SRBs, which represents a reference quasi-static internal ballistics model normalized to the propellant burn depth. Dynamic states of mass overboard and propellant burn depth are included in the filter model to account for real-time deviations from the reference model used. Aerodynamic, plume, wind and main engine uncertainties are included.

Overview of Millimeter Wave Communications for Fifth-Generation (5G) Wireless Networks—With a Focus on Propagation Models

NASA Astrophysics Data System (ADS)

Rappaport, Theodore S.; Xing, Yunchou; MacCartney, George R.; Molisch, Andreas F.; Mellios, Evangelos; Zhang, Jianhua

2017-12-01

This paper provides an overview of the features of fifth generation (5G) wireless communication systems now being developed for use in the millimeter wave (mmWave) frequency bands. Early results and key concepts of 5G networks are presented, and the channel modeling efforts of many international groups for both licensed and unlicensed applications are described here. Propagation parameters and channel models for understanding mmWave propagation, such as line-of-sight (LOS) probabilities, large-scale path loss, and building penetration loss, as modeled by various standardization bodies, are compared over the 0.5-100 GHz range.

Digital Modeling and Testing Research on Digging Mechanism of Deep Rootstalk Crops

NASA Astrophysics Data System (ADS)

Yang, Chuanhua; Xu, Ma; Wang, Zhoufei; Yang, Wenwu; Liao, Xinglong

The digital model of the laboratory bench parts of digging deep rootstalk crops were established through adopting the parametric model technology based on feature. The virtual assembly of the laboratory bench of digging deep rootstalk crops was done and the digital model of the laboratory bench parts of digging deep rootstalk crops was gained. The vibrospade, which is the key part of the laboratory bench of digging deep rootstalk crops was simulated and the movement parametric curves of spear on the vibrospade were obtained. The results show that the spear was accorded with design requirements. It is propitious to the deep rootstalk.

Key Factors Influencing the Energy Absorption of Dual-Phase Steels: Multiscale Material Model Approach and Microstructural Optimization

NASA Astrophysics Data System (ADS)

Belgasam, Tarek M.; Zbib, Hussein M.

2018-06-01

The increase in use of dual-phase (DP) steel grades by vehicle manufacturers to enhance crash resistance and reduce body car weight requires the development of a clear understanding of the effect of various microstructural parameters on the energy absorption in these materials. Accordingly, DP steelmakers are interested in predicting the effect of various microscopic factors as well as optimizing microstructural properties for application in crash-relevant components of vehicle bodies. This study presents a microstructure-based approach using a multiscale material and structure model. In this approach, Digimat and LS-DYNA software were coupled and employed to provide a full micro-macro multiscale material model, which is then used to simulate tensile tests. Microstructures with varied ferrite grain sizes, martensite volume fractions, and carbon content in DP steels were studied. The impact of these microstructural features at different strain rates on energy absorption characteristics of DP steels is investigated numerically using an elasto-viscoplastic constitutive model. The model is implemented in a multiscale finite-element framework. A comprehensive statistical parametric study using response surface methodology is performed to determine the optimum microstructural features for a required tensile toughness at different strain rates. The simulation results are validated using experimental data found in the literature. The developed methodology proved to be effective for investigating the influence and interaction of key microscopic properties on the energy absorption characteristics of DP steels. Furthermore, it is shown that this method can be used to identify optimum microstructural conditions at different strain-rate conditions.

Key Factors Influencing the Energy Absorption of Dual-Phase Steels: Multiscale Material Model Approach and Microstructural Optimization

NASA Astrophysics Data System (ADS)

Belgasam, Tarek M.; Zbib, Hussein M.

2018-03-01

The increase in use of dual-phase (DP) steel grades by vehicle manufacturers to enhance crash resistance and reduce body car weight requires the development of a clear understanding of the effect of various microstructural parameters on the energy absorption in these materials. Accordingly, DP steelmakers are interested in predicting the effect of various microscopic factors as well as optimizing microstructural properties for application in crash-relevant components of vehicle bodies. This study presents a microstructure-based approach using a multiscale material and structure model. In this approach, Digimat and LS-DYNA software were coupled and employed to provide a full micro-macro multiscale material model, which is then used to simulate tensile tests. Microstructures with varied ferrite grain sizes, martensite volume fractions, and carbon content in DP steels were studied. The impact of these microstructural features at different strain rates on energy absorption characteristics of DP steels is investigated numerically using an elasto-viscoplastic constitutive model. The model is implemented in a multiscale finite-element framework. A comprehensive statistical parametric study using response surface methodology is performed to determine the optimum microstructural features for a required tensile toughness at different strain rates. The simulation results are validated using experimental data found in the literature. The developed methodology proved to be effective for investigating the influence and interaction of key microscopic properties on the energy absorption characteristics of DP steels. Furthermore, it is shown that this method can be used to identify optimum microstructural conditions at different strain-rate conditions.

Interictal epileptiform discharge characteristics underlying expert interrater agreement.

PubMed

Bagheri, Elham; Dauwels, Justin; Dean, Brian C; Waters, Chad G; Westover, M Brandon; Halford, Jonathan J

2017-10-01

The presence of interictal epileptiform discharges (IED) in the electroencephalogram (EEG) is a key finding in the medical workup of a patient with suspected epilepsy. However, inter-rater agreement (IRA) regarding the presence of IED is imperfect, leading to incorrect and delayed diagnoses. An improved understanding of which IED attributes mediate expert IRA might help in developing automatic methods for IED detection able to emulate the abilities of experts. Therefore, using a set of IED scored by a large number of experts, we set out to determine which attributes of IED predict expert agreement regarding the presence of IED. IED were annotated on a 5-point scale by 18 clinical neurophysiologists within 200 30-s EEG segments from recordings of 200 patients. 5538 signal analysis features were extracted from the waveforms, including wavelet coefficients, morphological features, signal energy, nonlinear energy operator response, electrode location, and spectrogram features. Feature selection was performed by applying elastic net regression and support vector regression (SVR) was applied to predict expert opinion, with and without the feature selection procedure and with and without several types of signal normalization. Multiple types of features were useful for predicting expert annotations, but particular types of wavelet features performed best. Local EEG normalization also enhanced best model performance. As the size of the group of EEGers used to train the models was increased, the performance of the models leveled off at a group size of around 11. The features that best predict inter-rater agreement among experts regarding the presence of IED are wavelet features, using locally standardized EEG. Our models for predicting expert opinion based on EEGer's scores perform best with a large group of EEGers (more than 10). By examining a large group of EEG signal analysis features we found that wavelet features with certain wavelet basis functions performed best to identify IEDs. Local normalization also improves predictability, suggesting the importance of IED morphology over amplitude-based features. Although most IED detection studies in the past have used opinion from three or fewer experts, our study suggests a "wisdom of the crowd" effect, such that pooling over a larger number of expert opinions produces a better correlation between expert opinion and objectively quantifiable features of the EEG. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Analysis of temporal transcription expression profiles reveal links between protein function and developmental stages of Drosophila melanogaster.

PubMed

Wan, Cen; Lees, Jonathan G; Minneci, Federico; Orengo, Christine A; Jones, David T

2017-10-01

Accurate gene or protein function prediction is a key challenge in the post-genome era. Most current methods perform well on molecular function prediction, but struggle to provide useful annotations relating to biological process functions due to the limited power of sequence-based features in that functional domain. In this work, we systematically evaluate the predictive power of temporal transcription expression profiles for protein function prediction in Drosophila melanogaster. Our results show significantly better performance on predicting protein function when transcription expression profile-based features are integrated with sequence-derived features, compared with the sequence-derived features alone. We also observe that the combination of expression-based and sequence-based features leads to further improvement of accuracy on predicting all three domains of gene function. Based on the optimal feature combinations, we then propose a novel multi-classifier-based function prediction method for Drosophila melanogaster proteins, FFPred-fly+. Interpreting our machine learning models also allows us to identify some of the underlying links between biological processes and developmental stages of Drosophila melanogaster.

Ordinal measures for iris recognition.

PubMed

Sun, Zhenan; Tan, Tieniu

2009-12-01

Images of a human iris contain rich texture information useful for identity authentication. A key and still open issue in iris recognition is how best to represent such textural information using a compact set of features (iris features). In this paper, we propose using ordinal measures for iris feature representation with the objective of characterizing qualitative relationships between iris regions rather than precise measurements of iris image structures. Such a representation may lose some image-specific information, but it achieves a good trade-off between distinctiveness and robustness. We show that ordinal measures are intrinsic features of iris patterns and largely invariant to illumination changes. Moreover, compactness and low computational complexity of ordinal measures enable highly efficient iris recognition. Ordinal measures are a general concept useful for image analysis and many variants can be derived for ordinal feature extraction. In this paper, we develop multilobe differential filters to compute ordinal measures with flexible intralobe and interlobe parameters such as location, scale, orientation, and distance. Experimental results on three public iris image databases demonstrate the effectiveness of the proposed ordinal feature models.

Polycystic ovary syndrome: perceptions and attitudes of women and primary health care physicians on features of PCOS and renaming the syndrome.

PubMed

Teede, Helena; Gibson-Helm, Melanie; Norman, Robert J; Boyle, Jacqueline

2014-01-01

Polycystic ovary syndrome (PCOS) is an under-recognized, common, and complex endocrinopathy. The name PCOS is a misnomer, and there have been calls for a change to reflect the broader clinical syndrome. The aim of the study was to determine perceptions held by women and primary health care physicians around key clinical features of PCOS and attitudes toward current and alternative names for the syndrome. We conducted a cross-sectional study utilizing a devised questionnaire. Participants were recruited throughout Australia via professional associations, women's health organizations, and a PCOS support group. Fifty-seven women with PCOS and 105 primary care physicians participated in the study. Perceptions of key clinical PCOS features and attitudes toward current and alternative syndrome names were investigated. Irregular periods were identified as a key clinical feature of PCOS by 86% of the women with PCOS and 90% of the primary care physicians. In both groups, 60% also identified hormone imbalance as a key feature. Among women with PCOS, 47% incorrectly identified ovarian cysts as key, 48% felt the current name is confusing, and 51% supported a change. Most primary care physicians agreed that the name is confusing (74%) and needs changing (81%); however, opinions on specific alternative names were divided. The name "polycystic ovary syndrome" is perceived as confusing, and there is general support for a change to reflect the broader clinical syndrome. Engagement of primary health care physicians and consumers is strongly recommended to ensure that an alternative name enhances understanding and recognition of the syndrome and its complex features.

Examining sustainability in a hospital setting: case of smoking cessation.

PubMed

Campbell, Sharon; Pieters, Karen; Mullen, Kerri-Anne; Reece, Robin; Reid, Robert D

2011-09-14

The Ottawa Model of Smoking Cessation (OMSC) is a hospital-based smoking cessation program that is expanding across Canada. While the short-term effectiveness of hospital cessation programs has been documented, less is known about long-term sustainability. The purpose of this exploratory study was to understand how hospitals using the OMSC were addressing sustainability and determine if there were critical factors or issues that should be addressed as the program expanded. Six hospitals that differed on OMSC program activities (identify and document smokers, advise quitting, provide medication, and offer follow-up) were intentionally selected, and two key informants per hospital were interviewed using a semi-structured interview guide. Key informants were asked to reflect on the initial decision to implement the OMSC, the current implementation process, and perceived sustainability of the program. Qualitative analysis of the interview transcripts was conducted and themes related to problem definition, stakeholder influence, and program features emerged. Sustainability was operationalized as higher performance of OMSC activities than at baseline. Factors identified in the literature as important for sustainability, such as program design, differences in implementation, organizational characteristics, and the community environment did not explain differences in program sustainability. Instead, key informants identified factors that reflected the interaction between how the health problem was defined by stakeholders, how priorities and concerns were addressed, features of the program itself, and fit within the hospital context and resources as being influential to the sustainability of the program. Applying a sustainability model to a hospital smoking cessation program allowed for an examination of how decisions made during implementation may impact sustainability. Examining these factors during implementation may provide insight into issues affecting program sustainability, and foster development of a sustainability plan. Based on this study, we suggest that sustainability plans should focus on enhancing interactions between the health problem, program features, and stakeholder influence.

Examining sustainability in a hospital setting: Case of smoking cessation

PubMed Central

2011-01-01

Background The Ottawa Model of Smoking Cessation (OMSC) is a hospital-based smoking cessation program that is expanding across Canada. While the short-term effectiveness of hospital cessation programs has been documented, less is known about long-term sustainability. The purpose of this exploratory study was to understand how hospitals using the OMSC were addressing sustainability and determine if there were critical factors or issues that should be addressed as the program expanded. Methods Six hospitals that differed on OMSC program activities (identify and document smokers, advise quitting, provide medication, and offer follow-up) were intentionally selected, and two key informants per hospital were interviewed using a semi-structured interview guide. Key informants were asked to reflect on the initial decision to implement the OMSC, the current implementation process, and perceived sustainability of the program. Qualitative analysis of the interview transcripts was conducted and themes related to problem definition, stakeholder influence, and program features emerged. Results Sustainability was operationalized as higher performance of OMSC activities than at baseline. Factors identified in the literature as important for sustainability, such as program design, differences in implementation, organizational characteristics, and the community environment did not explain differences in program sustainability. Instead, key informants identified factors that reflected the interaction between how the health problem was defined by stakeholders, how priorities and concerns were addressed, features of the program itself, and fit within the hospital context and resources as being influential to the sustainability of the program. Conclusions Applying a sustainability model to a hospital smoking cessation program allowed for an examination of how decisions made during implementation may impact sustainability. Examining these factors during implementation may provide insight into issues affecting program sustainability, and foster development of a sustainability plan. Based on this study, we suggest that sustainability plans should focus on enhancing interactions between the health problem, program features, and stakeholder influence. PMID:21917156

A comparative study of sequence- and structure-based features of small RNAs and other RNAs of bacteria.

PubMed

Barik, Amita; Das, Santasabuj

2018-01-02

Small RNAs (sRNAs) in bacteria have emerged as key players in transcriptional and post-transcriptional regulation of gene expression. Here, we present a statistical analysis of different sequence- and structure-related features of bacterial sRNAs to identify the descriptors that could discriminate sRNAs from other bacterial RNAs. We investigated a comprehensive and heterogeneous collection of 816 sRNAs, identified by northern blotting across 33 bacterial species and compared their various features with other classes of bacterial RNAs, such as tRNAs, rRNAs and mRNAs. We observed that sRNAs differed significantly from the rest with respect to G+C composition, normalized minimum free energy of folding, motif frequency and several RNA-folding parameters like base-pairing propensity, Shannon entropy and base-pair distance. Based on the selected features, we developed a predictive model using Random Forests (RF) method to classify the above four classes of RNAs. Our model displayed an overall predictive accuracy of 89.5%. These findings would help to differentiate bacterial sRNAs from other RNAs and further promote prediction of novel sRNAs in different bacterial species.

A survey of hybrid Unmanned Aerial Vehicles

NASA Astrophysics Data System (ADS)

Saeed, Adnan S.; Younes, Ahmad Bani; Cai, Chenxiao; Cai, Guowei

2018-04-01

This article presents a comprehensive overview on the recent advances of miniature hybrid Unmanned Aerial Vehicles (UAVs). For now, two conventional types, i.e., fixed-wing UAV and Vertical Takeoff and Landing (VTOL) UAV, dominate the miniature UAVs. Each type has its own inherent limitations on flexibility, payload, flight range, cruising speed, takeoff and landing requirements and endurance. Enhanced popularity and interest are recently gained by the newer type, named hybrid UAV, that integrates the beneficial features of both conventional ones. In this survey paper, a systematic categorization method for the hybrid UAV's platform designs is introduced, first presenting the technical features and representative examples. Next, the hybrid UAV's flight dynamics model and flight control strategies are explained addressing several representative modeling and control work. In addition, key observations, existing challenges and conclusive remarks based on the conducted review are discussed accordingly.

Design and application of BIM based digital sand table for construction management

NASA Astrophysics Data System (ADS)

Fuquan, JI; Jianqiang, LI; Weijia, LIU

2018-05-01

This paper explores the design and application of BIM based digital sand table for construction management. Aiming at the demands and features of construction management plan for bridge and tunnel engineering, the key functional features of digital sand table should include three-dimensional GIS, model navigation, virtual simulation, information layers, and data exchange, etc. That involving the technology of 3D visualization and 4D virtual simulation of BIM, breakdown structure of BIM model and project data, multi-dimensional information layers, and multi-source data acquisition and interaction. Totally, the digital sand table is a visual and virtual engineering information integrated terminal, under the unified data standard system. Also, the applications shall contain visual constructing scheme, virtual constructing schedule, and monitoring of construction, etc. Finally, the applicability of several basic software to the digital sand table is analyzed.

Novel unimorph deformable mirror for space applications

NASA Astrophysics Data System (ADS)

Verpoort, Sven; Rausch, Peter; Wittrock, Ulrich

2017-11-01

We have developed a new type of unimorph deformable mirror, designed to correct for low-order Zernike modes. The mirror has a clear optical aperture of 50 mm combined with large peak-to-valley Zernike amplitudes of up to 35 μm. Newly developed fabrication processes allow the use of prefabricated super-polished and coated glass substrates. The mirror's unique features suggest the use in several astronomical applications like the precompensation of atmospheric aberrations seen by laser beacons and the use in woofer-tweeter systems. Additionally, the design enables an efficient correction of the inevitable wavefront error imposed by the floppy structure of primary mirrors in future large space-based telescopes. We have modeled the mirror by using analytical as well as finite element models. We will present design, key features and manufacturing steps of the deformable mirror.

From big data to rich data: The key features of athlete wheelchair mobility performance.

PubMed

van der Slikke, R M A; Berger, M A M; Bregman, D J J; Veeger, H E J

2016-10-03

Quantitative assessment of an athlete׳s individual wheelchair mobility performance is one prerequisite needed to evaluate game performance, improve wheelchair settings and optimize training routines. Inertial Measurement Unit (IMU) based methods can be used to perform such quantitative assessment, providing a large number of kinematic data. The goal of this research was to reduce that large amount of data to a set of key features best describing wheelchair mobility performance in match play and present them in meaningful way for both scientists and athletes. To test the discriminative power, wheelchair mobility characteristics of athletes with different performance levels were compared. The wheelchair kinematics of 29 (inter-)national level athletes were measured during a match using three inertial sensors mounted on the wheelchair. Principal component analysis was used to reduce 22 kinematic outcomes to a set of six outcomes regarding linear and rotational movement; speed and acceleration; average and best performance. In addition, it was explored whether groups of athletes with known performance differences based on their impairment classification also differed with respect to these key outcomes using univariate general linear models. For all six key outcomes classification showed to be a significant factor (p<0.05). We composed a set of six key kinematic outcomes that accurately describe wheelchair mobility performance in match play. The key kinematic outcomes were displayed in an easy to interpret way, usable for athletes, coaches and scientists. This standardized representation enables comparison of different wheelchair sports regarding wheelchair mobility, but also evaluation at the level of an individual athlete. By this means, the tool could enhance further development of wheelchair sports in general. Copyright © 2016 Elsevier Ltd. All rights reserved.

Activity-based funding model provides foundation for province-wide best practices in renal care.

PubMed

Levin, Adeera; Lo, Clifford; Noel, Kevin; Djurdjev, Ogjnenka; Amano, Erlyn C

2013-01-01

British Columbia has a unique funding model for renal care in Canada. Patient care is delivered through six health authorities, while funding is administered by the Provincial Renal Agency using an activity-based funding model. The model allocates funding based on a schedule of costs for every element of renal care, excluding physician fees. Accountability, transparency of allocation and tracking of outcomes are key features that ensure successful implementation. The model supports province-wide best practices and equitable care and fosters innovation. Since its introduction, the outpatient renal services budget has grown less than the population, while maintaining or improving clinical outcomes. Copyright © 2013 Longwoods Publishing.

Evaluation of Image Segmentation and Object Recognition Algorithms for Image Parsing

DTIC Science & Technology

2013-09-01

generation of the features from the key points. OpenCV uses Euclidean distance to match the key points and has the option to use Manhattan distance...feature vector includes polarity and intensity information. Final step is matching the key points. In OpenCV , Euclidean distance or Manhattan...the code below is one way and OpenCV offers the function radiusMatch (a pair must have a distance less than a given maximum distance). OpenCV’s

Mouse strain and injection site are crucial for detecting linked suppression in transplant recipients by trans-vivo DTH assay.

PubMed

Burlingham, W J; Jankowska-Gan, E

2007-02-01

Chemokine-driven accumulation of lymphocytes, mononuclear and polymorphonuclear proinflammatory cells in antigenic tissue sites is a key feature of several types of T-cell-dependent autoimmunity and transplant rejection pathology. It is now clear that the immune system expends considerable energy to control this process, exemplified by the sequential layers of regulatory cell input, both innate and adaptive, designed to prevent a classical Type IV or 'delayed-type' hypersensitivity (DTH) reaction from occurring in the visual field of the eye. Yet, despite an abundance of in vitro assays currently available to the human T-cell immunologist, none of them adequately models the human DTH response and its various control features. The theme of this article is that it is relatively easy to model the effector side of the human DTH response with xenogeneic adoptive transfer models. However, we show that in order to detect inhibition of a recall DTH in response to colocalized donor antigen (linked suppression)--a characteristic feature of peripheral tolerance to an organ transplant--both the challenge site and the immunocompetence of the mouse adoptive host are critical factors limiting the sensitivity of the trans-vivo DTH test.

Deformed Palmprint Matching Based on Stable Regions.

PubMed

Wu, Xiangqian; Zhao, Qiushi

2015-12-01

Palmprint recognition (PR) is an effective technology for personal recognition. A main problem, which deteriorates the performance of PR, is the deformations of palmprint images. This problem becomes more severe on contactless occasions, in which images are acquired without any guiding mechanisms, and hence critically limits the applications of PR. To solve the deformation problems, in this paper, a model for non-linearly deformed palmprint matching is derived by approximating non-linear deformed palmprint images with piecewise-linear deformed stable regions. Based on this model, a novel approach for deformed palmprint matching, named key point-based block growing (KPBG), is proposed. In KPBG, an iterative M-estimator sample consensus algorithm based on scale invariant feature transform features is devised to compute piecewise-linear transformations to approximate the non-linear deformations of palmprints, and then, the stable regions complying with the linear transformations are decided using a block growing algorithm. Palmprint feature extraction and matching are performed over these stable regions to compute matching scores for decision. Experiments on several public palmprint databases show that the proposed models and the KPBG approach can effectively solve the deformation problem in palmprint verification and outperform the state-of-the-art methods.

The US business cycle: power law scaling for interacting units with complex internal structure

NASA Astrophysics Data System (ADS)

Ormerod, Paul

2002-11-01

In the social sciences, there is increasing evidence of the existence of power law distributions. The distribution of recessions in capitalist economies has recently been shown to follow such a distribution. The preferred explanation for this is self-organised criticality. Gene Stanley and colleagues propose an alternative, namely that power law scaling can arise from the interplay between random multiplicative growth and the complex structure of the units composing the system. This paper offers a parsimonious model of the US business cycle based on similar principles. The business cycle, along with long-term growth, is one of the two features which distinguishes capitalism from all previously existing societies. Yet, economics lacks a satisfactory theory of the cycle. The source of cycles is posited in economic theory to be a series of random shocks which are external to the system. In this model, the cycle is an internal feature of the system, arising from the level of industrial concentration of the agents and the interactions between them. The model-in contrast to existing economic theories of the cycle-accounts for the key features of output growth in the US business cycle in the 20th century.

Finite element modeling of truss structures with frequency-dependent material damping

NASA Technical Reports Server (NTRS)

Lesieutre, George A.

1991-01-01

A physically motivated modelling technique for structural dynamic analysis that accommodates frequency dependent material damping was developed. Key features of the technique are the introduction of augmenting thermodynamic fields (AFT) to interact with the usual mechanical displacement field, and the treatment of the resulting coupled governing equations using finite element analysis methods. The AFT method is fully compatible with current structural finite element analysis techniques. The method is demonstrated in the dynamic analysis of a 10-bay planar truss structure, a structure representative of those contemplated for use in future space systems.

[Atmospheric parameter estimation for LAMOST/GUOSHOUJING spectra].

PubMed

Lu, Yu; Li, Xiang-Ru; Yang, Tan

2014-11-01

It is a key task to estimate the atmospheric parameters from the observed stellar spectra in exploring the nature of stars and universe. With our Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST) which begun its formal Sky Survey in September 2012, we are obtaining a mass of stellar spectra in an unprecedented speed. It has brought a new opportunity and a challenge for the research of galaxies. Due to the complexity of the observing system, the noise in the spectrum is relatively large. At the same time, the preprocessing procedures of spectrum are also not ideal, such as the wavelength calibration and the flow calibration. Therefore, there is a slight distortion of the spectrum. They result in the high difficulty of estimating the atmospheric parameters for the measured stellar spectra. It is one of the important issues to estimate the atmospheric parameters for the massive stellar spectra of LAMOST. The key of this study is how to eliminate noise and improve the accuracy and robustness of estimating the atmospheric parameters for the measured stellar spectra. We propose a regression model for estimating the atmospheric parameters of LAMOST stellar(SVM(lasso)). The basic idea of this model is: First, we use the Haar wavelet to filter spectrum, suppress the adverse effects of the spectral noise and retain the most discrimination information of spectrum. Secondly, We use the lasso algorithm for feature selection and extract the features of strongly correlating with the atmospheric parameters. Finally, the features are input to the support vector regression model for estimating the parameters. Because the model has better tolerance to the slight distortion and the noise of the spectrum, the accuracy of the measurement is improved. To evaluate the feasibility of the above scheme, we conduct experiments extensively on the 33 963 pilot surveys spectrums by LAMOST. The accuracy of three atmospheric parameters is log Teff: 0.006 8 dex, log g: 0.155 1 dex, [Fe/H]: 0.104 0 dex.

The role of collagen on the structural response of dermal layers in mammals and fish

NASA Astrophysics Data System (ADS)

Sherman, Vincent Robert

We study in depth the role of collagen in the protective layers of mammals (skin) and fish (scales) in depth to reveal its contribution to their mechanical performance. In order to gain an understanding of the structure property relations, we investigate its hierarchical arrangement and how it results in a specialized response. For rabbit skin, chosen as a model material for the dermis of vertebrates, deformation is expressed in terms of four mechanisms of collagen fibril activity that virtually eliminate the possibility of tearing in notched samples: fibril straightening, fibril reorientation towards the tensile direction, elastic stretching, and interfibrillar sliding. A model reflecting the in vivo shape of collagen is derived. The model incorporates the effects of its elasticity, viscoelasticity, and orientation. For arapaima and alligator gar scales, we investigate their protective function and identify key features which result in their resistance to failure. For the elasmoid scales of the arapaima, we show that the scale has a Bouligand-like arrangement of collagen layers which stretch, rotate, and delaminate to dissipate energy and arrest cracking prior to catastrophic failure. Atop the foundation are mineral ridges; this arrangement provides high toughness and resistance to penetration by predator teeth. We show that the ganoid scales of the alligator gar have a boney composite foundation of collagen and hydroxyapatite as well as an external surface of pure hydroxyapatite. Failure averting features of the gar scale include: crack inhibiting mineral decussation in the external ganoine layer; mineral crystals and tubules which deflect cracks in the bony region; and saw-tooth ridges along the interface between the two scale layers which direct cracks away from the weak interface. Furthermore, the scale's geometry is optimized to provide full coverage while accommodating physiological motion. Key features of the scale morphology are replicated in a bioinspired model which retains protection and flexibility.

