The Experts below are selected from a list of 318 Experts worldwide ranked by ideXlab platform
R. Martin - One of the best experts on this subject based on the ideXlab platform.
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A Simple and Versatile Field Prediction Model for Indoor and Indoor-to-Outdoor Propagation
IEEE Access, 2017Co-Authors: Vittorio Degli-esposti, Enrico M. Vitucci, R. MartinAbstract:A simple field prediction model based on a combination of a two-parameter propagation formula and a multi-wall model is proposed for fast and yet accurate indoor and indoor-to-outdoor field prediction. The model’s approach is based on: 1) simplicity; 2) physical soundness; and 3) adaptability to the available environment-Database Format. The model is validated versus both ray tracing and measurements in different environments and it is shown to perform very well in all cases. Moreover, the model is very fast and can exploit the accuracy plus of deterministic prediction based on the 3-D indoor building map whenever it is available.
John L. Markley - One of the best experts on this subject based on the ideXlab platform.
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A relational Database for sequence-specific protein NMR data
Journal of Biomolecular NMR, 1991Co-Authors: Beverly R. Seavey, Elizabeth A. Farr, William M. Westler, John L. MarkleyAbstract:A protein NMR Database has been designed and is being implemented. The Database is intended to contain solution NMR results from proteins and peptides (larger than 12 residues). A relational Database Format has been chosen that indexes data by: primary journal citation, molecular species, sequence-related and atom-specific assignments, and experimental conditions. At present, all data are entered from the primary refereed literature. Examples are given of sample queries to the Database. Possible distribution Formats are discussed.
Fabienne Maignan - One of the best experts on this subject based on the ideXlab platform.
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A BRDF–BPDF Database for the analysis of Earth target reflectances
Earth System Science Data, 2017Co-Authors: Francois-marie Breon, Fabienne MaignanAbstract:Land surface reflectance is not isotropic. It varies with the observation geometry that is defined by the sun, view zenith angles, and the relative azimuth. In addition, the reflectance is linearly polarized. The reflectance anisotropy is quantified by the bidirectional reflectance distribution function (BRDF), while its polarization properties are defined by the bidirectional polarization distribution function (BPDF). The POLDER radiometer that flew onboard the PARASOL microsatellite remains the only space instrument that measured numerous samples of the BRDF and BPDF of Earth targets. Here, we describe a Database of representative BRDFs and BPDFs derived from the POLDER measurements. From the huge number of data acquired by the spaceborne instrument over a period of 7 years, we selected a set of targets with high-quality observations. The selection aimed for a large number of observations, free of significant cloud or aerosol contamination, acquired in diverse observation geometries with a focus on the backscatter direction that shows the specific hot spot signature. The targets are sorted according to the 16-class International Geosphere-Biosphere Programme (IGBP) land cover classification system, and the target selection aims at a spatial representativeness within the class. The Database thus provides a set of high-quality BRDF and BPDF samples that can be used to assess the typical variability of natural surface reflectances or to evaluate models. It is available freely from the PANGAEA website (doi:10.1594/PANGAEA.864090). In addition to the Database, we provide a visualization and analysis tool based on the Interactive Data Language (IDL). It allows an interactive analysis of the measurements and a comparison against various BRDF and BPDF analytical models. The present paper describes the input data, the selection principles, the Database Format, and the analysis tool
Julie Barnes - One of the best experts on this subject based on the ideXlab platform.
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Ontology-based interactive inFormation extraction from scientific abstracts: Conference Papers
Comparative and Functional Genomics, 2005Co-Authors: David Milward, Marcus Bjäreland, William Hayes, Michelle Maxwell, Lisa Öberg, Nick Tilford, James Thomas, Roger Hale, Sylvia Knight, Julie BarnesAbstract:Over recent years, there has been a growing interest in extracting inFormation automatically or semi-automatically from the scientific literature. This paper describes a novel ontology-based interactive inFormation extraction (OBIIE) framework and a specific OBIIE system. We describe how this system enables life scientists to make ad hoc queries similar to using a standard search engine, but where the results are obtained in a Database Format similar to a pre-programmed inFormation extraction engine. We present a case study in which the system was evaluated for extracting co-factors from EMBASE and MEDLINE. Copyright © 2005 John Wiley & Sons, Ltd.