A Validated Multiscale In-Silico Model for Mechano-sensitive Tumour Angiogenesis and Growth

PubMed Central

Loizidou, Marilena; Stylianopoulos, Triantafyllos; Hawkes, David J.

2017-01-01

Vascularisation is a key feature of cancer growth, invasion and metastasis. To better understand the governing biophysical processes and their relative importance, it is instructive to develop physiologically representative mathematical models with which to compare to experimental data. Previous studies have successfully applied this approach to test the effect of various biochemical factors on tumour growth and angiogenesis. However, these models do not account for the experimentally observed dependency of angiogenic network evolution on growth-induced solid stresses. This work introduces two novel features: the effects of hapto- and mechanotaxis on vessel sprouting, and mechano-sensitive dynamic vascular remodelling. The proposed three-dimensional, multiscale, in-silico model of dynamically coupled angiogenic tumour growth is specified to in-vivo and in-vitro data, chosen, where possible, to provide a physiologically consistent description. The model is then validated against in-vivo data from murine mammary carcinomas, with particular focus placed on identifying the influence of mechanical factors. Crucially, we find that it is necessary to include hapto- and mechanotaxis to recapitulate observed time-varying spatial distributions of angiogenic vasculature. PMID:28125582

A Hierarchical Multiple-Level Approach to the Assessment of Interpersonal Relatedness and Self-Definition: Implications for Research, Clinical Practice, and DSM Planning.

PubMed

Luyten, Patrick; Blatt, Sidney J

2016-01-01

Extant research suggests there is considerable overlap between so-called 2-polarities models of personality development; that is, models that propose that personality development evolves through a dialectic synergistic interaction between 2 key developmental tasks across the life span-the development of self-definition on the one hand and of relatedness on the other. These models have attracted considerable research attention and play a central role in DSM planning. This article provides a researcher- and clinician-friendly guide to the assessment of these personality theories. We argue that current theoretical models focus on issues of relatedness and self-definition at different hierarchically organized levels of analysis; that is (a) at the level of broad personality features, (b) at the motivational level (i.e., the motivational processes underlying the development of these dimensions), and (c) at the level of underlying internal working models or cognitive affective schemas, and the specific interpersonal features and problems in which they are expressed. Implications for further research and DSM planning are outlined.

Identifying key features of early stressful experiences that produce stress vulnerability and resilience in primates

PubMed Central

Parker, Karen J.; Maestripieri, Dario

2010-01-01

This article examines the complex role of early stressful experiences in producing both vulnerability and resilience to later stress-related psychopathology in a variety of primate models of human development. Two types of models are reviewed: Parental Separation Models (e.g., isolate-rearing, peer-rearing, parental separations, and stress inoculation) and Maternal Behavior Models (e.g., foraging demands, variation in maternal style, and maternal abuse). Based on empirical evidence, it is argued that early life stress exposure does not increase adult vulnerability to stress-related psychopathology as a linear function, as is generally believed, but instead reflects a quadratic function. Features of early stress exposure including the type, duration, frequency, ecological validity, sensory modality, and developmental timing, within and between species, are identified to better understand how early stressful experiences alter neurobiological systems to produce such diverse developmental outcomes. This article concludes by identifying gaps in our current knowledge, providing directions for future research, and discussing the translational implications of these primate models for human development and psychopathology. PMID:20851145

Mapping the global football field: a sociological model of transnational forces within the world game.

PubMed

Giulianotti, Richard; Robertson, Roland

2012-06-01

This paper provides a sociological model of the key transnational political and economic forces that are shaping the 'global football field'. The model draws upon, and significantly extends, the theory of the 'global field' developed previously by Robertson. The model features four quadrants, each of which contains a dominant operating principle, an 'elemental reference point', and an 'elemental theme'. The quadrants contain, first, neo-liberalism, associated with the individual and elite football clubs; second, neo-mercantilism, associated with nation-states and national football systems; third, international relations, associated with international governing bodies; and fourth, global civil society, associated with diverse institutions that pursue human development and/or social justice. We examine some of the interactions and tensions between the major institutional and ideological forces across the four quadrants. We conclude by examining how the weakest quadrant, featuring global civil society, may gain greater prominence within football. In broad terms, we argue that our four-fold model may be utilized to map and to examine other substantive research fields with reference to globalization. © London School of Economics and Political Science 2012.

A Solution to the Cosmic Conundrum including Cosmological Constant and Dark Energy Problems

NASA Astrophysics Data System (ADS)

Singh, A.

2009-12-01

A comprehensive solution to the cosmic conundrum is presented that also resolves key paradoxes of quantum mechanics and relativity. A simple mathematical model, the Gravity Nullification model (GNM), is proposed that integrates the missing physics of the spontaneous relativistic conversion of mass to energy into the existing physics theories, specifically a simplified general theory of relativity. Mechanistic mathematical expressions are derived for a relativistic universe expansion, which predict both the observed linear Hubble expansion in the nearby universe and the accelerating expansion exhibited by the supernova observations. The integrated model addresses the key questions haunting physics and Big Bang cosmology. It also provides a fresh perspective on the misconceived birth and evolution of the universe, especially the creation and dissolution of matter. The proposed model eliminates singularities from existing models and the need for the incredible and unverifiable assumptions including the superluminous inflation scenario, multiple universes, multiple dimensions, Anthropic principle, and quantum gravity. GNM predicts the observed features of the universe without any explicit consideration of time as a governing parameter.

Rabbit and Mouse Models of HSV-1 Latency, Reactivation, and Recurrent Eye Diseases

PubMed Central

Webre, Jody M.; Hill, James M.; Nolan, Nicole M.; Clement, Christian; McFerrin, Harris E.; Bhattacharjee, Partha S.; Hsia, Victor; Neumann, Donna M.; Foster, Timothy P.; Lukiw, Walter J.; Thompson, Hilary W.

2012-01-01

The exact mechanisms of HSV-1 establishment, maintenance, latency, reactivation, and also the courses of recurrent ocular infections remain a mystery. Comprehensive understanding of the HSV-1 disease process could lead to prevention of HSV-1 acute infection, reactivation, and more effective treatments of recurrent ocular disease. Animal models have been used for over sixty years to investigate our concepts and hypotheses of HSV-1 diseases. In this paper we present descriptions and examples of rabbit and mouse eye models of HSV-1 latency, reactivation, and recurrent diseases. We summarize studies in animal models of spontaneous and induced HSV-1 reactivation and recurrent disease. Numerous stimuli that induce reactivation in mice and rabbits are described, as well as factors that inhibit viral reactivation from latency. The key features, advantages, and disadvantages of the mouse and rabbit models in relation to the study of ocular HSV-1 are discussed. This paper is pertinent but not intended to be all inclusive. We will give examples of key papers that have reported novel discoveries related to the review topics. PMID:23091352

Statistical Methods for Proteomic Biomarker Discovery based on Feature Extraction or Functional Modeling Approaches.

PubMed

Morris, Jeffrey S

2012-01-01

In recent years, developments in molecular biotechnology have led to the increased promise of detecting and validating biomarkers, or molecular markers that relate to various biological or medical outcomes. Proteomics, the direct study of proteins in biological samples, plays an important role in the biomarker discovery process. These technologies produce complex, high dimensional functional and image data that present many analytical challenges that must be addressed properly for effective comparative proteomics studies that can yield potential biomarkers. Specific challenges include experimental design, preprocessing, feature extraction, and statistical analysis accounting for the inherent multiple testing issues. This paper reviews various computational aspects of comparative proteomic studies, and summarizes contributions I along with numerous collaborators have made. First, there is an overview of comparative proteomics technologies, followed by a discussion of important experimental design and preprocessing issues that must be considered before statistical analysis can be done. Next, the two key approaches to analyzing proteomics data, feature extraction and functional modeling, are described. Feature extraction involves detection and quantification of discrete features like peaks or spots that theoretically correspond to different proteins in the sample. After an overview of the feature extraction approach, specific methods for mass spectrometry ( Cromwell ) and 2D gel electrophoresis ( Pinnacle ) are described. The functional modeling approach involves modeling the proteomic data in their entirety as functions or images. A general discussion of the approach is followed by the presentation of a specific method that can be applied, wavelet-based functional mixed models, and its extensions. All methods are illustrated by application to two example proteomic data sets, one from mass spectrometry and one from 2D gel electrophoresis. While the specific methods presented are applied to two specific proteomic technologies, MALDI-TOF and 2D gel electrophoresis, these methods and the other principles discussed in the paper apply much more broadly to other expression proteomics technologies.

A novel content-based medical image retrieval method based on query topic dependent image features (QTDIF)

NASA Astrophysics Data System (ADS)

Xiong, Wei; Qiu, Bo; Tian, Qi; Mueller, Henning; Xu, Changsheng

2005-04-01

Medical image retrieval is still mainly a research domain with a large variety of applications and techniques. With the ImageCLEF 2004 benchmark, an evaluation framework has been created that includes a database, query topics and ground truth data. Eleven systems (with a total of more than 50 runs) compared their performance in various configurations. The results show that there is not any one feature that performs well on all query tasks. Key to successful retrieval is rather the selection of features and feature weights based on a specific set of input features, thus on the query task. In this paper we propose a novel method based on query topic dependent image features (QTDIF) for content-based medical image retrieval. These feature sets are designed to capture both inter-category and intra-category statistical variations to achieve good retrieval performance in terms of recall and precision. We have used Gaussian Mixture Models (GMM) and blob representation to model medical images and construct the proposed novel QTDIF for CBIR. Finally, trained multi-class support vector machines (SVM) are used for image similarity ranking. The proposed methods have been tested over the Casimage database with around 9000 images, for the given 26 image topics, used for imageCLEF 2004. The retrieval performance has been compared with the medGIFT system, which is based on the GNU Image Finding Tool (GIFT). The experimental results show that the proposed QTDIF-based CBIR can provide significantly better performance than systems based general features only.

Simple dynamical models capturing the key features of the Central Pacific El Niño.

PubMed

Chen, Nan; Majda, Andrew J

2016-10-18

The Central Pacific El Niño (CP El Niño) has been frequently observed in recent decades. The phenomenon is characterized by an anomalous warm sea surface temperature (SST) confined to the central Pacific and has different teleconnections from the traditional El Niño. Here, simple models are developed and shown to capture the key mechanisms of the CP El Niño. The starting model involves coupled atmosphere-ocean processes that are deterministic, linear, and stable. Then, systematic strategies are developed for incorporating several major mechanisms of the CP El Niño into the coupled system. First, simple nonlinear zonal advection with no ad hoc parameterization of the background SST gradient is introduced that creates coupled nonlinear advective modes of the SST. Secondly, due to the recent multidecadal strengthening of the easterly trade wind, a stochastic parameterization of the wind bursts including a mean easterly trade wind anomaly is coupled to the simple atmosphere-ocean processes. Effective stochastic noise in the wind burst model facilitates the intermittent occurrence of the CP El Niño with realistic amplitude and duration. In addition to the anomalous warm SST in the central Pacific, other major features of the CP El Niño such as the rising branch of the anomalous Walker circulation being shifted to the central Pacific and the eastern Pacific cooling with a shallow thermocline are all captured by this simple coupled model. Importantly, the coupled model succeeds in simulating a series of CP El Niño that lasts for 5 y, which resembles the two CP El Niño episodes during 1990-1995 and 2002-2006.

Statistical analysis of dendritic spine distributions in rat hippocampal cultures

PubMed Central

2013-01-01

Background Dendritic spines serve as key computational structures in brain plasticity. Much remains to be learned about their spatial and temporal distribution among neurons. Our aim in this study was to perform exploratory analyses based on the population distributions of dendritic spines with regard to their morphological characteristics and period of growth in dissociated hippocampal neurons. We fit a log-linear model to the contingency table of spine features such as spine type and distance from the soma to first determine which features were important in modeling the spines, as well as the relationships between such features. A multinomial logistic regression was then used to predict the spine types using the features suggested by the log-linear model, along with neighboring spine information. Finally, an important variant of Ripley’s K-function applicable to linear networks was used to study the spatial distribution of spines along dendrites. Results Our study indicated that in the culture system, (i) dendritic spine densities were "completely spatially random", (ii) spine type and distance from the soma were independent quantities, and most importantly, (iii) spines had a tendency to cluster with other spines of the same type. Conclusions Although these results may vary with other systems, our primary contribution is the set of statistical tools for morphological modeling of spines which can be used to assess neuronal cultures following gene manipulation such as RNAi, and to study induced pluripotent stem cells differentiated to neurons. PMID:24088199

PrPC Governs Susceptibility to Prion Strains in Bank Vole, While Other Host Factors Modulate Strain Features

PubMed Central

Espinosa, J. C.; Nonno, R.; Di Bari, M.; Aguilar-Calvo, P.; Pirisinu, L.; Fernández-Borges, N.; Vanni, I.; Vaccari, G.; Marín-Moreno, A.; Frassanito, P.; Lorenzo, P.; Agrimi, U.

2016-01-01

ABSTRACT Bank vole is a rodent species that shows differential susceptibility to the experimental transmission of different prion strains. In this work, the transmission features of a panel of diverse prions with distinct origins were assayed both in bank vole expressing methionine at codon 109 (Bv109M) and in transgenic mice expressing physiological levels of bank vole PrPC (the BvPrP-Tg407 mouse line). This work is the first systematic comparison of the transmission features of a collection of prion isolates, representing a panel of diverse prion strains, in a transgenic-mouse model and in its natural counterpart. The results showed very similar transmission properties in both the natural species and the transgenic-mouse model, demonstrating the key role of the PrP amino acid sequence in prion transmission susceptibility. However, differences in the PrPSc types propagated by Bv109M and BvPrP-Tg407 suggest that host factors other than PrPC modulate prion strain features. IMPORTANCE The differential susceptibility of bank voles to prion strains can be modeled in transgenic mice, suggesting that this selective susceptibility is controlled by the vole PrP sequence alone rather than by other species-specific factors. Differences in the phenotypes observed after prion transmissions in bank voles and in the transgenic mice suggest that host factors other than the PrPC sequence may affect the selection of the substrain replicating in the animal model. PMID:27654300

The Effects of Peer Teaching on the University Students' Achievements in Cognitive, Affective, Psychomotor Domains and Game Performances in Volleyball Courses

ERIC Educational Resources Information Center

Mirzeoglu, Ayse Dilsad

2014-01-01

This study is related to one of the teaching models, peer teaching which is used in physical education courses. The fundamental feature of peer teaching is defined "to structure a learning environment in which some students assume and carry out many of the key operations of instruction to assist other students in the learning process".…

OAO battery data analysis

NASA Technical Reports Server (NTRS)

Gaston, S.; Wertheim, M.; Orourke, J. A.

1973-01-01

Summary, consolidation and analysis of specifications, manufacturing process and test controls, and performance results for OAO-2 and OAO-3 lot 20 Amp-Hr sealed nickel cadmium cells and batteries are reported. Correlation of improvements in control requirements with performance is a key feature. Updates for a cell/battery computer model to improve performance prediction capability are included. Applicability of regression analysis computer techniques to relate process controls to performance is checked.

Hierarchical Engine for Large-scale Infrastructure Co-Simulation

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

2017-04-24

HELICS is designed to support very-large-scale (100,000+ federates) cosimulations with off-the-shelf power-system, communication, market, and end-use tools. Other key features include cross platform operating system support, the integration of both event driven (e.g., packetized communication) and time-series (e.g., power flow) simulations, and the ability to co-iterate among federates to ensure physical model convergence at each time step.

ACER Mathematics Profile Series: Number Test. (Test Booklet, Answer and Record Sheet, Score Key, and Teachers Handbook).

ERIC Educational Resources Information Center

Cornish, Greg; Wines, Robin

The Number Test of the ACER Mathematics Profile Series, contains 30 items, for each of three suggested grade levels: 7-8, 8-9, and 9-10. Raw scores on all tests in the ACER Mathematics Profile Series (Number, Operations, Space and Measurement) are converted to a common scale called MAPS, a major feature of the Series. Based on the Rasch Model,…

Simulations of Long-Term Community Dynamics in Coral Reefs - How Perturbations Shape Trajectories

PubMed Central

Kubicek, Andreas; Muhando, Christopher; Reuter, Hauke

2012-01-01

Tropical coral reefs feature extraordinary biodiversity and high productivity rates in oligotrophic waters. Due to increasing frequencies of perturbations – anthropogenic and natural – many reefs are under threat. Such perturbations often have devastating effects on these unique ecosystems and especially if they occur simultaneously and amplify each other's impact, they might trigger a phase shift and create irreversible conditions. We developed a generic, spatially explicit, individual-based model in which competition drives the dynamics of a virtual benthic reef community – comprised of scleractinian corals and algae – under different environmental settings. Higher system properties, like population dynamics or community composition arise through self-organization as emergent properties. The model was parameterized for a typical coral reef site at Zanzibar, Tanzania and features coral bleaching and physical disturbance regimes as major sources of perturbations. Our results show that various types and modes (intensities and frequencies) of perturbations create diverse outcomes and that the switch from high diversity to single species dominance can be evoked by small changes in a key parameter. Here we extend the understanding of coral reef resilience and the identification of key processes, drivers and respective thresholds, responsible for changes in local situations. One future goal is to provide a tool which may aid decision making processes in management of coral reefs. PMID:23209397

Effects of Formalin-Inactivated Respiratory Syncytial Virus (FI-RSV) in the Perinatal Lamb Model of RSV

PubMed Central

Derscheid, Rachel J.; Gallup, Jack M.; Knudson, Cory J.; Varga, Steven M.; Grosz, Drew D.; van Geelen, Albert; Hostetter, Shannon J.; Ackermann, Mark R.

2013-01-01

Respiratory syncytial virus (RSV) is the most frequent cause of bronchiolitis in infants and children worldwide. There are currently no licensed vaccines or effective antivirals. The lack of a vaccine is partly due to increased caution following the aftermath of a failed clinical trial of a formalin-inactivated RSV vaccine (FI-RSV) conducted in the 1960’s that led to enhanced disease, necessitating hospitalization of 80% of vaccine recipients and resulting in two fatalities. Perinatal lamb lungs are similar in size, structure and physiology to those of human infants and are susceptible to human strains of RSV that induce similar lesions as those observed in infected human infants. We sought to determine if perinatal lambs immunized with FI-RSV would develop key features of vaccine-enhanced disease. This was tested in colostrum-deprived lambs immunized at 3–5 days of age with FI-RSV followed two weeks later by RSV infection. The FI-RSV-vaccinated lambs exhibited several key features of RSV vaccine-enhanced disease, including reduced RSV titers in bronchoalveolar lavage fluid and lung, and increased infiltration of peribronchiolar and perivascular lymphocytes compared to lambs either undergoing an acute RSV infection or naïve controls; all features of RSV vaccine-enhanced disease. These results represent a first step proof-of-principle demonstration that the lamb can develop altered responses to RSV following FI-RSV vaccination. The lamb model may be useful for future mechanistic studies as well as the assessment of RSV vaccines designed for infants. PMID:24324695

Search performance is better predicted by tileability than presence of a unique basic feature.

PubMed

Chang, Honghua; Rosenholtz, Ruth

2016-08-01

Traditional models of visual search such as feature integration theory (FIT; Treisman & Gelade, 1980), have suggested that a key factor determining task difficulty consists of whether or not the search target contains a "basic feature" not found in the other display items (distractors). Here we discriminate between such traditional models and our recent texture tiling model (TTM) of search (Rosenholtz, Huang, Raj, Balas, & Ilie, 2012b), by designing new experiments that directly pit these models against each other. Doing so is nontrivial, for two reasons. First, the visual representation in TTM is fully specified, and makes clear testable predictions, but its complexity makes getting intuitions difficult. Here we elucidate a rule of thumb for TTM, which enables us to easily design new and interesting search experiments. FIT, on the other hand, is somewhat ill-defined and hard to pin down. To get around this, rather than designing totally new search experiments, we start with five classic experiments that FIT already claims to explain: T among Ls, 2 among 5s, Q among Os, O among Qs, and an orientation/luminance-contrast conjunction search. We find that fairly subtle changes in these search tasks lead to significant changes in performance, in a direction predicted by TTM, providing definitive evidence in favor of the texture tiling model as opposed to traditional views of search.

Explaining neural signals in human visual cortex with an associative learning model.

PubMed

Jiang, Jiefeng; Schmajuk, Nestor; Egner, Tobias

2012-08-01

"Predictive coding" models posit a key role for associative learning in visual cognition, viewing perceptual inference as a process of matching (learned) top-down predictions (or expectations) against bottom-up sensory evidence. At the neural level, these models propose that each region along the visual processing hierarchy entails one set of processing units encoding predictions of bottom-up input, and another set computing mismatches (prediction error or surprise) between predictions and evidence. This contrasts with traditional views of visual neurons operating purely as bottom-up feature detectors. In support of the predictive coding hypothesis, a recent human neuroimaging study (Egner, Monti, & Summerfield, 2010) showed that neural population responses to expected and unexpected face and house stimuli in the "fusiform face area" (FFA) could be well-described as a summation of hypothetical face-expectation and -surprise signals, but not by feature detector responses. Here, we used computer simulations to test whether these imaging data could be formally explained within the broader framework of a mathematical neural network model of associative learning (Schmajuk, Gray, & Lam, 1996). Results show that FFA responses could be fit very closely by model variables coding for conditional predictions (and their violations) of stimuli that unconditionally activate the FFA. These data document that neural population signals in the ventral visual stream that deviate from classic feature detection responses can formally be explained by associative prediction and surprise signals.

A review of international pharmacy-based minor ailment services and proposed service design model.

PubMed

Aly, Mariyam; García-Cárdenas, Victoria; Williams, Kylie; Benrimoj, Shalom I

2018-01-05

The need to consider sustainable healthcare solutions is essential. An innovative strategy used to promote minor ailment care is the utilisation of community pharmacists to deliver minor ailment services (MASs). Promoting higher levels of self-care can potentially reduce the strain on existing resources. To explore the features of international MASs, including their similarities and differences, and consider the essential elements to design a MAS model. A grey literature search strategy was completed in June 2017 to comply with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses standard. This included (1) Google/Yahoo! search engines, (2) targeted websites, and (3) contact with commissioning organisations. Executive summaries, table of contents and title pages of documents were reviewed. Key characteristics of MASs were extracted and a MAS model was developed. A total of 147 publications were included in the review. Key service elements identified included eligibility, accessibility, staff involvement, reimbursement systems. Several factors need to be considered when designing a MAS model; including contextualisation of MAS to the market. Stakeholder engagement, service planning, governance, implementation and review have emerged as key aspects involved with a design model. MASs differ in their structural parameters. Consideration of these parameters is necessary when devising MAS aims and assessing outcomes to promote sustainability and success of the service. Copyright © 2018 Elsevier Inc. All rights reserved.

An elasto-viscoplastic interface model for investigating the constitutive behavior of nacre

NASA Astrophysics Data System (ADS)

Tang, H.; Barthelat, F.; Espinosa, H. D.

2007-07-01

In order to better understand the strengthening mechanism observed in nacre, we have developed an interface computational model to simulate the behavior of the organic present at the interface between aragonite tablets. In the model, the single polymer-chain behavior is characterized by the worm-like-chain (WLC) model, which is in turn incorporated into the eight-chain cell model developed by Arruda and Boyce [Arruda, E.M., Boyce, M.C., 1993a. A three-dimensional constitutive model for the large stretches, with application to polymeric glasses. Int. J. Solids Struct. 40, 389-412] to achieve a continuum interface constitutive description. The interface model is formulated within a finite-deformation framework. A fully implicit time-integration algorithm is used for solving the discretized governing equations. Finite element simulations were performed on a representative volume element (RVE) to investigate the tensile response of nacre. The staggered arrangement of tablets and interface waviness obtained experimentally by Barthelat et al. [Barthelat, F., Tang, H., Zavattieri, P.D., Li, C.-M., Espinosa, H.D., 2007. On the mechanics of mother-of-pearl: a key feature in the material hierarchical structure. J. Mech. Phys. Solids 55 (2), 306-337] was included in the RVE simulations. The simulations showed that both the rate-dependence of the tensile response and hysteresis loops during loading, unloading and reloading cycles were captured by the model. Through a parametric study, the effect of the polymer constitutive response during tablet-climbing and its relation to interface hardening was investigated. It is shown that stiffening of the organic material is not required to achieve the experimentally observed strain hardening of nacre during tension. In fact, when ratios of contour length/persistent length experimentally identified are employed in the simulations, the predicted stress-strain behavior exhibits a deformation hardening consistent with the one measured experimentally and also captured by the phenomenological cohesive model used in the study carried out by Barthelat et al. [Barthelat, F., Tang, H., Zavattieri, P.D., Li, C.-M., Espinosa, H.D., 2007. On the mechanics of mother-of-pearl: a key feature in the material hierarchical structure. J. Mech. Phys. Solids 55 (2), 306-337]. The simulation results also reveal that the bulk modulus of the polymer controls the rate of hardening, feature not captured by more simple cohesive laws.

Asymptotic behaviour of two-point functions in multi-species models

NASA Astrophysics Data System (ADS)

Kozlowski, Karol K.; Ragoucy, Eric

2016-05-01

We extract the long-distance asymptotic behaviour of two-point correlation functions in massless quantum integrable models containing multi-species excitations. For such a purpose, we extend to these models the method of a large-distance regime re-summation of the form factor expansion of correlation functions. The key feature of our analysis is a technical hypothesis on the large-volume behaviour of the form factors of local operators in such models. We check the validity of this hypothesis on the example of the SU (3)-invariant XXX magnet by means of the determinant representations for the form factors of local operators in this model. Our approach confirms the structure of the critical exponents obtained previously for numerous models solvable by the nested Bethe Ansatz.

SymptomCare@Home: Developing an Integrated Symptom Monitoring and Management System for Outpatients Receiving Chemotherapy.

PubMed

Beck, Susan L; Eaton, Linda H; Echeverria, Christina; Mooney, Kathi H

2017-10-01

SymptomCare@Home, an integrated symptom monitoring and management system, was designed as part of randomized clinical trials to help patients with cancer who receive chemotherapy in ambulatory clinics and often experience significant symptoms at home. An iterative design process was informed by chronic disease management theory and features of assessment and clinical decision support systems used in other diseases. Key stakeholders participated in the design process: nurse scientists, clinical experts, bioinformatics experts, and computer programmers. Especially important was input from end users, patients, and nurse practitioners participating in a series of studies testing the system. The system includes both a patient and clinician interface and fully integrates two electronic subsystems: a telephone computer-linked interactive voice response system and a Web-based Decision Support-Symptom Management System. Key features include (1) daily symptom monitoring, (2) self-management coaching, (3) alerting, and (4) nurse practitioner follow-up. The nurse practitioner is distinctively positioned to provide assessment, education, support, and pharmacologic and nonpharmacologic interventions to intensify management of poorly controlled symptoms at home. SymptomCare@Home is a model for providing telehealth. The system facilitates using evidence-based guidelines as part of a comprehensive symptom management approach. The design process and system features can be applied to other diseases and conditions.

Solution Binding and Structural Analyses Reveal Potential Multidrug Resistance Functions for SAV2435 and CTR107 and Other GyrI-like Proteins.

PubMed

Moreno, Andrew; Froehlig, John R; Bachas, Sharrol; Gunio, Drew; Alexander, Teressa; Vanya, Aaron; Wade, Herschel

2016-08-30

Multidrug resistance (MDR) refers to the acquired ability of cells to tolerate a broad range of toxic compounds. One mechanism cells employ is to increase the level of expression of efflux pumps for the expulsion of xenobiotics. A key feature uniting efflux-related mechanisms is multidrug (MD) recognition, either by efflux pumps themselves or by their transcriptional regulators. However, models describing MD binding by MDR effectors are incomplete, underscoring the importance of studies focused on the recognition elements and key motifs that dictate polyspecific binding. One such motif is the GyrI-like domain, which is found in several MDR proteins and is postulated to have been adapted for small-molecule binding and signaling. Here we report the solution binding properties and crystal structures of two proteins containing GyrI-like domains, SAV2435 and CTR107, bound to various ligands. Furthermore, we provide a comparison with deposited crystal structures of GyrI-like proteins, revealing key features of GyrI-like domains that not only support polyspecific binding but also are conserved among GyrI-like domains. Together, our studies suggest that GyrI-like domains perform evolutionarily conserved functions connected to multidrug binding and highlight the utility of these types of studies for elucidating mechanisms of MDR.

Contribution of low-temperature single-molecule techniques to structural issues of pigment–protein complexes from photosynthetic purple bacteria

PubMed Central

Löhner, Alexander; Cogdell, Richard

2018-01-01

As the electronic energies of the chromophores in a pigment–protein complex are imposed by the geometrical structure of the protein, this allows the spectral information obtained to be compared with predictions derived from structural models. Thereby, the single-molecule approach is particularly suited for the elucidation of specific, distinctive spectral features that are key for a particular model structure, and that would not be observable in ensemble-averaged spectra due to the heterogeneity of the biological objects. In this concise review, we illustrate with the example of the light-harvesting complexes from photosynthetic purple bacteria how results from low-temperature single-molecule spectroscopy can be used to discriminate between different structural models. Thereby the low-temperature approach provides two advantages: (i) owing to the negligible photobleaching, very long observation times become possible, and more importantly, (ii) at cryogenic temperatures, vibrational degrees of freedom are frozen out, leading to sharper spectral features and in turn to better resolved spectra. PMID:29321265

Quantum Tunneling Affects Engine Performance.

PubMed

Som, Sibendu; Liu, Wei; Zhou, Dingyu D Y; Magnotti, Gina M; Sivaramakrishnan, Raghu; Longman, Douglas E; Skodje, Rex T; Davis, Michael J

2013-06-20

We study the role of individual reaction rates on engine performance, with an emphasis on the contribution of quantum tunneling. It is demonstrated that the effect of quantum tunneling corrections for the reaction HO2 + HO2 = H2O2 + O2 can have a noticeable impact on the performance of a high-fidelity model of a compression-ignition (e.g., diesel) engine, and that an accurate prediction of ignition delay time for the engine model requires an accurate estimation of the tunneling correction for this reaction. The three-dimensional model includes detailed descriptions of the chemistry of a surrogate for a biodiesel fuel, as well as all the features of the engine, such as the liquid fuel spray and turbulence. This study is part of a larger investigation of how the features of the dynamics and potential energy surfaces of key reactions, as well as their reaction rate uncertainties, affect engine performance, and results in these directions are also presented here.

A mathematical model for malaria transmission with asymptomatic carriers and two age groups in the human population.

PubMed

Beretta, Edoardo; Capasso, Vincenzo; Garao, Dario G

2018-06-01

In this paper a conceptual mathematical model of malaria transmission proposed in a previous paper has been analyzed in a deeper detail. Among its key epidemiological features of this model, two-age-classes (child and adult) and asymptomatic carriers have been included. The extra mortality of mosquitoes due to the use of long-lasting treated mosquito nets (LLINs) and Indoor Residual Spraying (IRS) has been included too. By taking advantage of the natural double time scale of the parasite and the human populations, it has been possible to provide interesting threshold results. In particular it has been shown that key parameters can be identified such that below a threshold level, built on these parameters, the epidemic tends to extinction, while above another threshold level it tends to a nontrivial endemic state, for which an interval estimate has been provided. Numerical simulations confirm the analytical results. Copyright © 2018 Elsevier Inc. All rights reserved.

Ares-I-X Stability and Control Flight Test: Analysis and Plans

NASA Technical Reports Server (NTRS)

Brandon, Jay M.; Derry, Stephen D.; Heim, Eugene H.; Hueschen, Richard M.; Bacon, Barton J.

2008-01-01

The flight test of the Ares I-X vehicle provides a unique opportunity to reduce risk of the design of the Ares I vehicle and test out design, math modeling, and analysis methods. One of the key features of the Ares I design is the significant static aerodynamic instability coupled with the relatively flexible vehicle - potentially resulting in a challenging controls problem to provide adequate flight path performance while also providing adequate structural mode damping and preventing adverse control coupling to the flexible structural modes. Another challenge is to obtain enough data from the single flight to be able to conduct analysis showing the effectiveness of the controls solutions and have data to inform design decisions for Ares I. This paper will outline the modeling approaches and control system design to conduct this flight test, and also the system identification techniques developed to extract key information such as control system performance (gain/phase margins, for example), structural dynamics responses, and aerodynamic model estimations.

Computing by robust transience: How the fronto-parietal network performs sequential category-based decisions

PubMed Central

Chaisangmongkon, Warasinee; Swaminathan, Sruthi K.; Freedman, David J.; Wang, Xiao-Jing

2017-01-01

Summary Decision making involves dynamic interplay between internal judgements and external perception, which has been investigated in delayed match-to-category (DMC) experiments. Our analysis of neural recordings shows that, during DMC tasks, LIP and PFC neurons demonstrate mixed, time-varying, and heterogeneous selectivity, but previous theoretical work has not established the link between these neural characteristics and population-level computations. We trained a recurrent network model to perform DMC tasks and found that the model can remarkably reproduce key features of neuronal selectivity at the single-neuron and population levels. Analysis of the trained networks elucidates that robust transient trajectories of the neural population are the key driver of sequential categorical decisions. The directions of trajectories are governed by network self-organized connectivity, defining a ‘neural landscape’, consisting of a task-tailored arrangement of slow states and dynamical tunnels. With this model, we can identify functionally-relevant circuit motifs and generalize the framework to solve other categorization tasks. PMID:28334612

Computationally efficient approach for solving time dependent diffusion equation with discrete temporal convolution applied to granular particles of battery electrodes

NASA Astrophysics Data System (ADS)

Senegačnik, Jure; Tavčar, Gregor; Katrašnik, Tomaž

2015-03-01

The paper presents a computationally efficient method for solving the time dependent diffusion equation in a granule of the Li-ion battery's granular solid electrode. The method, called Discrete Temporal Convolution method (DTC), is based on a discrete temporal convolution of the analytical solution of the step function boundary value problem. This approach enables modelling concentration distribution in the granular particles for arbitrary time dependent exchange fluxes that do not need to be known a priori. It is demonstrated in the paper that the proposed method features faster computational times than finite volume/difference methods and Padé approximation at the same accuracy of the results. It is also demonstrated that all three addressed methods feature higher accuracy compared to the quasi-steady polynomial approaches when applied to simulate the current densities variations typical for mobile/automotive applications. The proposed approach can thus be considered as one of the key innovative methods enabling real-time capability of the multi particle electrochemical battery models featuring spatial and temporal resolved particle concentration profiles.

Combining the genetic algorithm and successive projection algorithm for the selection of feature wavelengths to evaluate exudative characteristics in frozen-thawed fish muscle.

PubMed

Cheng, Jun-Hu; Sun, Da-Wen; Pu, Hongbin

2016-04-15

The potential use of feature wavelengths for predicting drip loss in grass carp fish, as affected by being frozen at -20°C for 24 h and thawed at 4°C for 1, 2, 4, and 6 days, was investigated. Hyperspectral images of frozen-thawed fish were obtained and their corresponding spectra were extracted. Least-squares support vector machine and multiple linear regression (MLR) models were established using five key wavelengths, selected by combining a genetic algorithm and successive projections algorithm, and this showed satisfactory performance in drip loss prediction. The MLR model with a determination coefficient of prediction (R(2)P) of 0.9258, and lower root mean square error estimated by a prediction (RMSEP) of 1.12%, was applied to transfer each pixel of the image and generate the distribution maps of exudation changes. The results confirmed that it is feasible to identify the feature wavelengths using variable selection methods and chemometric analysis for developing on-line multispectral imaging. Copyright © 2015 Elsevier Ltd. All rights reserved.

The experimental verification on the shear bearing capacity of exposed steel column foot

NASA Astrophysics Data System (ADS)

Xijin, LIU

2017-04-01

In terms of the shear bearing capacity of the exposed steel column foot, there are many researches both home and abroad. However, the majority of the researches are limited to the theoretical analysis sector and few of them make the experimental analysis. In accordance with the prototype of an industrial plant in Beijing, this paper designs the experimental model. The experimental model is composed of six steel structural members in two groups, with three members without shear key and three members with shear key. The paper checks the shear bearing capacity of two groups respectively under different axial forces. The experiment shows: The anchor bolt of the exposed steel column foot features relatively large shear bearing capacity which could not be neglected. The results deducted through calculation methods proposed by this paper under two situations match the experimental results in terms of the shear bearing capacity of the steel column foot. Besides, it also proposed suggestions on revising the Code for Design of Steel Structure in the aspect of setting the shear key in the steel column foot.

A Robust Method for Ego-Motion Estimation in Urban Environment Using Stereo Camera.

PubMed

Ci, Wenyan; Huang, Yingping

2016-10-17

Visual odometry estimates the ego-motion of an agent (e.g., vehicle and robot) using image information and is a key component for autonomous vehicles and robotics. This paper proposes a robust and precise method for estimating the 6-DoF ego-motion, using a stereo rig with optical flow analysis. An objective function fitted with a set of feature points is created by establishing the mathematical relationship between optical flow, depth and camera ego-motion parameters through the camera's 3-dimensional motion and planar imaging model. Accordingly, the six motion parameters are computed by minimizing the objective function, using the iterative Levenberg-Marquard method. One of key points for visual odometry is that the feature points selected for the computation should contain inliers as much as possible. In this work, the feature points and their optical flows are initially detected by using the Kanade-Lucas-Tomasi (KLT) algorithm. A circle matching is followed to remove the outliers caused by the mismatching of the KLT algorithm. A space position constraint is imposed to filter out the moving points from the point set detected by the KLT algorithm. The Random Sample Consensus (RANSAC) algorithm is employed to further refine the feature point set, i.e., to eliminate the effects of outliers. The remaining points are tracked to estimate the ego-motion parameters in the subsequent frames. The approach presented here is tested on real traffic videos and the results prove the robustness and precision of the method.

A Robust Method for Ego-Motion Estimation in Urban Environment Using Stereo Camera

PubMed Central

Ci, Wenyan; Huang, Yingping

2016-01-01

Visual odometry estimates the ego-motion of an agent (e.g., vehicle and robot) using image information and is a key component for autonomous vehicles and robotics. This paper proposes a robust and precise method for estimating the 6-DoF ego-motion, using a stereo rig with optical flow analysis. An objective function fitted with a set of feature points is created by establishing the mathematical relationship between optical flow, depth and camera ego-motion parameters through the camera’s 3-dimensional motion and planar imaging model. Accordingly, the six motion parameters are computed by minimizing the objective function, using the iterative Levenberg–Marquard method. One of key points for visual odometry is that the feature points selected for the computation should contain inliers as much as possible. In this work, the feature points and their optical flows are initially detected by using the Kanade–Lucas–Tomasi (KLT) algorithm. A circle matching is followed to remove the outliers caused by the mismatching of the KLT algorithm. A space position constraint is imposed to filter out the moving points from the point set detected by the KLT algorithm. The Random Sample Consensus (RANSAC) algorithm is employed to further refine the feature point set, i.e., to eliminate the effects of outliers. The remaining points are tracked to estimate the ego-motion parameters in the subsequent frames. The approach presented here is tested on real traffic videos and the results prove the robustness and precision of the method. PMID:27763508

Chemical event chain model of coupled genetic oscillators.

PubMed

Jörg, David J; Morelli, Luis G; Jülicher, Frank

2018-03-01

We introduce a stochastic model of coupled genetic oscillators in which chains of chemical events involved in gene regulation and expression are represented as sequences of Poisson processes. We characterize steady states by their frequency, their quality factor, and their synchrony by the oscillator cross correlation. The steady state is determined by coupling and exhibits stochastic transitions between different modes. The interplay of stochasticity and nonlinearity leads to isolated regions in parameter space in which the coupled system works best as a biological pacemaker. Key features of the stochastic oscillations can be captured by an effective model for phase oscillators that are coupled by signals with distributed delays.

An economic theory of cigarette addiction.

PubMed

Suranovic, S M; Goldfarb, R S; Leonard, T C

1999-01-01

In this paper we present a model in which individuals act in their own best interest, to explain many behaviors associated with cigarette addiction. There are two key features of the model. First, there is an explicit representation of the withdrawal effects experienced when smokers attempt to quit smoking. Second, there is explicit recognition that the negative effects of smoking generally appear late in an individual's life. Among the things we use the model to explain are: (1) how individuals can become trapped in their decision to smoke; (2) the conditions under which cold-turkey quitting and gradual quitting may occur; and (3) a reason for the existence of quit-smoking treatments.

Modeling the voltage loss mechanisms in lithium-sulfur cells: the importance of electrolyte resistance and precipitation kinetics.

PubMed

Zhang, Teng; Marinescu, Monica; O'Neill, Laura; Wild, Mark; Offer, Gregory

2015-09-21

Understanding of the complex electrochemical, transport, and phase-change phenomena in Li-S cells requires experimental characterization in tandem with mechanistic modeling. However, existing Li-S models currently contradict some key features of experimental findings, particularly the evolution of cell resistance during discharge. We demonstrate that, by introducing a concentration-dependent electrolyte conductivity, the correct trends in voltage drop due to electrolyte resistance and activation overpotentials are retrieved. In addition, we reveal the existence of an often overlooked potential drop mechanism in the low voltage-plateau which originates from the limited rate of Li2S precipitation.

Chemical event chain model of coupled genetic oscillators

NASA Astrophysics Data System (ADS)

Jörg, David J.; Morelli, Luis G.; Jülicher, Frank

2018-03-01

We introduce a stochastic model of coupled genetic oscillators in which chains of chemical events involved in gene regulation and expression are represented as sequences of Poisson processes. We characterize steady states by their frequency, their quality factor, and their synchrony by the oscillator cross correlation. The steady state is determined by coupling and exhibits stochastic transitions between different modes. The interplay of stochasticity and nonlinearity leads to isolated regions in parameter space in which the coupled system works best as a biological pacemaker. Key features of the stochastic oscillations can be captured by an effective model for phase oscillators that are coupled by signals with distributed delays.

CWRF performance at downscaling China climate characteristics

NASA Astrophysics Data System (ADS)

Liang, Xin-Zhong; Sun, Chao; Zheng, Xiaohui; Dai, Yongjiu; Xu, Min; Choi, Hyun I.; Ling, Tiejun; Qiao, Fengxue; Kong, Xianghui; Bi, Xunqiang; Song, Lianchun; Wang, Fang

2018-05-01

The performance of the regional Climate-Weather Research and Forecasting model (CWRF) for downscaling China climate characteristics is evaluated using a 1980-2015 simulation at 30 km grid spacing driven by the ECMWF Interim reanalysis (ERI). It is shown that CWRF outperforms the popular Regional Climate Modeling system (RegCM4.6) in key features including monsoon rain bands, diurnal temperature ranges, surface winds, interannual precipitation and temperature anomalies, humidity couplings, and 95th percentile daily precipitation. Even compared with ERI, which assimilates surface observations, CWRF better represents the geographic distributions of seasonal mean climate and extreme precipitation. These results indicate that CWRF may significantly enhance China climate modeling capabilities.

Universal lineshapes at the crossover between weak and strong critical coupling in Fano-resonant coupled oscillators

NASA Astrophysics Data System (ADS)

Zanotto, Simone; Tredicucci, Alessandro

2016-04-01

In this article we discuss a model describing key features concerning the lineshapes and the coherent absorption conditions in Fano-resonant dissipative coupled oscillators. The model treats on the same footing the weak and strong coupling regimes, and includes the critical coupling concept, which is of great relevance in numerous applications; in addition, the role of asymmetry is thoroughly analyzed. Due to the wide generality of the model, which can be adapted to various frameworks like nanophotonics, plasmonics, and optomechanics, we envisage that the analytical formulas presented here will be crucial to effectively design devices and to interpret experimental results.

Tempest: Tools for Addressing the Needs of Next-Generation Climate Models

NASA Astrophysics Data System (ADS)

Ullrich, P. A.; Guerra, J. E.; Pinheiro, M. C.; Fong, J.

2015-12-01

Tempest is a comprehensive simulation-to-science infrastructure that tackles the needs of next-generation, high-resolution, data intensive climate modeling activities. This project incorporates three key components: TempestDynamics, a global modeling framework for experimental numerical methods and high-performance computing; TempestRemap, a toolset for arbitrary-order conservative and consistent remapping between unstructured grids; and TempestExtremes, a suite of detection and characterization tools for identifying weather extremes in large climate datasets. In this presentation, the latest advances with the implementation of this framework will be discussed, and a number of projects now utilizing these tools will be featured.

Nanogel Carrier Design for Targeted Drug Delivery

PubMed Central

Eckmann, D. M.; Composto, R. J.; Tsourkas, A.; Muzykantov, V. R.

2014-01-01

Polymer-based nanogel formulations offer features attractive for drug delivery, including ease of synthesis, controllable swelling and viscoelasticity as well as drug loading and release characteristics, passive and active targeting, and the ability to formulate nanogel carriers that can respond to biological stimuli. These unique features and low toxicity make the nanogels a favorable option for vascular drug targeting. In this review, we address key chemical and biological aspects of nanogel drug carrier design. In particular, we highlight published studies of nanogel design, descriptions of nanogel functional characteristics and their behavior in biological models. These studies form a compendium of information that supports the scientific and clinical rationale for development of this carrier for targeted therapeutic interventions. PMID:25485112

Nonlinear transient waves in coupled phase oscillators with inertia.

PubMed

Jörg, David J

2015-05-01

Like the inertia of a physical body describes its tendency to resist changes of its state of motion, inertia of an oscillator describes its tendency to resist changes of its frequency. Here, we show that finite inertia of individual oscillators enables nonlinear phase waves in spatially extended coupled systems. Using a discrete model of coupled phase oscillators with inertia, we investigate these wave phenomena numerically, complemented by a continuum approximation that permits the analytical description of the key features of wave propagation in the long-wavelength limit. The ability to exhibit traveling waves is a generic feature of systems with finite inertia and is independent of the details of the coupling function.

Improving Teaching through Continuous Learning: The Inquiry Process John Wooden Used to Become Coach of the Century

ERIC Educational Resources Information Center

Ermeling, Bradley Alan

2012-01-01

Past and contemporary scholars have emphasized the importance of job-embedded, systematic instructional inquiry for educators. A recent review of the literature highlights four key features shared by several well documented inquiry approaches for classroom teachers. Interestingly, another line of research suggests that these key features also…

Salient Key Features of Actual English Instructional Practices in Saudi Arabia

ERIC Educational Resources Information Center

Al-Seghayer, Khalid

2015-01-01

This is a comprehensive review of the salient key features of the actual English instructional practices in Saudi Arabia. The goal of this work is to gain insights into the practices and pedagogic approaches to English as a foreign language (EFL) teaching currently employed in this country. In particular, we identify the following central features…

Improving Latino Children's Early Language and Literacy Development: Key Features of Early Childhood Education within Family Literacy Programmes

ERIC Educational Resources Information Center

Jung, Youngok; Zuniga, Stephen; Howes, Carollee; Jeon, Hyun-Joo; Parrish, Deborah; Quick, Heather; Manship, Karen; Hauser, Alison

2016-01-01

Noting the lack of research on how early childhood education (ECE) programmes within family literacy programmes influence Latino children's early language and literacy development, this study examined key features of ECE programmes, specifically teacher-child interactions and child engagement in language and literacy activities and how these…

An ignition key for atomic-scale engines

NASA Astrophysics Data System (ADS)

Dundas, Daniel; Cunningham, Brian; Buchanan, Claire; Terasawa, Asako; Paxton, Anthony T.; Todorov, Tchavdar N.

2012-10-01

A current-carrying resonant nanoscale device, simulated by non-adiabatic molecular dynamics, exhibits sharp activation of non-conservative current-induced forces with bias. The result, above the critical bias, is generalized rotational atomic motion with a large gain in kinetic energy. The activation exploits sharp features in the electronic structure, and constitutes, in effect, an ignition key for atomic-scale motors. A controlling factor for the effect is the non-equilibrium dynamical response matrix for small-amplitude atomic motion under current. This matrix can be found from the steady-state electronic structure by a simpler static calculation, providing a way to detect the likely appearance, or otherwise, of non-conservative dynamics, in advance of real-time modelling.

Simulating Complex, Cold-region Process Interactions Using a Multi-scale, Variable-complexity Hydrological Model

NASA Astrophysics Data System (ADS)

Marsh, C.; Pomeroy, J. W.; Wheater, H. S.

2017-12-01

Accurate management of water resources is necessary for social, economic, and environmental sustainability worldwide. In locations with seasonal snowcovers, the accurate prediction of these water resources is further complicated due to frozen soils, solid-phase precipitation, blowing snow transport, and snowcover-vegetation-atmosphere interactions. Complex process interactions and feedbacks are a key feature of hydrological systems and may result in emergent phenomena, i.e., the arising of novel and unexpected properties within a complex system. One example is the feedback associated with blowing snow redistribution, which can lead to drifts that cause locally-increased soil moisture, thus increasing plant growth that in turn subsequently impacts snow redistribution, creating larger drifts. Attempting to simulate these emergent behaviours is a significant challenge, however, and there is concern that process conceptualizations within current models are too incomplete to represent the needed interactions. An improved understanding of the role of emergence in hydrological systems often requires high resolution distributed numerical hydrological models that incorporate the relevant process dynamics. The Canadian Hydrological Model (CHM) provides a novel tool for examining cold region hydrological systems. Key features include efficient terrain representation, allowing simulations at various spatial scales, reduced computational overhead, and a modular process representation allowing for an alternative-hypothesis framework. Using both physics-based and conceptual process representations sourced from long term process studies and the current cold regions literature allows for comparison of process representations and importantly, their ability to produce emergent behaviours. Examining the system in a holistic, process-based manner can hopefully derive important insights and aid in development of improved process representations.

Modeling and Characterization of cMUT-based Devices Applied to Galvanic Isolation

NASA Astrophysics Data System (ADS)

Heller, Jacques; Boulmé, Audren; Alquier, Daniel; Ngo, Sophie; Perroteau, Marie; Certon, Domnique

This paper describes a new way of using cMUT technology: galvanic isolation for power electronics. These devices work like acoustic transformers, except that piezoelectricity is replaced by cMUT technology. Primary and secondary circuits are two cMUT-based transducers respectively layered on each side of a silicon substrate, through which the ultrasonic triggering signal is transmitted. A specific model based on a commercial finite element code was implemented to simulate these devices. A particular attention was paid on the modeling of the cMUT/substrate coupling which is a key feature for the intended application. First experimental results performed for model validation are presented here and discussed.

Dimensional Reduction for the General Markov Model on Phylogenetic Trees.

PubMed

Sumner, Jeremy G

2017-03-01

We present a method of dimensional reduction for the general Markov model of sequence evolution on a phylogenetic tree. We show that taking certain linear combinations of the associated random variables (site pattern counts) reduces the dimensionality of the model from exponential in the number of extant taxa, to quadratic in the number of taxa, while retaining the ability to statistically identify phylogenetic divergence events. A key feature is the identification of an invariant subspace which depends only bilinearly on the model parameters, in contrast to the usual multi-linear dependence in the full space. We discuss potential applications including the computation of split (edge) weights on phylogenetic trees from observed sequence data.

Analysis and Prediction of Myristoylation Sites Using the mRMR Method, the IFS Method and an Extreme Learning Machine Algorithm.

PubMed

Wang, ShaoPeng; Zhang, Yu-Hang; Huang, GuoHua; Chen, Lei; Cai, Yu-Dong

2017-01-01

Myristoylation is an important hydrophobic post-translational modification that is covalently bound to the amino group of Gly residues on the N-terminus of proteins. The many diverse functions of myristoylation on proteins, such as membrane targeting, signal pathway regulation and apoptosis, are largely due to the lipid modification, whereas abnormal or irregular myristoylation on proteins can lead to several pathological changes in the cell. To better understand the function of myristoylated sites and to correctly identify them in protein sequences, this study conducted a novel computational investigation on identifying myristoylation sites in protein sequences. A training dataset with 196 positive and 84 negative peptide segments were obtained. Four types of features derived from the peptide segments following the myristoylation sites were used to specify myristoylatedand non-myristoylated sites. Then, feature selection methods including maximum relevance and minimum redundancy (mRMR), incremental feature selection (IFS), and a machine learning algorithm (extreme learning machine method) were adopted to extract optimal features for the algorithm to identify myristoylation sites in protein sequences, thereby building an optimal prediction model. As a result, 41 key features were extracted and used to build an optimal prediction model. The effectiveness of the optimal prediction model was further validated by its performance on a test dataset. Furthermore, detailed analyses were also performed on the extracted 41 features to gain insight into the mechanism of myristoylation modification. This study provided a new computational method for identifying myristoylation sites in protein sequences. We believe that it can be a useful tool to predict myristoylation sites from protein sequences. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Evolutionary mysteries in meiosis.

PubMed

Lenormand, Thomas; Engelstädter, Jan; Johnston, Susan E; Wijnker, Erik; Haag, Christoph R

2016-10-19

Meiosis is a key event of sexual life cycles in eukaryotes. Its mechanistic details have been uncovered in several model organisms, and most of its essential features have received various and often contradictory evolutionary interpretations. In this perspective, we present an overview of these often 'weird' features. We discuss the origin of meiosis (origin of ploidy reduction and recombination, two-step meiosis), its secondary modifications (in polyploids or asexuals, inverted meiosis), its importance in punctuating life cycles (meiotic arrests, epigenetic resetting, meiotic asymmetry, meiotic fairness) and features associated with recombination (disjunction constraints, heterochiasmy, crossover interference and hotspots). We present the various evolutionary scenarios and selective pressures that have been proposed to account for these features, and we highlight that their evolutionary significance often remains largely mysterious. Resolving these mysteries will likely provide decisive steps towards understanding why sex and recombination are found in the majority of eukaryotes.This article is part of the themed issue 'Weird sex: the underappreciated diversity of sexual reproduction'. © 2016 The Author(s).

Evolutionary mysteries in meiosis

PubMed Central

2016-01-01

Meiosis is a key event of sexual life cycles in eukaryotes. Its mechanistic details have been uncovered in several model organisms, and most of its essential features have received various and often contradictory evolutionary interpretations. In this perspective, we present an overview of these often ‘weird’ features. We discuss the origin of meiosis (origin of ploidy reduction and recombination, two-step meiosis), its secondary modifications (in polyploids or asexuals, inverted meiosis), its importance in punctuating life cycles (meiotic arrests, epigenetic resetting, meiotic asymmetry, meiotic fairness) and features associated with recombination (disjunction constraints, heterochiasmy, crossover interference and hotspots). We present the various evolutionary scenarios and selective pressures that have been proposed to account for these features, and we highlight that their evolutionary significance often remains largely mysterious. Resolving these mysteries will likely provide decisive steps towards understanding why sex and recombination are found in the majority of eukaryotes. This article is part of the themed issue ‘Weird sex: the underappreciated diversity of sexual reproduction’. PMID:27619705

Learning templates for artistic portrait lighting analysis.