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Ontology-Based Interactive InFormation Extraction From Scientific Abstracts
Comparative and functional genomics, 2005Co-Authors: David Milward, Marcus Bjäreland, William Hayes, Michelle Maxwell, Lisa Öberg, Nick Tilford, James Thomas, Roger Hale, Sylvia Knight, Julie BarnesAbstract:Over recent years, there has been a growing interest in extracting inFormation automatically or semi-automatically from the scientific literature. This paper describes a novel ontology-based interactive inFormation extraction (OBIIE) framework and a specific OBIIE system. We describe how this system enables life scientists to make ad hoc queries similar to using a standard search engine, but where the results are obtained in a Database Format similar to a pre-programmed inFormation extraction engine. We present a case study in which the system was evaluated for extracting co-factors from EMBASE and MEDLINE.
Elizabeth S. Burnside - One of the best experts on this subject based on the ideXlab platform.
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Probabilistic Computer Model Developed from Clinical Data in National Mammography Database Format to Classify Mammographic Findings
Radiology, 2009Co-Authors: Elizabeth S. Burnside, Jagpreet Chhatwal, Oguzhan Alagoz, Mary J. Lindstrom, Charles E. Kahn, Katherine A. Shaffer, Jesse Davis, Berta M. Geller, Benjamin Littenberg, C. David PageAbstract:Purpose: To determine whether a Bayesian network trained on a large Database of patient demographic risk factors and radiologist-observed findings from consecutive clinical mammography examinations can exceed radiologist performance in the classification of mammographic findings as benign or malignant. Materials and Methods: The institutional review board exempted this HIPAA-compliant retrospective study from requiring informed consent. Structured reports from 48 744 consecutive pooled screening and diagnostic mammography examinations in 18 269 patients from April 5, 1999 to February 9, 2004 were collected. Mammographic findings were matched with a state cancer registry, which served as the reference standard. By using 10-fold cross validation, the Bayesian network was tested and trained to estimate breast cancer risk by using demographic risk factors (age, family and personal history of breast cancer, and use of hormone replacement therapy) and mammographic findings recorded in the Breast Imaging Reporting and Data System lexicon. The performance of radiologists compared with the Bayesian network was evaluated by using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Results: The Bayesian network significantly exceeded the performance of interpreting radiologists in terms of AUC (0.960 vs 0.939, P = .002), sensitivity (90.0% vs 85.3%, P < .001), and specificity (93.0% vs 88.1%, P < .001). Conclusion: On the basis of prospectively collected variables, the evaluated Bayesian network can predict the probability of breast cancer and exceed interpreting radiologist performance. Bayesian networks may help radiologists improve mammographic interpretation. © RSNA, 2009
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A logistic regression model based on the national mammography Database Format to aid breast cancer diagnosis
AJR. American journal of roentgenology, 2009Co-Authors: Jagpreet Chhatwal, Oguzhan Alagoz, Mary J. Lindstrom, Charles E. Kahn, Katherine A. Shaffer, Elizabeth S. BurnsideAbstract:OBJECTIVE. The purpose of our study was to create a breast cancer risk estimation model based on the descriptors of the National Mammography Database using logistic regression that can aid in decision making for the early detection of breast cancer.MATERIALS AND METHODS. We created two logistic regression models based on the mammography features and demographic data for 62,219 consecutive mammography records from 48,744 studies in 18,270 patients reported using the Breast Imaging Reporting and Data System (BI-RADS) lexicon and the National Mammography Database Format between April 5, 1999 and February 9, 2004. State cancer registry outcomes matched with our data served as the reference standard. The probability of cancer was the outcome in both models. Model 2 was built using all variables in Model 1 plus radiologists' BI-RADS assessment categories. We used 10-fold cross-validation to train and test the model and to calculate the area under the receiver operating characteristic curves (Az) to measure the ...