PubMed

Chen, Xiaowu; Jin, Xin; Wu, Hongyu; Zhao, Qinping

2015-02-01

Lighting is a key factor in creating impressive artistic portraits. In this paper, we propose to analyze portrait lighting by learning templates of lighting styles. Inspired by the experience of artists, we first define several novel features that describe the local contrasts in various face regions. The most informative features are then selected with a stepwise feature pursuit algorithm to derive the templates of various lighting styles. After that, the matching scores that measure the similarity between a testing portrait and those templates are calculated for lighting style classification. Furthermore, we train a regression model by the subjective scores and the feature responses of a template to predict the score of a portrait lighting quality. Based on the templates, a novel face illumination descriptor is defined to measure the difference between two portrait lightings. Experimental results show that the learned templates can well describe the lighting styles, whereas the proposed approach can assess the lighting quality of artistic portraits as human being does.

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

NASA Astrophysics Data System (ADS)

He, Li; Song, Xuan

2018-03-01

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

Testing earthquake source inversion methodologies

USGS Publications Warehouse

Page, M.; Mai, P.M.; Schorlemmer, D.

2011-01-01

Source Inversion Validation Workshop; Palm Springs, California, 11-12 September 2010; Nowadays earthquake source inversions are routinely performed after large earthquakes and represent a key connection between recorded seismic and geodetic data and the complex rupture process at depth. The resulting earthquake source models quantify the spatiotemporal evolution of ruptures. They are also used to provide a rapid assessment of the severity of an earthquake and to estimate losses. However, because of uncertainties in the data, assumed fault geometry and velocity structure, and chosen rupture parameterization, it is not clear which features of these source models are robust. Improved understanding of the uncertainty and reliability of earthquake source inversions will allow the scientific community to use the robust features of kinematic inversions to more thoroughly investigate the complexity of the rupture process and to better constrain other earthquakerelated computations, such as ground motion simulations and static stress change calculations.

A predication model for combustion modes of the scramjet-powered aerospace vehicle based on the nonlinear features of the isolator flow field

NASA Astrophysics Data System (ADS)

Yang, Qingchun; Wang, Hongxin; Chetehouna, Khaled; Gascoin, Nicolas

2017-01-01

The supersonic combustion ramjet (scramjet) engine remains the most promising airbreathing engine cycle for hypersonic flight, particularly the high-performance dual-mode scramjet in the range of flight Mach number from 4 to 7, because it can operates under different combustion modes. Isolator is a very key component of the dual-mode scramjet engine. In this paper, nonlinear characteristics of combustion mode transition is theoretically analyzed. The discontinuous sudden changes of static pressure and Mach number are obtained as the mode transition occurs, which emphasizing the importance of predication and control of combustion modes. In this paper, a predication model of different combustion modes is developed based on these these nonlinear features in the isolator flow field. it can provide a valuable reference for control system design of the scramjet-powered aerospace vehicle.

"Why would I want to go out?": Age-related Vision Loss and Social Participation.

PubMed

Laliberte Rudman, Debbie; Gold, Deborah; McGrath, Colleen; Zuvela, Biljana; Spafford, Marlee M; Renwick, Rebecca

2016-12-01

Social participation, a key determinant of healthy aging, is often negatively impacted by age-related vision loss (ARVL). This grounded theory study aimed to understand social participation as a process negotiated in everyday life by older adults with ARVL. Interviews, audio diaries, and life space maps were used to collect data with 21 older adults in two Ontario cities. Inductive data analysis resulted in a transactional model of the process of negotiating social participation in context. This model depicts how environmental features and resources, skills and abilities, and risks and vulnerabilities transacted with values and priorities to affect if and how social participation occurred within the context of daily life. The findings point to several ways that research and services addressing the social participation of older adults with ARVL need to expand, particularly in relation to environmental features and resources, risk, and the prioritization of independence.

SIFT optimization and automation for matching images from multiple temporal sources

NASA Astrophysics Data System (ADS)

Castillo-Carrión, Sebastián; Guerrero-Ginel, José-Emilio

2017-05-01

Scale Invariant Feature Transformation (SIFT) was applied to extract tie-points from multiple source images. Although SIFT is reported to perform reliably under widely different radiometric and geometric conditions, using the default input parameters resulted in too few points being found. We found that the best solution was to focus on large features as these are more robust and not prone to scene changes over time, which constitutes a first approach to the automation of processes using mapping applications such as geometric correction, creation of orthophotos and 3D models generation. The optimization of five key SIFT parameters is proposed as a way of increasing the number of correct matches; the performance of SIFT is explored in different images and parameter values, finding optimization values which are corroborated using different validation imagery. The results show that the optimization model improves the performance of SIFT in correlating multitemporal images captured from different sources.

Mapping pathological phenotypes in a mouse model of CDKL5 disorder.

PubMed

Amendola, Elena; Zhan, Yang; Mattucci, Camilla; Castroflorio, Enrico; Calcagno, Eleonora; Fuchs, Claudia; Lonetti, Giuseppina; Silingardi, Davide; Vyssotski, Alexei L; Farley, Dominika; Ciani, Elisabetta; Pizzorusso, Tommaso; Giustetto, Maurizio; Gross, Cornelius T

2014-01-01

Mutations in cyclin-dependent kinase-like 5 (CDKL5) cause early-onset epileptic encephalopathy, a neurodevelopmental disorder with similarities to Rett Syndrome. Here we describe the physiological, molecular, and behavioral phenotyping of a Cdkl5 conditional knockout mouse model of CDKL5 disorder. Behavioral analysis of constitutive Cdkl5 knockout mice revealed key features of the human disorder, including limb clasping, hypoactivity, and abnormal eye tracking. Anatomical, physiological, and molecular analysis of the knockout uncovered potential pathological substrates of the disorder, including reduced dendritic arborization of cortical neurons, abnormal electroencephalograph (EEG) responses to convulsant treatment, decreased visual evoked responses (VEPs), and alterations in the Akt/rpS6 signaling pathway. Selective knockout of Cdkl5 in excitatory and inhibitory forebrain neurons allowed us to map the behavioral features of the disorder to separable cell-types. These findings identify physiological and molecular deficits in specific forebrain neuron populations as possible pathological substrates in CDKL5 disorder.

Predicting DNA hybridization kinetics from sequence

NASA Astrophysics Data System (ADS)

Zhang, Jinny X.; Fang, John Z.; Duan, Wei; Wu, Lucia R.; Zhang, Angela W.; Dalchau, Neil; Yordanov, Boyan; Petersen, Rasmus; Phillips, Andrew; Zhang, David Yu

2018-01-01

Hybridization is a key molecular process in biology and biotechnology, but so far there is no predictive model for accurately determining hybridization rate constants based on sequence information. Here, we report a weighted neighbour voting (WNV) prediction algorithm, in which the hybridization rate constant of an unknown sequence is predicted based on similarity reactions with known rate constants. To construct this algorithm we first performed 210 fluorescence kinetics experiments to observe the hybridization kinetics of 100 different DNA target and probe pairs (36 nt sub-sequences of the CYCS and VEGF genes) at temperatures ranging from 28 to 55 °C. Automated feature selection and weighting optimization resulted in a final six-feature WNV model, which can predict hybridization rate constants of new sequences to within a factor of 3 with ∼91% accuracy, based on leave-one-out cross-validation. Accurate prediction of hybridization kinetics allows the design of efficient probe sequences for genomics research.

System Engineering Infrastructure Evolution Galileo IOV and the Steps Beyond

NASA Astrophysics Data System (ADS)

Eickhoff, J.; Herpel, H.-J.; Steinle, T.; Birn, R.; Steiner, W.-D.; Eisenmann, H.; Ludwig, T.

2009-05-01

The trends to more and more constrained financial budgets in satellite engineering require a permanent optimization of the S/C system engineering processes and infrastructure. Astrium in the recent years already has built up a system simulation infrastructure - the "Model-based Development & Verification Environment" - which meanwhile is well known all over Europe and is established as Astrium's standard approach for ESA, DLR projects and now even the EU/ESA-Project Galileo IOV. The key feature of the MDVE / FVE approach is to provide entire S/C simulation (with full featured OBC simulation) already in early phases to start OBSW code tests on a simulated S/C and to later add hardware in the loop step by step up to an entire "Engineering Functional Model (EFM)" or "FlatSat". The subsequent enhancements to this simulator infrastructure w.r.t. spacecraft design data handling are reported in the following sections.

A hyperacute neurology team - transforming emergency neurological care.

PubMed

Nitkunan, Arani; MacDonald, Bridget K; Boodhoo, Ajay; Tomkins, Andrew; Smyth, Caitlin; Southam, Medina; Schon, Fred

2017-07-01

We present the results of an 18-month study of a new model of how to care for emergency neurological admissions. We have established a hyperacute neurology team at a single district general hospital. Key features are a senior acute neurology nurse coordinator, an exclusively consultant-delivered service, acute epilepsy nurses, an acute neurophysiology service supported by neuroradiology and acute physicians and based within the acute medical admissions unit. Key improvements are a major increase in the number of patients seen, the speed with which they are seen and the percentage seen on acute medical unit before going to the general wards. We have shown a reduced length of stay and readmission rates for patients with epilepsy. Epilepsy accounted for 30% of all referrals. The cost implications of running this service are modest. We feel that this model is worthy of widespread consideration. © Royal College of Physicians 2017. All rights reserved.

Standard Clock in primordial density perturbations and cosmic microwave background

NASA Astrophysics Data System (ADS)

Chen, Xingang; Namjoo, Mohammad Hossein

2014-12-01

Standard Clocks in the primordial epoch leave a special type of features in the primordial perturbations, which can be used to directly measure the scale factor of the primordial universe as a function of time a (t), thus discriminating between inflation and alternatives. We have started to search for such signals in the Planck 2013 data using the key predictions of the Standard Clock. In this Letter, we summarize the key predictions of the Standard Clock and present an interesting candidate example in Planck 2013 data. Motivated by this candidate, we construct and compute full Standard Clock models and use the more complete prediction to make more extensive comparison with data. Although this candidate is not yet statistically significant, we use it to illustrate how Standard Clocks appear in Cosmic Microwave Background (CMB) and how they can be further tested by future data. We also use it to motivate more detailed theoretical model building.

An appraisal of the literature on teaching physical examination skills.

PubMed

Easton, Graham; Stratford-Martin, James; Atherton, Helen

2012-07-01

To discover which models for teaching physical examination skills have been proposed, and to appraise the evidence for each. We conducted a narrative review of relevant literature from 1990-2010. We searched the databases MEDLINE, PsycINFO, and ERIC (The Education Resource Information Centre) for the terms: 'physical examination' AND 'teaching' as both MESH terms and keyword searches. We excluded web-based or video teaching, non-physical examination skills (e.g. communication skills), and articles about simulated patients or models. We identified five relevant articles. These five studies outlined several approaches to teaching physical examination skills, including Peyton's 4-step model, an adaptation of his model to a 6-step model; the silent run through; and collaborative discovery. There was little evidence to support one method over others. One controlled trial suggested that silent run-through could improve performance of complex motor tasks, and another suggested that collaborative discovery improves students' ability to recognise key findings in cardiac examinations. There are several models for teaching physical examinations, but few are designed specifically for that purpose and there is little evidence to back any one model over another. We propose an approach which adopts several key features of these models. Future research could usefully evaluate the effectiveness of the proposed models, or develop innovative practical models for teaching examination skills.

A signal-detection-based diagnostic-feature-detection model of eyewitness identification.

PubMed

Wixted, John T; Mickes, Laura

2014-04-01

The theoretical understanding of eyewitness identifications made from a police lineup has long been guided by the distinction between absolute and relative decision strategies. In addition, the accuracy of identifications associated with different eyewitness memory procedures has long been evaluated using measures like the diagnosticity ratio (the correct identification rate divided by the false identification rate). Framed in terms of signal-detection theory, both the absolute/relative distinction and the diagnosticity ratio are mainly relevant to response bias while remaining silent about the key issue of diagnostic accuracy, or discriminability (i.e., the ability to tell the difference between innocent and guilty suspects in a lineup). Here, we propose a signal-detection-based model of eyewitness identification, one that encourages the use of (and helps to conceptualize) receiver operating characteristic (ROC) analysis to measure discriminability. Recent ROC analyses indicate that the simultaneous presentation of faces in a lineup yields higher discriminability than the presentation of faces in isolation, and we propose a diagnostic feature-detection hypothesis to account for that result. According to this hypothesis, the simultaneous presentation of faces allows the eyewitness to appreciate that certain facial features (viz., those that are shared by everyone in the lineup) are non-diagnostic of guilt. To the extent that those non-diagnostic features are discounted in favor of potentially more diagnostic features, the ability to discriminate innocent from guilty suspects will be enhanced.

Heterosynaptic metaplasticity in the hippocampus in vivo: A BCM-like modifiable threshold for LTP

PubMed Central

Abraham, Wickliffe C.; Mason-Parker, Sara E.; Bear, Mark F.; Webb, Sarah; Tate, Warren P.

2001-01-01

The homeostatic maintenance of the “modification threshold” for inducing long-term potentiation (LTP) is a fundamental feature of the Bienenstock, Cooper, and Munro (BCM) model of synaptic plasticity. In the present study, two key features of the modification threshold, its heterosynaptic expression and its regulation by postsynaptic neural activity, were tested experimentally in the dentate gyrus of awake, freely moving rats. Conditioning stimulation ranging from 10 to 1,440 brief 400-Hz trains, when applied to medial perforant path afferents, raised the threshold for LTP induction heterosynaptically in the neighboring lateral perforant path synapses. This effect recovered slowly over a 7- to 35-day period. The same conditioning paradigms, however, did not affect the reversal of long-term depression. The inhibition of LTP by medial-path conditioning stimulation was N-methyl-D-aspartate (NMDA) receptor-dependent, but antidromic stimulation of the granule cells could also inhibit lateral path LTP induction, independently of NMDA receptor activation. Increased calcium buffering is a potential mechanism underlying the altered LTP threshold, but the levels of two important calcium-binding proteins did not increase after conditioning stimulation, nor was de novo protein synthesis required for generating the threshold shift. These data confirm, in an in vivo model, two key postulates of the BCM model regarding the LTP threshold. They also provide further evidence for the broad sensitivity of synaptic plasticity mechanisms to the history of prior activity, i.e., metaplasticity. PMID:11517323

Individualized Prediction of Reading Comprehension Ability Using Gray Matter Volume.

PubMed

Cui, Zaixu; Su, Mengmeng; Li, Liangjie; Shu, Hua; Gong, Gaolang

2018-05-01

Reading comprehension is a crucial reading skill for learning and putatively contains 2 key components: reading decoding and linguistic comprehension. Current understanding of the neural mechanism underlying these reading comprehension components is lacking, and whether and how neuroanatomical features can be used to predict these 2 skills remain largely unexplored. In the present study, we analyzed a large sample from the Human Connectome Project (HCP) dataset and successfully built multivariate predictive models for these 2 skills using whole-brain gray matter volume features. The results showed that these models effectively captured individual differences in these 2 skills and were able to significantly predict these components of reading comprehension for unseen individuals. The strict cross-validation using the HCP cohort and another independent cohort of children demonstrated the model generalizability. The identified gray matter regions contributing to the skill prediction consisted of a wide range of regions covering the putative reading, cerebellum, and subcortical systems. Interestingly, there were gender differences in the predictive models, with the female-specific model overestimating the males' abilities. Moreover, the identified contributing gray matter regions for the female-specific and male-specific models exhibited considerable differences, supporting a gender-dependent neuroanatomical substrate for reading comprehension.

The Geoengineering Model Intercomparison Project Phase 6 (GeoMIP6): Simulation design and preliminary results

DOE PAGES

Kravitz, Benjamin S.; Robock, Alan; Tilmes, S.; ...

2015-10-27

We present a suite of new climate model experiment designs for the Geoengineering Model Intercomparison Project (GeoMIP). This set of experiments, named GeoMIP6 (to be consistent with the Coupled Model Intercomparison Project Phase 6), builds on the previous GeoMIP project simulations, and has been expanded to address several further important topics, including key uncertainties in extreme events, the use of geoengineering as part of a portfolio of responses to climate change, and the relatively new idea of cirrus cloud thinning to allow more long wave radiation to escape to space. We discuss experiment designs, as well as the rationale formore » those designs, showing preliminary results from individual models when available. We also introduce a new feature, called the GeoMIP Testbed, which provides a platform for simulations that will be performed with a few models and subsequently assessed to determine whether the proposed experiment designs will be adopted as core (Tier 1) GeoMIP experiments. In conclusion, this is meant to encourage various stakeholders to propose new targeted experiments that address their key open science questions, with the goal of making GeoMIP more relevant to a broader set of communities.« less

State estimation and prediction using clustered particle filters.

PubMed

Lee, Yoonsang; Majda, Andrew J

2016-12-20

Particle filtering is an essential tool to improve uncertain model predictions by incorporating noisy observational data from complex systems including non-Gaussian features. A class of particle filters, clustered particle filters, is introduced for high-dimensional nonlinear systems, which uses relatively few particles compared with the standard particle filter. The clustered particle filter captures non-Gaussian features of the true signal, which are typical in complex nonlinear dynamical systems such as geophysical systems. The method is also robust in the difficult regime of high-quality sparse and infrequent observations. The key features of the clustered particle filtering are coarse-grained localization through the clustering of the state variables and particle adjustment to stabilize the method; each observation affects only neighbor state variables through clustering and particles are adjusted to prevent particle collapse due to high-quality observations. The clustered particle filter is tested for the 40-dimensional Lorenz 96 model with several dynamical regimes including strongly non-Gaussian statistics. The clustered particle filter shows robust skill in both achieving accurate filter results and capturing non-Gaussian statistics of the true signal. It is further extended to multiscale data assimilation, which provides the large-scale estimation by combining a cheap reduced-order forecast model and mixed observations of the large- and small-scale variables. This approach enables the use of a larger number of particles due to the computational savings in the forecast model. The multiscale clustered particle filter is tested for one-dimensional dispersive wave turbulence using a forecast model with model errors.

State estimation and prediction using clustered particle filters

PubMed Central

Lee, Yoonsang; Majda, Andrew J.

2016-01-01

Particle filtering is an essential tool to improve uncertain model predictions by incorporating noisy observational data from complex systems including non-Gaussian features. A class of particle filters, clustered particle filters, is introduced for high-dimensional nonlinear systems, which uses relatively few particles compared with the standard particle filter. The clustered particle filter captures non-Gaussian features of the true signal, which are typical in complex nonlinear dynamical systems such as geophysical systems. The method is also robust in the difficult regime of high-quality sparse and infrequent observations. The key features of the clustered particle filtering are coarse-grained localization through the clustering of the state variables and particle adjustment to stabilize the method; each observation affects only neighbor state variables through clustering and particles are adjusted to prevent particle collapse due to high-quality observations. The clustered particle filter is tested for the 40-dimensional Lorenz 96 model with several dynamical regimes including strongly non-Gaussian statistics. The clustered particle filter shows robust skill in both achieving accurate filter results and capturing non-Gaussian statistics of the true signal. It is further extended to multiscale data assimilation, which provides the large-scale estimation by combining a cheap reduced-order forecast model and mixed observations of the large- and small-scale variables. This approach enables the use of a larger number of particles due to the computational savings in the forecast model. The multiscale clustered particle filter is tested for one-dimensional dispersive wave turbulence using a forecast model with model errors. PMID:27930332

Age structure is critical to the population dynamics and survival of honeybee colonies

PubMed Central

Betti, M. I.; Wahl, L. M.

2016-01-01

Age structure is an important feature of the division of labour within honeybee colonies, but its effects on colony dynamics have rarely been explored. We present a model of a honeybee colony that incorporates this key feature, and use this model to explore the effects of both winter and disease on the fate of the colony. The model offers a novel explanation for the frequently observed phenomenon of ‘spring dwindle’, which emerges as a natural consequence of the age-structured dynamics. Furthermore, the results indicate that a model taking age structure into account markedly affects the predicted timing and severity of disease within a bee colony. The timing of the onset of disease with respect to the changing seasons may also have a substantial impact on the fate of a honeybee colony. Finally, simulations predict that an infection may persist in a honeybee colony over several years, with effects that compound over time. Thus, the ultimate collapse of the colony may be the result of events several years past. PMID:28018627

An integrate-and-fire model for synchronized bursting in a network of cultured cortical neurons.

PubMed

French, D A; Gruenstein, E I

2006-12-01

It has been suggested that spontaneous synchronous neuronal activity is an essential step in the formation of functional networks in the central nervous system. The key features of this type of activity consist of bursts of action potentials with associated spikes of elevated cytoplasmic calcium. These features are also observed in networks of rat cortical neurons that have been formed in culture. Experimental studies of these cultured networks have led to several hypotheses for the mechanisms underlying the observed synchronized oscillations. In this paper, bursting integrate-and-fire type mathematical models for regular spiking (RS) and intrinsic bursting (IB) neurons are introduced and incorporated through a small-world connection scheme into a two-dimensional excitatory network similar to those in the cultured network. This computer model exhibits spontaneous synchronous activity through mechanisms similar to those hypothesized for the cultured experimental networks. Traces of the membrane potential and cytoplasmic calcium from the model closely match those obtained from experiments. We also consider the impact on network behavior of the IB neurons, the geometry and the small world connection scheme.

Modelling fragile X syndrome in the laboratory setting: A behavioral perspective.

PubMed

Melancia, Francesca; Trezza, Viviana

2018-04-25

Fragile X syndrome is the most common form of inherited mental retardation and the most frequent monogenic cause of syndromic autism spectrum disorders. The syndrome is caused by the loss of the Fragile X Mental Retardation Protein (FMRP), a key RNA-binding protein involved in synaptic plasticity and neuronal morphology. Patients show intellectual disability, social deficits, repetitive behaviors and impairments in social communication. The aim of this review is to outline the importance of behavioral phenotyping of animal models of FXS from a developmental perspective, by showing how the behavioral characteristics of FXS at the clinical level can be translated into effective, developmentally-specific and clinically meaningful behavioral readouts in the laboratory setting. After introducing the behavioral features, diagnostic criteria and off-label pharmacotherapy of FXS, we outline how FXS-relevant behavioral features can be modelled in laboratory animals in the course of development: we review the progress to date, discuss how behavioral phenotyping in animal models of FXS is essential to identify potential treatments, and discuss caveats and future directions in this research field. Copyright © 2018. Published by Elsevier B.V.

Simulating Thermal Cycling and Isothermal Deformation Response of Polycrystalline NiTi

NASA Technical Reports Server (NTRS)

Manchiraju, Sivom; Gaydosh, Darrell J.; Noebe, Ronald D.; Anderson, Peter M.

2011-01-01

A microstructure-based FEM model that couples crystal plasticity, crystallographic descriptions of the B2-B19' martensitic phase transformation, and anisotropic elasticity is used to simulate thermal cycling and isothermal deformation in polycrystalline NiTi (49.9at% Ni). The model inputs include anisotropic elastic properties, polycrystalline texture, DSC data, and a subset of isothermal deformation and load-biased thermal cycling data. A key experimental trend is captured.namely, the transformation strain during thermal cycling is predicted to reach a peak with increasing bias stress, due to the onset of plasticity at larger bias stress. Plasticity induces internal stress that affects both thermal cycling and isothermal deformation responses. Affected thermal cycling features include hysteretic width, two-way shape memory effect, and evolution of texture with increasing bias stress. Affected isothermal deformation features include increased hardening during loading and retained martensite after unloading. These trends are not captured by microstructural models that lack plasticity, nor are they all captured in a robust manner by phenomenological approaches. Despite this advance in microstructural modeling, quantitative differences exist, such as underprediction of open loop strain during thermal cycling.

Probabilistic models for capturing more physicochemical properties on protein-protein interface.

PubMed

Guo, Fei; Li, Shuai Cheng; Du, Pufeng; Wang, Lusheng

2014-06-23

Protein-protein interactions play a key role in a multitude of biological processes, such as signal transduction, de novo drug design, immune responses, and enzymatic activities. It is of great interest to understand how proteins interact with each other. The general approach is to explore all possible poses and identify near-native ones with the energy function. The key issue here is to design an effective energy function, based on various physicochemical properties. In this paper, we first identify two new features, the coupled dihedral angles on the interfaces and the geometrical information on π-π interactions. We study these two features through statistical methods: a mixture of bivariate von Mises distributions is used to model the correlation of the coupled dihedral angles, while a mixture of bivariate normal distributions is used to model the orientation of the aromatic rings on π-π interactions. Using 6438 complexes, we parametrize the joint distribution of each new feature. Then, we propose a novel method to construct the energy function for protein-protein interface prediction, which includes the new features as well as the existing energy items such as dDFIRE energy, side-chain energy, atom contact energy, and amino acid energy. Experiments show that our method outperforms the state-of-the-art methods, ZRANK and ClusPro. We use the CAPRI evaluation criteria, Irmsd value, and Fnat value. On Benchmark v4.0, our method has an average Irmsd value of 3.39 Å and Fnat value of 62%, which improves upon the average Irmsd value of 3.89 Å and Fnat value of 49% for ZRANK, and the average Irmsd value of 3.99 Å and Fnat value of 46% for ClusPro. On the CAPRI targets, our method has an average Irmsd value of 3.56 Å and Fnat value of 42%, which improves upon the average Irmsd value of 4.27 Å and Fnat value of 39% for ZRANK, the average Irmsd value of 5.15 Å and Fnat value of 30% for ClusPro.

A high-frequency warm shallow water acoustic communications channel model and measurements.

PubMed

Chitre, Mandar

2007-11-01

Underwater acoustic communication is a core enabling technology with applications in ocean monitoring using remote sensors and autonomous underwater vehicles. One of the more challenging underwater acoustic communication channels is the medium-range very shallow warm-water channel, common in tropical coastal regions. This channel exhibits two key features-extensive time-varying multipath and high levels of non-Gaussian ambient noise due to snapping shrimp-both of which limit the performance of traditional communication techniques. A good understanding of the communications channel is key to the design of communication systems. It aids in the development of signal processing techniques as well as in the testing of the techniques via simulation. In this article, a physics-based channel model for the very shallow warm-water acoustic channel at high frequencies is developed, which are of interest to medium-range communication system developers. The model is based on ray acoustics and includes time-varying statistical effects as well as non-Gaussian ambient noise statistics observed during channel studies. The model is calibrated and its accuracy validated using measurements made at sea.

EnergyPlus Run Time Analysis

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

Hong, Tianzhen; Buhl, Fred; Haves, Philip

2008-09-20

EnergyPlus is a new generation building performance simulation program offering many new modeling capabilities and more accurate performance calculations integrating building components in sub-hourly time steps. However, EnergyPlus runs much slower than the current generation simulation programs. This has become a major barrier to its widespread adoption by the industry. This paper analyzed EnergyPlus run time from comprehensive perspectives to identify key issues and challenges of speeding up EnergyPlus: studying the historical trends of EnergyPlus run time based on the advancement of computers and code improvements to EnergyPlus, comparing EnergyPlus with DOE-2 to understand and quantify the run time differences,more » identifying key simulation settings and model features that have significant impacts on run time, and performing code profiling to identify which EnergyPlus subroutines consume the most amount of run time. This paper provides recommendations to improve EnergyPlus run time from the modeler?s perspective and adequate computing platforms. Suggestions of software code and architecture changes to improve EnergyPlus run time based on the code profiling results are also discussed.« less

SURF Model Calibration Strategy

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

Menikoff, Ralph

2017-03-10

SURF and SURFplus are high explosive reactive burn models for shock initiation and propagation of detonation waves. They are engineering models motivated by the ignition & growth concept of high spots and for SURFplus a second slow reaction for the energy release from carbon clustering. A key feature of the SURF model is that there is a partial decoupling between model parameters and detonation properties. This enables reduced sets of independent parameters to be calibrated sequentially for the initiation and propagation regimes. Here we focus on a methodology for tting the initiation parameters to Pop plot data based on 1-Dmore » simulations to compute a numerical Pop plot. In addition, the strategy for tting the remaining parameters for the propagation regime and failure diameter is discussed.« less

Implementing a Trauma-Informed Model of Care in a Community Acute Mental Health Team.

PubMed

Moloney, Bill; Cameron, Ian; Baker, Ashley; Feeney, Johanna; Korner, Anthony; Kornhaber, Rachel; Cleary, Michelle; McLean, Loyola

2018-04-12

In this paper, we demonstrate the value of implementing a Trauma-Informed Model of Care in a Community Acute Mental Health Team by providing brief intensive treatment (comprising risk interventions, brief counselling, collaborative formulation and pharmacological treatment). The team utilised the Conversational Model (CM), a psychotherapeutic approach for complex trauma. Key features of the CM are described in this paper using a clinical case study. The addition of the Conversational Model approach to practice has enabled better understandings of consumers' capacities and ways to then engage, converse, and intervene. The implementation of this intervention has led to a greater sense of self-efficacy amongst clinicians, who can now articulate a clear counselling model of care.

Managing professional work: three models of control for health organizations.

PubMed Central

Scott, W R

1982-01-01

Three arrangements for structuring the work of professional participants in professional organizations are described, contrasted and evaluated. Arguments are illustrated by application to the organization of physicians within hospitals. The primary rationale, the support structures that have fostered its development, the key structural features and the advantages and disadvantages of each arrangement are described. The effect on these arrangements of structures and forces external to any particular professional organization is emphasized. PMID:6749761

Subject-based discriminative sparse representation model for detection of concealed information.

PubMed

Akhavan, Amir; Moradi, Mohammad Hassan; Vand, Safa Rafiei

2017-05-01

The use of machine learning approaches in concealed information test (CIT) plays a key role in the progress of this neurophysiological field. In this paper, we presented a new machine learning method for CIT in which each subject is considered independent of the others. The main goal of this study is to adapt the discriminative sparse models to be applicable for subject-based concealed information test. In order to provide sufficient discriminability between guilty and innocent subjects, we introduced a novel discriminative sparse representation model and its appropriate learning methods. For evaluation of the method forty-four subjects participated in a mock crime scenario and their EEG data were recorded. As the model input, in this study the recurrence plot features were extracted from single trial data of different stimuli. Then the extracted feature vectors were reduced using statistical dependency method. The reduced feature vector went through the proposed subject-based sparse model in which the discrimination power of sparse code and reconstruction error were applied simultaneously. Experimental results showed that the proposed approach achieved better performance than other competing discriminative sparse models. The classification accuracy, sensitivity and specificity of the presented sparsity-based method were about 93%, 91% and 95% respectively. Using the EEG data of a single subject in response to different stimuli types and with the aid of the proposed discriminative sparse representation model, one can distinguish guilty subjects from innocent ones. Indeed, this property eliminates the necessity of several subject EEG data in model learning and decision making for a specific subject. Copyright © 2017 Elsevier B.V. All rights reserved.

SVGenes: a library for rendering genomic features in scalable vector graphic format.

PubMed

Etherington, Graham J; MacLean, Daniel

2013-08-01

Drawing genomic features in attractive and informative ways is a key task in visualization of genomics data. Scalable Vector Graphics (SVG) format is a modern and flexible open standard that provides advanced features including modular graphic design, advanced web interactivity and animation within a suitable client. SVGs do not suffer from loss of image quality on re-scaling and provide the ability to edit individual elements of a graphic on the whole object level independent of the whole image. These features make SVG a potentially useful format for the preparation of publication quality figures including genomic objects such as genes or sequencing coverage and for web applications that require rich user-interaction with the graphical elements. SVGenes is a Ruby-language library that uses SVG primitives to render typical genomic glyphs through a simple and flexible Ruby interface. The library implements a simple Page object that spaces and contains horizontal Track objects that in turn style, colour and positions features within them. Tracks are the level at which visual information is supplied providing the full styling capability of the SVG standard. Genomic entities like genes, transcripts and histograms are modelled in Glyph objects that are attached to a track and take advantage of SVG primitives to render the genomic features in a track as any of a selection of defined glyphs. The feature model within SVGenes is simple but flexible and not dependent on particular existing gene feature formats meaning graphics for any existing datasets can easily be created without need for conversion. The library is provided as a Ruby Gem from https://rubygems.org/gems/bio-svgenes under the MIT license, and open source code is available at https://github.com/danmaclean/bioruby-svgenes also under the MIT License. dan.maclean@tsl.ac.uk.

Identifying Key Features of Student Performance in Educational Video Games and Simulations through Cluster Analysis

ERIC Educational Resources Information Center

Kerr, Deirdre; Chung, Gregory K. W. K.

2012-01-01

The assessment cycle of "evidence-centered design" (ECD) provides a framework for treating an educational video game or simulation as an assessment. One of the main steps in the assessment cycle of ECD is the identification of the key features of student performance. While this process is relatively simple for multiple choice tests, when…

77 FR 28541 - Request for Comments on the Recommendation for the Disclosure of Sequence Listings Using XML...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-15

... (EPO) as the lead, to propose a revised standard for the filing of nucleotide and/or amino acid.... ST.25 uses a controlled vocabulary of feature keys to describe nucleic acid and amino acid sequences... patent data purposes. The XML standard also includes four qualifiers for amino acids. These feature keys...

Crafting your Elevator Pitch: Key Features of an Elevator Speech to Help You Reach the Top Floor

EPA Science Inventory

You never know when you will end up talking to someone who will end up helping to shape your career. Many of these chance meetings are brief and when you only get 2-3 minutes to make your case everything that you say has to count. This presentation will cover the key features o...

Hybrid multiscale modeling and prediction of cancer cell behavior

PubMed Central

Habibi, Jafar

2017-01-01

Background Understanding cancer development crossing several spatial-temporal scales is of great practical significance to better understand and treat cancers. It is difficult to tackle this challenge with pure biological means. Moreover, hybrid modeling techniques have been proposed that combine the advantages of the continuum and the discrete methods to model multiscale problems. Methods In light of these problems, we have proposed a new hybrid vascular model to facilitate the multiscale modeling and simulation of cancer development with respect to the agent-based, cellular automata and machine learning methods. The purpose of this simulation is to create a dataset that can be used for prediction of cell phenotypes. By using a proposed Q-learning based on SVR-NSGA-II method, the cells have the capability to predict their phenotypes autonomously that is, to act on its own without external direction in response to situations it encounters. Results Computational simulations of the model were performed in order to analyze its performance. The most striking feature of our results is that each cell can select its phenotype at each time step according to its condition. We provide evidence that the prediction of cell phenotypes is reliable. Conclusion Our proposed model, which we term a hybrid multiscale modeling of cancer cell behavior, has the potential to combine the best features of both continuum and discrete models. The in silico results indicate that the 3D model can represent key features of cancer growth, angiogenesis, and its related micro-environment and show that the findings are in good agreement with biological tumor behavior. To the best of our knowledge, this paper is the first hybrid vascular multiscale modeling of cancer cell behavior that has the capability to predict cell phenotypes individually by a self-generated dataset. PMID:28846712

Hybrid multiscale modeling and prediction of cancer cell behavior.

PubMed

Zangooei, Mohammad Hossein; Habibi, Jafar

2017-01-01

Understanding cancer development crossing several spatial-temporal scales is of great practical significance to better understand and treat cancers. It is difficult to tackle this challenge with pure biological means. Moreover, hybrid modeling techniques have been proposed that combine the advantages of the continuum and the discrete methods to model multiscale problems. In light of these problems, we have proposed a new hybrid vascular model to facilitate the multiscale modeling and simulation of cancer development with respect to the agent-based, cellular automata and machine learning methods. The purpose of this simulation is to create a dataset that can be used for prediction of cell phenotypes. By using a proposed Q-learning based on SVR-NSGA-II method, the cells have the capability to predict their phenotypes autonomously that is, to act on its own without external direction in response to situations it encounters. Computational simulations of the model were performed in order to analyze its performance. The most striking feature of our results is that each cell can select its phenotype at each time step according to its condition. We provide evidence that the prediction of cell phenotypes is reliable. Our proposed model, which we term a hybrid multiscale modeling of cancer cell behavior, has the potential to combine the best features of both continuum and discrete models. The in silico results indicate that the 3D model can represent key features of cancer growth, angiogenesis, and its related micro-environment and show that the findings are in good agreement with biological tumor behavior. To the best of our knowledge, this paper is the first hybrid vascular multiscale modeling of cancer cell behavior that has the capability to predict cell phenotypes individually by a self-generated dataset.

Chaotic Dynamics of Linguistic-Like Processes at the Syntactical and Semantic Levels: in the Pursuit of a Multifractal Attractor

NASA Astrophysics Data System (ADS)

Nicolis, John S.; Katsikas, Anastassis A.

Collective parameters such as the Zipf's law-like statistics, the Transinformation, the Block Entropy and the Markovian character are compared for natural, genetic, musical and artificially generated long texts from generating partitions (alphabets) on homogeneous as well as on multifractal chaotic maps. It appears that minimal requirements for a language at the syntactical level such as memory, selectivity of few keywords and broken symmetry in one dimension (polarity) are more or less met by dynamically iterating simple maps or flows e.g. very simple chaotic hardware. The same selectivity is observed at the semantic level where the aim refers to partitioning a set of enviromental impinging stimuli onto coexisting attractors-categories. Under the regime of pattern recognition and classification, few key features of a pattern or few categories claim the lion's share of the information stored in this pattern and practically, only these key features are persistently scanned by the cognitive processor. A multifractal attractor model can in principle explain this high selectivity, both at the syntactical and the semantic levels.

Genetic Removal of Matrix Metalloproteinase 9 Rescues the Symptoms of Fragile X Syndrome in a Mouse Model

PubMed Central

Sidhu, Harpreet; Dansie, Lorraine E.; Hickmott, Peter W.

2014-01-01

Fmr1 knock-out (ko) mice display key features of fragile X syndrome (FXS), including delayed dendritic spine maturation and FXS-associated behaviors, such as poor socialization, obsessive-compulsive behavior, and hyperactivity. Here we provide conclusive evidence that matrix metalloproteinase-9 (MMP-9) is necessary to the development of FXS-associated defects in Fmr1 ko mice. Genetic disruption of Mmp-9 rescued key aspects of Fmr1 deficiency, including dendritic spine abnormalities, abnormal mGluR5-dependent LTD, as well as aberrant behaviors in open field and social novelty tests. Remarkably, MMP-9 deficiency also corrected non-neural features of Fmr1 deficiency—specifically macroorchidism—indicating that MMP-9 dysregulation contributes to FXS-associated abnormalities outside the CNS. Further, MMP-9 deficiency suppressed elevations of Akt, mammalian target of rapamycin, and eukaryotic translation initiation factor 4E phosphorylation seen in Fmr1 ko mice, which are also associated with other autistic spectrum disorders. These findings establish that MMP-9 is critical to the mechanisms responsible for neural and non-neural aspects of the FXS phenotype. PMID:25057190

Modelling of information diffusion on social networks with applications to WeChat

NASA Astrophysics Data System (ADS)

Liu, Liang; Qu, Bo; Chen, Bin; Hanjalic, Alan; Wang, Huijuan

2018-04-01

Traces of user activities recorded in online social networks open new possibilities to systematically understand the information diffusion process on social networks. From the online social network WeChat, we collected a large number of information cascade trees, each of which tells the spreading trajectory of a message/information such as which user creates the information and which users view or forward the information shared by which neighbours. In this work, we propose two heterogeneous non-linear models, one for the topologies of the information cascade trees and the other for the stochastic process of information diffusion on a social network. Both models are validated by the WeChat data in reproducing and explaining key features of cascade trees. Specifically, we apply the Random Recursive Tree (RRT) to model the growth of cascade trees. The RRT model could capture key features, i.e. the average path length and degree variance of a cascade tree in relation to the number of nodes (size) of the tree. Its single identified parameter quantifies the relative depth or broadness of the cascade trees and indicates that information propagates via a star-like broadcasting or viral-like hop by hop spreading. The RRT model explains the appearance of hubs, thus a possibly smaller average path length as the cascade size increases, as observed in WeChat. We further propose the stochastic Susceptible View Forward Removed (SVFR) model to depict the dynamic user behaviour including creating, viewing, forwarding and ignoring a message on a given social network. Beside the average path length and degree variance of the cascade trees in relation to their sizes, the SVFR model could further explain the power-law cascade size distribution in WeChat and unravel that a user with a large number of friends may actually have a smaller probability to read a message (s)he receives due to limited attention.

Salient features of the ciliated organ of asymmetry

PubMed Central

Amack, Jeffrey D.

2014-01-01

Many internal organs develop distinct left and right sides that are essential for their functions. In several vertebrate embryos, motile cilia generate an asymmetric fluid flow that plays an important role in establishing left-right (LR) signaling cascades. These ‘LR cilia’ are found in the ventral node and posterior notochordal plate in mammals, the gastrocoel roof plate in amphibians and Kupffer’s vesicle in teleost fish. I consider these transient ciliated structures as the ‘organ of asymmetry’ that directs LR patterning of the developing embryo. Variations in size and morphology of the organ of asymmetry in different vertebrate species have raised questions regarding the fundamental features that are required for LR determination. Here, I review current models for how LR asymmetry is established in vertebrates, discuss the cellular architecture of the ciliated organ of asymmetry and then propose key features of this organ that are critical for orienting the LR body axis. PMID:24481178

ICAN: Integrated composites analyzer

NASA Technical Reports Server (NTRS)

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

1984-01-01

The ICAN computer program performs all the essential aspects of mechanics/analysis/design of multilayered fiber composites. Modular, open-ended and user friendly, the program can handle a variety of composite systems having one type of fiber and one matrix as constituents as well as intraply and interply hybrid composite systems. It can also simulate isotropic layers by considering a primary composite system with negligible fiber volume content. This feature is specifically useful in modeling thin interply matrix layers. Hygrothermal conditions and various combinations of in-plane and bending loads can also be considered. Usage of this code is illustrated with a sample input and the generated output. Some key features of output are stress concentration factors around a circular hole, locations of probable delamination, a summary of the laminate failure stress analysis, free edge stresses, microstresses and ply stress/strain influence coefficients. These features make ICAN a powerful, cost-effective tool to analyze/design fiber composite structures and components.

Study on Huizhou architecture of point cloud registration based on optimized ICP algorithm

NASA Astrophysics Data System (ADS)

Zhang, Runmei; Wu, Yulu; Zhang, Guangbin; Zhou, Wei; Tao, Yuqian

2018-03-01

In view of the current point cloud registration software has high hardware requirements, heavy workload and moltiple interactive definition, the source of software with better processing effect is not open, a two--step registration method based on normal vector distribution feature and coarse feature based iterative closest point (ICP) algorithm is proposed in this paper. This method combines fast point feature histogram (FPFH) algorithm, define the adjacency region of point cloud and the calculation model of the distribution of normal vectors, setting up the local coordinate system for each key point, and obtaining the transformation matrix to finish rough registration, the rough registration results of two stations are accurately registered by using the ICP algorithm. Experimental results show that, compared with the traditional ICP algorithm, the method used in this paper has obvious time and precision advantages for large amount of point clouds.

Funding of drugs: do vaccines warrant a different approach?

PubMed

Beutels, Philippe; Scuffham, Paul A; MacIntyre, C Raina

2008-11-01

Vaccines have features that require special consideration when assessing their cost-effectiveness. These features are related to herd immunity, quality-of-life losses in young children, parental care and work loss, time preference, uncertainty, eradication, macroeconomics, and tiered pricing. Advisory committees on public funding for vaccines, or for pharmaceuticals in general, should be knowledgable about these special features. We discuss key issues and difficulties in decision making for vaccines against rotavirus, human papillomavirus, varicella-zoster virus, influenza virus, and Streptococcus pneumoniae. We argue that guidelines for economic evaluation should be reconsidered generally to recommend (1) modelling options for the assessment of interventions against infectious diseases; (2) a wider perspective to account for impacts on third parties, if relevant; (3) a wider scope of costs than health-care system costs alone, if appropriate; and (4) alternative discounting techniques to explore social time preference over long periods.

Hierarchy of Gambling Choices: A Framework for Examining EGM Gambling Environment Preferences.

PubMed

Thorne, Hannah Briony; Rockloff, Matthew Justus; Langham, Erika; Li, En

2016-12-01

This paper presents the Hierarchy of Gambling Choices (HGC), which is a consumer-oriented framework for understanding the key environmental and contextual features that influence peoples' selections of online and venue-based electronic gaming machines (EGMs). The HGC framework proposes that EGM gamblers make choices in selection of EGM gambling experiences utilising Tversky's (Psychol Rev 79(4):281-299, 1972). Elimination-by-Aspects model, and organise their choice in a hierarchical manner by virtue of EGMs being an "experience good" (Nelson in J Polit Econ 78(2):311-329, 1970). EGM features are divided into three levels: the platform-including, online, mobile or land-based; the provider or specific venue in which the gambling occurs; and the game or machine characteristics, such as graphical themes and bonus features. This framework will contribute to the gambling field by providing a manner in which to systematically explore the environment surrounding EGM gambling and how it affects behaviour.

Can interface features affect aggression resulting from violent video game play? An examination of realistic controller and large screen size.

PubMed

Kim, Ki Joon; Sundar, S Shyam

2013-05-01

Aggressiveness attributed to violent video game play is typically studied as a function of the content features of the game. However, can interface features of the game also affect aggression? Guided by the General Aggression Model (GAM), we examine the controller type (gun replica vs. mouse) and screen size (large vs. small) as key technological aspects that may affect the state aggression of gamers, with spatial presence and arousal as potential mediators. Results from a between-subjects experiment showed that a realistic controller and a large screen display induced greater aggression, presence, and arousal than a conventional mouse and a small screen display, respectively, and confirmed that trait aggression was a significant predictor of gamers' state aggression. Contrary to GAM, however, arousal showed no effects on aggression; instead, presence emerged as a significant mediator.

SIMPL Systems, or: Can We Design Cryptographic Hardware without Secret Key Information?

NASA Astrophysics Data System (ADS)

Rührmair, Ulrich

This paper discusses a new cryptographic primitive termed SIMPL system. Roughly speaking, a SIMPL system is a special type of Physical Unclonable Function (PUF) which possesses a binary description that allows its (slow) public simulation and prediction. Besides this public key like functionality, SIMPL systems have another advantage: No secret information is, or needs to be, contained in SIMPL systems in order to enable cryptographic protocols - neither in the form of a standard binary key, nor as secret information hidden in random, analog features, as it is the case for PUFs. The cryptographic security of SIMPLs instead rests on (i) a physical assumption on their unclonability, and (ii) a computational assumption regarding the complexity of simulating their output. This novel property makes SIMPL systems potentially immune against many known hardware and software attacks, including malware, side channel, invasive, or modeling attacks.

On analyzing colour constancy approach for improving SURF detector performance

NASA Astrophysics Data System (ADS)

Zulkiey, Mohd Asyraf; Zaki, Wan Mimi Diyana Wan; Hussain, Aini; Mustafa, Mohd. Marzuki

2012-04-01

Robust key point detector plays a crucial role in obtaining a good tracking feature. The main challenge in outdoor tracking is the illumination change due to various reasons such as weather fluctuation and occlusion. This paper approaches the illumination change problem by transforming the input image through colour constancy algorithm before applying the SURF detector. Masked grey world approach is chosen because of its ability to perform well under local as well as global illumination change. Every image is transformed to imitate the canonical illuminant and Gaussian distribution is used to model the global change. The simulation results show that the average number of detected key points have increased by 69.92%. Moreover, the average of improved performance cases far out weight the degradation case where the former is improved by 215.23%. The approach is suitable for tracking implementation where sudden illumination occurs frequently and robust key point detection is needed.

Parametric Human Body Reconstruction Based on Sparse Key Points.

PubMed

Cheng, Ke-Li; Tong, Ruo-Feng; Tang, Min; Qian, Jing-Ye; Sarkis, Michel

2016-11-01

We propose an automatic parametric human body reconstruction algorithm which can efficiently construct a model using a single Kinect sensor. A user needs to stand still in front of the sensor for a couple of seconds to measure the range data. The user's body shape and pose will then be automatically constructed in several seconds. Traditional methods optimize dense correspondences between range data and meshes. In contrast, our proposed scheme relies on sparse key points for the reconstruction. It employs regression to find the corresponding key points between the scanned range data and some annotated training data. We design two kinds of feature descriptors as well as corresponding regression stages to make the regression robust and accurate. Our scheme follows with dense refinement where a pre-factorization method is applied to improve the computational efficiency. Compared with other methods, our scheme achieves similar reconstruction accuracy but significantly reduces runtime.

PrPC Governs Susceptibility to Prion Strains in Bank Vole, While Other Host Factors Modulate Strain Features.

PubMed

Espinosa, J C; Nonno, R; Di Bari, M; Aguilar-Calvo, P; Pirisinu, L; Fernández-Borges, N; Vanni, I; Vaccari, G; Marín-Moreno, A; Frassanito, P; Lorenzo, P; Agrimi, U; Torres, J M

2016-12-01

Bank vole is a rodent species that shows differential susceptibility to the experimental transmission of different prion strains. In this work, the transmission features of a panel of diverse prions with distinct origins were assayed both in bank vole expressing methionine at codon 109 (Bv109M) and in transgenic mice expressing physiological levels of bank vole PrP C (the BvPrP-Tg407 mouse line). This work is the first systematic comparison of the transmission features of a collection of prion isolates, representing a panel of diverse prion strains, in a transgenic-mouse model and in its natural counterpart. The results showed very similar transmission properties in both the natural species and the transgenic-mouse model, demonstrating the key role of the PrP amino acid sequence in prion transmission susceptibility. However, differences in the PrP Sc types propagated by Bv109M and BvPrP-Tg407 suggest that host factors other than PrP C modulate prion strain features. The differential susceptibility of bank voles to prion strains can be modeled in transgenic mice, suggesting that this selective susceptibility is controlled by the vole PrP sequence alone rather than by other species-specific factors. Differences in the phenotypes observed after prion transmissions in bank voles and in the transgenic mice suggest that host factors other than the PrP C sequence may affect the selection of the substrain replicating in the animal model. Copyright © 2016, American Society for Microbiology. All Rights Reserved.

Network Receptive Field Modeling Reveals Extensive Integration and Multi-feature Selectivity in Auditory Cortical Neurons.

PubMed

Harper, Nicol S; Schoppe, Oliver; Willmore, Ben D B; Cui, Zhanfeng; Schnupp, Jan W H; King, Andrew J

2016-11-01

Cortical sensory neurons are commonly characterized using the receptive field, the linear dependence of their response on the stimulus. In primary auditory cortex neurons can be characterized by their spectrotemporal receptive fields, the spectral and temporal features of a sound that linearly drive a neuron. However, receptive fields do not capture the fact that the response of a cortical neuron results from the complex nonlinear network in which it is embedded. By fitting a nonlinear feedforward network model (a network receptive field) to cortical responses to natural sounds, we reveal that primary auditory cortical neurons are sensitive over a substantially larger spectrotemporal domain than is seen in their standard spectrotemporal receptive fields. Furthermore, the network receptive field, a parsimonious network consisting of 1-7 sub-receptive fields that interact nonlinearly, consistently better predicts neural responses to auditory stimuli than the standard receptive fields. The network receptive field reveals separate excitatory and inhibitory sub-fields with different nonlinear properties, and interaction of the sub-fields gives rise to important operations such as gain control and conjunctive feature detection. The conjunctive effects, where neurons respond only if several specific features are present together, enable increased selectivity for particular complex spectrotemporal structures, and may constitute an important stage in sound recognition. In conclusion, we demonstrate that fitting auditory cortical neural responses with feedforward network models expands on simple linear receptive field models in a manner that yields substantially improved predictive power and reveals key nonlinear aspects of cortical processing, while remaining easy to interpret in a physiological context.

Network Receptive Field Modeling Reveals Extensive Integration and Multi-feature Selectivity in Auditory Cortical Neurons

PubMed Central

Willmore, Ben D. B.; Cui, Zhanfeng; Schnupp, Jan W. H.; King, Andrew J.

2016-01-01

Cortical sensory neurons are commonly characterized using the receptive field, the linear dependence of their response on the stimulus. In primary auditory cortex neurons can be characterized by their spectrotemporal receptive fields, the spectral and temporal features of a sound that linearly drive a neuron. However, receptive fields do not capture the fact that the response of a cortical neuron results from the complex nonlinear network in which it is embedded. By fitting a nonlinear feedforward network model (a network receptive field) to cortical responses to natural sounds, we reveal that primary auditory cortical neurons are sensitive over a substantially larger spectrotemporal domain than is seen in their standard spectrotemporal receptive fields. Furthermore, the network receptive field, a parsimonious network consisting of 1–7 sub-receptive fields that interact nonlinearly, consistently better predicts neural responses to auditory stimuli than the standard receptive fields. The network receptive field reveals separate excitatory and inhibitory sub-fields with different nonlinear properties, and interaction of the sub-fields gives rise to important operations such as gain control and conjunctive feature detection. The conjunctive effects, where neurons respond only if several specific features are present together, enable increased selectivity for particular complex spectrotemporal structures, and may constitute an important stage in sound recognition. In conclusion, we demonstrate that fitting auditory cortical neural responses with feedforward network models expands on simple linear receptive field models in a manner that yields substantially improved predictive power and reveals key nonlinear aspects of cortical processing, while remaining easy to interpret in a physiological context. PMID:27835647

Understanding Cooperative Chirality at the Nanoscale

NASA Astrophysics Data System (ADS)

Yu, Shangjie; Wang, Pengpeng; Govorov, Alexander; Ouyang, Min

Controlling chirality of organic and inorganic structures plays a key role in many physical, chemical and biochemical processes, and may offer new opportunity to create technology applications based on chiroptical effect. In this talk, we will present a theoretical model and simulation to demonstrate how to engineer nanoscale chirality in inorganic nanostructures via synergistic control of electromagnetic response of both lattice and geometry, leading to rich tunability of chirality at the nanoscale. Our model has also been applied to understand recent materials advancement of related control with excellent agreement, and can elucidate physical origins of circular dichroism features in the experiment.

Network structure of production

PubMed Central

Atalay, Enghin; Hortaçsu, Ali; Roberts, James; Syverson, Chad

2011-01-01

Complex social networks have received increasing attention from researchers. Recent work has focused on mechanisms that produce scale-free networks. We theoretically and empirically characterize the buyer–supplier network of the US economy and find that purely scale-free models have trouble matching key attributes of the network. We construct an alternative model that incorporates realistic features of firms’ buyer–supplier relationships and estimate the model’s parameters using microdata on firms’ self-reported customers. This alternative framework is better able to match the attributes of the actual economic network and aids in further understanding several important economic phenomena. PMID:21402924

Statechart-based design controllers for FPGA partial reconfiguration

NASA Astrophysics Data System (ADS)

Łabiak, Grzegorz; Wegrzyn, Marek; Rosado Muñoz, Alfredo

2015-09-01

Statechart diagram and UML technique can be a vital part of early conceptual modeling. At the present time there is no much support in hardware design methodologies for reconfiguration features of reprogrammable devices. Authors try to bridge the gap between imprecise UML model and formal HDL description. The key concept in author's proposal is to describe the behavior of the digital controller by statechart diagrams and to map some parts of the behavior into reprogrammable logic by means of group of states which forms sequential automaton. The whole process is illustrated by the example with experimental results.

Self-injection-locking linewidth narrowing in a semiconductor laser coupled to an external fiber-optic ring resonator

NASA Astrophysics Data System (ADS)

Korobko, Dmitry A.; Zolotovskii, Igor O.; Panajotov, Krassimir; Spirin, Vasily V.; Fotiadi, Andrei A.

2017-12-01

We develop a theoretical framework for modeling of semiconductor laser coupled to an external fiber-optic ring resonator. The developed approach has shown good qualitative agreement between theoretical predictions and experimental results for particular configuration of a self-injection locked DFB laser delivering narrow-band radiation. The model is capable of describing the main features of the experimentally measured laser outputs such as laser line narrowing, spectral shape of generated radiation, mode-hoping instabilities and makes possible exploring the key physical mechanisms responsible for the laser operation stability.

A combined coarse-grained and all-atom simulation of TRPV1 channel gating and heat activation

PubMed Central

Qin, Feng

2015-01-01

The transient receptor potential (TRP) channels act as key sensors of various chemical and physical stimuli in eukaryotic cells. Despite years of study, the molecular mechanisms of TRP channel activation remain unclear. To elucidate the structural, dynamic, and energetic basis of gating in TRPV1 (a founding member of the TRPV subfamily), we performed coarse-grained modeling and all-atom molecular dynamics (MD) simulation based on the recently solved high resolution structures of the open and closed form of TRPV1. Our coarse-grained normal mode analysis captures two key modes of collective motions involved in the TRPV1 gating transition, featuring a quaternary twist motion of the transmembrane domains (TMDs) relative to the intracellular domains (ICDs). Our transition pathway modeling predicts a sequence of structural movements that propagate from the ICDs to the TMDs via key interface domains (including the membrane proximal domain and the C-terminal domain), leading to sequential opening of the selectivity filter followed by the lower gate in the channel pore (confirmed by modeling conformational changes induced by the activation of ICDs). The above findings of coarse-grained modeling are robust to perturbation by lipids. Finally, our MD simulation of the ICD identifies key residues that contribute differently to the nonpolar energy of the open and closed state, and these residues are predicted to control the temperature sensitivity of TRPV1 gating. These computational predictions offer new insights to the mechanism for heat activation of TRPV1 gating, and will guide our future electrophysiology and mutagenesis studies. PMID:25918362

Microstructure-Tensile Properties Correlation for the Ti-6Al-4V Titanium Alloy

NASA Astrophysics Data System (ADS)

Shi, Xiaohui; Zeng, Weidong; Sun, Yu; Han, Yuanfei; Zhao, Yongqing; Guo, Ping

2015-04-01

Finding the quantitative microstructure-tensile properties correlations is the key to achieve performance optimization for various materials. However, it is extremely difficult due to their non-linear and highly interactive interrelations. In the present investigation, the lamellar microstructure features-tensile properties correlations of the Ti-6Al-4V alloy are studied using an error back-propagation artificial neural network (ANN-BP) model. Forty-eight thermomechanical treatments were conducted to prepare the Ti-6Al-4V alloy with different lamellar microstructure features. In the proposed model, the input variables are microstructure features including the α platelet thickness, colony size, and β grain size, which were extracted using Image Pro Plus software. The output variables are the tensile properties, including ultimate tensile strength, yield strength, elongation, and reduction of area. Fourteen hidden-layer neurons which can make ANN-BP model present the most excellent performance were applied. The training results show that all the relative errors between the predicted and experimental values are within 6%, which means that the trained ANN-BP model is capable of providing precise prediction of the tensile properties for Ti-6Al-4V alloy. Based on the corresponding relations between the tensile properties predicted by ANN-BP model and the lamellar microstructure features, it can be found that the yield strength decreases with increasing α platelet thickness continuously. However, the α platelet thickness exerts influence on the elongation in a more complicated way. In addition, for a given α platelet thickness, the yield strength and the elongation both increase with decreasing β grain size and colony size. In general, the β grain size and colony size play a more important role in affecting the tensile properties of Ti-6Al-4V alloy than the α platelet thickness.

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

PubMed Central

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

2016-01-01

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

Characterization of Retinal Vascular and Neural Damage in a Novel Model of Diabetic Retinopathy.

PubMed

Weerasekera, Lakshini Y; Balmer, Lois A; Ram, Ramesh; Morahan, Grant

2015-06-01

Diabetic retinopathy (DR) is a major cause of blindness globally. Investigating the underlying mechanisms of DR would be aided by a suitable mouse model that developed key features seen in the human disease, and did so without carrying genetic modifications. This study was undertaken to produce such a model. Our panel of Collaborative Cross strains was screened for DR-like features after induction of diabetes by intravenous injection with alloxan or streptozotocin. Both flat-mounted whole-retina and histologic sections were studied for the presence of retinal lesions. Progression of DR was also studied by histologic examination of the retinal vascular and neural structure at various time points after diabetes onset. In addition, microarray investigations were conducted on retinas from control and diabetic mice. Features of DR such as degenerated pericytes, acellular capillaries, minor vascular proliferation, gliosis of Müller cells, and loss of ganglion cells were noted as early as day 7 in some mice. These lesions became more evident with time. After 21 days of diabetes, severe vascular proliferation, microaneurysms, preretinal damage, increased Müller cell gliosis, and damage to the outer retina were all obvious. Microarray studies found significant differential expression of multiple genes known to be involved in DR. The FOT_FB strain provides a useful model to investigate the pathogenesis of DR and to develop treatments for this vision-threatening disease.

GIS prospectivity mapping and 3D modeling validation for potential uranium deposit targets in Shangnan district, China

NASA Astrophysics Data System (ADS)

Xie, Jiayu; Wang, Gongwen; Sha, Yazhou; Liu, Jiajun; Wen, Botao; Nie, Ming; Zhang, Shuai

2017-04-01

Integrating multi-source geoscience information (such as geology, geophysics, geochemistry, and remote sensing) using GIS mapping is one of the key topics and frontiers in quantitative geosciences for mineral exploration. GIS prospective mapping and three-dimensional (3D) modeling can be used not only to extract exploration criteria and delineate metallogenetic targets but also to provide important information for the quantitative assessment of mineral resources. This paper uses the Shangnan district of Shaanxi province (China) as a case study area. GIS mapping and potential granite-hydrothermal uranium targeting were conducted in the study area combining weights of evidence (WofE) and concentration-area (C-A) fractal methods with multi-source geoscience information. 3D deposit-scale modeling using GOCAD software was performed to validate the shapes and features of the potential targets at the subsurface. The research results show that: (1) the known deposits have potential zones at depth, and the 3D geological models can delineate surface or subsurface ore-forming features, which can be used to analyze the uncertainty of the shape and feature of prospectivity mapping at the subsurface; (2) single geochemistry anomalies or remote sensing anomalies at the surface require combining the depth exploration criteria of geophysics to identify potential targets; and (3) the single or sparse exploration criteria zone with few mineralization spots at the surface has high uncertainty in terms of the exploration target.

Non-invasive classification of gas-liquid two-phase horizontal flow regimes using an ultrasonic Doppler sensor and a neural network

NASA Astrophysics Data System (ADS)

Musa Abbagoni, Baba; Yeung, Hoi

2016-08-01

The identification of flow pattern is a key issue in multiphase flow which is encountered in the petrochemical industry. It is difficult to identify the gas-liquid flow regimes objectively with the gas-liquid two-phase flow. This paper presents the feasibility of a clamp-on instrument for an objective flow regime classification of two-phase flow using an ultrasonic Doppler sensor and an artificial neural network, which records and processes the ultrasonic signals reflected from the two-phase flow. Experimental data is obtained on a horizontal test rig with a total pipe length of 21 m and 5.08 cm internal diameter carrying air-water two-phase flow under slug, elongated bubble, stratified-wavy and, stratified flow regimes. Multilayer perceptron neural networks (MLPNNs) are used to develop the classification model. The classifier requires features as an input which is representative of the signals. Ultrasound signal features are extracted by applying both power spectral density (PSD) and discrete wavelet transform (DWT) methods to the flow signals. A classification scheme of ‘1-of-C coding method for classification’ was adopted to classify features extracted into one of four flow regime categories. To improve the performance of the flow regime classifier network, a second level neural network was incorporated by using the output of a first level networks feature as an input feature. The addition of the two network models provided a combined neural network model which has achieved a higher accuracy than single neural network models. Classification accuracies are evaluated in the form of both the PSD and DWT features. The success rates of the two models are: (1) using PSD features, the classifier missed 3 datasets out of 24 test datasets of the classification and scored 87.5% accuracy; (2) with the DWT features, the network misclassified only one data point and it was able to classify the flow patterns up to 95.8% accuracy. This approach has demonstrated the success of a clamp-on ultrasound sensor for flow regime classification that would be possible in industry practice. It is considerably more promising than other techniques as it uses a non-invasive and non-radioactive sensor.

Real-Time Lane Region Detection Using a Combination of Geometrical and Image Features

PubMed Central

Cáceres Hernández, Danilo; Kurnianggoro, Laksono; Filonenko, Alexander; Jo, Kang Hyun

2016-01-01

Over the past few decades, pavement markings have played a key role in intelligent vehicle applications such as guidance, navigation, and control. However, there are still serious issues facing the problem of lane marking detection. For example, problems include excessive processing time and false detection due to similarities in color and edges between traffic signs (channeling lines, stop lines, crosswalk, arrows, etc.). This paper proposes a strategy to extract the lane marking information taking into consideration its features such as color, edge, and width, as well as the vehicle speed. Firstly, defining the region of interest is a critical task to achieve real-time performance. In this sense, the region of interest is dependent on vehicle speed. Secondly, the lane markings are detected by using a hybrid color-edge feature method along with a probabilistic method, based on distance-color dependence and a hierarchical fitting model. Thirdly, the following lane marking information is extracted: the number of lane markings to both sides of the vehicle, the respective fitting model, and the centroid information of the lane. Using these parameters, the region is computed by using a road geometric model. To evaluate the proposed method, a set of consecutive frames was used in order to validate the performance. PMID:27869657

Quantitative Analysis of Intracellular Motility Based on Optical Flow Model

PubMed Central

Li, Heng

2017-01-01

Analysis of cell mobility is a key issue for abnormality identification and classification in cell biology research. However, since cell deformation induced by various biological processes is random and cell protrusion is irregular, it is difficult to measure cell morphology and motility in microscopic images. To address this dilemma, we propose an improved variation optical flow model for quantitative analysis of intracellular motility, which not only extracts intracellular motion fields effectively but also deals with optical flow computation problem at the border by taking advantages of the formulation based on L1 and L2 norm, respectively. In the energy functional of our proposed optical flow model, the data term is in the form of L2 norm; the smoothness of the data changes with regional features through an adaptive parameter, using L1 norm near the edge of the cell and L2 norm away from the edge. We further extract histograms of oriented optical flow (HOOF) after optical flow field of intracellular motion is computed. Then distances of different HOOFs are calculated as the intracellular motion features to grade the intracellular motion. Experimental results show that the features extracted from HOOFs provide new insights into the relationship between the cell motility and the special pathological conditions. PMID:29065574

Developing a Framework to Link Catchment Modelling tools to Decision Support Systems for Catchment Management and Planning

NASA Astrophysics Data System (ADS)

Adams, Russell; Owen, Gareth

2015-04-01

Over the past few years a series of catchment monitoring studies in the UK have developed a wide range of tools to enable managers and planners to make informed decisions to target several key outcomes. These outcomes include the mitigation of diffuse pollution and the reduction of flood risk. Good progress has been but additional steps are still required to link together more detailed models that represent catchment processes with the decision support systems (often termed matrices; i.e. DSMs) which form the basis of these planning and management tools. Examples include: (i) the FARM tools developed by the PROACTIVE team at Newcastle University to assess different catchment management options for mitigating against flooding events, (ii) TOPMANAGE, a suite of algorithms that link with high resolution DEMs to enable surface flow pathways, having the potential to be mitigated by Natural Flood Management (NFM) features (in order to target diffuse pollution due to nutrients and sediments) to be identified. To date, these DSMs have not been underpinned by models that can be run in real-time to quantify the benefits in terms of measurable reductions in flood or nutrient pollution risks. Their use has therefore been mostly as qualitative assessment tools. This study aims to adapt an existing spreadsheet-based model, the CRAFT, in order for it to become fully coupled to a DSM approach. Previous catchment scale applications of the CRAFT have focussed on meso-scale studies where any management interventions at a local scale are unlikely to be detectable at the monitoring point (the catchment outlet). The model has however been reasonably successful in identifying potential flow and transport pathways that link the headwater subcatchments to the outlet. Furthermore, recent enhancements to the model enable features such as sedimentation ponds and lagoons that can trap and remove nutrients and sediments to be added, once data become available from different types of NFM features to parameterise these. The model can be used to investigate runoff attenuation (in this case primarily through a lagged routing term applied to surface runoff) as a result of implementing mitigation measures. However to be fully integrated within a DSM framework requires the CRAFT to be linked to a user-friendly interface that will allow the user to modify key parameters, preferably using a web-based expert system, which will be explored further.

Model of visual contrast gain control and pattern masking

NASA Technical Reports Server (NTRS)

Watson, A. B.; Solomon, J. A.

1997-01-01

We have implemented a model of contrast gain and control in human vision that incorporates a number of key features, including a contrast sensitivity function, multiple oriented bandpass channels, accelerating nonlinearities, and a devisive inhibitory gain control pool. The parameters of this model have been optimized through a fit to the recent data that describe masking of a Gabor function by cosine and Gabor masks [J. M. Foley, "Human luminance pattern mechanisms: masking experiments require a new model," J. Opt. Soc. Am. A 11, 1710 (1994)]. The model achieves a good fit to the data. We also demonstrate how the concept of recruitment may accommodate a variant of this model in which excitatory and inhibitory paths have a common accelerating nonlinearity, but which include multiple channels tuned to different levels of contrast.

Future Directions in Simulating Solar Geoengineering

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

Kravitz, Benjamin S.; Robock, Alan; Boucher, Olivier

2014-08-05

Solar geoengineering is a proposed set of technologies to temporarily alleviate some of the consequences of anthropogenic greenhouse gas emissions. The Geoengineering Model Intercomparison Project (GeoMIP) created a framework of geoengineering simulations in climate models that have been performed by modeling centers throughout the world (B. Kravitz et al., The Geoengineering Model Intercomparison Project (GeoMIP), Atmospheric Science Letters, 12(2), 162-167, doi:10.1002/asl.316, 2011). These experiments use state-of-the-art climate models to simulate solar geoengineering via uniform solar reduction, creation of stratospheric sulfate aerosol layers, or injecting sea spray into the marine boundary layer. GeoMIP has been quite successful in its mission ofmore » revealing robust features and key uncertainties of the modeled effects of solar geoengineering.« less

Market mature 1998 hybrid electric vehicles

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

Wyczalek, F.A.

Beginning in 1990, the major automotive passenger vehicle manufacturers once again re-evaluated the potential of the battery powered electric vehicle (EV). This intensive effort to reduce the battery EV to commercial practice focused attention on the key issue of limited vehicle range, resulting from the low energy density and high mass characteristics of batteries, in comparison to the high energy density of liquid hydrocarbon (HC) fuels. Consequently, by 1995, vehicle manufacturers turned their attention to hybrid electric vehicles (HEV). This redirection of EV effort was highlighted finally, in 1997, at the 57th Frankfurt Motor Show, the Audi Duo parallel typemore » hybrid was released for the domestic market as a 1998 model vehicle. Also at the 1997 32nd Tokyo Motor Show, the Toyota Hybrid System (THS) Prius was released for the domestic market as a 1998 model vehicle. This paper presents a comparative analysis of the key features of these two 1998 model year production hybrid propulsion systems. Among the conclusions, two issues are evident: one, the major manufacturers have turned to the hybrid concept in their search for solutions to the key EV issues of limited range and heating/air conditioning; and, two, the focus is now on introducing hybrid EV for test marketing domestically.« less

Psychological factors mediate key symptoms of fibromyalgia through their influence on stress.

PubMed

Malin, Katrina; Littlejohn, Geoffrey Owen

2016-09-01

The clinical features of fibromyalgia are associated with various psychological factors, including stress. We examined the hypothesis that the path that psychological factors follow in influencing fibromyalgia symptoms is through their direct effect on stress. Ninety-eight females with ACR 1990 classified fibromyalgia completed the following questionnaires: The Big 5 Personality Inventory, Fibromyalgia Impact Questionnaire, Perceived Stress Scale, Profile of Mood States, Mastery Scale, and Perceived Control of Internal States Scale. SPSS (PASW version 22) was used to perform basic t tests, means, and standard deviations to show difference between symptom characteristics. Pathway analysis using structural equation modelling (Laavan) examined the effect of stress on the relationships between psychological factors and the elements that define the fibromyalgia phenotype. The preferred model showed that the identified path clearly linked the psychological variables of anxiety, neuroticism and mastery, but not internal control, to the three key elements of fibromyalgia, namely pain, fatigue and sleep (p < 0.001), via the person's perceived stress. Confusion, however, did not fit the preferred model. This study confirms that stress is a necessary link in the pathway between certain identified, established and significant psychological factors and key fibromyalgia symptoms. This has implications for the understanding of contributing mechanisms and the clinical care of patients with fibromyalgia.

A new mouse model to explore therapies for preeclampsia.

PubMed

Ahmed, Abdulwahab; Singh, Jameel; Khan, Ysodra; Seshan, Surya V; Girardi, Guillermina

2010-10-27

Pre-eclampsia, a pregnancy-specific multisystemic disorder is a leading cause of maternal and perinatal mortality and morbidity. This syndrome has been known to medical science since ancient times. However, despite considerable research, the cause/s of preeclampsia remain unclear, and there is no effective treatment. Development of an animal model that recapitulates this complex pregnancy-related disorder may help to expand our understanding and may hold great potential for the design and implementation of effective treatment. Here we show that the CBA/J x DBA/2 mouse model of recurrent miscarriage is also a model of immunologically-mediated preeclampsia (PE). DBA/J mated CBA/J females spontaneously develop many features of human PE (primigravidity, albuminuria, endotheliosis, increased sensitivity to angiotensin II and increased plasma leptin levels) that correlates with bad pregnancy outcomes. We previously reported that antagonism of vascular endothelial growth factor (VEGF) signaling by soluble VEGF receptor 1 (sFlt-1) is involved in placental and fetal injury in CBA/J x DBA/2 mice. Using this animal model that recapitulates many of the features of preeclampsia in women, we found that pravastatin restores angiogenic balance, ameliorates glomerular injury, diminishes hypersensitivity to angiotensin II and protects pregnancies. We described a new mouse model of PE, were the relevant key features of human preeclampsia develop spontaneously. The CBA/J x DBA/2 model, that recapitulates this complex disorder, helped us identify pravastatin as a candidate therapy to prevent preeclampsia and its related complications. We recognize that these studies were conducted in mice and that clinical trials are needed to confirm its application to humans.

Are quantum-mechanical-like models possible, or necessary, outside quantum physics?

NASA Astrophysics Data System (ADS)

Plotnitsky, Arkady

2014-12-01

This article examines some experimental conditions that invite and possibly require recourse to quantum-mechanical-like mathematical models (QMLMs), models based on the key mathematical features of quantum mechanics, in scientific fields outside physics, such as biology, cognitive psychology, or economics. In particular, I consider whether the following two correlative features of quantum phenomena that were decisive for establishing the mathematical formalism of quantum mechanics play similarly important roles in QMLMs elsewhere. The first is the individuality and discreteness of quantum phenomena, and the second is the irreducibly probabilistic nature of our predictions concerning them, coupled to the particular character of the probabilities involved, as different from the character of probabilities found in classical physics. I also argue that these features could be interpreted in terms of a particular form of epistemology that suspends and even precludes a causal and, in the first place, realist description of quantum objects and processes. This epistemology limits the descriptive capacity of quantum theory to the description, classical in nature, of the observed quantum phenomena manifested in measuring instruments. Quantum mechanics itself only provides descriptions, probabilistic in nature, concerning numerical data pertaining to such phenomena, without offering a physical description of quantum objects and processes. While QMLMs share their use of the quantum-mechanical or analogous mathematical formalism, they may differ by the roles, if any, the two features in question play in them and by different ways of interpreting the phenomena they considered and this formalism itself. This article will address those differences as well.

Interactions of social, terrestrial, and marine sub-systems in the Galapagos Islands, Ecuador.

PubMed

Walsh, Stephen J; Mena, Carlos F

2016-12-20

Galapagos is often cited as an example of the conflicts that are emerging between resource conservation and economic development in island ecosystems, as the pressures associated with tourism threaten nature, including the iconic and emblematic species, unique terrestrial landscapes, and special marine environments. In this paper, two projects are described that rely upon dynamic systems models and agent-based models to examine human-environment interactions. We use a theoretical context rooted in complexity theory to guide the development of our models that are linked to social-ecological dynamics. The goal of this paper is to describe key elements, relationships, and processes to inform and enhance our understanding of human-environment interactions in the Galapagos Islands of Ecuador. By formalizing our knowledge of how systems operate and the manner in which key elements are linked in coupled human-natural systems, we specify rules, relationships, and rates of exchange between social and ecological features derived through statistical functions and/or functions specified in theory or practice. The processes described in our models also have practical applications in that they emphasize how political policies generate different human responses and model outcomes, many detrimental to the social-ecological sustainability of the Galapagos Islands.

An efficient and stable hydrodynamic model with novel source term discretization schemes for overland flow and flood simulations

NASA Astrophysics Data System (ADS)

Xia, Xilin; Liang, Qiuhua; Ming, Xiaodong; Hou, Jingming

2017-05-01

Numerical models solving the full 2-D shallow water equations (SWEs) have been increasingly used to simulate overland flows and better understand the transient flow dynamics of flash floods in a catchment. However, there still exist key challenges that have not yet been resolved for the development of fully dynamic overland flow models, related to (1) the difficulty of maintaining numerical stability and accuracy in the limit of disappearing water depth and (2) inaccurate estimation of velocities and discharges on slopes as a result of strong nonlinearity of friction terms. This paper aims to tackle these key research challenges and present a new numerical scheme for accurately and efficiently modeling large-scale transient overland flows over complex terrains. The proposed scheme features a novel surface reconstruction method (SRM) to correctly compute slope source terms and maintain numerical stability at small water depth, and a new implicit discretization method to handle the highly nonlinear friction terms. The resulting shallow water overland flow model is first validated against analytical and experimental test cases and then applied to simulate a hypothetic rainfall event in the 42 km2 Haltwhistle Burn, UK.

Interactions of social, terrestrial, and marine sub-systems in the Galapagos Islands, Ecuador

PubMed Central

Walsh, Stephen J.; Mena, Carlos F.

2016-01-01

Galapagos is often cited as an example of the conflicts that are emerging between resource conservation and economic development in island ecosystems, as the pressures associated with tourism threaten nature, including the iconic and emblematic species, unique terrestrial landscapes, and special marine environments. In this paper, two projects are described that rely upon dynamic systems models and agent-based models to examine human–environment interactions. We use a theoretical context rooted in complexity theory to guide the development of our models that are linked to social–ecological dynamics. The goal of this paper is to describe key elements, relationships, and processes to inform and enhance our understanding of human–environment interactions in the Galapagos Islands of Ecuador. By formalizing our knowledge of how systems operate and the manner in which key elements are linked in coupled human–natural systems, we specify rules, relationships, and rates of exchange between social and ecological features derived through statistical functions and/or functions specified in theory or practice. The processes described in our models also have practical applications in that they emphasize how political policies generate different human responses and model outcomes, many detrimental to the social–ecological sustainability of the Galapagos Islands. PMID:27791072

Natural climate variability and teleconnections to precipitation over the Pacific-North American region in CMIP3 and CMIP5 models

NASA Astrophysics Data System (ADS)

Polade, Suraj D.; Gershunov, Alexander; Cayan, Daniel R.; Dettinger, Michael D.; Pierce, David W.

2013-05-01

climate variability will continue to be an important aspect of future regional climate even in the midst of long-term secular changes. Consequently, the ability of climate models to simulate major natural modes of variability and their teleconnections provides important context for the interpretation and use of climate change projections. Comparisons reported here indicate that the CMIP5 generation of global climate models shows significant improvements in simulations of key Pacific climate mode and their teleconnections to North America compared to earlier CMIP3 simulations. The performance of 14 models with simulations in both the CMIP3 and CMIP5 archives are assessed using singular value decomposition analysis of simulated and observed winter Pacific sea surface temperatures (SSTs) and concurrent precipitation over the contiguous United States and northwestern Mexico. Most of the models reproduce basic features of the key natural mode and their teleconnections, albeit with notable regional deviations from observations in both SST and precipitation. Increasing horizontal resolution in the CMIP5 simulations is an important, but not a necessary, factor in the improvement from CMIP3 to CMIP5.

Natural climate variability and teleconnections to precipitation over the Pacific-North American region in CMIP3 and CMIP5 models

USGS Publications Warehouse

Polade, Suraj D.; Gershunov, Alexander; Cayan, Daniel R.; Dettinger, Michael D.; Pierce, David W.

2013-01-01

Natural climate variability will continue to be an important aspect of future regional climate even in the midst of long-term secular changes. Consequently, the ability of climate models to simulate major natural modes of variability and their teleconnections provides important context for the interpretation and use of climate change projections. Comparisons reported here indicate that the CMIP5 generation of global climate models shows significant improvements in simulations of key Pacific climate mode and their teleconnections to North America compared to earlier CMIP3 simulations. The performance of 14 models with simulations in both the CMIP3 and CMIP5 archives are assessed using singular value decomposition analysis of simulated and observed winter Pacific sea surface temperatures (SSTs) and concurrent precipitation over the contiguous United States and northwestern Mexico. Most of the models reproduce basic features of the key natural mode and their teleconnections, albeit with notable regional deviations from observations in both SST and precipitation. Increasing horizontal resolution in the CMIP5 simulations is an important, but not a necessary, factor in the improvement from CMIP3 to CMIP5.

Work Keys USA.

ERIC Educational Resources Information Center

Work Keys USA, 1998

1998-01-01

"Work Keys" is a comprehensive program for assessing and teaching workplace skills. This serial "special issue" features 18 first-hand reports on Work Keys projects in action in states across North America. They show how the Work Keys is helping businesses and educators solve the challenge of building a world-class work force.…

Multiple Paths to Mathematics Practice in Al-Kashi's "Key to Arithmetic"

ERIC Educational Resources Information Center

Taani, Osama

2014-01-01

In this paper, I discuss one of the most distinguishing features of Jamshid al-Kashi's pedagogy from his "Key to Arithmetic", a well-known Arabic mathematics textbook from the fifteenth century. This feature is the multiple paths that he includes to find a desired result. In the first section light is shed on al-Kashi's life…

An Analysis of the Contents and Pedagogy of Al-Kashi's 1427 "Key to Arithmetic" (Miftah Al-Hisab)

ERIC Educational Resources Information Center

Ta'ani, Osama Hekmat

2011-01-01

Al-Kashi's 1427 "Key to Arithmetic" had important use over several hundred years in mathematics teaching in Medieval Islam throughout the time of the Ottoman Empire. Its pedagogical features have never been studied before. In this dissertation I have made a close pedagogical analysis of these features and discovered several teaching…

Ice-Ridge Pile Up and the Genesis of Martian "Shorelines"

NASA Technical Reports Server (NTRS)

Barnhart, C. J.; Tulaczyk, S.; Asphaug, E.; Kraal, E. R.; Moore, J.

2005-01-01

Unique geomorphologic features such as basin terraces exhibiting topographic continuity have been found within several Martian craters as shown in Viking, MOC, and THEMIS images. These features, showing similarity to terrestrial shorelines, have been mapped and cataloged with significant effort [1]. Currently, open wave action on the surface of paleolakes has been hypothesized as the geomorphologic agent responsible for the generation of these features [2]. As consequence, feature interpretations, including shorelines, wave-cut benches, and bars are, befittingly, lacustrine. Because such interpretations and their formation mechanisms have profound implications for the climate and potential biological history of Mars, confidence is crucial. The insight acquired through linked quantitative modeling of geomorphologic agents and processes is key to accurately interpreting these features. In this vein, recent studies [3,4] involving the water wave energy in theoretical open water basins on Mars show minimal erosional effects due to water waves under Martian conditions. Consequently, sub-glacial lake flattens the surface, produces a local velocity increase over the lake, and creates a deviation of the ice flow from the main flow direction [11]. These consequences of ice flow are observed at Lake Vostok, Antarctica an excellent Martian analogue [11]. Martian observations include reticulate terrain exhibiting sharp inter-connected ridges speculated to reflect the deposition and reworking of ice blocks at the periphery of ice-covered lakes throughout Hellas [12]. Our model determines to what extent ice, a terrestrial geomorphologic agent, can alter the Martian landscape. Method: We study the evolution of crater ice plugs as the formation mechanism of surface features frequently identified as shorelines. In particular, we perform model integrations involving parameters such as ice slope and purity, atmospheric pressure and temperature, crater shape and composition, and an energy balance between solar flux, geothermal flux, latent heat, and ablation. Our ultimate goal is to understand how an intracrater ice plug could create the observed shoreline features and how these

Identification of key regulators for the migration and invasion of rheumatoid synoviocytes through a systems approach

PubMed Central

You, Sungyong; Yoo, Seung-Ah; Choi, Susanna; Kim, Ji-Young; Park, Su-Jung; Ji, Jong Dae; Kim, Tae-Hwan; Kim, Ki-Jo; Cho, Chul-Soo; Hwang, Daehee; Kim, Wan-Uk

2014-01-01

Rheumatoid synoviocytes, which consist of fibroblast-like synoviocytes (FLSs) and synovial macrophages (SMs), are crucial for the progression of rheumatoid arthritis (RA). Particularly, FLSs of RA patients (RA-FLSs) exhibit invasive characteristics reminiscent of cancer cells, destroying cartilage and bone. RA-FLSs and SMs originate differently from mesenchymal and myeloid cells, respectively, but share many pathologic functions. However, the molecular signatures and biological networks representing the distinct and shared features of the two cell types are unknown. We performed global transcriptome profiling of FLSs and SMs obtained from RA and osteoarthritis patients. By comparing the transcriptomes, we identified distinct molecular signatures and cellular processes defining invasiveness of RA-FLSs and proinflammatory properties of RA-SMs, respectively. Interestingly, under the interleukin-1β (IL-1β)–stimulated condition, the RA-FLSs newly acquired proinflammatory signature dominant in RA-SMs without losing invasive properties. We next reconstructed a network model that delineates the shared, RA-FLS–dominant (invasive), and RA-SM–dominant (inflammatory) processes. From the network model, we selected 13 genes, including periostin, osteoblast-specific factor (POSTN) and twist basic helix–loop–helix transcription factor 1 (TWIST1), as key regulator candidates responsible for FLS invasiveness. Of note, POSTN and TWIST1 expressions were elevated in independent RA-FLSs and further instigated by IL-1β. Functional assays demonstrated the requirement of POSTN and TWIST1 for migration and invasion of RA-FLSs stimulated with IL-1β. Together, our systems approach to rheumatoid synovitis provides a basis for identifying key regulators responsible for pathological features of RA-FLSs and -SMs, demonstrating how a certain type of cells acquires functional redundancy under chronic inflammatory conditions. PMID:24374632

A neurally plausible parallel distributed processing model of event-related potential word reading data.

PubMed

Laszlo, Sarah; Plaut, David C

2012-03-01

The Parallel Distributed Processing (PDP) framework has significant potential for producing models of cognitive tasks that approximate how the brain performs the same tasks. To date, however, there has been relatively little contact between PDP modeling and data from cognitive neuroscience. In an attempt to advance the relationship between explicit, computational models and physiological data collected during the performance of cognitive tasks, we developed a PDP model of visual word recognition which simulates key results from the ERP reading literature, while simultaneously being able to successfully perform lexical decision-a benchmark task for reading models. Simulations reveal that the model's success depends on the implementation of several neurally plausible features in its architecture which are sufficiently domain-general to be relevant to cognitive modeling more generally. Copyright © 2011 Elsevier Inc. All rights reserved.

Redd Site Selection and Spawning Habitat Use by Fall Chinook Salmon: The Importance of Geomorphic Features in Large Rivers

PubMed

Geist; Dauble

1998-09-01

/ Knowledge of the three-dimensional connectivity between rivers and groundwater within the hyporheic zone can be used to improve the definition of fall chinook salmon (Oncorhynchus tshawytscha) spawning habitat. Information exists on the microhabitat characteristics that define suitable salmon spawning habitat. However, traditional spawning habitat models that use these characteristics to predict available spawning habitat are restricted because they can not account for the heterogeneous nature of rivers. We present a conceptual spawning habitat model for fall chinook salmon that describes how geomorphic features of river channels create hydraulic processes, including hyporheic flows, that influence where salmon spawn in unconstrained reaches of large mainstem alluvial rivers. Two case studies based on empirical data from fall chinook salmon spawning areas in the Hanford Reach of the Columbia River are presented to illustrate important aspects of our conceptual model. We suggest that traditional habitat models and our conceptual model be combined to predict the limits of suitable fall chinook salmon spawning habitat. This approach can incorporate quantitative measures of river channel morphology, including general descriptors of geomorphic features at different spatial scales, in order to understand the processes influencing redd site selection and spawning habitat use. This information is needed in order to protect existing salmon spawning habitat in large rivers, as well as to recover habitat already lost.KEY WORDS: Hyporheic zone; Geomorphology; Spawning habitat; Large rivers; Fall chinook salmon; Habitat management

Lithospheric radial anisotropy beneath the Gulf of Mexico

NASA Astrophysics Data System (ADS)

Chu, Risheng; Ko, Justin Yen-Ting; Wei, Shengji; Zhan, Zhongwen; Helmberger, Don

2017-05-01

The Lithosphere-Asthenosphere Boundary (LAB), where a layer of low viscosity asthenosphere decouples with the upper plate motion, plays an essential role in plate tectonics. Most dynamic modeling assumes that the shear velocity can be used as a surrogate for viscosity which provides key information about mantle flow. Here, we derive a shear velocity model for the LAB structure beneath the Gulf of Mexico allowing a detailed comparison with that beneath the Pacific (PAC) and Atlantic (ATL). Our study takes advantage of the USArray data from the March 25th, 2013 Guatemala earthquake at a depth of 200 km. Such data is unique in that we can observe a direct upward traveling lid arrival which remains the first arrival ahead of the triplications beyond 18°. This extra feature in conjunction with upper-mantle triplication sampling allows good depth control of the LAB and a new upper-mantle seismic model ATM, a modification of ATL, to be developed. ATM has a prominent low velocity zone similar to the structure beneath the western Atlantic. The model contains strong radial anisotropy in the lid where VSH is about 6% faster than VSV. This anisotropic feature ends at the bottom of the lithosphere at about the depth of 175 km in contrast to the Pacific where it extends to over 300 km. Another important feature of ATM is the weaker velocity gradient from the depth of 175 to 350 km compared to Pacific models, which may be related to differences in mantle flow.

Structural hierarchy of autism spectrum disorder symptoms: an integrative framework.

PubMed

Kim, Hyunsik; Keifer, Cara M; Rodriguez-Seijas, Craig; Eaton, Nicholas R; Lerner, Matthew D; Gadow, Kenneth D

2018-01-01

In an attempt to resolve questions regarding the symptom classification of autism spectrum disorder (ASD), previous research generally aimed to demonstrate superiority of one model over another. Rather than adjudicating which model may be optimal, we propose an alternative approach that integrates competing models using Goldberg's bass-ackwards method, providing a comprehensive understanding of the underlying symptom structure of ASD. The study sample comprised 3,825 individuals, consecutive referrals to a university hospital developmental disabilities specialty clinic or a child psychiatry outpatient clinic. This study analyzed DSM-IV-referenced ASD symptom statements from parent and teacher versions of the Child and Adolescent Symptom Inventory-4R. A series of exploratory structural equation models was conducted in order to produce interpretable latent factors that account for multivariate covariance. Results indicated that ASD symptoms were structured into an interpretable hierarchy across multiple informants. This hierarchy includes five levels; key features of ASD bifurcate into different constructs with increasing specificity. This is the first study to examine an underlying structural hierarchy of ASD symptomatology using the bass-ackwards method. This hierarchy demonstrates how core features of ASD relate at differing levels of resolution, providing a model for conceptualizing ASD heterogeneity and a structure for integrating divergent theories of cognitive processes and behavioral features that define the disorder. These findings suggest that a more coherent and complete understanding of the structure of ASD symptoms may be reflected in a metastructure rather than at one level of resolution. © 2017 Association for Child and Adolescent Mental Health.

Making Better Use of Bandwidth: Data Compression and Network Management Technologies

DTIC Science & Technology

2005-01-01

data , the compression would not be a success. A key feature of the Lempel - Ziv family of algorithms is that the...citeseer.nj.nec.com/yu02motion.html. Ziv , J., and A. Lempel , “A Universal Algorithm for Sequential Data Compression ,” IEEE Transac- tions on Information Theory, Vol. 23, 1977, pp. 337–342. ...probability models – Lempel - Ziv – Prediction by partial matching The central component of a lossless compression algorithm

In Flight Evaluation of Active Inceptor Force-Feel Characteristics and Handling Qualities

DTIC Science & Technology

2012-05-01

DEGRADED ACCEPTABLE Mitchell Aponso (1995) Watson Schroeder (1990) 0.75 lb/in 2.3 lb/in2.9 lb/in5.9 lb/in Side Stk - lon Side Stk - lat Center Stk Figure...vestibular feedback ( and respectively), and the visual error compensation ( ). A key feature of this approach is the modeling of proprioceptive...and vestibular feedback, and is the proportional component of the visual compensation strategy. At its core the fundamental concept of the HQSF

Introduction to Python for CMF Authority Users

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

Pritchett-Sheats, Lori A.

This talk is a very broad over view of Python that highlights key features in the language used in the Common Model Framework (CMF). I assume that the audience has some programming experience in a shell scripting language (C shell, Bash, PERL) or other high level language (C/C++/ Fortran). The talk will cover Python data types, classes (objects) and basic programming constructs. The talk concludes with slides describing how I developed the basic classes for a TITANS homework assignment.

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.

Modelling Aṣṭādhyāyī: An Approach Based on the Methodology of Ancillary Disciplines (Vedāṅga)

NASA Astrophysics Data System (ADS)

Mishra, Anand

This article proposes a general model based on the common methodological approach of the ancillary disciplines (Vedāṅga) associated with the Vedas taking examples from Śikṣā, Chandas, Vyākaraṇa and Prātiśā khya texts. It develops and elaborates this model further to represent the contents and processes of Aṣṭādhyāyī. Certain key features are added to my earlier modelling of Pāṇinian system of Sanskrit grammar. This includes broader coverage of the Pāṇinian meta-language, mechanism for automatic application of rules and positioning the grammatical system within the procedural complexes of ancillary disciplines.

Shock Structure Analysis and Aerodynamics in a Weakly Ionized Gas Flow

NASA Technical Reports Server (NTRS)

Saeks, R.; Popovic, S.; Chow, A. S.

2006-01-01

The structure of a shock wave propagating through a weakly ionized gas is analyzed using an electrofluid dynamics model composed of classical conservation laws and Gauss Law. A viscosity model is included to correctly model the spatial scale of the shock structure, and quasi-neutrality is not assumed. A detailed analysis of the structure of a shock wave propagating in a weakly ionized gas is presented, together with a discussion of the physics underlying the key features of the shock structure. A model for the flow behind a shock wave propagating through a weakly ionized gas is developed and used to analyze the effect of the ionization on the aerodynamics and performance of a two-dimensional hypersonic lifting body.

Generic features of the wealth distribution in ideal-gas-like markets.

PubMed

Mohanty, P K

2006-07-01

We provide an exact solution to the ideal-gas-like models studied in econophysics to understand the microscopic origin of Pareto law. In these classes of models the key ingredient necessary for having a self-organized scale-free steady-state distribution is the trading or collision rule where agents or particles save a definite fraction of their wealth or energy and invest the rest for trading. Using a Gibbs ensemble approach we could obtain the exact distribution of wealth in this model. Moreover we show that in this model (a) good savers are always rich and (b) every agent poor or rich invests the same amount for trading. Nonlinear trading rules could alter the generic scenario observed here.

10 CFR 1045.17 - Classification levels.

Code of Federal Regulations, 2014 CFR

2014-01-01

... classification include detailed technical descriptions of critical features of a nuclear explosive design that... classification include designs for specific weapon components (not revealing critical features), key features of uranium enrichment technologies, or specifications of weapon materials. (3) Confidential. The Director of...

10 CFR 1045.17 - Classification levels.

Code of Federal Regulations, 2013 CFR

2013-01-01

... classification include detailed technical descriptions of critical features of a nuclear explosive design that... classification include designs for specific weapon components (not revealing critical features), key features of uranium enrichment technologies, or specifications of weapon materials. (3) Confidential. The Director of...

10 CFR 1045.17 - Classification levels.

Code of Federal Regulations, 2011 CFR

2011-01-01

... classification include detailed technical descriptions of critical features of a nuclear explosive design that... classification include designs for specific weapon components (not revealing critical features), key features of uranium enrichment technologies, or specifications of weapon materials. (3) Confidential. The Director of...

10 CFR 1045.17 - Classification levels.

Code of Federal Regulations, 2012 CFR

2012-01-01

... classification include detailed technical descriptions of critical features of a nuclear explosive design that... classification include designs for specific weapon components (not revealing critical features), key features of uranium enrichment technologies, or specifications of weapon materials. (3) Confidential. The Director of...

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

PubMed

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

2016-01-01

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

Building intelligent communication systems for handicapped aphasiacs.

PubMed

Fu, Yu-Fen; Ho, Cheng-Seen

2010-01-01

This paper presents an intelligent system allowing handicapped aphasiacs to perform basic communication tasks. It has the following three key features: (1) A 6-sensor data glove measures the finger gestures of a patient in terms of the bending degrees of his fingers. (2) A finger language recognition subsystem recognizes language components from the finger gestures. It employs multiple regression analysis to automatically extract proper finger features so that the recognition model can be fast and correctly constructed by a radial basis function neural network. (3) A coordinate-indexed virtual keyboard allows the users to directly access the letters on the keyboard at a practical speed. The system serves as a viable tool for natural and affordable communication for handicapped aphasiacs through continuous finger language input.

Support vector machine for automatic pain recognition

NASA Astrophysics Data System (ADS)

Monwar, Md Maruf; Rezaei, Siamak

2009-02-01

Facial expressions are a key index of emotion and the interpretation of such expressions of emotion is critical to everyday social functioning. In this paper, we present an efficient video analysis technique for recognition of a specific expression, pain, from human faces. We employ an automatic face detector which detects face from the stored video frame using skin color modeling technique. For pain recognition, location and shape features of the detected faces are computed. These features are then used as inputs to a support vector machine (SVM) for classification. We compare the results with neural network based and eigenimage based automatic pain recognition systems. The experiment results indicate that using support vector machine as classifier can certainly improve the performance of automatic pain recognition system.

Cloud-assisted mobile-access of health data with privacy and auditability.

PubMed

Tong, Yue; Sun, Jinyuan; Chow, Sherman S M; Li, Pan

2014-03-01

Motivated by the privacy issues, curbing the adoption of electronic healthcare systems and the wild success of cloud service models, we propose to build privacy into mobile healthcare systems with the help of the private cloud. Our system offers salient features including efficient key management, privacy-preserving data storage, and retrieval, especially for retrieval at emergencies, and auditability for misusing health data. Specifically, we propose to integrate key management from pseudorandom number generator for unlinkability, a secure indexing method for privacy-preserving keyword search which hides both search and access patterns based on redundancy, and integrate the concept of attribute-based encryption with threshold signing for providing role-based access control with auditability to prevent potential misbehavior, in both normal and emergency cases.

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

PubMed Central

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

2017-01-01

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

Multiple R&D projects scheduling optimization with improved particle swarm algorithm.

PubMed

Liu, Mengqi; Shan, Miyuan; Wu, Juan

2014-01-01

For most enterprises, in order to win the initiative in the fierce competition of market, a key step is to improve their R&D ability to meet the various demands of customers more timely and less costly. This paper discusses the features of multiple R&D environments in large make-to-order enterprises under constrained human resource and budget, and puts forward a multi-project scheduling model during a certain period. Furthermore, we make some improvements to existed particle swarm algorithm and apply the one developed here to the resource-constrained multi-project scheduling model for a simulation experiment. Simultaneously, the feasibility of model and the validity of algorithm are proved in the experiment.

Finite driving rate and anisotropy effects in landslide modeling

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

Piegari, E.; Cataudella, V.; Di Maio, R.

2006-02-15

In order to characterize landslide frequency-size distributions and individuate hazard scenarios and their possible precursors, we investigate a cellular automaton where the effects of a finite driving rate and the anisotropy are taken into account. The model is able to reproduce observed features of landslide events, such as power-law distributions, as experimentally reported. We analyze the key role of the driving rate and show that, as it is increased, a crossover from power-law to non-power-law behaviors occurs. Finally, a systematic investigation of the model on varying its anisotropy factors is performed and the full diagram of its dynamical behaviors ismore » presented.« less

Effects of the local structure dependence of evaporation fields on field evaporation behavior

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

Yao, Lan; Marquis, Emmanuelle A., E-mail: emarq@umich.edu; Withrow, Travis

2015-12-14

Accurate three dimensional reconstructions of atomic positions and full quantification of the information contained in atom probe microscopy data rely on understanding the physical processes taking place during field evaporation of atoms from needle-shaped specimens. However, the modeling framework for atom probe microscopy has only limited quantitative justification. Building on the continuum field models previously developed, we introduce a more physical approach with the selection of evaporation events based on density functional theory calculations. This model reproduces key features observed experimentally in terms of sequence of evaporation, evaporation maps, and depth resolution, and provides insights into the physical limit formore » spatial resolution.« less

Incorporating Satellite Time-Series Data into Modeling

NASA Technical Reports Server (NTRS)

Gregg, Watson

2008-01-01

In situ time series observations have provided a multi-decadal view of long-term changes in ocean biology. These observations are sufficiently reliable to enable discernment of even relatively small changes, and provide continuous information on a host of variables. Their key drawback is their limited domain. Satellite observations from ocean color sensors do not suffer the drawback of domain, and simultaneously view the global oceans. This attribute lends credence to their use in global and regional model validation and data assimilation. We focus on these applications using the NASA Ocean Biogeochemical Model. The enhancement of the satellite data using data assimilation is featured and the limitation of tongterm satellite data sets is also discussed.

Deriving Physical Properties from Broadband Photometry with Prospector: Description of the Model and a Demonstration of its Accuracy Using 129 Galaxies in the Local Universe

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

Leja, Joel; Johnson, Benjamin D.; Conroy, Charlie

2017-03-10

Broadband photometry of galaxies measures an unresolved mix of complex stellar populations, gas, and dust. Interpreting these data is a challenge for models: many studies have shown that properties derived from modeling galaxy photometry are uncertain by a factor of two or more, and yet answering key questions in the field now requires higher accuracy than this. Here, we present a new model framework specifically designed for these complexities. Our model, Prospector- α , includes dust attenuation and re-radiation, a flexible attenuation curve, nebular emission, stellar metallicity, and a six-component nonparametric star formation history. The flexibility and range of themore » parameter space, coupled with Monte Carlo Markov chain sampling within the Prospector inference framework, is designed to provide unbiased parameters and realistic error bars. We assess the accuracy of the model with aperture-matched optical spectroscopy, which was excluded from the fits. We compare spectral features predicted solely from fits to the broadband photometry to the observed spectral features. Our model predicts H α luminosities with a scatter of ∼0.18 dex and an offset of ∼0.1 dex across a wide range of morphological types and stellar masses. This agreement is remarkable, as the H α luminosity is dependent on accurate star formation rates, dust attenuation, and stellar metallicities. The model also accurately predicts dust-sensitive Balmer decrements, spectroscopic stellar metallicities, polycyclic aromatic hydrocarbon mass fractions, and the age- and metallicity-sensitive features D{sub n}4000 and H δ . Although the model passes all these tests, we caution that we have not yet assessed its performance at higher redshift or the accuracy of recovered stellar masses.« less

Estimation of key parameters in adaptive neuron model according to firing patterns based on improved particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Yuan, Chunhua; Wang, Jiang; Yi, Guosheng

2017-03-01

Estimation of ion channel parameters is crucial to spike initiation of neurons. The biophysical neuron models have numerous ion channel parameters, but only a few of them play key roles in the firing patterns of the models. So we choose three parameters featuring the adaptation in the Ermentrout neuron model to be estimated. However, the traditional particle swarm optimization (PSO) algorithm is still easy to fall into local optimum and has the premature convergence phenomenon in the study of some problems. In this paper, we propose an improved method that uses a concave function and dynamic logistic chaotic mapping mixed to adjust the inertia weights of the fitness value, effectively improve the global convergence ability of the algorithm. The perfect predicting firing trajectories of the rebuilt model using the estimated parameters prove that only estimating a few important ion channel parameters can establish the model well and the proposed algorithm is effective. Estimations using two classic PSO algorithms are also compared to the improved PSO to verify that the algorithm proposed in this paper can avoid local optimum and quickly converge to the optimal value. The results provide important theoretical foundations for building biologically realistic neuron models.

[Prediction of ETA oligopeptides antagonists from Glycine max based on in silico proteolysis].

PubMed

Qiao, Lian-Sheng; Jiang, Lu-di; Luo, Gang-Gang; Lu, Fang; Chen, Yan-Kun; Wang, Ling-Zhi; Li, Gong-Yu; Zhang, Yan-Ling

2017-02-01

Oligopeptides are one of the the key pharmaceutical effective constituents of traditional Chinese medicine(TCM). Systematic study on composition and efficacy of TCM oligopeptides is essential for the analysis of material basis and mechanism of TCM. In this study, the potential anti-hypertensive oligopeptides from Glycine max and their endothelin receptor A (ETA) antagonistic activity were discovered and predicted based on in silico technologies.Main protein sequences of G. max were collected and oligopeptides were obtained using in silico gastrointestinal tract proteolysis. Then, the pharmacophore of ETA antagonistic peptides was constructed and included one hydrophobic feature, one ionizable negative feature, one ring aromatic feature and five excluded volumes. Meanwhile, three-dimensional structure of ETA was developed by homology modeling methods for further docking studies. According to docking analysis and consensus score, the key amino acid of GLN165 was identified for ETA antagonistic activity. And 27 oligopeptides from G. max were predicted as the potential ETA antagonists by pharmacophore and docking studies.In silico proteolysis could be used to analyze the protein sequences from TCM. According to combination of in silico proteolysis and molecular simulation, the biological activities of oligopeptides could be predicted rapidly based on the known TCM protein sequence. It might provide the methodology basis for rapidly and efficiently implementing the mechanism analysis of TCM oligopeptides. Copyright© by the Chinese Pharmaceutical Association.

Discovering rules for protein-ligand specificity using support vector inductive logic programming.

PubMed

Kelley, Lawrence A; Shrimpton, Paul J; Muggleton, Stephen H; Sternberg, Michael J E

2009-09-01

Structural genomics initiatives are rapidly generating vast numbers of protein structures. Comparative modelling is also capable of producing accurate structural models for many protein sequences. However, for many of the known structures, functions are not yet determined, and in many modelling tasks, an accurate structural model does not necessarily tell us about function. Thus, there is a pressing need for high-throughput methods for determining function from structure. The spatial arrangement of key amino acids in a folded protein, on the surface or buried in clefts, is often the determinants of its biological function. A central aim of molecular biology is to understand the relationship between such substructures or surfaces and biological function, leading both to function prediction and to function design. We present a new general method for discovering the features of binding pockets that confer specificity for particular ligands. Using a recently developed machine-learning technique which couples the rule-discovery approach of inductive logic programming with the statistical learning power of support vector machines, we are able to discriminate, with high precision (90%) and recall (86%) between pockets that bind FAD and those that bind NAD on a large benchmark set given only the geometry and composition of the backbone of the binding pocket without the use of docking. In addition, we learn rules governing this specificity which can feed into protein functional design protocols. An analysis of the rules found suggests that key features of the binding pocket may be tied to conformational freedom in the ligand. The representation is sufficiently general to be applicable to any discriminatory binding problem. All programs and data sets are freely available to non-commercial users at http://www.sbg.bio.ic.ac.uk/svilp_ligand/.

FY17 Status Report on Testing Supporting the Inclusion of Grade 91 Steel as an Acceptable Material for Application of the EPP Methodology

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

Messner, Mark C.; Sham, Sam; Wang, Yanli

This report summarizes the experiments performed in FY17 on Gr. 91 steels. The testing of Gr. 91 has technical significance because, currently, it is the only approved material for Class A construction that is strongly cyclic softening. Specific FY17 testing includes the following activities for Gr. 91 steel. First, two types of key feature testing have been initiated, including two-bar thermal ratcheting and Simplified Model Testing (SMT). The goal is to qualify the Elastic – Perfectly Plastic (EPP) design methodologies and to support incorporation of these rules for Gr. 91 into the ASME Division 5 Code. The preliminary SMT testmore » results show that Gr. 91 is most damaging when tested with compression hold mode under the SMT creep fatigue testing condition. Two-bar thermal ratcheting test results at a temperature range between 350 to 650o C were compared with the EPP strain limits code case evaluation, and the results show that the EPP strain limits code case is conservative. The material information obtained from these key feature tests can also be used to verify its material model. Second, to provide experimental data in support of the viscoplastic material model development at Argonne National Laboratory, selective tests were performed to evaluate the effect of cyclic softening on strain rate sensitivity and creep rates. The results show the prior cyclic loading history decreases the strain rate sensitivity and increases creep rates. In addition, isothermal cyclic stress-strain curves were generated at six different temperatures, and a nonisothermal thermomechanical testing was also performed to provide data to calibrate the viscoplastic material model.« less

Patch models and their applications to multivehicle command and control.

PubMed

Rao, Venkatesh G; D'Andrea, Raffaello

2007-06-01

We introduce patch models, a computational modeling formalism for multivehicle combat domains, based on spatiotemporal abstraction methods developed in the computer science community. The framework yields models that are expressive enough to accommodate nontrivial controlled vehicle dynamics while being within the representational capabilities of common artificial intelligence techniques used in the construction of autonomous systems. The framework allows several key design requirements of next-generation network-centric command and control systems, such as maintenance of shared situation awareness, to be achieved. Major features include support for multiple situation models at each decision node and rapid mission plan adaptation. We describe the formal specification of patch models and our prototype implementation, i.e., Patchworks. The capabilities of patch models are validated through a combat mission simulation in Patchworks, which involves two defending teams protecting a camp from an enemy attacking team.

Analysis and Management of Animal Populations: Modeling, Estimation and Decision Making

USGS Publications Warehouse

Williams, B.K.; Nichols, J.D.; Conroy, M.J.

2002-01-01

This book deals with the processes involved in making informed decisions about the management of animal populations. It covers the modeling of population responses to management actions, the estimation of quantities needed in the modeling effort, and the application of these estimates and models to the development of sound management decisions. The book synthesizes and integrates in a single volume the methods associated with these themes, as they apply to ecological assessment and conservation of animal populations. KEY FEATURES * Integrates population modeling, parameter estimation and * decision-theoretic approaches to management in a single, cohesive framework * Provides authoritative, state-of-the-art descriptions of quantitative * approaches to modeling, estimation and decision-making * Emphasizes the role of mathematical modeling in the conduct of science * and management * Utilizes a unifying biological context, consistent mathematical notation, * and numerous biological examples

Machinery running state identification based on discriminant semi-supervised local tangent space alignment for feature fusion and extraction

NASA Astrophysics Data System (ADS)

Su, Zuqiang; Xiao, Hong; Zhang, Yi; Tang, Baoping; Jiang, Yonghua

2017-04-01

Extraction of sensitive features is a challenging but key task in data-driven machinery running state identification. Aimed at solving this problem, a method for machinery running state identification that applies discriminant semi-supervised local tangent space alignment (DSS-LTSA) for feature fusion and extraction is proposed. Firstly, in order to extract more distinct features, the vibration signals are decomposed by wavelet packet decomposition WPD, and a mixed-domain feature set consisted of statistical features, autoregressive (AR) model coefficients, instantaneous amplitude Shannon entropy and WPD energy spectrum is extracted to comprehensively characterize the properties of machinery running state(s). Then, the mixed-dimension feature set is inputted into DSS-LTSA for feature fusion and extraction to eliminate redundant information and interference noise. The proposed DSS-LTSA can extract intrinsic structure information of both labeled and unlabeled state samples, and as a result the over-fitting problem of supervised manifold learning and blindness problem of unsupervised manifold learning are overcome. Simultaneously, class discrimination information is integrated within the dimension reduction process in a semi-supervised manner to improve sensitivity of the extracted fusion features. Lastly, the extracted fusion features are inputted into a pattern recognition algorithm to achieve the running state identification. The effectiveness of the proposed method is verified by a running state identification case in a gearbox, and the results confirm the improved accuracy of the running state identification.

The NASA/industry Design Analysis Methods for Vibrations (DAMVIBS) program: McDonnell-Douglas Helicopter Company achievements

NASA Technical Reports Server (NTRS)

Toossi, Mostafa; Weisenburger, Richard; Hashemi-Kia, Mostafa

1993-01-01

This paper presents a summary of some of the work performed by McDonnell Douglas Helicopter Company under NASA Langley-sponsored rotorcraft structural dynamics program known as DAMVIBS (Design Analysis Methods for VIBrationS). A set of guidelines which is applicable to dynamic modeling, analysis, testing, and correlation of both helicopter airframes and a large variety of structural finite element models is presented. Utilization of these guidelines and the key features of their applications to vibration modeling of helicopter airframes are discussed. Correlation studies with the test data, together with the development and applications of a set of efficient finite element model checkout procedures, are demonstrated on a large helicopter airframe finite element model. Finally, the lessons learned and the benefits resulting from this program are summarized.

Intelligent Chatter Bot for Regulation Search

NASA Astrophysics Data System (ADS)

De Luise, María Daniela López; Pascal, Andrés; Saad, Ben; Álvarez, Claudia; Pescio, Pablo; Carrilero, Patricio; Malgor, Rafael; Díaz, Joaquín

2016-01-01

This communication presents a functional prototype, named PTAH, implementing a linguistic model focused on regulations in Spanish. Its global architecture, the reasoning model and short statistics are provided for the prototype. It is mainly a conversational robot linked to an Expert System by a module with many intelligent linguistic filters, implementing the reasoning model of an expert. It is focused on bylaws, regulations, jurisprudence and customized background representing entity mission, vision and profile. This Structure and model are generic enough to self-adapt to any regulatory environment, but as a first step, it was limited to an academic field. This way it is possible to limit the slang and data numbers. The foundations of the linguistic model are also outlined and the way the architecture implements the key features of the behavior.

Features of spatial and functional segregation and integration of the primate connectome revealed by trade-off between wiring cost and efficiency

PubMed Central

Chen, Yuhan; Wang, Shengjun

2017-01-01

The primate connectome, possessing a characteristic global topology and specific regional connectivity profiles, is well organized to support both segregated and integrated brain function. However, the organization mechanisms shaping the characteristic connectivity and its relationship to functional requirements remain unclear. The primate brain connectome is shaped by metabolic economy as well as functional values. Here, we explored the influence of two competing factors and additional advanced functional requirements on the primate connectome employing an optimal trade-off model between neural wiring cost and the representative functional requirement of processing efficiency. Moreover, we compared this model with a generative model combining spatial distance and topological similarity, with the objective of statistically reproducing multiple topological features of the network. The primate connectome indeed displays a cost-efficiency trade-off and that up to 67% of the connections were recovered by optimal combination of the two basic factors of wiring economy and processing efficiency, clearly higher than the proportion of connections (56%) explained by the generative model. While not explicitly aimed for, the trade-off model captured several key topological features of the real connectome as the generative model, yet better explained the connectivity of most regions. The majority of the remaining 33% of connections unexplained by the best trade-off model were long-distance links, which are concentrated on few cortical areas, termed long-distance connectors (LDCs). The LDCs are mainly non-hubs, but form a densely connected group overlapping on spatially segregated functional modalities. LDCs are crucial for both functional segregation and integration across different scales. These organization features revealed by the optimization analysis provide evidence that the demands of advanced functional segregation and integration among spatially distributed regions may play a significant role in shaping the cortical connectome, in addition to the basic cost-efficiency trade-off. These findings also shed light on inherent vulnerabilities of brain networks in diseases. PMID:28961235

Features of spatial and functional segregation and integration of the primate connectome revealed by trade-off between wiring cost and efficiency.

PubMed

Chen, Yuhan; Wang, Shengjun; Hilgetag, Claus C; Zhou, Changsong

2017-09-01

The primate connectome, possessing a characteristic global topology and specific regional connectivity profiles, is well organized to support both segregated and integrated brain function. However, the organization mechanisms shaping the characteristic connectivity and its relationship to functional requirements remain unclear. The primate brain connectome is shaped by metabolic economy as well as functional values. Here, we explored the influence of two competing factors and additional advanced functional requirements on the primate connectome employing an optimal trade-off model between neural wiring cost and the representative functional requirement of processing efficiency. Moreover, we compared this model with a generative model combining spatial distance and topological similarity, with the objective of statistically reproducing multiple topological features of the network. The primate connectome indeed displays a cost-efficiency trade-off and that up to 67% of the connections were recovered by optimal combination of the two basic factors of wiring economy and processing efficiency, clearly higher than the proportion of connections (56%) explained by the generative model. While not explicitly aimed for, the trade-off model captured several key topological features of the real connectome as the generative model, yet better explained the connectivity of most regions. The majority of the remaining 33% of connections unexplained by the best trade-off model were long-distance links, which are concentrated on few cortical areas, termed long-distance connectors (LDCs). The LDCs are mainly non-hubs, but form a densely connected group overlapping on spatially segregated functional modalities. LDCs are crucial for both functional segregation and integration across different scales. These organization features revealed by the optimization analysis provide evidence that the demands of advanced functional segregation and integration among spatially distributed regions may play a significant role in shaping the cortical connectome, in addition to the basic cost-efficiency trade-off. These findings also shed light on inherent vulnerabilities of brain networks in diseases.

Transient and sustained elementary flux mode networks on a catalytic string-based chemical evolution model.

PubMed

Pereira, José A

2014-08-01

Theoretical models designed to test the metabolism-first hypothesis for prebiotic evolution have yield strong indications about the hypothesis validity but could sometimes use a more extensive identification between model objects and real objects towards a more meaningful interpretation of results. In an attempt to go in that direction, the string-based model SSE ("steady state evolution") was developed, where abstract molecules (strings) and catalytic interaction rules are based on some of the most important features of carbon compounds in biological chemistry. The system is open with a random inflow and outflow of strings but also with a permanent string food source. Although specific catalysis is a key aspect of the model, used to define reaction rules, the focus is on energetics rather than kinetics. Standard energy change tables were constructed and used with standard formation reactions to track energy flows through the interpretation of equilibrium constant values. Detection of metabolic networks on the reaction system was done with elementary flux mode (EFM) analysis. The combination of these model design and analysis options enabled obtaining metabolic and catalytic networks showing several central features of biological metabolism, some more clearly than in previous models: metabolic networks with stepwise synthesis, energy coupling, catalysts regulation, SN2 coupling, redox coupling, intermediate cycling, coupled inverse pathways (metabolic cycling), autocatalytic cycles and catalytic cascades. The results strongly suggest that the main biological metabolism features, including the genotype-phenotype interpretation, are caused by the principles of catalytic systems and are prior to modern genetic systems principles. It also gives further theoretical support to the thesis that the basic features of biologic metabolism are a consequence of the time evolution of a random catalyst search working on an open system with a permanent food source. The importance of the food source characteristics and evolutionary possibilities are discussed. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Research on gesture recognition of augmented reality maintenance guiding system based on improved SVM

NASA Astrophysics Data System (ADS)

Zhao, Shouwei; Zhang, Yong; Zhou, Bin; Ma, Dongxi

2014-09-01

Interaction is one of the key techniques of augmented reality (AR) maintenance guiding system. Because of the complexity of the maintenance guiding system's image background and the high dimensionality of gesture characteristics, the whole process of gesture recognition can be divided into three stages which are gesture segmentation, gesture characteristic feature modeling and trick recognition. In segmentation stage, for solving the misrecognition of skin-like region, a segmentation algorithm combing background mode and skin color to preclude some skin-like regions is adopted. In gesture characteristic feature modeling of image attributes stage, plenty of characteristic features are analyzed and acquired, such as structure characteristics, Hu invariant moments features and Fourier descriptor. In trick recognition stage, a classifier based on Support Vector Machine (SVM) is introduced into the augmented reality maintenance guiding process. SVM is a novel learning method based on statistical learning theory, processing academic foundation and excellent learning ability, having a lot of issues in machine learning area and special advantages in dealing with small samples, non-linear pattern recognition at high dimension. The gesture recognition of augmented reality maintenance guiding system is realized by SVM after the granulation of all the characteristic features. The experimental results of the simulation of number gesture recognition and its application in augmented reality maintenance guiding system show that the real-time performance and robustness of gesture recognition of AR maintenance guiding system can be greatly enhanced by improved SVM.

Prediction of active sites of enzymes by maximum relevance minimum redundancy (mRMR) feature selection.

PubMed

Gao, Yu-Fei; Li, Bi-Qing; Cai, Yu-Dong; Feng, Kai-Yan; Li, Zhan-Dong; Jiang, Yang

2013-01-27

Identification of catalytic residues plays a key role in understanding how enzymes work. Although numerous computational methods have been developed to predict catalytic residues and active sites, the prediction accuracy remains relatively low with high false positives. In this work, we developed a novel predictor based on the Random Forest algorithm (RF) aided by the maximum relevance minimum redundancy (mRMR) method and incremental feature selection (IFS). We incorporated features of physicochemical/biochemical properties, sequence conservation, residual disorder, secondary structure and solvent accessibility to predict active sites of enzymes and achieved an overall accuracy of 0.885687 and MCC of 0.689226 on an independent test dataset. Feature analysis showed that every category of the features except disorder contributed to the identification of active sites. It was also shown via the site-specific feature analysis that the features derived from the active site itself contributed most to the active site determination. Our prediction method may become a useful tool for identifying the active sites and the key features identified by the paper may provide valuable insights into the mechanism of catalysis.

Parallel Key Frame Extraction for Surveillance Video Service in a Smart City.

PubMed

Zheng, Ran; Yao, Chuanwei; Jin, Hai; Zhu, Lei; Zhang, Qin; Deng, Wei

2015-01-01

Surveillance video service (SVS) is one of the most important services provided in a smart city. It is very important for the utilization of SVS to provide design efficient surveillance video analysis techniques. Key frame extraction is a simple yet effective technique to achieve this goal. In surveillance video applications, key frames are typically used to summarize important video content. It is very important and essential to extract key frames accurately and efficiently. A novel approach is proposed to extract key frames from traffic surveillance videos based on GPU (graphics processing units) to ensure high efficiency and accuracy. For the determination of key frames, motion is a more salient feature in presenting actions or events, especially in surveillance videos. The motion feature is extracted in GPU to reduce running time. It is also smoothed to reduce noise, and the frames with local maxima of motion information are selected as the final key frames. The experimental results show that this approach can extract key frames more accurately and efficiently compared with several other methods.

Closing in on the Mechanisms of Pulsatile Insulin Secretion.

PubMed

Bertram, Richard; Satin, Leslie S; Sherman, Arthur S

2018-03-01

Insulin secretion from pancreatic islet β-cells occurs in a pulsatile fashion, with a typical period of ∼5 min. The basis of this pulsatility in mouse islets has been investigated for more than four decades, and the various theories have been described as either qualitative or mathematical models. In many cases the models differ in their mechanisms for rhythmogenesis, as well as other less important details. In this Perspective, we describe two main classes of models: those in which oscillations in the intracellular Ca 2+ concentration drive oscillations in metabolism, and those in which intrinsic metabolic oscillations drive oscillations in Ca 2+ concentration and electrical activity. We then discuss nine canonical experimental findings that provide key insights into the mechanism of islet oscillations and list the models that can account for each finding. Finally, we describe a new model that integrates features from multiple earlier models and is thus called the Integrated Oscillator Model. In this model, intracellular Ca 2+ acts on the glycolytic pathway in the generation of oscillations, and it is thus a hybrid of the two main classes of models. It alone among models proposed to date can explain all nine key experimental findings, and it serves as a good starting point for future studies of pulsatile insulin secretion from human islets. © 2018 by the American Diabetes Association.

Topological and kinetic determinants of the modal matrices of dynamic models of metabolism

PubMed Central

2017-01-01

Large-scale kinetic models of metabolism are becoming increasingly comprehensive and accurate. A key challenge is to understand the biochemical basis of the dynamic properties of these models. Linear analysis methods are well-established as useful tools for characterizing the dynamic response of metabolic networks. Central to linear analysis methods are two key matrices: the Jacobian matrix (J) and the modal matrix (M-1) arising from its eigendecomposition. The modal matrix M-1 contains dynamically independent motions of the kinetic model near a reference state, and it is sparse in practice for metabolic networks. However, connecting the structure of M-1 to the kinetic properties of the underlying reactions is non-trivial. In this study, we analyze the relationship between J, M-1, and the kinetic properties of the underlying network for kinetic models of metabolism. Specifically, we describe the origin of mode sparsity structure based on features of the network stoichiometric matrix S and the reaction kinetic gradient matrix G. First, we show that due to the scaling of kinetic parameters in real networks, diagonal dominance occurs in a substantial fraction of the rows of J, resulting in simple modal structures with clear biological interpretations. Then, we show that more complicated modes originate from topologically-connected reactions that have similar reaction elasticities in G. These elasticities represent dynamic equilibrium balances within reactions and are key determinants of modal structure. The work presented should prove useful towards obtaining an understanding of the dynamics of kinetic models of metabolism, which are rooted in the network structure and the kinetic properties of reactions. PMID:29267329

A universal deep learning approach for modeling the flow of patients under different severities.

PubMed

Jiang, Shancheng; Chin, Kwai-Sang; Tsui, Kwok L

2018-02-01

The Accident and Emergency Department (A&ED) is the frontline for providing emergency care in hospitals. Unfortunately, relative A&ED resources have failed to keep up with continuously increasing demand in recent years, which leads to overcrowding in A&ED. Knowing the fluctuation of patient arrival volume in advance is a significant premise to relieve this pressure. Based on this motivation, the objective of this study is to explore an integrated framework with high accuracy for predicting A&ED patient flow under different triage levels, by combining a novel feature selection process with deep neural networks. Administrative data is collected from an actual A&ED and categorized into five groups based on different triage levels. A genetic algorithm (GA)-based feature selection algorithm is improved and implemented as a pre-processing step for this time-series prediction problem, in order to explore key features affecting patient flow. In our improved GA, a fitness-based crossover is proposed to maintain the joint information of multiple features during iterative process, instead of traditional point-based crossover. Deep neural networks (DNN) is employed as the prediction model to utilize their universal adaptability and high flexibility. In the model-training process, the learning algorithm is well-configured based on a parallel stochastic gradient descent algorithm. Two effective regularization strategies are integrated in one DNN framework to avoid overfitting. All introduced hyper-parameters are optimized efficiently by grid-search in one pass. As for feature selection, our improved GA-based feature selection algorithm has outperformed a typical GA and four state-of-the-art feature selection algorithms (mRMR, SAFS, VIFR, and CFR). As for the prediction accuracy of proposed integrated framework, compared with other frequently used statistical models (GLM, seasonal-ARIMA, ARIMAX, and ANN) and modern machine models (SVM-RBF, SVM-linear, RF, and R-LASSO), the proposed integrated "DNN-I-GA" framework achieves higher prediction accuracy on both MAPE and RMSE metrics in pairwise comparisons. The contribution of our study is two-fold. Theoretically, the traditional GA-based feature selection process is improved to have less hyper-parameters and higher efficiency, and the joint information of multiple features is maintained by fitness-based crossover operator. The universal property of DNN is further enhanced by merging different regularization strategies. Practically, features selected by our improved GA can be used to acquire an underlying relationship between patient flows and input features. Predictive values are significant indicators of patients' demand and can be used by A&ED managers to make resource planning and allocation. High accuracy achieved by the present framework in different cases enhances the reliability of downstream decision makings. Copyright © 2017 Elsevier B.V. All rights reserved.

Extreme Rainfall from Hurricane Harvey (2017): Intercomparisons of WRF Simulations and Polarimetric Radar Fields

NASA Astrophysics Data System (ADS)

Yang, L.; Smith, J. A.; Liu, M.; Baeck, M. L.; Chaney, M. M.; Su, Y.

2017-12-01

Hurricane Harvey made landfall on 25 August 2017 and produced more than a meter of rain during a four-day period over eastern Texas, making it the wettest tropical cyclone on record in the United States. Extreme rainfall from Harvey was predominantly related to the dynamics and structure of outer rain bands. In this study, we provide details of the extreme rainfall produced by Hurricane Harvey. The principal research questions that motivate this study are: (1) what are the key microphysical properties of extreme rainfall from landfalling tropical cyclones and (2) what are the capabilities and deficiencies of existing bulk microphysics parameterizations from the physical models in capturing them. Our analyses are centered on intercomparisons of high-resolution simulations using the Weather Research and Forecasting (WRF) model and polarimetric radar fields from KHGX (Houston, Texas) WSR-88D. The WRF simulations accurately capture the track and intensity of Hurricane Harvey. Multi-rainband structure and its key evolution features are also well represented in the simulations. Two microphysics parameterizations (WSM6 and WDM6) are tested in this study. Radar reflectivity and differential reflectivity fields simulated by the WRF model are compared with polarimetric radar observations. An important feature for the extreme rainfall from Hurricane Harvey is the sharp boundary of spatial rainfall accumulation along the coast (with torrential rainfall distributed over Houston and its surrounding inland areas). We will examine the role of land-sea contrasts in dictating storm structure and evolution from both WRF simulations and polarimetric radar fields. Implications for improving hurricane rainfall forecasts and estimates will be provided.

A model of face selection in viewing video stories.

PubMed

Suda, Yuki; Kitazawa, Shigeru

2015-01-19

When typical adults watch TV programs, they show surprisingly stereo-typed gaze behaviours, as indicated by the almost simultaneous shifts of their gazes from one face to another. However, a standard saliency model based on low-level physical features alone failed to explain such typical gaze behaviours. To find rules that explain the typical gaze behaviours, we examined temporo-spatial gaze patterns in adults while they viewed video clips with human characters that were played with or without sound, and in the forward or reverse direction. We here show the following: 1) the "peak" face scanpath, which followed the face that attracted the largest number of views but ignored other objects in the scene, still retained the key features of actual scanpaths, 2) gaze behaviours remained unchanged whether the sound was provided or not, 3) the gaze behaviours were sensitive to time reversal, and 4) nearly 60% of the variance of gaze behaviours was explained by the face saliency that was defined as a function of its size, novelty, head movements, and mouth movements. These results suggest that humans share a face-oriented network that integrates several visual features of multiple faces, and directs our eyes to the most salient face at each moment.

A two parameter family of travelling waves with a singular barrier arising from the modelling of extracellular matrix mediated cellular invasion

NASA Astrophysics Data System (ADS)

Perumpanani, Abbey J.; Sherratt, Jonathan A.; Norbury, John; Byrne, Helen M.

1999-02-01

Invasive cells variously show changes in adhesion, protease production and motility. In this paper the authors develop and analyse a model for malignant invasion, brought about by a combination of proteolysis and haptotaxis. A common feature of these two mechanisms is that they can be produced by contact with the extracellular matrix through the mediation of a class of surface receptors called integrins. An unusual feature of the model is the absence of cell diffusion. By seeking travelling wave solutions the model is reduced to a system of ordinary differential equations which can be studied using phase plane analysis. The authors demonstrate the presence of a singular barrier in the phase plane and a “hole” in this singular barrier which admits a phase trajectory. The model admits a family of travelling waves which depend on two parameters, i.e. the tissue concentration of connective tissue and the rate of decay of the initial spatial profile of the invading cells. The slowest member of this family corresponds to the phase trajectory which goes through the “hole” in the singular barrier. Using a power series method the authors derive an expression relating the minimum wavespeed to the tissue concentration of the extracellular matrix which is arbitrary. The model is applicable in a wide variety of biological settings which combine haptotaxis with proteolysis. By considering various functional forms the authors show that the key mathematical features of the particular model studied in the early parts of the paper are exhibited by a wider class of models which characterise the behaviour of invading cells.

Modernization and multiscale databases at the U.S. geological survey

USGS Publications Warehouse

Morrison, J.L.

1992-01-01

The U.S. Geological Survey (USGS) has begun a digital cartographic modernization program. Keys to that program are the creation of a multiscale database, a feature-based file structure that is derived from a spatial data model, and a series of "templates" or rules that specify the relationships between instances of entities in reality and features in the database. The database will initially hold data collected from the USGS standard map products at scales of 1:24,000, 1:100,000, and 1:2,000,000. The spatial data model is called the digital line graph-enhanced model, and the comprehensive rule set consists of collection rules, product generation rules, and conflict resolution rules. This modernization program will affect the USGS mapmaking process because both digital and graphic products will be created from the database. In addition, non-USGS map users will have more flexibility in uses of the databases. These remarks are those of the session discussant made in response to the six papers and the keynote address given in the session. ?? 1992.

Supraglacial channel inception: Modeling and processes

NASA Astrophysics Data System (ADS)

Mantelli, E.; Camporeale, C.; Ridolfi, L.

2015-09-01

Supraglacial drainage systems play a key role in glacial hydrology. Nevertheless, physical processes leading to spatial organization in supraglacial networks are still an open issue. In the present work we thus address from a quantitative point of view the question of what is the physics leading to widely observed patterns made up of evenly spaced channels. To this aim, we set up a novel mathematical model describing a condition antecedent channel formation, i.e., the down-glacier flow of a distributed meltwater film. We then perform a linear stability analysis to assess whether the ice-water interface undergoes a morphological instability compatible with observed patterns. The instability is detected, its features depending on glacier surface slope, ice friction factor, and water as well as ice thermal conditions. By contrast, in our model channel spacing is solely hydrodynamically driven and relies on the interplay between pressure perturbations, flow depth response, and Reynolds stresses. Geometrical features of the predicted pattern are quantitatively consistent with available field data. The hydrodynamic origin of supraglacial channel morphogenesis suggests that alluvial patterns might share the same physical controls.

Chimeric Mice with Competent Hematopoietic Immunity Reproduce Key Features of Severe Lassa Fever.

PubMed

Oestereich, Lisa; Lüdtke, Anja; Ruibal, Paula; Pallasch, Elisa; Kerber, Romy; Rieger, Toni; Wurr, Stephanie; Bockholt, Sabrina; Pérez-Girón, José V; Krasemann, Susanne; Günther, Stephan; Muñoz-Fontela, César

2016-05-01

Lassa fever (LASF) is a highly severe viral syndrome endemic to West African countries. Despite the annual high morbidity and mortality caused by LASF, very little is known about the pathophysiology of the disease. Basic research on LASF has been precluded due to the lack of relevant small animal models that reproduce the human disease. Immunocompetent laboratory mice are resistant to infection with Lassa virus (LASV) and, to date, only immunodeficient mice, or mice expressing human HLA, have shown some degree of susceptibility to experimental infection. Here, transplantation of wild-type bone marrow cells into irradiated type I interferon receptor knockout mice (IFNAR-/-) was used to generate chimeric mice that reproduced important features of severe LASF in humans. This included high lethality, liver damage, vascular leakage and systemic virus dissemination. In addition, this model indicated that T cell-mediated immunopathology was an important component of LASF pathogenesis that was directly correlated with vascular leakage. Our strategy allows easy generation of a suitable small animal model to test new vaccines and antivirals and to dissect the basic components of LASF pathophysiology.

Modeling of microwave-sustained plasmas at atmospheric pressure with application to discharge contraction.

PubMed

Castaños Martinez, E; Kabouzi, Y; Makasheva, K; Moisan, M

2004-12-01

The modeling of microwave-sustained discharges at atmospheric pressure is much less advanced than at reduced pressure (<10 Torr) because of the greater complexity of the mechanisms involved. In particular, discharge contraction, a characteristic feature of high-pressure discharges, is not well understood. To describe adequately this phenomenon, one needs to consider that the charged-particle balance in atmospheric-pressure discharges relies on the kinetics of molecular ions, including their dissociation through electron impact. Nonuniform gas heating plays a key role in the radial distribution of the density of molecular ions. The onset of contraction is shown to depend only on radially nonuniform gas heating. The radial nonuniformity of the electric field intensity also plays an important role allowing one, for instance, to explain the lower degree of contraction observed in microwave discharges compared to dc discharges. We present a numerical fluid-plasma model that aims to bring into relief the main features of discharge contraction in rare gases. It calls for surface-wave discharges because of their wide range of operating conditions, enabling a closer check between theory and experiment.